diff --git a/.gitignore b/.gitignore index bf917f79..64b64a68 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,15 @@ temp_data *.egg* __pycache__ build/ +*.log + +#ignore example result +examples/**/*.h5ad +examples/tuning/**/*.yaml +examples/tuning/**/*.csv +examples/tuning/**/*.sh +examples/tuning/**/*.h5 +examples/tuning/**/*.tar.gz +examples/tuning/**/*.tif +examples/tuning/**/*.txt +examples/atlas/config/run_config.csv diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c580386f..3af9d357 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -33,7 +33,7 @@ repos: args: [--line-width, "120", --profile, black] - repo: https://github.com/PyCQA/docformatter - rev: v1.7.5 + rev: eb1df34 hooks: - id: docformatter name: Format docstring diff --git a/README.md b/README.md index bec823f5..5d1fcfb2 100644 --- a/README.md +++ b/README.md @@ -193,14 +193,14 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | ------------------- | ------------ | ------------------------------------------------------------------------------------------------------------ | ---- | ------- | -| GNN | GraphSCI | Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks | 2021 | ✅ | +| GNN | GraphSCI | Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks | 2021 | ✅ | | GNN | scGNN (2020) | SCGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph | 2020 | P1 | -| GNN | scGNN (2021) | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses | 2021 | ✅ | +| GNN | scGNN (2021) | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses | 2021 | ✅ | | GNN | GNNImpute | An efficient scRNA-seq dropout imputation method using graph attention network | 2021 | P1 | | Graph Diffusion | MAGIC | MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data | 2018 | P1 | | Probabilistic Model | scImpute | An accurate and robust imputation method scImpute for single-cell RNA-seq data | 2018 | P1 | | GAN | scGAIN | scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks | 2019 | P1 | -| NN | DeepImpute | DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | 2019 | ✅ | +| NN | DeepImpute | DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | 2019 | ✅ | | NN + TF | Saver-X | Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery | 2019 | P1 | | Model | Evaluation Metric | Mouse Brain (current/reported) | Mouse Embryo (current/reported) | PBMC (current/reported) | @@ -215,12 +215,12 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | ----------------------- | ------------- | ------------------------------------------------------------------------------------------------------------- | ---- | ------- | -| GNN | ScDeepsort | Single-cell transcriptomics with weighted GNN | 2021 | ✅ | -| Logistic Regression | Celltypist | Cross-tissue immune cell analysis reveals tissue-specific features in humans. | 2021 | ✅ | -| Random Forest | singleCellNet | SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. | 2019 | ✅ | -| Neural Network | ACTINN | ACTINN: automated identification of cell types in single cell RNA sequencing. | 2020 | ✅ | +| GNN | ScDeepsort | Single-cell transcriptomics with weighted GNN | 2021 | ✅ | +| Logistic Regression | Celltypist | Cross-tissue immune cell analysis reveals tissue-specific features in humans. | 2021 | ✅ | +| Random Forest | singleCellNet | SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. | 2019 | ✅ | +| Neural Network | ACTINN | ACTINN: automated identification of cell types in single cell RNA sequencing. | 2020 | ✅ | | Hierarchical Clustering | SingleR | Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. | 2019 | P1 | -| SVM | SVM | A comparison of automatic cell identification methods for single-cell RNA sequencing data. | 2018 | ✅ | +| SVM | SVM | A comparison of automatic cell identification methods for single-cell RNA sequencing data. | 2018 | ✅ | | Model | Evaluation Metric | Mouse Brain 2695 (current/reported) | Mouse Spleen 1759 (current/reported) | Mouse Kidney 203 (current/reported) | | ------------- | ----------------- | ----------------------------------- | ------------------------------------ | ----------------------------------- | @@ -234,12 +234,12 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | ----------- | ------------- | ------------------------------------------------------------------------------------------------------------ | ---- | ------- | -| GNN | graph-sc | GNN-based embedding for clustering scRNA-seq data | 2022 | ✅ | -| GNN | scTAG | ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations | 2022 | ✅ | -| GNN | scDSC | Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network | 2022 | ✅ | +| GNN | graph-sc | GNN-based embedding for clustering scRNA-seq data | 2022 | ✅ | +| GNN | scTAG | ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations | 2022 | ✅ | +| GNN | scDSC | Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network | 2022 | ✅ | | GNN | scGAC | scGAC: a graph attentional architecture for clustering single-cell RNA-seq data | 2022 | P1 | -| AutoEncoder | scDeepCluster | Clustering single-cell RNA-seq data with a model-based deep learning approach | 2019 | ✅ | -| AutoEncoder | scDCC | Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data | 2021 | ✅ | +| AutoEncoder | scDeepCluster | Clustering single-cell RNA-seq data with a model-based deep learning approach | 2019 | ✅ | +| AutoEncoder | scDCC | Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data | 2021 | ✅ | | AutoEncoder | scziDesk | Deep soft K-means clustering with self-training for single-cell RNA sequence data | 2020 | P1 | | Model | Evaluation Metric | 10x PBMC (current/reported) | Mouse ES (current/reported) | Worm Neuron (current/reported) | Mouse Bladder (current/reported) | @@ -256,12 +256,12 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | ---------------- | ------------------------ | -------------------------------------------------------------------------------------------------- | ---- | ------- | -| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ | +| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ | | GNN | ScMoLP | Link Prediction Variant of ScMoGCN | 2022 | P1 | | GNN | GRAPE | Handling Missing Data with Graph Representation Learning | 2020 | P1 | -| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ | -| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ | -| Auto-encoder | BABEL | BABEL enables cross-modality translation between multiomic profiles at single-cell resolution | 2021 | ✅ | +| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ | +| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ | +| Auto-encoder | BABEL | BABEL enables cross-modality translation between multiomic profiles at single-cell resolution | 2021 | ✅ | | Model | Evaluation Metric | GEX2ADT (current/reported) | ADT2GEX (current/reported) | GEX2ATAC (current/reported) | ATAC2GEX (current/reported) | | ------------------------ | ----------------- | -------------------------- | -------------------------- | --------------------------- | --------------------------- | @@ -274,10 +274,10 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | ---------------- | ------------------------ | -------------------------------------------------------------------------------------------------- | ---- | ------- | -| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ | +| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ | | GNN/Auto-ecnoder | GLUE | Multi-omics single-cell data integration and regulatory inference with graph-linked embedding | 2021 | P1 | -| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ | -| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ | +| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | ✅ | +| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | ✅ | | Model | Evaluation Metric | GEX2ADT (current/reported) | GEX2ATAC (current/reported) | | ------------------------ | ----------------- | -------------------------- | --------------------------- | @@ -289,11 +289,11 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | ---------------- | ------- | ----------------------------------------------------------------------------------------------------- | ---- | ------- | -| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ | -| Auto-encoder | scMVAE | Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data | 2020 | ✅ | -| Auto-encoder | scDEC | Simultaneous deep generative modelling and clustering of single-cell genomic data | 2021 | ✅ | +| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | ✅ | +| Auto-encoder | scMVAE | Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data | 2020 | ✅ | +| Auto-encoder | scDEC | Simultaneous deep generative modelling and clustering of single-cell genomic data | 2021 | ✅ | | GNN/Auto-ecnoder | GLUE | Multi-omics single-cell data integration and regulatory inference with graph-linked embedding | 2021 | P1 | -| Auto-encoder | DCCA | Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data | 2021 | ✅ | +| Auto-encoder | DCCA | Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data | 2021 | ✅ | | Model | Evaluation Metric | GEX2ADT (current/reported) | GEX2ATAC (current/reported) | | ---------- | ----------------- | -------------------------- | --------------------------- | @@ -329,11 +329,11 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | -------------------------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | ------- | -| GNN | SpaGCN | SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network | 2021 | ✅ | -| GNN | STAGATE | Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder | 2021 | ✅ | +| GNN | SpaGCN | SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network | 2021 | ✅ | +| GNN | STAGATE | Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder | 2021 | ✅ | | Bayesian | BayesSpace | Spatial transcriptomics at subspot resolution with BayesSpace | 2021 | P1 | -| Pseudo-space-time (PST) Distance | stLearn | stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues | 2020 | ✅ | -| Heuristic | Louvain | Fast unfolding of community hierarchies in large networks | 2008 | ✅ | +| Pseudo-space-time (PST) Distance | stLearn | stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues | 2020 | ✅ | +| Heuristic | Louvain | Fast unfolding of community hierarchies in large networks | 2008 | ✅ | | Model | Evaluation Metric | 151673 (current/reported) | 151676 (current/reported) | 151507 (current/reported) | | ------- | ----------------- | ------------------------- | ------------------------- | ------------------------- | @@ -346,10 +346,10 @@ pip install -e . | BackBone | Model | Algorithm | Year | CheckIn | | -------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------- | ---- | ------- | -| GNN | DSTG | DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence | 2021 | ✅ | -| logNormReg | SpatialDecon | Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data | 2022 | ✅ | -| NNMFreg | SPOTlight | SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes | 2021 | ✅ | -| NN Linear + CAR assumption | CARD | Spatially informed cell-type deconvolution for spatial transcriptomics | 2022 | ✅ | +| GNN | DSTG | DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence | 2021 | ✅ | +| logNormReg | SpatialDecon | Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data | 2022 | ✅ | +| NNMFreg | SPOTlight | SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes | 2021 | ✅ | +| NN Linear + CAR assumption | CARD | Spatially informed cell-type deconvolution for spatial transcriptomics | 2022 | ✅ | | Model | Evaluation Metric | GSE174746 (current/reported) | CARD Synthetic (current/reported) | SPOTlight Synthetic (current/reported) | | ------------ | ----------------- | ---------------------------- | --------------------------------- | -------------------------------------- | diff --git a/dance/atlas/data_dropbox_upload.py b/dance/atlas/data_dropbox_upload.py new file mode 100644 index 00000000..87b6d78e --- /dev/null +++ b/dance/atlas/data_dropbox_upload.py @@ -0,0 +1,155 @@ +import json +import os +import pathlib + +import dropbox +import numpy as np +import pandas as pd +import scanpy as sc +from dropbox.exceptions import ApiError, AuthError + +from dance.utils import logger + + +def upload_file_to_dropbox(dropbox_path, access_token, local_path): + """Upload a local file to Dropbox. + + Parameters + ---------- + dropbox_path : str + Destination path in Dropbox + access_token : str + Dropbox API access token + local_path : str or pathlib.Path + Path to local file to upload + + Returns + ------- + None + Returns None if upload fails + + """ + dbx = dropbox.Dropbox(access_token) + + # Verify access token + try: + dbx.users_get_current_account() + except AuthError as err: + print("ERROR: Invalid access token; please check your access token.") + return None + try: + file_upload(dbx=dbx, local_path=local_path, remote_path=dropbox_path) + print("Upload successful.") + except ApiError as err: + print(f"API error: {err}") + return None + + +def file_upload(dbx: dropbox.Dropbox, local_path: pathlib.Path, remote_path: str): + """Upload large files to Dropbox using chunked upload. + + Parameters + ---------- + dbx : dropbox.Dropbox + Authenticated Dropbox client + local_path : pathlib.Path + Path to local file + remote_path : str + Destination path in Dropbox + + """ + CHUNKSIZE = 100 * 1024 * 1024 + upload_session_start_result = dbx.files_upload_session_start(b'') + cursor = dropbox.files.UploadSessionCursor(session_id=upload_session_start_result.session_id, offset=0) + with local_path.open("rb") as f: + while True: + data = f.read(CHUNKSIZE) + if data == b"": + break + logger.debug("Pushing %d bytes", len(data)) + dbx.files_upload_session_append_v2(data, cursor) + cursor.offset += len(data) + commit = dropbox.files.CommitInfo(path=remote_path) + dbx.files_upload_session_finish(b'', cursor, commit) + + +def create_shared_link(dbx, dropbox_path): + """Create or get existing shared link. + + :param dbx: Dropbox object + :param dropbox_path: File path on Dropbox + :return: Shared link URL + + """ + try: + links = dbx.sharing_list_shared_links(path=dropbox_path, direct_only=True).links + if links: + # If shared link already exists, return the first one + return links[0].url + else: + # Create a new shared link + link = dbx.sharing_create_shared_link_with_settings(dropbox_path) + return link.url + except ApiError as err: + print(f"Error creating shared link: {err}") + return None + + +def get_link(data_fname, local_path, ACCESS_TOKEN, DROPBOX_DEST_PATH): + DROPBOX_DEST_PATH = DROPBOX_DEST_PATH + "/" + data_fname + + upload_file_to_dropbox(dropbox_path=DROPBOX_DEST_PATH, access_token=ACCESS_TOKEN, local_path=local_path) + + # Create Dropbox object to get shared link + dbx = dropbox.Dropbox(ACCESS_TOKEN) + # Get shared link + shared_link = create_shared_link(dbx, DROPBOX_DEST_PATH) + if shared_link: + # Dropbox shared link defaults to `dl=0` at the end, which means preview in browser. + # change it to `dl=1`. + download_link = shared_link.replace('&dl=0', '&dl=1') + print(f"Download link: {download_link}") + return download_link + else: + print("Unable to get shared link.") + + +def get_ans(data: sc.AnnData, tissue: str, dataset_id: str, local_path, ACCESS_TOKEN, DROPBOX_DEST_PATH): + """Generate metadata dictionary for dataset and upload to Dropbox. + + Parameters + ---------- + data : sc.AnnData + Annotated data matrix + tissue : str + Tissue type + dataset_id : str + Unique identifier for dataset + local_path : str or pathlib.Path + Path to local data file + ACCESS_TOKEN : str + Dropbox API access token + DROPBOX_DEST_PATH : str + Base path in Dropbox for uploads + + Returns + ------- + dict + Metadata dictionary containing dataset information and Dropbox URLs + + """ + # keys=["species","tissue","dataset","split","celltype_fname","celltype_url","data_fname","data_url"] + # Create metadata dictionary with dataset info + ans = {} + ans["species"] = "human" + ans["tissue"] = tissue.capitalize() + # Store number of observations (cells) in dataset + ans["dataset"] = data.n_obs + ans["split"] = "train" + ans["celltype_fname"] = "" + ans["celltype_url"] = "" + ans["data_fname"] = f"train_human_{tissue.capitalize()}{dataset_id}_data.h5ad" + ans["data_url"] = get_link(data_fname=ans["data_fname"].split("_", 1)[1], local_path=local_path, + ACCESS_TOKEN=ACCESS_TOKEN, DROPBOX_DEST_PATH=DROPBOX_DEST_PATH) + ans["is_ALL_Integer"] = np.all(np.equal(data.X.data, data.X.data.astype(int))) + return ans diff --git a/dance/atlas/sc_similarity/anndata_similarity.py b/dance/atlas/sc_similarity/anndata_similarity.py new file mode 100644 index 00000000..25c9fab3 --- /dev/null +++ b/dance/atlas/sc_similarity/anndata_similarity.py @@ -0,0 +1,634 @@ +# anndata_similarity.py +import re +import warnings +from typing import Dict, List, Optional + +import anndata +import anndata as ad +import numpy as np +import ot +import pandas as pd +import scanpy as sc +import scipy +import yaml +from omegaconf import OmegaConf +from scipy.linalg import sqrtm +from scipy.spatial import cKDTree +from scipy.spatial.distance import cdist, directed_hausdorff, jensenshannon +from sklearn.metrics.pairwise import cosine_similarity, rbf_kernel + +from dance import logger +from dance.settings import METADIR + +# Suppress scipy warnings for constant input in Pearson correlation +warnings.filterwarnings("ignore", message="An input array is constant") +from dance.datasets.singlemodality import CellTypeAnnotationDataset + + +def get_anndata(tissue: str = "Blood", species: str = "human", filetype: str = "h5ad", train_dataset=[], + test_dataset=[], valid_dataset=[], data_dir="../temp_data"): + + def find_dataset_in_metadata(datasets, tissue): + datasets_in_metadata = [] + for dataset_id in datasets: + all_datasets = pd.read_csv(METADIR / "scdeepsort.csv", header=0, skiprows=[i for i in range(1, 68)]) + for collect_dataset in all_datasets[all_datasets["tissue"] == tissue]["data_fname"].tolist(): + if dataset_id in collect_dataset: + datasets_in_metadata.append( + (collect_dataset.split(tissue)[1] + + (tissue + collect_dataset.split(tissue)[2] if len(collect_dataset.split(tissue)) >= 3 else '') + ).split('_')[0]) + break + return datasets_in_metadata + + train_dataset = find_dataset_in_metadata(train_dataset, tissue) + valid_dataset = find_dataset_in_metadata(valid_dataset, tissue) + test_dataset = find_dataset_in_metadata(test_dataset, tissue) + data = CellTypeAnnotationDataset(train_dataset=train_dataset, test_dataset=test_dataset, + valid_dataset=valid_dataset, data_dir=data_dir, tissue=tissue, species=species, + filetype=filetype).load_data() + return data.data + + +class AnnDataSimilarity: + """A class to compute various similarity metrics between two AnnData objects. + + Parameters + ---------- + adata1 : anndata.AnnData + First AnnData object for comparison + adata2 : anndata.AnnData + Second AnnData object for comparison + sample_size : Optional[int] + Number of cells to sample from each dataset. If None, uses min(adata1.n_obs, adata2.n_obs) + init_random_state : Optional[int] + Random seed for reproducibility + n_runs : int + Number of times to run each similarity computation + ground_truth_conf_path : Optional[str] + Path to ground truth configuration file + adata1_name : Optional[str] + Name identifier for first dataset + adata2_name : Optional[str] + Name identifier for second dataset + methods : List[str] + List of cell type annotation methods to use + tissue : str + Tissue type being analyzed + + """ + + def __init__(self, adata1: anndata.AnnData, adata2: anndata.AnnData, sample_size: Optional[int] = None, + init_random_state: Optional[int] = None, n_runs: int = 10, + ground_truth_conf_path: Optional[str] = None, adata1_name: Optional[str] = None, + adata2_name: Optional[str] = None, + methods=['cta_actinn', 'cta_celltypist', 'cta_scdeepsort', 'cta_singlecellnet'], tissue="blood"): + """Initialize the AnnDataSimilarity object and perform data preprocessing.""" + self.origin_adata1 = adata1.copy() + self.origin_adata2 = adata2.copy() + self.sample_size = sample_size + self.init_random_state = init_random_state + self.preprocess() + self.results = {} + self.ground_truth_conf_path = ground_truth_conf_path + self.adata1_name = adata1_name + self.adata2_name = adata2_name + self.methods = methods + self.tissue = tissue + self.n_runs = n_runs + + def filter_gene(self, n_top_genes=3000): + """Filter genes to keep only highly variable genes common between datasets. + + Parameters + ---------- + n_top_genes : int + Number of top variable genes to select + + """ + sc.pp.filter_genes(self.origin_adata1, min_counts=3) + sc.pp.filter_genes(self.origin_adata2, min_counts=3) + sc.pp.highly_variable_genes(self.origin_adata1, n_top_genes=n_top_genes, flavor='seurat_v3') + sc.pp.highly_variable_genes(self.origin_adata2, n_top_genes=n_top_genes, flavor='seurat_v3') + + common_hvg = self.origin_adata1.var_names[self.origin_adata1.var['highly_variable']].intersection( + self.origin_adata2.var_names[self.origin_adata2.var['highly_variable']]) + + self.origin_adata1 = self.origin_adata1[:, common_hvg].copy() + self.origin_adata2 = self.origin_adata2[:, common_hvg].copy() + self.common_genes = common_hvg + + def preprocess(self): + """Preprocess the data, including log normalization and normalization to + probability distribution.""" + self.filter_gene() + + def sample_cells(self, random_state): + """Randomly sample cells from each dataset if sample_size is specified.""" + np.random.seed(random_state) + if self.sample_size is None: + self.sample_size = min(self.adata1.n_obs, self.adata2.n_obs) #need to think + if self.adata1.n_obs > self.sample_size: + indices1 = np.random.choice(self.adata1.n_obs, size=self.sample_size, replace=False) + self.sampled_adata1 = self.adata1[indices1, :].copy() + else: + self.sampled_adata1 = self.adata1.copy() + if self.adata2.n_obs > self.sample_size: + indices2 = np.random.choice(self.adata2.n_obs, size=self.sample_size, replace=False) + self.sampled_adata2 = self.adata2[indices2, :].copy() + else: + self.sampled_adata2 = self.adata2.copy() + + def normalize_data(self): # I am not sure + """Normalize the data by total counts per cell and log-transform.""" + sc.pp.normalize_total(self.adata1, target_sum=1e4) + sc.pp.log1p(self.adata1) + sc.pp.normalize_total(self.adata2, target_sum=1e4) + sc.pp.log1p(self.adata2) + + def set_prob_data(self, sampled=False): + # Normalize the data to probability distributions + if sampled: + prob_adata1 = self.sampled_adata1.X / self.sampled_adata1.X.sum(axis=1) + prob_adata2 = self.sampled_adata2.X / self.sampled_adata2.X.sum(axis=1) + else: + prob_adata1 = self.adata1.X / self.adata1.X.sum(axis=1) + prob_adata2 = self.adata2.X / self.adata2.X.sum(axis=1) + # Handle any NaN values resulting from division by zero + self.X = np.nan_to_num(prob_adata1).toarray() + self.Y = np.nan_to_num(prob_adata2).toarray() + + def cosine_sim_sampled(self) -> pd.DataFrame: + """Computes the average cosine similarity between all pairs of cells from the + two datasets.""" + # Compute cosine similarity matrix + sim_matrix = cosine_similarity(self.sampled_adata1.X, self.sampled_adata2.X) + # Return the average similarity + return sim_matrix.mean() + + def pearson_corr_sampled(self) -> pd.DataFrame: + """Computes the average Pearson correlation coefficient between all pairs of + cells from the two datasets.""" + # Compute Pearson correlation matrix + corr_matrix = np.corrcoef(self.sampled_adata1.X.toarray(), + self.sampled_adata2.X.toarray())[:self.sampled_adata1.n_obs, + self.sampled_adata1.n_obs:] + # Return the average correlation + return np.nanmean(corr_matrix) + + def jaccard_sim_sampled(self, threshold: float = 0.5) -> pd.DataFrame: + """Computes the average Jaccard similarity between all pairs of binarized cells + from the two datasets.""" + # Binarize the data + binary_adata1 = (self.sampled_adata1.X > threshold).astype(int) + binary_adata2 = (self.sampled_adata2.X > threshold).astype(int) + # Compute Jaccard distance matrix + distance_matrix = cdist(binary_adata1.A, binary_adata2.A, metric='jaccard') + # Convert to similarity and compute the average + similarity_matrix = 1 - distance_matrix + return similarity_matrix.mean() + + def js_divergence_sampled(self) -> float: + """Computes the average Jensen-Shannon divergence between all pairs of cells + from the two datasets.""" + # Normalize the data to probability distributions + prob_adata1 = self.sampled_adata1.X / self.sampled_adata1.X.sum(axis=1) + prob_adata2 = self.sampled_adata2.X / self.sampled_adata2.X.sum(axis=1) + # Handle any NaN values resulting from division by zero + prob_adata1 = np.nan_to_num(prob_adata1).toarray() + prob_adata2 = np.nan_to_num(prob_adata2).toarray() + + # Define a function to compute JS divergence for a pair of probability vectors + def jsd(p, q): + return jensenshannon(p, q) + + # Compute JS divergence matrix + jsd_vectorized = np.vectorize(jsd, signature='(n),(n)->()') + divergence_matrix = np.zeros((prob_adata1.shape[0], prob_adata2.shape[0])) + for i in range(prob_adata1.shape[0]): + divergence_matrix[i, :] = jsd_vectorized( + np.repeat(prob_adata1[i, :], prob_adata2.shape[0], axis=0).reshape(-1, prob_adata1.shape[1]), + prob_adata2) + + # Convert divergence to similarity and compute the average + similarity_matrix = 1 - divergence_matrix + return np.nanmean(similarity_matrix) + + def compute_mmd_alternative(self) -> float: + X = self.X + Y = self.Y + gamma = 1.0 + K_XX = rbf_kernel(X, X, gamma) + K_YY = rbf_kernel(Y, Y, gamma) + K_XY = rbf_kernel(X, Y, gamma) + n_x = X.shape[0] + n_y = Y.shape[0] + mmd = (K_XX.sum() - np.trace(K_XX)) / (n_x * (n_x - 1)) \ + + (K_YY.sum() - np.trace(K_YY)) / (n_y * (n_y - 1)) \ + - 2 * K_XY.mean() + return 1 / (1 + np.sqrt(max(mmd, 0))) + + def compute_mmd(self) -> float: + """Compute Maximum Mean Discrepancy between datasets. + + Returns + ------- + float + Normalized MMD similarity score between 0 and 1 + + """ + X = self.X + Y = self.Y + kernel = "rbf" + gamma = 1.0 + if kernel == 'rbf': + K_X = np.exp(-gamma * cdist(X, X, 'sqeuclidean')) + K_Y = np.exp(-gamma * cdist(Y, Y, 'sqeuclidean')) + K_XY = np.exp(-gamma * cdist(X, Y, 'sqeuclidean')) + elif kernel == 'linear': + K_X = np.dot(X, X.T) + K_Y = np.dot(Y, Y.T) + K_XY = np.dot(X, Y.T) + else: + raise ValueError("Unsupported kernel type") + + m = X.shape[0] + n = Y.shape[0] + + sum_X = (np.sum(K_X) - np.sum(np.diag(K_X))) / (m * (m - 1)) + sum_Y = (np.sum(K_Y) - np.sum(np.diag(K_Y))) / (n * (n - 1)) + sum_XY = np.sum(K_XY) / (m * n) + + mmd_squared = sum_X + sum_Y - 2 * sum_XY + mmd = np.sqrt(max(mmd_squared, 0)) + return 1 / (1 + mmd) + + def common_genes_num(self): + return len(self.common_genes) + + def otdd(self): + """Compute the OTDD between two data sets.""" + raise NotImplementedError("OTDD!") + + def data_company(self): + raise NotImplementedError("data company") + + def wasserstein_dist(self) -> float: + """Compute Wasserstein distance between datasets. + + Returns + ------- + float + Normalized Wasserstein similarity score between 0 and 1 + + """ + X = self.X + Y = self.Y + a = np.ones((X.shape[0], )) / X.shape[0] + b = np.ones((Y.shape[0], )) / Y.shape[0] + M = ot.dist(X, Y, metric='euclidean') + wasserstein_dist = ot.emd2(a, b, M) + return 1 / (1 + wasserstein_dist) + + def get_Hausdorff(self): + X = self.X + Y = self.Y + forward = directed_hausdorff(X, Y)[0] + backward = directed_hausdorff(X, Y)[0] + hausdorff_distance = max(forward, backward) + normalized_hausdorff = hausdorff_distance / np.sqrt(X.shape[1]) + similarity = 1 - normalized_hausdorff + return similarity + + def chamfer_distance(self): + X = self.X + Y = self.Y + tree_A = cKDTree(X) + tree_B = cKDTree(Y) + + distances_A_to_B, _ = tree_A.query(Y) + distances_B_to_A, _ = tree_B.query(X) + + chamfer_A_to_B = np.mean(distances_A_to_B) + chamfer_B_to_A = np.mean(distances_B_to_A) + distance = chamfer_A_to_B + chamfer_B_to_A + normalized_chamfer = distance / np.sqrt(X.shape[1]) + similarity = 1 - normalized_chamfer + return similarity + + def energy_distance_metric(self): + X = self.X + Y = self.Y + XX = cdist(X, X, 'euclidean') + YY = cdist(Y, Y, 'euclidean') + XY = cdist(X, Y, 'euclidean') + distance = 2 * np.mean(XY) - np.mean(XX) - np.mean(YY) + return 1 / (1 + distance) + + def get_sinkhorn2(self): + X = self.X + Y = self.Y + a = np.ones(X.shape[0]) / X.shape[0] + b = np.ones(Y.shape[0]) / Y.shape[0] + M = ot.dist(X, Y, metric='euclidean') + reg = 0.1 + sinkhorn_dist = ot.sinkhorn2(a, b, M, reg) + return 1 / (1 + sinkhorn_dist) + + def bures_distance(self): + X = self.X + Y = self.Y + C1 = np.cov(X, rowvar=False) + C2 = np.cov(Y, rowvar=False) + sqrt_C1 = sqrtm(C1) + product = sqrt_C1 @ C2 @ sqrt_C1 + sqrt_product = sqrtm(product) + trace = np.trace(C1) + np.trace(C2) - 2 * np.trace(sqrt_product) + return 1 / (1 + np.sqrt(max(trace, 0))) + + def spectral_distance(self): + X = self.X + Y = self.Y + C1 = np.cov(X, rowvar=False) + C2 = np.cov(Y, rowvar=False) + eig_A = np.linalg.eigvalsh(C1) + eig_B = np.linalg.eigvalsh(C2) + return 1 / (1 + np.linalg.norm(eig_A - eig_B)) + + def get_dataset_meta_sim(self): + """Compute metadata similarity between datasets based on discrete and continuous + features. + + Returns + ------- + float + Average similarity score across all metadata features + + """ + # dis_cols=['assay', 'cell_type', 'development_stage','disease','is_primary_data','self_reported_ethnicity','sex', 'suspension_type', 'tissue','tissue_type', 'tissue_general'] + con_cols = [ + "nnz_mean", "nnz_var", "nnz_counts_mean", "nnz_counts_var", "n_measured_vars", "n_counts_mean", + "n_counts_var", "var_n_counts_mean", "var_n_counts_var" + ] + dis_cols = ['assay', 'tissue'] + + def get_discrete_sim(col_list1, col_list2): + set1 = set(col_list1) + set2 = set(col_list2) + intersection = len(set1.intersection(set2)) + union = len(set1.union(set2)) + return intersection / union + + def get_con_sim(con_data_1, con_data_2): + return abs(con_data_1 - con_data_2) / max(con_data_1, con_data_2) + + def get_dataset_info(data: ad.AnnData): + con_sim = {} + con_sim["nnz_mean"] = np.mean(data.obs["nnz"]) + con_sim["nnz_var"] = np.var(data.obs["nnz"]) + nnz_values = data.X[data.X.nonzero()] + con_sim["nnz_counts_mean"] = np.mean(nnz_values) + con_sim["nnz_counts_var"] = np.var(nnz_values) + con_sim["n_measured_vars"] = np.mean(data.obs["n_measured_vars"]) + con_sim["cell_num"] = len(data.obs) + con_sim["gene_num"] = len(data.var) + if "n_counts" not in data.obs.columns: + if scipy.sparse.issparse(data.X): + cell_counts = np.array(data.X.sum(axis=1)).flatten() + else: + cell_counts = data.X.sum(axis=1) + data.obs["n_counts"] = cell_counts + con_sim["n_counts_mean"] = np.mean(data.obs["n_counts"]) + con_sim["n_counts_var"] = np.var(data.obs["n_counts"]) + # if "n_counts" not in data.var.columns: + if scipy.sparse.issparse(data.X): + gene_counts = np.array(data.X.sum(axis=0)).flatten() + else: + gene_counts = data.X.sum(axis=0) + data.var["n_counts"] = gene_counts + data.var["n_counts"] = data.var["n_counts"].astype(float) + con_sim["var_n_counts_mean"] = np.mean(data.var["n_counts"]) + con_sim["var_n_counts_var"] = np.var(data.var["n_counts"]) + data.uns["con_sim"] = con_sim + return data + + data_1 = self.adata1.copy() + data_2 = self.adata2.copy() + data_1 = get_dataset_info(data_1) + data_2 = get_dataset_info(data_2) + ans = {} + obs_1 = data_1.obs + obs_2 = data_2.obs + con_sim_1 = data_1.uns["con_sim"] + con_sim_2 = data_2.uns["con_sim"] + for dis_col in dis_cols: + ans[f"{dis_col}_sim"] = get_discrete_sim(obs_1[dis_col].values, obs_2[dis_col].values) + for con_col in con_cols: + ans[f"{con_col}_sim"] = get_con_sim(con_sim_1[con_col], con_sim_2[con_col]) + return np.mean(list(ans.values())) + + def get_ground_truth(self): + assert self.ground_truth_conf_path is not None + assert self.adata1_name is not None + assert self.adata2_name is not None + ground_truth_conf = pd.read_excel(self.ground_truth_conf_path, sheet_name=self.tissue, index_col=0) + + def get_targets(dataset_truth: str): + dataset_truth = OmegaConf.create(fix_yaml_string(dataset_truth)) + targets = [] + for item in dataset_truth: + targets.append(item["target"]) + return targets + + sim_targets = [] + for method in self.methods: + query_dataset_truth = ground_truth_conf.loc[ground_truth_conf["dataset_id"] == self.adata1_name, + f"{method}_step2_best_yaml"].iloc[0] + atlas_dataset_truth = ground_truth_conf.loc[ground_truth_conf["dataset_id"] == self.adata2_name, + f"{method}_step2_best_yaml"].iloc[0] + if (type(atlas_dataset_truth) == float + and np.isnan(atlas_dataset_truth)) or (type(query_dataset_truth) == float + and np.isnan(query_dataset_truth)): + return 0 + + query_targets = get_targets(query_dataset_truth) + atlas_targets = get_targets(atlas_dataset_truth) + assert len(query_targets) == len(atlas_targets) + sim_targets.append((sum(a == b for a, b in zip(query_targets, atlas_targets)), len(query_targets))) + sim_targets.append((sum(x for x, y in sim_targets), sum(y for x, y in sim_targets))) + return sim_targets + + def compute_similarity( + self, random_state: int, methods: List[str] = [ + 'cosine', 'pearson', 'jaccard', 'js_distance', 'otdd', 'common_genes_num', "ground_truth", "metadata_sim" + ] + ) -> Dict[str, float]: + """Compute multiple similarity metrics between datasets. + + Parameters + ---------- + random_state : int + Random seed for cell sampling + methods : List[str] + List of similarity methods to compute + + Returns + ------- + Dict[str, float] + Dictionary mapping method names to similarity scores + + """ + self.adata1 = self.origin_adata1.copy() + self.adata2 = self.origin_adata2.copy() + self.normalize_data() + self.sample_cells(random_state) + self.set_prob_data() + + results = {} + for method in methods: + logger.info(f"method: {method}") + if method == 'cosine': + results['cosine'] = self.cosine_sim_sampled() + elif method == 'pearson': + results['pearson'] = self.pearson_corr_sampled() + elif method == 'jaccard': + results['jaccard'] = self.jaccard_sim_sampled() + elif method == 'js_distance': + results['js_distance'] = self.js_divergence_sampled() + elif method == 'wasserstein': + results['wasserstein'] = self.wasserstein_dist() + elif method == "common_genes_num": + results["common_genes_num"] = self.common_genes_num() + elif method == "Hausdorff": + results["Hausdorff"] = self.get_Hausdorff() + elif method == "chamfer": + results["chamfer"] = self.chamfer_distance() + elif method == "energy": + results["energy"] = self.energy_distance_metric() + elif method == "sinkhorn2": + results["sinkhorn2"] = self.get_sinkhorn2() + elif method == "bures": + results["bures"] = self.bures_distance() + elif method == "spectral": + results["spectral"] = self.spectral_distance() + elif method == "otdd": + results['otdd'] = self.otdd() + elif method == "ground_truth": + results["ground_truth"] = self.get_ground_truth() + elif method == "metadata_sim": + results["metadata_sim"] = self.get_dataset_meta_sim() + elif method == "mmd": + results["mmd"] = self.compute_mmd() + else: + raise ValueError(f"Unsupported similarity method: {method}") + return results + + def get_similarity_matrix_A2B( + self, methods: List[str] = [ + "wasserstein", "Hausdorff", "chamfer", "energy", "sinkhorn2", "bures", "spectral", "common_genes_num", + "ground_truth", "metadata_sim", "mmd" + ] + ) -> Dict[str, float]: + """Same as compute_similarity, keeping method name consistency.""" + cumulative_results = {method: 0.0 for method in methods} + + for run in range(self.n_runs): + # Update random state for each run + if self.init_random_state is not None: + current_random_state = self.init_random_state + run + else: + current_random_state = None + run_results = self.compute_similarity(methods=methods, random_state=current_random_state) + for method in methods: + if method in ["ground_truth"]: + cumulative_results[method] = run_results[method] + else: + cumulative_results[method] += run_results[method] + # Average the results over the number of runs + averaged_results = { + method: + cumulative_results[method] if method in ["ground_truth"] else cumulative_results[method] / self.n_runs + for method in methods + } + return averaged_results + + # def get_max_similarity_A_to_B(self): + # if self.results is None: + # raise ValueError(f"need results!") + # else: + # self.results_score = {} + # for key in self.results: + # if key not in ["common_genes_num", "ground_truth", "metadata_sim"]: + # self.results_score[key] = self._get_max_similarity(self.results[key]) + # else: + # self.results_score[key] = self.results[key] + # return self.results_score + + # def _get_max_similarity(self, similarity_matrix: pd.DataFrame): + # """Maximum matching average similarity score.""" + # matched_values = [ + # similarity_matrix.loc[label, + # label] if label in similarity_matrix.columns else similarity_matrix.loc[label].max() + # for label in similarity_matrix.index + # ] # need to ask + # overall_similarity = np.mean(matched_values) + # return overall_similarity + + +def extract_type_target_params(item_text): + lines = item_text.strip().split('\n') + item_dict = {} + params_dict = {} + current_param_key = None + in_params = False + for line in lines: + stripped_line = line.strip() + if stripped_line.startswith('- type:'): + item_dict['type'] = stripped_line.split(':', 1)[1].strip() + elif stripped_line.startswith('target:'): + item_dict['target'] = stripped_line.split(':', 1)[1].strip() + elif stripped_line.startswith('params:'): + params_content = stripped_line.split(':', 1)[1].strip() + if params_content == '{}': + params_dict = {} + in_params = False + else: + params_dict = {} + in_params = True + elif in_params: + if re.match(r'^\w+:$', stripped_line): + current_param_key = stripped_line[:-1].strip() + params_dict[current_param_key] = {} + elif re.match(r'^- ', stripped_line): + list_item = stripped_line[2:].strip() + if current_param_key: + if not isinstance(params_dict[current_param_key], list): + params_dict[current_param_key] = [] + params_dict[current_param_key].append(list_item) + elif ':' in stripped_line: + key, value = map(str.strip, stripped_line.split(':', 1)) + if current_param_key and isinstance(params_dict.get(current_param_key, None), dict): + params_dict[current_param_key][key] = yaml.safe_load(value) + else: + params_dict[key] = yaml.safe_load(value) + item_dict['params'] = params_dict + return item_dict + + +def fix_yaml_string(original_str): + #It will be deleted + + yaml_str = original_str.replace('\\n', '\n').strip() + items = re.split(r'(?=-\s*type:)', yaml_str) + config_list = [] + for item in items: + if not item.strip(): + continue + if not item.strip().startswith('- type:'): + print(item) + print("Warning: An item does not start with '- type:', skipping this item.") + continue + item_dict = extract_type_target_params(item) + config_list.append(item_dict) + fixed_yaml = yaml.dump(config_list, sort_keys=False) + return fixed_yaml diff --git a/dance/datasets/multimodality.py b/dance/datasets/multimodality.py index 477aff94..87c8689c 100644 --- a/dance/datasets/multimodality.py +++ b/dance/datasets/multimodality.py @@ -7,7 +7,10 @@ import mudata as md import numpy as np import scanpy as sc +import scipy import scipy.sparse as sp +import sklearn +from sklearn.utils import issparse from dance import logger from dance.data import Data @@ -572,6 +575,7 @@ def __init__(self, subtask, root="./data", preprocess=None, normalize=False, pre def _raw_to_dance(self, raw_data): mod1, mod2, meta1, meta2, test_sol = self._maybe_preprocess(raw_data) + self._to_csr([mod1, mod2, meta1, meta2, test_sol]) assert all(mod2.obs_names == mod1.obs_names), "Modalities not aligned" mdata = md.MuData({"mod1": mod1, "mod2": mod2, "meta1": meta1, "meta2": meta2, "test_sol": test_sol}) @@ -581,6 +585,17 @@ def _raw_to_dance(self, raw_data): return data + def _to_csr(self, datas): + for data in datas: + if scipy.sparse.issparse(data.X): + if not isinstance(data.X, scipy.sparse.csr_matrix): + data.X = data.X.tocsr() + # data.X = np.array(data.X.todense()).astype(float) + if "counts" in data.layers and scipy.sparse.issparse(data.layers["counts"]): + if not isinstance(data.layers["counts"], scipy.sparse.csr_matrix): + data.layers["counts"] = data.layers["counts"].tocsr() + # data.layers["counts"] = np.array(data.layers["counts"].todense()).astype(float) + def _maybe_preprocess(self, raw_data): if self.preprocess is None: return raw_data @@ -744,7 +759,7 @@ def _maybe_preprocess(self, raw_data): if mod1.shape[1] > self.selection_threshold: sc.pp.highly_variable_genes(mod1, layer="counts", flavor="seurat_v3", n_top_genes=self.selection_threshold, span=self.span) - mod1 = mod1[:, mod1.var["highly_variable"]] + mod1 = mod1[:, mod1.var["highly_variable"]] # Equivalent to subset=True and _inplace_subset_var if mod2.shape[1] > self.selection_threshold: sc.pp.highly_variable_genes(mod2, layer="counts", flavor="seurat_v3", n_top_genes=self.selection_threshold, span=self.span) diff --git a/dance/datasets/singlemodality.py b/dance/datasets/singlemodality.py index 60998be7..302b44b8 100644 --- a/dance/datasets/singlemodality.py +++ b/dance/datasets/singlemodality.py @@ -53,7 +53,7 @@ class CellTypeAnnotationDataset(BaseDataset): def __init__(self, full_download=False, train_dataset=None, test_dataset=None, species=None, tissue=None, valid_dataset=None, train_dir="train", test_dir="test", valid_dir="valid", map_path="map", - data_dir="./", train_as_valid=False, val_size=0.2): + data_dir="./", train_as_valid=False, val_size=0.2, test_size=0.2, filetype: str = "csv"): super().__init__(data_dir, full_download) self.data_dir = data_dir @@ -73,6 +73,8 @@ def __init__(self, full_download=False, train_dataset=None, test_dataset=None, s self.valid_dataset = train_dataset self.train2valid() self.val_size = val_size + self.test_size = test_size + self.filetype = filetype def train2valid(self): logger.info("Copy train_dataset and use it as valid_dataset") @@ -109,12 +111,12 @@ def download_all(self): pass os.rename(download_path, move_path) - def get_all_filenames(self, filetype: str = "csv", feat_suffix: str = "data", label_suffix: str = "celltype"): + def get_all_filenames(self, feat_suffix: str = "data", label_suffix: str = "celltype"): filenames = [] - for id in self.train_dataset + self.test_dataset + (self.valid_dataset - if self.valid_dataset is not None else []): - filenames.append(f"{self.species}_{self.tissue}{id}_{feat_suffix}.{filetype}") - filenames.append(f"{self.species}_{self.tissue}{id}_{label_suffix}.{filetype}") + for id in self.train_dataset + (self.test_dataset if self.test_dataset is not None else + []) + (self.valid_dataset if self.valid_dataset is not None else []): + filenames.append(f"{self.species}_{self.tissue}{id}_{feat_suffix}.{self.filetype}") + filenames.append(f"{self.species}_{self.tissue}{id}_{label_suffix}.{self.filetype}") return filenames def download(self, download_map=True): @@ -175,6 +177,8 @@ def _load_raw_data(self, ct_col: str = "Cell_type") -> Tuple[ad.AnnData, List[Se species = self.species tissue = self.tissue valid_feat = None + if self.test_dataset is None or self.test_dataset == []: + return self._load_raw_data_single_h5ad() if self.valid_dataset is not None: train_dataset_ids = self.train_dataset test_dataset_ids = self.test_dataset @@ -270,6 +274,72 @@ def _load_raw_data(self, ct_col: str = "Cell_type") -> Tuple[ad.AnnData, List[Se return adata, labels, idx_to_label, train_size, 0 + def _load_raw_data_single_h5ad(self, + ct_col: str = "cell_type") -> Tuple[ad.AnnData, List[Set[str]], List[str], int]: + species = self.species + tissue = self.tissue + valid_feat = None + data_dir = self.data_dir + train_dir = osp.join(data_dir, self.train_dir) + data_path = osp.join(train_dir, species, f"{species}_{tissue}{self.train_dataset[0]}_data.h5ad") + adata = sc.read_h5ad(data_path) + map_path = osp.join(data_dir, self.map_path, self.species) + X_train_temp, X_test = train_test_split(adata, test_size=0.2) + X_train, X_val = train_test_split(X_train_temp, test_size=0.25) + train_feat, valid_feat, test_feat = X_train.X, X_val.X, X_test.X + train_label, valid_label, test_label = X_train.obs, X_val.obs, X_test.obs + if valid_feat is not None: + # Combine features (only use features that are present in the training data) + train_size = train_feat.shape[0] + valid_size = valid_feat.shape[0] + # Convert cell type labels and map test cell type names to train + cell_types = set(train_label[ct_col].unique()) + idx_to_label = sorted(cell_types) + cell_type_mappings: Dict[str, Set[str]] = self.get_map_dict(map_path, tissue) + train_labels, valid_labels, test_labels = train_label[ct_col].tolist(), [], [] + for i in valid_label[ct_col]: + valid_labels.append(i if i in cell_types else cell_type_mappings.get(i)) + for i in test_label[ct_col]: + test_labels.append(i if i in cell_types else cell_type_mappings.get(i)) + labels: List[Set[str]] = train_labels + valid_labels + test_labels + + logger.debug("Mapped valid cell-types:") + for i, j, k in zip(valid_label.index, valid_label[ct_col], valid_labels): + logger.debug(f"{i}:{j}\t-> {k}") + + logger.debug("Mapped test cell-types:") + for i, j, k in zip(test_label.index, test_label[ct_col], test_labels): + logger.debug(f"{i}:{j}\t-> {k}") + + logger.info(f"Loaded expression data: {adata}") + logger.info(f"Number of training samples: {train_feat.shape[0]:,}") + logger.info(f"Number of valid samples: {valid_feat.shape[0]:,}") + logger.info(f"Number of testing samples: {test_feat.shape[0]:,}") + logger.info(f"Cell-types (n={len(idx_to_label)}):\n{pprint.pformat(idx_to_label)}") + + return adata, labels, idx_to_label, train_size, valid_size + else: + # Combine features (only use features that are present in the training data) + train_size = train_feat.shape[0] + cell_types = set(train_label[ct_col].unique()) + idx_to_label = sorted(cell_types) + cell_type_mappings: Dict[str, Set[str]] = self.get_map_dict(map_path, tissue) + train_labels, test_labels = train_label[ct_col].tolist(), [] + for i in test_label[ct_col]: + test_labels.append(i if i in cell_types else cell_type_mappings.get(i)) + labels: List[Set[str]] = train_labels + test_labels + + logger.debug("Mapped test cell-types:") + for i, j, k in zip(test_label.index, test_label[ct_col], test_labels): + logger.debug(f"{i}:{j}\t-> {k}") + + logger.info(f"Loaded expression data: {adata}") + logger.info(f"Number of training samples: {train_feat.shape[0]:,}") + logger.info(f"Number of testing samples: {test_feat.shape[0]:,}") + logger.info(f"Cell-types (n={len(idx_to_label)}):\n{pprint.pformat(idx_to_label)}") + + return adata, labels, idx_to_label, train_size, 0 + def _raw_to_dance(self, raw_data): adata, cell_labels, idx_to_label, train_size, valid_size = raw_data adata.obsm["cell_type"] = cell_label_to_df(cell_labels, idx_to_label, index=adata.obs.index) diff --git a/dance/metadata/scdeepsort.csv b/dance/metadata/scdeepsort.csv index 79dfc5c1..97e0acc6 100644 --- a/dance/metadata/scdeepsort.csv +++ b/dance/metadata/scdeepsort.csv @@ -66,3 +66,141 @@ mouse,Brain,3285,train,train_mouse_Brain3285_celltype.csv,https://www.dropbox.co mouse,Brain,753,train,train_mouse_Brain753_celltype.csv,https://www.dropbox.com/s/x2katwk93z06sgw?dl=1,train_mouse_Brain753_data.csv,https://www.dropbox.com/s/3f3wbplgo3xa4ww?dl=1 mouse,Kidney,4682,train,train_mouse_Kidney4682_celltype.csv,https://www.dropbox.com/s/3plrve7g9v428ec?dl=1,train_mouse_Kidney4682_data.csv,https://www.dropbox.com/s/olf5nirtieu1ikq?dl=1 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+human,Pancreas,2544,train,,,train_human_Pancreas66d15835-5dc8-4e96-b0eb-f48971cb65e8_data.h5ad,https://www.dropbox.com/scl/fi/p2zo6qt4j0hq2xtd8yudn/human_Pancreas66d15835-5dc8-4e96-b0eb-f48971cb65e8_data.h5ad?rlkey=hum53j5mvk3vs3ybqwe0fb2r1&dl=1 +human,Pancreas,8215,train,,,train_human_Pancreas97a17473-e2b1-4f31-a544-44a60773e2dd(Pancreas)_data.h5ad,https://www.dropbox.com/scl/fi/43r5btoo1z1r43xwlg1st/human_Pancreas97a17473-e2b1-4f31-a544-44a60773e2dd-Pancreas-_data.h5ad?rlkey=2zlbl33carcm5xsyp9dbazn84&st=hhpl86bj&dl=1 diff --git a/dance/modules/multi_modality/joint_embedding/dcca.py b/dance/modules/multi_modality/joint_embedding/dcca.py index b5356ada..bb6d9a69 100644 --- a/dance/modules/multi_modality/joint_embedding/dcca.py +++ b/dance/modules/multi_modality/joint_embedding/dcca.py @@ -11,6 +11,7 @@ import collections import math import os +import sys import time import warnings from collections import OrderedDict @@ -385,7 +386,7 @@ def fit(self, train_loader, test_loader, total_loader, model_pre, args, criterio train_loss_list = [] reco_epoch_test = 0 - test_like_max = 100000 + test_like_max = sys.maxsize flag_break = 0 patience_epoch = 0 @@ -394,7 +395,7 @@ def fit(self, train_loader, test_loader, total_loader, model_pre, args, criterio model_pre.eval() start = time.time() - + best_dict = None for epoch in range(1, args.max_epoch + 1): self.train() @@ -636,7 +637,7 @@ def fit(self, train_loader, test_loader, total_loader, model_pre, args, criterio break duration = time.time() - start - self.load_state_dict(best_dict) + self.load_state_dict(best_dict if best_dict is not None else self.state_dict()) print('Finish training, total time is: ' + str(duration) + 's') self.eval() diff --git a/dance/modules/multi_modality/joint_embedding/scmogcn.py b/dance/modules/multi_modality/joint_embedding/scmogcn.py index f0567293..a5fd4bd0 100644 --- a/dance/modules/multi_modality/joint_embedding/scmogcn.py +++ b/dance/modules/multi_modality/joint_embedding/scmogcn.py @@ -116,7 +116,7 @@ def fit(self, g_mod1, g_mod2, train_size, cell_type, batch_label, phase_score): Bipartite expression feature graph for modality 1. g_mod2 : dgl.DGLGraph Bipartite expression feature graph for modality 2. - train_size : int + train_size : int or array_like Number of training samples. labels : torch.Tensor Labels for training samples. diff --git a/dance/modules/multi_modality/joint_embedding/scmvae.py b/dance/modules/multi_modality/joint_embedding/scmvae.py index 3905f2fe..0173ea39 100644 --- a/dance/modules/multi_modality/joint_embedding/scmvae.py +++ b/dance/modules/multi_modality/joint_embedding/scmvae.py @@ -369,7 +369,7 @@ def _inference(self, X1=None, X2=None): if X1 is not None: if self.log_variational: - X1_ = torch.log(X1_ + 1) + X1_ = torch.log(torch.clamp(X1_, min=1e-7) + 1) mean_l, logvar_l, library = self.X1_encoder_l(X1_) @@ -380,7 +380,8 @@ def _inference(self, X1=None, X2=None): if self.Type == 'ZINB': if self.log_variational: - X2_ = torch.log(X2_ + 1) + # X2_ = torch.log(X2_ + 1) + X2_ = torch.log(torch.clamp(X2_, min=1e-7) + 1) mean_l2, logvar_l2, library2 = self.X2_encoder_l(X2_) means, logvar = self._encode_modalities(X1_, X2_) diff --git a/dance/pipeline.py b/dance/pipeline.py index d831ab10..aa4a0c00 100644 --- a/dance/pipeline.py +++ b/dance/pipeline.py @@ -8,6 +8,7 @@ from operator import mul from pprint import pformat +import omegaconf import pandas as pd from omegaconf import DictConfig, OmegaConf @@ -793,6 +794,14 @@ def _params_search_space(self) -> Dict[str, Dict[str, Optional[Union[str, float] def wandb_sweep_config(self) -> Dict[str, Any]: if self.wandb_config is None: raise ValueError("wandb config not specified in the raw config.") + if "run_kwargs" in self.config: + return { + **self.wandb_config, "parameters": { + "run_kwargs": { + "values": omegaconf.OmegaConf.to_object(self.config.run_kwargs) + } + } + } return {**self.wandb_config, "parameters": self.search_space()} def wandb_sweep(self) -> Tuple[str, str, str]: @@ -807,7 +816,7 @@ def wandb_sweep(self) -> Tuple[str, str, str]: f"'entity' and 'project': {wandb_entity=!r}, {wandb_project=!r}") sweep_config = self.wandb_sweep_config() - logger.info(f"Sweep config:\n{pformat(sweep_config)}") + # logger.info(f"Sweep config:\n{pformat(sweep_config)}") wandb_sweep_id = wandb.sweep(sweep=sweep_config, entity=wandb_entity, project=wandb_project) logger.info(Color("blue")(f"\n\n\t[*] Sweep ID: {wandb_sweep_id}\n")) @@ -1032,7 +1041,9 @@ def get_step3_yaml(conf_save_path="config_yamls/params/", conf_load_path="step3_ conf = OmegaConf.load(conf_load_path) pipeline_top_k = default(step2_pipeline_planer.config.pipeline_tuning_top_k, DEFAULT_PIPELINE_TUNING_TOP_K) result = pd.read_csv(result_load_path).sort_values(by=metric, ascending=ascending).head(pipeline_top_k) - columns = sorted([col for col in result.columns if col.startswith("pipeline")]) + columns = sorted( + [col for col in result.columns if (col.startswith("pipeline") or col.startswith("run_kwargs_pipeline"))], + key=lambda x: float(x.split('.')[1])) pipeline_names = result.loc[:, columns].values count = 0 for row in pipeline_names: @@ -1041,11 +1052,12 @@ def get_step3_yaml(conf_save_path="config_yamls/params/", conf_load_path="step3_ for x in row: for k in conf.pipeline: if k["target"] == x: - pipeline.append(k) + pipeline.append(deepcopy(k)) for i, f in zip(required_indexes, required_funs): for k in step2_pipeline_planer.config.pipeline: if "target" in k and k["target"] == f: - pipeline.insert(i, k) + pipeline.insert(i, deepcopy(k)) + break for p1 in step2_pipeline_planer.config.pipeline: if "step3_frozen" in p1 and p1["step3_frozen"]: for p2 in pipeline: @@ -1056,6 +1068,16 @@ def get_step3_yaml(conf_save_path="config_yamls/params/", conf_load_path="step3_ for target, d_p in p1.default_params.items(): if target == p2["target"]: p2["params"] = d_p + #The order is wrong, refer to _sanitize_pipeline for modification TODO use test to check + step2_pipeline = step2_pipeline_planer.config.pipeline + # step2_pipeline=sorted(step2_pipeline_planer.config.pipeline,key=lambda x: float(x.split('.')[1])) + for p1, p2 in zip(step2_pipeline, pipeline): #need order + if "params" in p1: + p2.params = p1.params + # for key, value in p1.params.items(): + # if "params" not in p2: + # p2.params = {} + # p2.params[key] = value temp_conf = conf.copy() temp_conf.pipeline = pipeline temp_conf.wandb = step2_pipeline_planer.config.wandb @@ -1086,9 +1108,9 @@ def run_step3(root_path, evaluate_pipeline, step2_pipeline_planer: PipelinePlane step3_k = default(step2_pipeline_planer.config.parameter_tuning_freq_n, DEFAULT_PARAMETER_TUNING_FREQ_N) # Skip some of the already run step3 because in pandas, when you sort columns with exactly the same values, the results are not random. # Instead, pandas preserves the order of the original data. So we can skip it without causing any impact. - step3_start_k = default(step2_pipeline_planer.config.step3_start_k, 0) + step3_start_k = step2_pipeline_planer.config.step3_start_k if "step3_start_k" in step2_pipeline_planer.config else 0 #Some sweep_ids of step3 that have already been run - step3_sweep_ids = step2_pipeline_planer.config.step3_sweep_ids + step3_sweep_ids = step2_pipeline_planer.config.step3_sweep_ids if "step3_sweep_ids" in step2_pipeline_planer.config else None step3_sweep_ids = [None] * (pipeline_top_k - step3_start_k) if step3_sweep_ids is None else ( step3_sweep_ids + [None] * (pipeline_top_k - step3_start_k - len(step3_sweep_ids))) diff --git a/dance/settings.py b/dance/settings.py index df7846cb..0fdbb713 100644 --- a/dance/settings.py +++ b/dance/settings.py @@ -47,7 +47,10 @@ def change_log_level(name: str = "dance", /, *, level: Union[str, int]): DANCEDIR = Path(__file__).resolve().parents[1] DANCEPKGDIR = DANCEDIR / "dance" METADIR = DANCEPKGDIR / "metadata" - +ATLASDIR = DANCEDIR / "examples/atlas" +SIMILARITYDIR = ATLASDIR / "sc_similarity_examples" +entity = "xzy11632" +project = "dance-dev" __all__ = [ "change_log_level", ] diff --git a/dance/transforms/cell_feature.py b/dance/transforms/cell_feature.py index 465a12b0..2af1c3a6 100644 --- a/dance/transforms/cell_feature.py +++ b/dance/transforms/cell_feature.py @@ -1,6 +1,6 @@ import numpy as np import pandas as pd -from sklearn.decomposition import PCA, TruncatedSVD +from sklearn.decomposition import PCA, SparsePCA, TruncatedSVD from sklearn.random_projection import GaussianRandomProjection from dance.registry import register_preprocessor @@ -8,9 +8,11 @@ from dance.typing import Optional, Union from dance.utils.matrix import normalize from dance.utils.status import deprecated +from dance.utils.wrappers import add_mod_and_transform @register_preprocessor("feature", "cell") +@add_mod_and_transform class WeightedFeaturePCA(BaseTransform): """Compute the weighted gene PCA as cell features. @@ -66,6 +68,7 @@ def __call__(self, data): @register_preprocessor("feature", "cell") +@add_mod_and_transform class WeightedFeatureSVD(BaseTransform): """Compute the weighted gene SVD as cell features. @@ -127,6 +130,7 @@ def __call__(self, data): @register_preprocessor("feature", "cell") +@add_mod_and_transform class CellPCA(BaseTransform): """Reduce cell feature matrix with PCA. @@ -145,10 +149,9 @@ def __init__(self, n_components: Union[float, int] = 400, *, channel: Optional[s self.n_components = n_components self.channel = channel - self.mod = mod def __call__(self, data): - feat = data.get_feature(return_type="numpy", channel=self.channel, mod=self.mod) + feat = data.get_feature(return_type="numpy", channel=self.channel) if self.n_components > min(feat.shape): self.logger.warning( f"n_components={self.n_components} must be between 0 and min(n_samples, n_features)={min(feat.shape)} with svd_solver='full'" @@ -167,6 +170,46 @@ def __call__(self, data): @register_preprocessor("feature", "cell") +@add_mod_and_transform +class SparsePCA(BaseTransform): + """Reduce cell feature matrix with SparsePCA. + + Parameters + ---------- + n_components + Number of SparsePCA components to use. + + """ + + _DISPLAY_ATTRS = ("n_components", ) + + def __init__(self, n_components: Union[float, int] = 400, *, channel: Optional[str] = None, + mod: Optional[str] = None, **kwargs): + super().__init__(**kwargs) + + self.n_components = n_components + self.channel = channel + + def __call__(self, data): + feat = data.get_feature(return_type="numpy", channel=self.channel) + # if self.n_components > min(feat.shape): + # self.logger.warning( + # f"n_components={self.n_components} must be between 0 and min(n_samples, n_features)={min(feat.shape)} with svd_solver='full'" + # ) + # self.n_components = min(feat.shape) + pca = SparsePCA(n_components=self.n_components) + cell_feat = pca.fit_transform(feat) + self.logger.info(f"Generating cell SparsePCA features {feat.shape} (k={pca.n_components_})") + # evr = pca.explained_variance_ratio_ + # self.logger.info(f"Top 10 explained variances: {evr[:10]}") + # self.logger.info(f"Total explained variance: {evr.sum():.2%}") + data.data.obsm[self.out] = cell_feat + + return data + + +@register_preprocessor("feature", "cell") +@add_mod_and_transform class CellSVD(BaseTransform): """Reduce cell feature matrix with SVD. @@ -185,10 +228,9 @@ def __init__(self, n_components: Union[float, int] = 400, *, channel: Optional[s self.n_components = n_components self.channel = channel - self.mod = mod def __call__(self, data): - feat = data.get_feature(return_type="numpy", channel=self.channel, mod=self.mod) + feat = data.get_feature(return_type="numpy", channel=self.channel) if isinstance(self.n_components, float): n_components = min(feat.shape) - 1 svd = TruncatedSVD(n_components=n_components) @@ -215,7 +257,8 @@ def __call__(self, data): @register_preprocessor("feature", "cell") -@deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") +@add_mod_and_transform +# @deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") class FeatureCellPlaceHolder(BaseTransform): """Used as a placeholder to skip the process. @@ -229,13 +272,12 @@ class FeatureCellPlaceHolder(BaseTransform): def __init__(self, n_components: int = 400, *, channel: Optional[str] = None, mod: Optional[str] = None, **kwargs): super().__init__(**kwargs) self.channel = channel - self.mod = mod self.logger.info( "n_components in FeatureCellPlaceHolder is used to make the parameters consistent and will not have any actual effect." ) def __call__(self, data): - feat = data.get_feature(return_type="numpy", channel=self.channel, mod=self.mod) + feat = data.get_feature(return_type="numpy", channel=self.channel) cell_feat = feat gene_feat = feat.T data.data.obsm[self.out] = cell_feat @@ -305,6 +347,7 @@ def __call__(self, data): @register_preprocessor("feature", "cell") # NOTE: register any custom preprocessing function to be used for tuning +@add_mod_and_transform class GaussRandProjFeature(BaseTransform): """Custom preprocessing to extract cell feature via Gaussian random projection.""" diff --git a/dance/transforms/filter.py b/dance/transforms/filter.py index 78e6d83f..ef703d0d 100644 --- a/dance/transforms/filter.py +++ b/dance/transforms/filter.py @@ -3,6 +3,7 @@ from typing import get_args import anndata as ad +import mudata as md import numpy as np import pandas as pd import scanpy as sc @@ -20,6 +21,7 @@ from dance.typing import Dict, GeneSummaryMode, List, Literal, Logger, Optional, Tuple, Union from dance.utils import default from dance.utils.status import deprecated +from dance.utils.wrappers import add_mod_and_transform def get_count(count_or_ratio: Optional[Union[float, int]], total: int) -> Optional[int]: @@ -48,6 +50,7 @@ def get_count(count_or_ratio: Optional[Union[float, int]], total: int) -> Option @register_preprocessor("filter") +@add_mod_and_transform class FilterScanpy(BaseTransform): """Scanpy filtering transformation with additional options.""" @@ -145,9 +148,9 @@ def prepCounts(self, x): elif self._FILTER_TARGET == "cells": n_counts = np.sum(x, axis=1) if isinstance(self.min_counts, float) and 0 <= self.min_counts <= 1: - min_counts = np.percentile(n_counts, self.min_counts) + min_counts = np.percentile(n_counts, self.min_counts * 100) else: - max_counts = np.percentile(n_counts, self.max_counts) + max_counts = np.percentile(n_counts, self.max_counts * 100) return min_counts, max_counts else: return self.min_counts, self.max_counts @@ -268,6 +271,45 @@ def __init__( inplace=inplace, **kwargs) +@register_preprocessor("filter", "cell") +@add_mod_and_transform +class FilterCellsCommonMod(BaseTransform): + """Initialize the FilterCellsCommonMod class. + + Parameters + ---------- + mod1 : str + Name of the first modality in the single-cell dataset. + mod2 : str + Name of the second modality in the single-cell dataset. + sol : Optional[str], default=None + Name of the optional solution dataset containing cell labels or annotations. + **kwargs : dict + Additional keyword arguments passed to the base transformation class. + + """ + + def __init__(self, mod1: str, mod2: str, sol: Optional[str] = None, **kwargs): + super().__init__(**kwargs) + self.mod1 = mod1 + self.mod2 = mod2 + self.sol = sol + + def __call__(self, data: Data): + md_data = data.data + data_mod1 = md_data.mod[self.mod1] + data_mod2 = md_data.mod[self.mod2] + common_cells = list(set(data_mod1.obs.index) & set(data_mod2.obs.index)) + data_mod1 = data_mod1[common_cells, :] + data_mod2 = data_mod2[common_cells, :] + data.data.mod[self.mod1] = data_mod1 + data.data.mod[self.mod2] = data_mod2 + if self.sol is not None: + test_sol = md_data.mod[self.sol] + test_sol = test_sol[common_cells, :] + data.data.mod[self.sol] = test_sol + + @register_preprocessor("filter", "gene") class FilterGenesCommon(BaseTransform): """Filter genes by taking the common genes across batches or splits. @@ -472,6 +514,7 @@ def __call__(self, data): @register_preprocessor("filter", "gene") +@add_mod_and_transform class FilterGenesPercentile(FilterGenes): """Filter genes based on percentiles of the summarized gene expressions. @@ -540,6 +583,7 @@ def _get_preserve_mask(self, gene_summary): @register_preprocessor("filter", "gene") +@add_mod_and_transform class FilterGenesTopK(FilterGenes): """Select top/bottom genes based on the summarized gene expressions. @@ -708,6 +752,7 @@ def __call__(self, data): @register_preprocessor("filter", "gene") +@add_mod_and_transform class FilterGenesRegression(BaseTransform): """Select genes based on regression. @@ -733,18 +778,19 @@ class FilterGenesRegression(BaseTransform): _DISPLAY_ATTRS = ("num_genes", ) def __init__(self, method: str = "enclasc", num_genes: int = 1000, *, channel: Optional[str] = None, - mod: Optional[str] = None, skip_count_check: bool = False, inplace=True, **kwargs): + channel_type: Optional[str] = None, mod: Optional[str] = None, skip_count_check: bool = False, + inplace=True, **kwargs): super().__init__(**kwargs) self.num_genes = num_genes self.channel = channel - self.mod = mod self.method = method self.skip_count_check = skip_count_check self.inplace = inplace + self.channel_type = channel_type def __call__(self, data): - feat = data.get_feature(return_type="numpy", channel=self.channel, mod=self.mod) + feat = data.get_feature(return_type="numpy", channel=self.channel, channel_type=self.channel_type) if not self.skip_count_check and np.mod(feat, 1).sum(): warnings.warn("Expecting count data as input, but the input feature matrix does not appear to be count." @@ -995,6 +1041,7 @@ def gini_func(x, weights=None): @register_preprocessor("filter", "gene") +@add_mod_and_transform class FilterGenesScanpyOrder(BaseTransform): """Scanpy filtering gene transformation with additional options. @@ -1084,6 +1131,7 @@ def __call__(self, data: Data): @register_preprocessor("filter", "gene") +@add_mod_and_transform class HighlyVariableGenesRawCount(AnnDataTransform): """Filter for highly variable genes using raw count matrix. @@ -1120,9 +1168,10 @@ class HighlyVariableGenesRawCount(AnnDataTransform): """ - def __init__(self, layer: Optional[str] = None, n_top_genes: Optional[int] = 1000, span: Optional[float] = 0.3, - subset: bool = True, inplace: bool = True, batch_key: Optional[str] = None, check_values: bool = True, - **kwargs): + def __init__(self, channel: Optional[str] = None, channel_type: Optional[str] = None, + n_top_genes: Optional[int] = 1000, span: Optional[float] = 0.3, subset: bool = True, + inplace: bool = True, batch_key: Optional[str] = None, check_values: bool = True, **kwargs): + layer = channel if channel_type == "layers" else None super().__init__(sc.pp.highly_variable_genes, layer=layer, n_top_genes=n_top_genes, batch_key=batch_key, check_values=check_values, span=span, subset=subset, inplace=inplace, flavor="seurat_v3", **kwargs) @@ -1158,6 +1207,7 @@ def __call__(self, data): @register_preprocessor("filter", "gene") +@add_mod_and_transform class HighlyVariableGenesLogarithmizedByTopGenes(AnnDataTransform): """Filter for highly variable genes based on top genes. @@ -1197,16 +1247,19 @@ class HighlyVariableGenesLogarithmizedByTopGenes(AnnDataTransform): """ - def __init__(self, layer: Optional[str] = None, n_top_genes: Optional[int] = 1000, n_bins: int = 20, - flavor: Literal["seurat", "cell_ranger"] = "seurat", subset: bool = True, inplace: bool = True, - batch_key: Optional[str] = None, **kwargs): + def __init__(self, channel: Optional[str] = None, channel_type: Optional[str] = None, + n_top_genes: Optional[int] = 1000, n_bins: int = 20, flavor: Literal["seurat", + "cell_ranger"] = "seurat", + subset: bool = True, inplace: bool = True, batch_key: Optional[str] = None, **kwargs): + layer = channel if channel_type == "layers" else None super().__init__(sc.pp.highly_variable_genes, layer=layer, n_top_genes=n_top_genes, n_bins=n_bins, flavor=flavor, subset=subset, inplace=inplace, batch_key=batch_key, **kwargs) self.logger.info("Expects logarithmized data") @register_preprocessor("filter", "gene") -@deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") +@add_mod_and_transform +# @deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") class FilterGenesPlaceHolder(BaseTransform): """Used as a placeholder to skip the process.""" @@ -1237,10 +1290,11 @@ def __call__(self, data: Data) -> Data: @register_preprocessor("filter", "gene") -@deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") +@add_mod_and_transform +# @deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") class FilterGenesNumberPlaceHolder(BaseTransform): - def __init__(self, **kwargs): + def __init__(self, channel=None, channel_type=None, **kwargs): super().__init__(**kwargs) def __call__(self, data: Data) -> Data: @@ -1248,6 +1302,7 @@ def __call__(self, data: Data) -> Data: @register_preprocessor("filter", "gene") +@add_mod_and_transform class HighlyVariableGenesLogarithmizedByMeanAndDisp(AnnDataTransform): """Filter for highly variable genes based on mean and dispersion. @@ -1293,10 +1348,12 @@ class HighlyVariableGenesLogarithmizedByMeanAndDisp(AnnDataTransform): """ - def __init__(self, layer: Optional[str] = None, min_disp: Optional[float] = 0.5, max_disp: Optional[float] = np.inf, + def __init__(self, channel: Optional[str] = None, channel_type: Optional[str] = None, + min_disp: Optional[float] = 0.5, max_disp: Optional[float] = np.inf, min_mean: Optional[float] = 0.0125, max_mean: Optional[float] = 3, n_bins: int = 20, flavor: Literal["seurat", "cell_ranger"] = "seurat", subset: bool = True, inplace: bool = True, batch_key: Optional[str] = None, **kwargs): + layer = channel if channel_type == "layers" else None super().__init__(sc.pp.highly_variable_genes, layer=layer, min_disp=min_disp, max_disp=max_disp, min_mean=min_mean, max_mean=max_mean, n_bins=n_bins, flavor=flavor, subset=subset, inplace=inplace, batch_key=batch_key, **kwargs) @@ -1304,7 +1361,8 @@ def __init__(self, layer: Optional[str] = None, min_disp: Optional[float] = 0.5, @register_preprocessor("filter", "cell") -@deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") +@add_mod_and_transform +# @deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") class FilterCellsPlaceHolder(BaseTransform): """Used as a placeholder to skip the process.""" @@ -1335,6 +1393,7 @@ def __call__(self, data: Data) -> Data: @register_preprocessor("filter", "cell") +@add_mod_and_transform class FilterCellsScanpyOrder(BaseTransform): """Scanpy filtering cell transformation with additional options. diff --git a/dance/transforms/graph/dstg_graph.py b/dance/transforms/graph/dstg_graph.py index b261fede..5b6b792f 100644 --- a/dance/transforms/graph/dstg_graph.py +++ b/dance/transforms/graph/dstg_graph.py @@ -1,3 +1,5 @@ +from typing import Sequence, Union + import networkx as nx import numpy as np import pandas as pd @@ -32,17 +34,23 @@ class DSTGraph(BaseTransform): _DISPLAY_ATTRS = ("k_filter", "num_cc", "ref_split", "inf_split") - def __init__(self, k_filter=200, num_cc=30, *, ref_split: str = "train", inf_split: str = "test", **kwargs): + def __init__(self, k_filter=200, num_cc=30, *, ref_split: str = "train", inf_split: str = "test", + channels: Sequence[Union[str, None]] = (None, None), + channel_types: Sequence[Union[str, None]] = ("obsm", "obsm"), **kwargs): super().__init__(**kwargs) self.k_filter = k_filter self.num_cc = num_cc self.ref_split = ref_split self.inf_split = inf_split + self.channels = channels + self.channel_types = channel_types def __call__(self, data): - x_ref = data.get_feature(return_type="numpy", split_name=self.ref_split) - x_inf = data.get_feature(return_type="numpy", split_name=self.inf_split) + x_ref = data.get_feature(return_type="numpy", split_name=self.ref_split, channel=self.channels[0], + channel_type=self.channel_types[0]) + x_inf = data.get_feature(return_type="numpy", split_name=self.inf_split, channel=self.channels[1], + channel_type=self.channel_types[1]) adj = compute_dstg_adj(x_ref, x_inf, k_filter=self.k_filter, num_cc=self.num_cc) data.data.obsp[self.out] = adj diff --git a/dance/transforms/misc.py b/dance/transforms/misc.py index fa765928..8b47c8b5 100644 --- a/dance/transforms/misc.py +++ b/dance/transforms/misc.py @@ -1,6 +1,10 @@ from pprint import pformat +from typing import Optional + +import mudata as md from dance import logger +from dance.data.base import Data from dance.registry import register_preprocessor from dance.transforms.base import BaseTransform from dance.typing import Any, Dict, Tuple @@ -153,3 +157,21 @@ def __init__(self, *, split_name: str, **kwargs): def __call__(self, data): self.logger.info("Popping split: {self.split_name!r}") data.pop(split_name=self.split_name) + + +@register_preprocessor("misc") +class AlignMod(BaseTransform): + """Aligning mods and metadata in multimodal data.""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def __call__(self, data: Data) -> Data: + mod1, mod2, meta1, meta2, test_sol = data.data.mod.values() + meta1 = meta1[:, mod1.var.index] + meta2 = meta2[:, mod2.var.index] + test_sol = test_sol[:, mod1.var.index] + data.data.mod["meta1"] = meta1 + data.data.mod["meta2"] = meta2 + data.data.mod["test_sol"] = test_sol + return data diff --git a/dance/transforms/normalize.py b/dance/transforms/normalize.py index f2deedae..71ab6622 100644 --- a/dance/transforms/normalize.py +++ b/dance/transforms/normalize.py @@ -5,6 +5,7 @@ import numpy as np import pandas as pd import scanpy as sc +import scipy import scipy.sparse as sp import statsmodels.discrete.discrete_model import statsmodels.nonparametric.kernel_regression @@ -18,9 +19,11 @@ from dance.typing import Dict, Iterable, List, Literal, LogLevel, NormMode, Number, Optional, Union from dance.utils.matrix import normalize from dance.utils.status import deprecated +from dance.utils.wrappers import add_mod_and_transform @register_preprocessor("normalize") +@add_mod_and_transform class ScaleFeature(BaseTransform): """Scale the feature matrix in the AnnData object. @@ -169,6 +172,37 @@ def __call__(self, data: Data) -> Data: @register_preprocessor("normalize") +@add_mod_and_transform +class tfidfTransform(BaseTransform): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.idf = None + self.fitted = False + + def fit(self, X): + self.idf = X.shape[0] / X.sum(axis=0) + self.fitted = True + + def transform(self, X): + if not self.fitted: + raise RuntimeError('Transformer was not fitted on any data') + if scipy.sparse.issparse(X): + tf = X.multiply(1 / X.sum(axis=1)) + return tf.multiply(self.idf).tocsr() + else: + tf = X / X.sum(axis=1, keepdims=True) + return tf * self.idf + + def __call__(self, data): + X = data.data.X + self.fit(X) + data.data.X = self.transform(X) + return data + + +@register_preprocessor("normalize") +@add_mod_and_transform class ScTransform(BaseTransform): """ScTransform normalization and variance stabiliation. @@ -399,7 +433,8 @@ def __call__(self, data: Data): z[gn[genes_step1]] = 1 w = pd.Series(index=gn, data=np.zeros(gn.size, dtype='int')) - w[gn] = genes_log_gmean + # w[gn] = genes_log_gmean + w[gn] = genes_log_gmean.astype(int) #need to think selected_data.var['genes_step1_sct'] = z selected_data.var['log10_gmean_sct'] = w @@ -453,6 +488,8 @@ def _parallel_init(igenes_bin_regress, iumi_bin, ign, imm, ips): def _parallel_wrapper(j): name = gn[genes_bin_regress[j]] y = umi_bin[:, j].A.flatten() + y[np.isinf(y) | np.isnan(y)] = 0 + mm[np.isinf(mm) | np.isnan(mm)] = 0 pr = statsmodels.discrete.discrete_model.Poisson(y, mm) res = pr.fit(disp=False) mu = res.predict() @@ -490,6 +527,7 @@ def info(n, th, mu, y, w): @register_preprocessor("normalize") +@add_mod_and_transform class Log1P(AnnDataTransform): """Logarithmize the data matrix. @@ -527,6 +565,7 @@ def __init__(self, base: Optional[Number] = None, copy: bool = False, chunked: b @register_preprocessor("normalize") +@add_mod_and_transform class NormalizeTotal(AnnDataTransform): """Normalize counts per cell. @@ -583,9 +622,15 @@ def __init__(self, target_sum: Optional[float] = None, max_fraction: float = 0.0 if max_fraction == 1.0: self.logger.info("max_fraction set to 1.0, this is equivalent to setting exclude_highly_expressed=False.") + def __call__(self, data): + if scipy.sparse.issparse(data.data.X): + data.data.X = np.array(data.data.X.todense()) + return super().__call__(data) + @register_preprocessor("normalize") -@deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") +@add_mod_and_transform +# @deprecated(msg="will be replaced by builtin bypass mechanism in pipeline") class NormalizePlaceHolder(BaseTransform): """Used as a placeholder to skip the process.""" @@ -597,6 +642,7 @@ def __call__(self, data: Data) -> Data: @register_preprocessor("normalize") +@add_mod_and_transform class NormalizeTotalLog1P(BaseTransform): """Normalize total counts followed by log1p transformation. diff --git a/dance/transforms/scn_feature.py b/dance/transforms/scn_feature.py index da86e5d1..9a959a3a 100644 --- a/dance/transforms/scn_feature.py +++ b/dance/transforms/scn_feature.py @@ -49,7 +49,7 @@ def __call__(self, data): # sc.pp.scale(adata, max_value=10) # Filtering shouldn't be here norm_exp_df = adata.to_df() - cell_type_df = cell_type_df.loc[adata.obs_names] # not necessary, but kept here in case we subsample cells + # cell_type_df = cell_type_df.loc[adata.obs_names] # not necessary, but kept here in case we subsample cells # Get differentially expressed genes and gene pairs cell_type_array = cell_type_df.columns.values[cell_type_df.values.argmax(1)] diff --git a/dance/utils/wrappers.py b/dance/utils/wrappers.py index eb20eff7..a7f308ee 100644 --- a/dance/utils/wrappers.py +++ b/dance/utils/wrappers.py @@ -1,11 +1,15 @@ import datetime import functools import time +from typing import Union +import anndata +import mudata import numpy as np import torch from dance import logger +from dance.data.base import Data from dance.typing import Any, Callable @@ -85,3 +89,48 @@ def wrapped_func(*args): return func(*new_args) return wrapped_func + + +import functools + + +def add_mod_and_transform(cls): + """A decorator that modifies a class to add functionality for working with specific + modalities (`mod`) in a `mudata` object.""" + original_init = cls.__init__ + original_call = cls.__call__ + cls.add_mod_and_transform = "add_mod_and_transform" + + @functools.wraps(original_init) + def new_init(self, *args, **kwargs): + mod = kwargs.pop('mod', None) + original_init(self, *args, **kwargs) + self.mod = mod + + @functools.wraps(original_call) + def new_call(self, data: Data, *args, **kwargs): + """ + Parameters + ---------- + data : Data + The input data object containing the `mudata` with multiple modalities. + Returns + ------- + Any + The result of the original_call method. + """ + if hasattr(self, 'mod') and self.mod is not None: + md_data = data.data + ad_data = Data(data=transform_mod_to_anndata(md_data, self.mod)) + res = original_call(self, ad_data, *args, **kwargs) + data.data.mod[self.mod] = ad_data.data + else: + return original_call(self, data, *args, **kwargs) + + cls.__init__ = new_init + cls.__call__ = new_call + return cls + + +def transform_mod_to_anndata(mod_data: mudata.MuData, mod_key: str): + return mod_data.mod[mod_key] diff --git a/examples/atlas/__init__.py b/examples/atlas/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/atlas/config/atlas_template_yamls/cta_actinn/pipeline_params_tuning_config.yaml b/examples/atlas/config/atlas_template_yamls/cta_actinn/pipeline_params_tuning_config.yaml new file mode 100644 index 00000000..fb607022 --- /dev/null +++ b/examples/atlas/config/atlas_template_yamls/cta_actinn/pipeline_params_tuning_config.yaml @@ -0,0 +1,73 @@ +type: preprocessor +tune_mode: pipeline_params +pipeline_tuning_top_k: 3 +parameter_tuning_freq_n: 20 +pipeline: + - type: filter.gene + include: + - FilterGenesPercentile + - FilterGenesScanpyOrder + - FilterGenesPlaceHolder + default_params: + FilterGenesScanpyOrder: + order: ["min_counts", "min_cells", "max_counts", "max_cells"] + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 + - type: normalize + include: + - ScaleFeature + - ScTransform + - Log1P + - NormalizeTotal + - NormalizePlaceHolder + default_params: + ScTransform: + processes_num: 8 + - type: filter.gene + include: + - HighlyVariableGenesLogarithmizedByMeanAndDisp + - HighlyVariableGenesRawCount + - HighlyVariableGenesLogarithmizedByTopGenes + - FilterGenesTopK + - FilterGenesRegression + - FilterGenesNumberPlaceHolder + default_params: + FilterGenesTopK: + num_genes: 3000 + FilterGenesRegression: + num_genes: 3000 + HighlyVariableGenesRawCount: + n_top_genes: 3000 + HighlyVariableGenesLogarithmizedByTopGenes: + n_top_genes: 3000 + - type: feature.cell + include: + - WeightedFeaturePCA + - WeightedFeatureSVD + - CellPCA + - CellSVD + - GaussRandProjFeature # Registered custom preprocessing func + - FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO + default_params: + WeightedFeaturePCA: + split_name: train + WeightedFeatureSVD: + split_name: train + - type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +wandb: + entity: xzy11632 + project: dance-dev + method: grid #try grid to provide a comprehensive search + metric: + name: acc # val/acc + goal: maximize diff --git a/examples/atlas/config/atlas_template_yamls/cta_celltypist/pipeline_params_tuning_config.yaml b/examples/atlas/config/atlas_template_yamls/cta_celltypist/pipeline_params_tuning_config.yaml new file mode 100644 index 00000000..801da1d7 --- /dev/null +++ b/examples/atlas/config/atlas_template_yamls/cta_celltypist/pipeline_params_tuning_config.yaml @@ -0,0 +1,73 @@ +type: preprocessor +tune_mode: pipeline_params +pipeline_tuning_top_k: 3 +parameter_tuning_freq_n: 20 +pipeline: + - type: filter.gene + include: + - FilterGenesPercentile + - FilterGenesScanpyOrder + - FilterGenesPlaceHolder + default_params: + FilterGenesScanpyOrder: + order: ["min_counts", "min_cells", "max_counts", "max_cells"] + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 + - type: normalize + include: + - ScaleFeature + - ScTransform + - Log1P + - NormalizeTotal + - NormalizePlaceHolder + default_params: + ScTransform: + processes_num: 8 + - type: filter.gene + include: + - HighlyVariableGenesLogarithmizedByMeanAndDisp + - HighlyVariableGenesRawCount + - HighlyVariableGenesLogarithmizedByTopGenes + - FilterGenesTopK + - FilterGenesRegression + # - FilterGenesNumberPlaceHolder + default_params: + FilterGenesTopK: + num_genes: 3000 + FilterGenesRegression: + num_genes: 3000 + HighlyVariableGenesRawCount: + n_top_genes: 3000 + HighlyVariableGenesLogarithmizedByTopGenes: + n_top_genes: 3000 + - type: feature.cell + include: + - WeightedFeaturePCA + - WeightedFeatureSVD + - CellPCA + - CellSVD + - GaussRandProjFeature # Registered custom preprocessing func + - FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO + default_params: + WeightedFeaturePCA: + split_name: train + WeightedFeatureSVD: + split_name: train + - type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +wandb: + entity: xzy11632 + project: dance-dev + method: grid #try grid to provide a comprehensive search + metric: + name: acc # val/acc + goal: maximize diff --git a/examples/atlas/config/atlas_template_yamls/cta_scdeepsort/pipeline_params_tuning_config.yaml b/examples/atlas/config/atlas_template_yamls/cta_scdeepsort/pipeline_params_tuning_config.yaml new file mode 100644 index 00000000..220ad410 --- /dev/null +++ b/examples/atlas/config/atlas_template_yamls/cta_scdeepsort/pipeline_params_tuning_config.yaml @@ -0,0 +1,73 @@ +type: preprocessor +tune_mode: pipeline_params +pipeline_tuning_top_k: 3 +parameter_tuning_freq_n: 20 +pipeline: + - type: filter.gene + include: + - FilterGenesPercentile + - FilterGenesScanpyOrder + - FilterGenesPlaceHolder + default_params: + FilterGenesScanpyOrder: + order: ["min_counts", "min_cells", "max_counts", "max_cells"] + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 + - type: normalize + include: + - ScaleFeature + - ScTransform + - Log1P + - NormalizeTotal + - NormalizePlaceHolder + default_params: + ScTransform: + processes_num: 8 + - type: filter.gene + include: + - HighlyVariableGenesLogarithmizedByMeanAndDisp + - HighlyVariableGenesRawCount + - HighlyVariableGenesLogarithmizedByTopGenes + - FilterGenesTopK + - FilterGenesRegression + # - FilterGenesNumberPlaceHolder + default_params: + FilterGenesTopK: + num_genes: 3000 + FilterGenesRegression: + num_genes: 3000 + HighlyVariableGenesRawCount: + n_top_genes: 3000 + HighlyVariableGenesLogarithmizedByTopGenes: + n_top_genes: 3000 + - type: feature.cell + include: + - WeightedFeaturePCA + - WeightedFeatureSVD + # - FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO + default_params: + WeightedFeaturePCA: + split_name: train + WeightedFeatureSVD: + split_name: train + - type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell + - type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +wandb: + entity: xzy11632 + project: dance-dev + method: grid #try grid to provide a comprehensive search + metric: + name: acc # val/acc + goal: maximize diff --git a/examples/atlas/config/atlas_template_yamls/cta_singlecellnet/pipeline_params_tuning_config.yaml b/examples/atlas/config/atlas_template_yamls/cta_singlecellnet/pipeline_params_tuning_config.yaml new file mode 100644 index 00000000..d34f34f2 --- /dev/null +++ b/examples/atlas/config/atlas_template_yamls/cta_singlecellnet/pipeline_params_tuning_config.yaml @@ -0,0 +1,59 @@ +type: preprocessor +tune_mode: pipeline_params +pipeline_tuning_top_k: 3 +parameter_tuning_freq_n: 20 +pipeline: + - type: filter.gene + include: + - FilterGenesPercentile + - FilterGenesScanpyOrder + - FilterGenesPlaceHolder + default_params: + FilterGenesScanpyOrder: + order: ["min_counts", "min_cells", "max_counts", "max_cells"] + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 + - type: normalize + include: + - ScaleFeature + - ScTransform + - Log1P + - NormalizeTotal + - NormalizePlaceHolder + default_params: + ScTransform: + processes_num: 8 + - type: filter.gene + include: + - HighlyVariableGenesLogarithmizedByMeanAndDisp + - HighlyVariableGenesRawCount + - HighlyVariableGenesLogarithmizedByTopGenes + - FilterGenesTopK + - FilterGenesRegression + - FilterGenesNumberPlaceHolder + default_params: + FilterGenesTopK: + num_genes: 3000 + FilterGenesRegression: + num_genes: 3000 + HighlyVariableGenesRawCount: + n_top_genes: 3000 + HighlyVariableGenesLogarithmizedByTopGenes: + n_top_genes: 3000 + - type: feature.cell + target: SCNFeature + - type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +wandb: + entity: xzy11632 + project: dance-dev + method: grid # try grid to provide a comprehensive search + metric: + name: acc # val/acc + goal: maximize diff --git a/examples/atlas/config/commands.yaml b/examples/atlas/config/commands.yaml new file mode 100644 index 00000000..7d058c51 --- /dev/null +++ b/examples/atlas/config/commands.yaml @@ -0,0 +1,9 @@ +algorithms: + cta_actinn: + command: "python main.py --species {species} --tissue {tissue} --train_dataset {dataset_id} --filetype {filetype} --count {count} --device {device} > {dataset_id}/out.log 2>&1 &" + cta_singlecellnet: + command: "python main.py --species {species} --tissue {tissue} --train_dataset {dataset_id} --filetype {filetype} --count {count} > {dataset_id}/out.log 2>&1 &" + cta_celltypist: + command: "python main.py --species {species} --tissue {tissue} --train_dataset {dataset_id} --filetype {filetype} --count {count} > {dataset_id}/out.log 2>&1 &" + cta_scdeepsort: + command: "python main.py --species {species} --tissue {tissue} --train_dataset {dataset_id} --filetype {filetype} --count {count} --device {device} > {dataset_id}/out.log 2>&1 &" diff --git a/examples/atlas/config/readme.md b/examples/atlas/config/readme.md new file mode 100644 index 00000000..9494cd57 --- /dev/null +++ b/examples/atlas/config/readme.md @@ -0,0 +1,5 @@ +Generate configuration files through setup_run.py and execute + +run_config.csv contains runtime configurations including dataset_id, species, tissue, etc. +commands.yaml contains execution commands including algorithm_name, dataset_id, etc. +atlas_template_yamls contains template configuration files including preprocessing functions, etc. diff --git a/examples/atlas/config/run_config.csv b/examples/atlas/config/run_config.csv new file mode 100644 index 00000000..aaa4db91 --- /dev/null +++ b/examples/atlas/config/run_config.csv @@ -0,0 +1,21 @@ +algorithm_name,dataset_id,species,tissue,filetype,count,device +cta_actinn,fa27492b-82ff-4ab7-ac61-0e2b184eee67(Lung),human,Lung,h5ad,800,cuda:1 +cta_actinn,4ed927e9-c099-49af-b8ce-a2652d069333(Lung),human,Lung,h5ad,800,cuda:5 +cta_actinn,01209dce-3575-4bed-b1df-129f57fbc031(Lung),human,Lung,h5ad,800,cuda:6 +cta_actinn,c5d88abe-f23a-45fa-a534-788985e93dad(Lung),human,Lung,h5ad,800,cuda:7 +cta_actinn,9968be68-ab65-4a38-9e1a-c9b6abece194,human,Lung,h5ad,800,cuda:0 +cta_actinn,1e6a6ef9-7ec9-4c90-bbfb-2ad3c3165fd1,human,Lung,h5ad,800,cuda:1 +cta_actinn,486486d4-9462-43e5-9249-eb43fa5a49a6,human,Lung,h5ad,800,cuda:2 +cta_actinn,7b3368a5-c1a0-4973-9e75-d95b4150c7da,human,Lung,h5ad,800,cuda:3 +cta_actinn,e04daea4-4412-45b5-989e-76a9be070a89,human,Lung,h5ad,800,cuda:4 +cta_actinn,e9175006-8978-4417-939f-819855eab80e,human,Lung,h5ad,800,cuda:5 +cta_actinn,0ba16f4b-cb87-4fa3-9363-19fc51eec6e7,human,Lung,h5ad,800,cuda:6 +cta_actinn,a68b64d8-aee3-4947-81b7-36b8fe5a44d2(Lung),human,Lung,h5ad,800,cuda:7 +cta_actinn,8c42cfd0-0b0a-46d5-910c-fc833d83c45e,human,Lung,h5ad,800,cuda:0 +cta_actinn,2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Lung),human,Lung,h5ad,800,cuda:1 +cta_actinn,d8da613f-e681-4c69-b463-e94f5e66847f,human,Lung,h5ad,800,cuda:2 +cta_actinn,4023a2bc-6325-47db-bfdf-9639e91042c2,human,Lung,h5ad,800,cuda:3 +cta_actinn,71be997d-ff75-41b9-8a9f-1288c865f921(Lung),human,Lung,h5ad,800,cuda:4 +cta_actinn,53d208b0-2cfd-4366-9866-c3c6114081bc(Pancreas),human,Pancreas,h5ad,800,cuda:5 +cta_actinn,78f10833-3e61-4fad-96c9-4bbd4f14bdfa,human,Pancreas,h5ad,800,cuda:6 +cta_actinn,f7c1c579-2dc0-47e2-ba19-8165c5a0e353(Pancreas),human,Pancreas,h5ad,800,cuda:7 diff --git a/examples/atlas/demos/__init__.py b/examples/atlas/demos/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/atlas/demos/main.py b/examples/atlas/demos/main.py new file mode 100644 index 00000000..19e7f268 --- /dev/null +++ b/examples/atlas/demos/main.py @@ -0,0 +1,93 @@ +# This script finds the most similar dataset from the atlas for a given user-uploaded dataset +# It calculates similarity scores and returns the best matching dataset along with its configurations + +import argparse +import json + +import pandas as pd +import scanpy as sc + +from dance import logger +from dance.atlas.sc_similarity.anndata_similarity import AnnDataSimilarity, get_anndata +from dance.settings import DANCEDIR, SIMILARITYDIR + + +def calculate_similarity(source_data, tissue, atlas_datasets, reduce_error, in_query): + """Calculate similarity scores between source data and atlas datasets. + + Args: + source_data: User uploaded AnnData object + tissue: Target tissue type + atlas_datasets: List of candidate datasets from atlas + reduce_error: Flag for error reduction mode - when True, applies a significant penalty + to configurations in the atlas that produced errors + in_query: Flag for query mode - when True, ranks similarity based on query performance, + when False, ranks based on inter-atlas comparison + + Returns: + Dictionary containing similarity scores for each atlas dataset + + """ + with open( + SIMILARITYDIR / + f"data/similarity_weights_results/{'reduce_error_' if reduce_error else ''}{'in_query_' if in_query else ''}sim_dict.json", + encoding='utf-8') as f: + sim_dict = json.load(f) + feature_name = sim_dict[tissue]["feature_name"] + w1 = sim_dict[tissue]["weight1"] + w2 = 1 - w1 + ans = {} + for target_file in atlas_datasets: + logger.info(f"calculating similarity for {target_file}") + atlas_data = get_anndata(tissue=tissue.capitalize(), species="human", filetype="h5ad", + train_dataset=[f"{target_file}"], data_dir=str(DANCEDIR / "examples/tuning/temp_data")) + similarity_calculator = AnnDataSimilarity(adata1=source_data, adata2=atlas_data, sample_size=10, + init_random_state=42, n_runs=1, tissue=tissue) + sim_target = similarity_calculator.get_similarity_matrix_A2B(methods=[feature_name, "metadata_sim"]) + ans[target_file] = sim_target[feature_name] * w1 + sim_target["metadata_sim"] * w2 + return ans + + +def main(args): + """Main function to process user data and find the most similar atlas dataset. + + Args: + args: Arguments containing: + - tissue: Target tissue type + - data_dir: Directory containing the source data + - source_file: Name of the source file + + Returns: + tuple containing: + - ans_file: ID of the most similar dataset + - ans_conf: Preprocess configuration dictionary for different cell type annotation methods + - ans_value: Similarity score of the best matching dataset + + """ + reduce_error = False + in_query = True + tissue = args.tissue + tissue = tissue.lower() + conf_data = pd.read_excel(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", sheet_name=tissue) + atlas_datasets = list(conf_data[conf_data["queryed"] == False]["dataset_id"]) + source_data = sc.read_h5ad(f"{args.data_dir}/{args.source_file}.h5ad") + + ans = calculate_similarity(source_data, tissue, atlas_datasets, reduce_error, in_query) + ans_file = max(ans, key=ans.get) + ans_value = ans[ans_file] + ans_conf = { + method: conf_data.loc[conf_data["dataset_id"] == ans_file, f"{method}_step2_best_yaml"].iloc[0] + for method in ["cta_celltypist", "cta_scdeepsort", "cta_singlecellnet", "cta_actinn"] + } + return ans_file, ans_conf, ans_value + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--tissue", default="Brain") + parser.add_argument("--data_dir", default=str(DANCEDIR / "examples/tuning/temp_data/train/human")) + parser.add_argument("--source_file", default="human_Brain364348b4-bc34-4fe1-a851-60d99e36cafa_data") + + args = parser.parse_args() + ans_file, ans_conf, ans_value = main(args) + print(ans_file, ans_conf, ans_value) diff --git a/examples/atlas/get_result_web.py b/examples/atlas/get_result_web.py new file mode 100644 index 00000000..2cfdeb7c --- /dev/null +++ b/examples/atlas/get_result_web.py @@ -0,0 +1,435 @@ +import argparse +import json +import os +from functools import partial +from pathlib import Path + +import numpy as np +import pandas as pd +from natsort import os_sort_key +from omegaconf import OmegaConf +from sympy import im +from tqdm import tqdm + +from dance import logger +from dance.settings import ATLASDIR, DANCEDIR, METADIR +from dance.utils import try_import + +# get yaml of best method + + +def check_identical_strings(string_list): + """Compare strings in a list to check if they are identical. + + Parameters + ---------- + string_list : list + List of strings to compare + + Returns + ------- + str + The common string if all strings are identical + + Raises + ------ + ValueError + If list is empty or strings are different + + """ + if not string_list: + raise ValueError("The list is empty") + + arr = np.array(string_list) + if not np.all(arr == arr[0]): + raise ValueError("Different strings found") + + return string_list[0] + + # if not string_list: + # raise ValueError("The list is empty") + # first_string = string_list[0] + # for s in string_list[1:]: + # if s != first_string: + # raise ValueError(f"Different strings found: '{first_string}' and '{s}'") + # return first_string + + +def get_sweep_url(step_csv: pd.DataFrame, single=True): + """Extract Weights & Biases sweep URL from a DataFrame containing run IDs. + + Parameters + ---------- + step_csv : pd.DataFrame + DataFrame containing wandb run IDs in an 'id' column + single : bool, optional + If True, only process the first run, by default True + + Returns + ------- + str + The wandb sweep URL + + """ + ids = step_csv["id"] + sweep_urls = [] + test_accs_exists = False + if "test_acc" in step_csv.columns: + test_accs = reversed(step_csv["test_acc"]) + test_accs_exists = True + else: + test_accs = [np.nan] * len(ids) + + for run_id, test_acc in tqdm( + zip(reversed(ids), + test_accs), leave=False): #The reversal of order is related to additional_sweep_ids.append(sweep_id) + if test_accs_exists and pd.isna(test_acc): + continue + api = wandb.Api(timeout=1000) + run = api.run(f"/{entity}/{project}/runs/{run_id}") + sweep_urls.append(run.sweep.url) + if single: + break + sweep_url = check_identical_strings(sweep_urls) + return sweep_url + + +import re + + +def spilt_web(url: str): + """Parse Weights & Biases URL to extract entity, project and sweep components. + + Parameters + ---------- + url : str + Complete wandb sweep URL + + Returns + ------- + tuple or None + Tuple of (entity, project, sweep_id) if parsing succeeds + None if parsing fails + + """ + pattern = r"https://wandb\.ai/([^/]+)/([^/]+)/sweeps/([^/]+)" + + match = re.search(pattern, url) + + if match: + entity = match.group(1) + project = match.group(2) + pattern = r'/sweeps/([^/?]+)' # Regular expression pattern + match = re.search(pattern, url) + if match: + sweep_id = match.group(1) + return entity, project, sweep_id + return None + else: + print(url) + print("No match found") + + +def get_metric(run, metric_col): + """Extract metric value from wandb run. + + Parameters + ---------- + run : wandb.Run + Weights & Biases run object + + Returns + ------- + float + Metric value or negative infinity if metric not found + + """ + if metric_col not in run.summary: + return float('-inf') # Return -inf for missing metrics to handle in comparisons + return run.summary[metric_col] + + +def get_best_method(urls, metric_col="test_acc"): + """Find the best performing method across multiple wandb sweeps. + + Parameters + ---------- + urls : list + List of wandb sweep URLs to compare + metric_col : str, optional + Metric column name to use for comparison, by default "test_acc" + + Returns + ------- + tuple + (best_step_name, best_run, best_metric_value) where: + - best_step_name: name of the step with best performance + - best_run: wandb run object of best performing run + - best_metric_value: value of the metric for best run + + """ + all_best_run = None + all_best_step_name = None + step_names = ["step2", "step3_0", "step3_1", "step3_2"] + step2_best_run = None + # Track run statistics + run_states = {"all_total_runs": 0, "all_finished_runs": 0} + + for step_name, url in zip(step_names, urls): + _, _, sweep_id = spilt_web(url) + sweep = wandb.Api(timeout=1000).sweep(f"{entity}/{project}/{sweep_id}") + + # Update run statistics + finished_runs = [run for run in sweep.runs if run.state == "finished"] + run_states.update({ + f"{step_name}_total_runs": len(sweep.runs), + f"{step_name}_finished_runs": len(finished_runs) + }) + run_states["all_total_runs"] += run_states[f"{step_name}_total_runs"] + run_states["all_finished_runs"] += run_states[f"{step_name}_finished_runs"] + + # Find best run based on optimization goal + goal = sweep.config["metric"]["goal"] + best_run = max(sweep.runs, key=partial(get_metric, metric_col=metric_col)) if goal == "maximize" else \ + min(sweep.runs, key=partial(get_metric, metric_col=metric_col)) if goal == "minimize" else \ + None + + if best_run is None: + raise RuntimeError("Optimization goal must be either 'minimize' or 'maximize'") + + if metric_col not in best_run.summary: + continue + if all_best_run is None: + all_best_run = best_run + all_best_step_name = step_name + elif all_best_run.summary[metric_col] < best_run.summary[metric_col] and goal == "maximize": + all_best_run = best_run + all_best_step_name = step_name + elif all_best_run.summary[metric_col] > best_run.summary[metric_col] and goal == "minimize": + all_best_run = best_run + all_best_step_name = step_name + if step2_best_run is None and step_name == "step2": + step2_best_run = best_run + num = run_states["all_finished_runs"] / run_states["all_total_runs"] + run_states["finished_rate"] = f"{num:.2%}" + need_to_check = num < 0.6 + runs_states_str = "|".join([f"{k}:{v}" for k, v in run_states.items()]) + return all_best_step_name, all_best_run, all_best_run.summary[ + metric_col], runs_states_str, need_to_check, step2_best_run, step2_best_run.summary[metric_col] + + +def get_best_yaml(step_name, best_run, file_path): + """Generate YAML configuration for the best performing wandb run. + + Parameters + ---------- + step_name : str + Name of the step ('step2' or 'step3_X') + best_run : wandb.Run + Best performing wandb run object + file_path : str + Path to configuration files + + Returns + ------- + str + YAML string containing the best configuration + + """ + if step_name == "step2": + conf = OmegaConf.load(f"{file_path}/pipeline_params_tuning_config.yaml") + for i, fun in enumerate(conf["pipeline"]): + if "include" not in fun: + continue + type_fun = fun["type"] + prefix = f"pipeline.{i}.{type_fun}" + # filtered_dict = {k: v for k, v in b_run.config.items() if k==prefix}.items()[0] + fun_name = best_run.config[prefix] + fun['target'] = fun_name + if 'params' not in fun: + fun['params'] = {} + if "default_params" in fun and fun_name in fun["default_params"]: + fun['params'].update(fun["default_params"][fun_name]) + del fun["include"] + del fun["default_params"] + else: + step3_number = step_name.split("_")[1] + conf = OmegaConf.load(f"{file_path}/config_yamls/params/{step3_number}_test_acc_params_tuning_config.yaml") + for i, fun in enumerate(conf['pipeline']): + if 'params_to_tune' not in fun: + continue + target = fun["target"] + prefix = f"params.{i}.{target}" + filtered_dict = {k: v for k, v in best_run.config.items() if k.startswith(prefix)} + for k, v in filtered_dict.items(): + param_name = k.split(".")[-1] + fun['params_to_tune'][param_name] = v + if "params" not in fun: + fun["params"] = {} + fun["params"].update(fun['params_to_tune']) + del fun["params_to_tune"] + return OmegaConf.to_yaml(conf["pipeline"]) + + +def check_exist(file_path): + """Check if results directory exists and contains multiple result files. + + Parameters + ---------- + file_path : str + Path to check for results + + Returns + ------- + bool + True if valid results exist (directory exists and contains >1 file) + + """ + file_path = f"{file_path}/results/params/" + if os.path.exists(file_path) and os.path.isdir(file_path): + file_num = len(os.listdir(file_path)) + return file_num > 1 + else: + return False + + +def get_new_ans(tissue): + ans = [] + # temp=all_datasets[all_datasets["tissue"] == tissue]["data_fname"].tolist() + collect_datasets = [ + (collect_dataset.split(tissue)[1] + + (tissue + collect_dataset.split(tissue)[2] if len(collect_dataset.split(tissue)) >= 3 else '')).split('_')[0] + for collect_dataset in all_datasets[all_datasets["tissue"] == tissue]["data_fname"].tolist() + ] + + for method_folder in tqdm(methods): + for dataset_id in collect_datasets: + if dataset_id == "0b4a15a7-4e9e-4555-9733-2423e5c66469": #f72958f5-7f42-4ebb-98da-445b0c6de516 + pass + file_path = DANCEDIR / f"examples/tuning/{method_folder}/{dataset_id}" + if not check_exist(file_path): + continue + step2_url = get_sweep_url(pd.read_csv(f"{file_path}/results/pipeline/best_test_acc.csv")) + step3_urls = [] + for i in range(3): + file_csv = f"{file_path}/results/params/{i}_best_test_acc.csv" + if not os.path.exists(file_csv): + print(f"File {file_csv} does not exist, skipping.") + continue + step3_urls.append(get_sweep_url(pd.read_csv(file_csv))) + step3_str = ",".join(step3_urls) + step_str = f"step2:{step2_url}|step3:{step3_str}" + step_name, best_run, best_res, run_stats_str, need_to_check, step2_best_run, step2_best_res = get_best_method( + [step2_url] + step3_urls) + best_yaml = get_best_yaml(step_name, best_run, file_path) + step2_best_yaml = get_best_yaml("step2", step2_best_run, file_path) + ans.append({ + "Dataset_id": dataset_id, + method_folder: step_str, + f"{method_folder}_best_yaml": best_yaml, + f"{method_folder}_best_res": best_res, + f"{method_folder}_run_stats": run_stats_str, + f"{method_folder}_check": need_to_check, + f"{method_folder}_step2_best_yaml": step2_best_yaml, + f"{method_folder}_step2_best_res": step2_best_res + }) + # with open('temp_ans.json', 'w') as f: + # json.dump(ans, f,indent=4) + new_df = pd.DataFrame(ans) + return new_df + + +def write_ans(tissue, new_df, output_file=None): + """Process and write results for a specific tissue type to CSV. + + Updates all columns with matching method_folder prefix only when new _best_res + value is greater than existing value. + + Parameters + ---------- + tissue : str + Tissue type being processed + new_df : pd.DataFrame + New results to be written + output_file : str, optional + Output file path. Defaults to 'sweep_results/{tissue}_ans.csv' + + """ + if output_file is None: + output_file = f"sweep_results/{tissue}_ans.csv" + + if 'Dataset_id' not in new_df.columns: + logger.warning("Dataset_id column missing in input DataFrame") + return + + # Reset index to ensure Dataset_id is a regular column + new_df = new_df.reset_index(drop=True) + + # Process new data by merging rows with same Dataset_id + new_df_processed = pd.DataFrame() + for dataset_id in new_df['Dataset_id'].unique(): + row_data = {'Dataset_id': dataset_id} + subset = new_df[new_df['Dataset_id'] == dataset_id] + for col in new_df.columns: + if col != 'Dataset_id': + non_null_values = subset[col].dropna().unique() + if len(non_null_values) > 0: + row_data[col] = non_null_values[0] + new_df_processed = pd.concat([new_df_processed, pd.DataFrame([row_data])]) + + if os.path.exists(output_file): + existing_df = pd.read_csv(output_file) + existing_df = existing_df.loc[:, ~existing_df.columns.str.contains('^Unnamed')] + merged_df = existing_df.copy() + + for col in new_df_processed.columns: + if col not in merged_df.columns: + merged_df[col] = pd.NA + + # Iterate through each new data row + for _, new_row in new_df_processed.iterrows(): + dataset_id = new_row['Dataset_id'] + existing_row = merged_df[merged_df['Dataset_id'] == dataset_id] + + if len(existing_row) > 0: + # Check best_res for each method + for method in methods: + best_res_col = f"{method}_best_res" + if best_res_col in new_row and best_res_col in existing_row: + new_value = new_row[best_res_col] + existing_value = existing_row[best_res_col].iloc[0] + + # Only update when new value exists and is greater than existing value + if pd.notna(new_value) and (pd.isna(existing_value) + or float(new_value) > float(existing_value)): + # Update all columns starting with method + method_cols = [col for col in new_row.index if col.startswith(method)] + for col in method_cols: + merged_df.loc[merged_df['Dataset_id'] == dataset_id, col] = new_row[col] + elif pd.notna(new_value) and pd.notna(existing_value): + # Print debug information + print(f"Skipping update for {dataset_id}, {method}: " + f"existing value ({existing_value}) >= new value ({new_value})") + else: + # If it's a new Dataset_id, add the entire row + merged_df = pd.concat([merged_df, pd.DataFrame([new_row])], ignore_index=True) + + merged_df.to_csv(output_file, index=False) + else: + new_df_processed.to_csv(output_file, index=False) + + +wandb = try_import("wandb") +entity = "xzy11632" +project = "dance-dev" +methods = ["cta_actinn", "cta_celltypist", "cta_scdeepsort", "cta_singlecellnet"] +if __name__ == "__main__": + # Initialize wandb and set global configuration + # Load dataset configuration and process results for tissue + all_datasets = pd.read_csv(METADIR / "scdeepsort.csv", header=0, skiprows=[i for i in range(1, 68)]) + parser = argparse.ArgumentParser() + parser.add_argument("--tissue", type=str, default="Lung") + args = parser.parse_args() + tissue = args.tissue.capitalize() + new_df = get_new_ans(tissue) + write_ans(tissue, new_df) diff --git a/examples/atlas/metadatas/readme.md b/examples/atlas/metadatas/readme.md new file mode 100644 index 00000000..da65a38b --- /dev/null +++ b/examples/atlas/metadatas/readme.md @@ -0,0 +1,8 @@ +# metadata/README.md + +## Data Source Description + +- Data source: scGPT +- Original path: data/cellxgene/example_data_dataset/save_file/metadatas +- Sync time: 2025-01-03 +- Usage instructions: see 'examples/atlas/sc_similarity_examples/readme.md' diff --git a/examples/atlas/sc_similarity_examples/__init__.py b/examples/atlas/sc_similarity_examples/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/atlas/sc_similarity_examples/cache/sweep_cache.json b/examples/atlas/sc_similarity_examples/cache/sweep_cache.json new file mode 100644 index 00000000..88271e95 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/cache/sweep_cache.json @@ -0,0 +1 @@ +{"w9issa4d": 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0.3205, 0.082, 0.3255, 0.325, 0.3125, 0.073, 0.2975, 0.323, 0.313, 0.3035, 0.3045, 0.313, 0.3225]} diff --git a/examples/atlas/sc_similarity_examples/configs/exclude_dataset.json b/examples/atlas/sc_similarity_examples/configs/exclude_dataset.json new file mode 100644 index 00000000..53eafe07 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/configs/exclude_dataset.json @@ -0,0 +1 @@ +{"heart":["97a17473-e2b1-4f31-a544-44a60773e2dd","f75f2ff4-2884-4c2d-b375-70de37a34507"]} diff --git a/examples/atlas/sc_similarity_examples/data_processing/__init__.py b/examples/atlas/sc_similarity_examples/data_processing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/atlas/sc_similarity_examples/data_processing/merge_result_metadata.py b/examples/atlas/sc_similarity_examples/data_processing/merge_result_metadata.py new file mode 100644 index 00000000..106bba95 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/data_processing/merge_result_metadata.py @@ -0,0 +1,18 @@ +tissues = ["blood", "brain", "heart", "intestine", "kidney", "lung", "pancreas"] +import pandas as pd + +from dance.settings import ATLASDIR, SIMILARITYDIR + +if __name__ == "__main__": + for tissue in tissues: + metadata_df = pd.read_csv(ATLASDIR / f"metadatas/{tissue}_metadata.csv") + sweep_result_df = pd.read_csv(ATLASDIR / f"sweep_results/{tissue.capitalize()}_ans.csv") + sweep_result_df = sweep_result_df.rename(columns={"Dataset_id": "dataset_id"}) + sweep_result_df["dataset_id"] = sweep_result_df["dataset_id"].str.split('(').str[0] + result_df = metadata_df.merge(sweep_result_df, how="outer", on="dataset_id") + # result_df.to_csv(SIMILARITYDIR / f"data/results/{tissue}_result.csv") + # for tissue in tissues: + # df=pd.read_csv(SIMILARITYDIR / f"data/results/{tissue}_result.csv") + with pd.ExcelWriter(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", mode='a', + if_sheet_exists='replace') as writer: + result_df.to_excel(writer, sheet_name=tissue) diff --git a/examples/atlas/sc_similarity_examples/readme.md b/examples/atlas/sc_similarity_examples/readme.md new file mode 100644 index 00000000..07a6a3b8 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/readme.md @@ -0,0 +1,8 @@ +\[scGPT->metadatas\]+\[get_result_web->sweep_results\]+\[data_processing/merge_result_metadata.py\]->\[data/cell_type_annotation_atlas.xlsx\] +\[data/cell_type_annotation_atlas.xlsx\]+\[similarity/analyze_atlas_accuracy.py\]->\[data/in_atlas_datas\] +\[similarity/example_usage_anndata.py\]+\[data/in_atlas_datas\]+\[data/cell_type_annotation_atlas.xlsx\]->\[data/dataset_similarity\] +\[data/dataset_similarity\]+\[similarity/process_tissue_similarity_matrices.py\]->\[data/new_sim\] + +#run_similarity_optimization.sh +\[data/new_sim\]+\[similarity/optimize_similarity_weights.py\]+\[cache/sweep_cache.json\]->\[data/similarity_weights_results\] +\[data/similarity_weights_results\]+\[similarity/visualize_atlas_performance.py\]+\[cache/sweep_cache.json\]->\[data/imgs\] diff --git a/examples/atlas/sc_similarity_examples/run_similarity_optimization.sh b/examples/atlas/sc_similarity_examples/run_similarity_optimization.sh new file mode 100644 index 00000000..8125db5f --- /dev/null +++ b/examples/atlas/sc_similarity_examples/run_similarity_optimization.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +# Define array +array=("blood" "brain" "heart" "intestine" "kidney" "lung" "pancreas") +# Loop through array and run Python scripts +for tissue in "${array[@]}" +do + # python similarity/example_usage_anndata.py --tissue "$tissue" >> example_usage_anndata.log 2>&1 + python similarity/optimize_similarity_weights.py --tissue "$tissue" + python visualization/visualize_atlas_performance.py --tissue "$tissue" + python similarity/optimize_similarity_weights.py --tissue "$tissue" --in_query + python visualization/visualize_atlas_performance.py --tissue "$tissue" --in_query + python similarity/optimize_similarity_weights.py --tissue "$tissue" --reduce_error + python visualization/visualize_atlas_performance.py --tissue "$tissue" --reduce_error + python similarity/optimize_similarity_weights.py --tissue "$tissue" --in_query --reduce_error + python visualization/visualize_atlas_performance.py --tissue "$tissue" --in_query --reduce_error + echo "Started processing tissue: $tissue" +done + +# Wait for all background processes to complete +wait + +echo "All Python scripts have completed execution" diff --git a/examples/atlas/sc_similarity_examples/similarity/analyze_atlas_accuracy.py b/examples/atlas/sc_similarity_examples/similarity/analyze_atlas_accuracy.py new file mode 100644 index 00000000..be034f51 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/similarity/analyze_atlas_accuracy.py @@ -0,0 +1,231 @@ +import argparse +import os +import re +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import yaml +from tqdm import tqdm + +from dance.settings import DANCEDIR, SIMILARITYDIR + +sys.path.append(str(DANCEDIR)) +import ast + +from get_result_web import get_sweep_url, spilt_web + +from dance import logger +from dance.settings import entity, project +from dance.utils import try_import + +file_root = str(Path(__file__).resolve().parent.parent) + + +def find_unique_matching_row(df, config_col, input_dict_list): + """Find a unique matching row in DataFrame based on specified criteria. + + Parameters + ---------- + df : pandas.DataFrame + DataFrame containing the data to search + config_col : str + Name of the DataFrame column containing dictionary list strings + input_dict_list : list of dict + Input dictionary list for matching + + Returns + ------- + pandas.Series + The matching row from the DataFrame + + Raises + ------ + ValueError + If the number of matching rows is not exactly one + + """ + + # Define a function for parsing strings and comparing + def is_match(config_str): + try: + # Safely parse string to Python object using ast.literal_eval + config = ast.literal_eval(config_str) + return config == input_dict_list + except (ValueError, SyntaxError): + # If parsing fails, no match + return False + + # Apply comparison function to get a boolean series + matches = df[config_col].apply(is_match) + + # Get all matching rows + matching_rows = df[matches] + + # Check number of matching rows + num_matches = len(matching_rows) + if num_matches == 1: + return matching_rows.iloc[0] + elif num_matches == 0: + raise ValueError("No matching rows found.") + else: + raise ValueError(f"Found {num_matches} matching rows, expected exactly one.") + + +wandb = try_import("wandb") + + +def is_matching_dict(yaml_str, target_dict): + """Compare YAML configuration with target dictionary. + + Parameters + ---------- + yaml_str : str + YAML configuration string to parse + target_dict : dict + Target dictionary to compare against + + Returns + ------- + bool + True if dictionaries match, False otherwise + + """ + # Parse YAML string + yaml_config = yaml.safe_load(yaml_str) + + # Build expected dictionary format + expected_dict = {} + for i, item in enumerate(yaml_config): + # Skip misc and graph.cell types, or SCNFeature targets + if item['type'] in ['misc', 'graph.cell'] or item['target'] == 'SCNFeature': + continue + key = f"pipeline.{i}.{item['type']}" + value = item['target'] + expected_dict[key] = value + + return expected_dict == target_dict + + +def get_ans(query_dataset, method): + """Get test accuracy results for a given dataset and method. + + Parameters + ---------- + query_dataset : str + Dataset identifier + method : str + Method name to analyze + + Returns + ------- + pandas.DataFrame or None + DataFrame containing test accuracy results, None if results don't exist + + """ + result_path = f"{file_root}/tuning/{method}/{query_dataset}/results/atlas/best_test_acc.csv" + if not os.path.exists(result_path): + logger.warning(f"{result_path} not exists") + return None + data = pd.read_csv(result_path) + sweep_url = get_sweep_url(data) + _, _, sweep_id = spilt_web(sweep_url) + sweep = wandb.Api().sweep(f"{entity}/{project}/{sweep_id}") + ans = pd.DataFrame(index=[method], columns=atlas_datasets) + for i, run_kwarg in enumerate(sweep.config["parameters"]["run_kwargs"]["values"]): + ans.loc[method, atlas_datasets[i]] = find_unique_matching_row(data, "run_kwargs", run_kwarg)["test_acc"] + # ans.append({atlas_datasets[i]:find_unique_matching_row(data,"run_kwargs",run_kwarg)["test_acc"]}) + return ans + + +def get_ans_from_cache(query_dataset, method): + """Get cached test accuracy results for atlas datasets. + + Parameters + ---------- + query_dataset : str + Query dataset identifier + method : str + Method name to analyze + + Returns + ------- + pandas.DataFrame + DataFrame containing test accuracy results from cache + + """ + # Get best method from step2 of atlas datasets + # Search accuracy according to best method (all values should exist) + ans = pd.DataFrame(index=[method], columns=[f"{atlas_dataset}_from_cache" for atlas_dataset in atlas_datasets]) + step_str = conf_data[conf_data["dataset_id"] == query_dataset][method].iloc[0] + if pd.isna(step_str): + logger.warning(f"{query_dataset} is nan in {method}") + return ans + sweep_url = re.search(r"step2:([^|]+)", step_str).group(1) + _, _, sweep_id = spilt_web(sweep_url) + sweep = wandb.Api().sweep(f"{entity}/{project}/{sweep_id}") + runs = sweep.runs + for atlas_dataset in tqdm(atlas_datasets): + best_yaml = conf_data[conf_data["dataset_id"] == atlas_dataset][f"{method}_step2_best_yaml"].iloc[0] + match_run = None + + # Find matching run configuration + for run in tqdm(runs, leave=False): + if isinstance(best_yaml, float) and np.isnan(best_yaml): + continue + if is_matching_dict(best_yaml, run.config): + if match_run is not None: + raise ValueError("Multiple matching runs found when only one expected") + match_run = run + + if match_run is None: + logger.warning(f"No matching configuration found for {atlas_dataset} with method {method}") + else: + ans.loc[method, f"{atlas_dataset}_from_cache"] = (match_run.summary["test_acc"] + if "test_acc" in match_run.summary else np.nan) + + return ans + + +ans_all = {} +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) +parser.add_argument("--methods", default=["cta_actinn", "cta_celltypist", "cta_scdeepsort", "cta_singlecellnet"], + nargs="+") +parser.add_argument("--tissue", type=str, default="pancreas") +args = parser.parse_args() +methods = args.methods +tissue = args.tissue + +# atlas_datasets = [ +# "01209dce-3575-4bed-b1df-129f57fbc031", "055ca631-6ffb-40de-815e-b931e10718c0", +# "2a498ace-872a-4935-984b-1afa70fd9886", "2adb1f8a-a6b1-4909-8ee8-484814e2d4bf", +# "3faad104-2ab8-4434-816d-474d8d2641db", "471647b3-04fe-4c76-8372-3264feb950e8", +# "4c4cd77c-8fee-4836-9145-16562a8782fe", "84230ea4-998d-4aa8-8456-81dd54ce23af", +# "8a554710-08bc-4005-87cd-da9675bdc2e7", "ae29ebd0-1973-40a4-a6af-d15a5f77a80f", +# "bc260987-8ee5-4b6e-8773-72805166b3f7", "bc2a7b3d-f04e-477e-96c9-9d5367d5425c", +# "d3566d6a-a455-4a15-980f-45eb29114cab", "d9b4bc69-ed90-4f5f-99b2-61b0681ba436", +# "eeacb0c1-2217-4cf6-b8ce-1f0fedf1b569" +# ] +# query_datasets = [ +# "c7775e88-49bf-4ba2-a03b-93f00447c958", "456e8b9b-f872-488b-871d-94534090a865", +# "738942eb-ac72-44ff-a64b-8943b5ecd8d9", "a5d95a42-0137-496f-8a60-101e17f263c8", +# "71be997d-ff75-41b9-8a9f-1288c865f921" +# ] +conf_data = pd.read_excel(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", sheet_name=tissue) +# conf_data = pd.read_csv(f"results/{tissue}_result.csv", index_col=0) +atlas_datasets = list(conf_data[conf_data["queryed"] == False]["dataset_id"]) +query_datasets = list(conf_data[conf_data["queryed"] == True]["dataset_id"]) +if __name__ == "__main__": + for query_dataset in query_datasets: + ans = [] + for method in methods: + ans.append(get_ans_from_cache(query_dataset, method)) + ans = pd.concat(ans) + ans_all[query_dataset] = ans + print(query_dataset) + # for k, v in ans_all.items(): + file_path = SIMILARITYDIR / f"data/in_atlas_datas/{tissue}/{str(methods)}_{query_dataset}_in_atlas.csv" + folder_path = Path(file_path).parent + folder_path.mkdir(parents=True, exist_ok=True) + ans.to_csv(file_path) diff --git a/examples/atlas/sc_similarity_examples/similarity/example_usage_anndata.py b/examples/atlas/sc_similarity_examples/similarity/example_usage_anndata.py new file mode 100644 index 00000000..87f7f92d --- /dev/null +++ b/examples/atlas/sc_similarity_examples/similarity/example_usage_anndata.py @@ -0,0 +1,187 @@ +import argparse +import json +import os +from pathlib import Path + +import numpy as np +import pandas as pd +import scanpy as sc +import torch +from anndata import AnnData +from scipy.sparse import issparse +from torch.utils.data import TensorDataset + +from dance import logger +from dance.atlas.sc_similarity.anndata_similarity import AnnDataSimilarity, get_anndata +from dance.settings import DANCEDIR, SIMILARITYDIR +from dance.utils import set_seed + +# target_files = [ +# "01209dce-3575-4bed-b1df-129f57fbc031", "055ca631-6ffb-40de-815e-b931e10718c0", +# "2a498ace-872a-4935-984b-1afa70fd9886", "2adb1f8a-a6b1-4909-8ee8-484814e2d4bf", +# "3faad104-2ab8-4434-816d-474d8d2641db", "471647b3-04fe-4c76-8372-3264feb950e8", +# "4c4cd77c-8fee-4836-9145-16562a8782fe", "84230ea4-998d-4aa8-8456-81dd54ce23af", +# "8a554710-08bc-4005-87cd-da9675bdc2e7", "ae29ebd0-1973-40a4-a6af-d15a5f77a80f", +# "bc260987-8ee5-4b6e-8773-72805166b3f7", "bc2a7b3d-f04e-477e-96c9-9d5367d5425c", +# "d3566d6a-a455-4a15-980f-45eb29114cab", "d9b4bc69-ed90-4f5f-99b2-61b0681ba436", +# "eeacb0c1-2217-4cf6-b8ce-1f0fedf1b569" +# ] +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) +parser.add_argument("--tissue", type=str, default="pancreas") +parser.add_argument("--data_dir", default=DANCEDIR / f"examples/tuning/temp_data") +args = parser.parse_args() + +data_dir = args.data_dir +set_seed(42) +tissue = args.tissue +# conf_data = pd.read_csv(f"results/{tissue}_result.csv", index_col=0) +conf_data = pd.read_excel(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", sheet_name=tissue) +atlas_datasets = list(conf_data[conf_data["queryed"] == False]["dataset_id"]) +query_datasets = list(conf_data[conf_data["queryed"] == True]["dataset_id"]) + + +class CustomEncoder(json.JSONEncoder): + + def default(self, obj): + if isinstance(obj, (np.float32, np.float64)): + return float(obj) + if isinstance(obj, (np.int32, np.int64)): + return int(obj) + if isinstance(obj, pd.DataFrame): + return obj.to_dict(orient='records') + return super().default(obj) + + +def dataset_from_anndata(adata: AnnData, label_key: str = 'cell_type', classes=None): + """Convert AnnData object to PyTorch TensorDataset. + + Parameters + ---------- + adata : AnnData + Input AnnData object + label_key : str, default='cell_type' + Column name in adata.obs containing cell type labels + classes : list, optional + Predefined class labels. If None, will be inferred from data + + Returns + ------- + TensorDataset + PyTorch dataset with features and labels + + """ + X = adata.X + if issparse(X): + X = X.toarray() + X_tensor = torch.from_numpy(X).float() + Y = adata.obs[label_key].values + if pd.api.types.is_numeric_dtype(Y): + targets = torch.LongTensor(Y) + if classes is None: + classes = sorted(np.unique(Y)) + else: + unique_classes = sorted(np.unique(Y)) + # class_to_idx = {cls: idx for idx, cls in enumerate(unique_classes)} + # Y_encoded = np.array([class_to_idx[cls] for cls in Y]) + targets = torch.LongTensor(Y.codes) + if classes is None: + classes = unique_classes + ds = TensorDataset(X_tensor, targets) + ds.targets = targets + ds.classes = classes + return ds + + +# def run_test_otdd(): +# data_root = "/home/zyxing/dance/examples/tuning/temp_data/train/human" +# for target_file in target_files: +# source_data = sc.read_h5ad(f"{data_root}/human_{tissue.capitalize()}{source_file}_data.h5ad") +# target_data = sc.read_h5ad(f"{data_root}/human_{tissue.capitalize()}{target_file}_data.h5ad") +# source_ds = dataset_from_anndata(source_data) +# target_ds = dataset_from_anndata(target_data) +# dist = DatasetDistance(source_ds, target_ds) +# dist.distance() + + +def run_test_case(source_file): + """Calculate similarity matrices between source and target datasets. + + Parameters + ---------- + source_file : str + Name of the source dataset file + + Returns + ------- + pandas.DataFrame + Similarity scores for different metrics + + """ + ans = {} + source_data = get_anndata(train_dataset=[f"{source_file}"], data_dir=data_dir, tissue=tissue.capitalize()) + + for target_file in atlas_datasets: + # source_data=sc.read_h5ad(f"{data_root}/{source_file}.h5ad") + # target_data=sc.read_h5ad(f"{data_root}/{target_file}.h5ad") + target_data = get_anndata(train_dataset=[f"{target_file}"], data_dir=data_dir, tissue=tissue.capitalize()) + + # Initialize similarity calculator with multiple metrics + similarity_calculator = AnnDataSimilarity( + adata1=source_data, adata2=target_data, sample_size=10, init_random_state=42, n_runs=1, + ground_truth_conf_path=SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", adata1_name=source_file, + adata2_name=target_file, tissue=tissue) + + # Calculate similarity using multiple methods + ans[target_file] = similarity_calculator.get_similarity_matrix_A2B(methods=[ + "wasserstein", "Hausdorff", "chamfer", "energy", "sinkhorn2", "bures", "spectral", "common_genes_num", + "ground_truth", "mmd", "metadata_sim" + ]) + + # Convert results to DataFrame and save + ans = pd.DataFrame(ans) + # ans_to_path = f'sims/{tissue}/sim_{source_file}.csv' + # os.makedirs(os.path.dirname(ans_to_path), exist_ok=True) + # ans.to_csv(ans_to_path) + return ans + + +start = False +query_data = os.listdir(SIMILARITYDIR / "data/in_atlas_datas" / f"{tissue}") +excel_path = SIMILARITYDIR / f"data/dataset_similarity/{tissue}_similarity.xlsx" +# with pd.ExcelWriter(file_root / f"{tissue}_similarity.xlsx", engine='openpyxl') as writer: +logger.info(f"tissue: {tissue}") +for source_file in query_datasets: + # if source_file[:4]=='e868': + # start=True + # if not start: + # continue + logger.info(f"source_file: {source_file}") + query_ans = pd.concat([ + pd.read_csv(SIMILARITYDIR / "data/in_atlas_datas" / f"{tissue}" / element, index_col=0) + for element in query_data if element.split("_")[-3] == source_file + ]) + rename_dict = {col: col.replace('_from_cache', '') for col in query_ans.columns if '_from_cache' in col} + query_ans = query_ans.rename(columns=rename_dict) + ans = run_test_case(source_file) + merged_df = pd.concat([query_ans, ans], join='inner') + if os.path.exists(excel_path): + excel = pd.ExcelFile(excel_path, engine='openpyxl') + if source_file[:4] in excel.sheet_names: + # Try to read the specified sheet + existing_df = pd.read_excel(SIMILARITYDIR / f"data/dataset_similarity/{tissue}_similarity.xlsx", + sheet_name=source_file[:4], engine="openpyxl", index_col=0) + # Find rows that exist in the new dataframe but not in the existing table + merged_df = pd.concat([existing_df, merged_df]) + merged_df = merged_df.applymap(lambda x: tuple(x) if isinstance(x, list) else x) + # Then deduplicate, using keep='last' instead of subset parameter + merged_df = merged_df[~merged_df.index.duplicated(keep='last')] + # merged_df = merged_df.drop_duplicates(subset=merged_df.index.name, keep='last') + excel.close() + if os.path.exists(excel_path): + mode = 'a' + if_sheet_exists = "replace" + else: + mode = 'w' + if_sheet_exists = None + with pd.ExcelWriter(excel_path, engine='openpyxl', mode=mode, if_sheet_exists=if_sheet_exists) as writer: + merged_df.to_excel(writer, sheet_name=source_file[:4]) diff --git a/examples/atlas/sc_similarity_examples/similarity/optimize_similarity_weights.py b/examples/atlas/sc_similarity_examples/similarity/optimize_similarity_weights.py new file mode 100644 index 00000000..030a2cef --- /dev/null +++ b/examples/atlas/sc_similarity_examples/similarity/optimize_similarity_weights.py @@ -0,0 +1,199 @@ +"""Calculate optimal weights for similarity metrics in cell type annotation. + +This script analyzes different similarity metrics (like Wasserstein, Hausdorff, etc.) and metadata similarity +to find optimal weights that minimize the total rank of correct cell type predictions across multiple datasets. + +The script: +1. Loads similarity scores from Excel files +2. Computes rankings for different cell type annotation methods +3. Finds optimal weights (w1, w2) for combining feature-based and metadata-based similarity +4. Outputs the best performing feature and its corresponding weight + +Returns +------- +DataFrame + Results containing feature names, weights, and corresponding total ranks + +""" + +import argparse +import os +import sys + +import numpy as np +import pandas as pd + +from dance.settings import SIMILARITYDIR + +sys.path.append(str(SIMILARITYDIR)) +print(sys.path) +from similarity.process_tissue_similarity_matrices import convert_to_complex +from visualization.visualize_atlas_performance import get_runs + +from dance.utils import set_seed, try_import + + +def get_ans(): + """Load similarity scores from Excel files for each dataset. + + Returns + ------- + dict + Dictionary mapping dataset IDs to their similarity score DataFrames + + """ + ans = {} + for query_dataset in query_datasets: + if query_dataset in exclude_dataset: + continue + data = pd.read_excel(SIMILARITYDIR / f"data/new_sim/{tissue}_similarity.xlsx", sheet_name=query_dataset[:4], + index_col=0) + ans[query_dataset] = data + return ans + + +def get_rank(reduce_error, in_query): + """Calculate rankings for each cell type annotation method. + + Updates the input DataFrames with rank columns for each method, where lower ranks + indicate better performance. + + """ + + def query_rank(x, test_accs, reduce_error): + if pd.isna(x): + if reduce_error: + return 10000 + else: + x = -0.01 + rank = sum(1 for test_acc in test_accs if test_acc > x) + 1 + return rank + + if in_query: + for query_dataset, data in ans.items(): + for method in methods: + rank_col = 'rank_' + method + data.loc[rank_col, :] = data.loc[method, :].apply( + lambda x: query_rank(x, get_runs(conf_data, query_dataset, method), reduce_error)) + else: + for query_dataset, data in ans.items(): + for method in methods: + rank_col = 'rank_' + method + # if method not in data.index: + # data.loc[rank_col, :] = 10000 + # continue + if reduce_error: + data.loc[rank_col, :] = data.loc[method, :].rank(ascending=False, method='min', na_option='keep') + data.loc[rank_col, :] = data.loc[rank_col, :].fillna(10000) + else: + data.loc[rank_col, :] = data.loc[method, :].rank(ascending=False, method='min', na_option='bottom') + + +def objective(w1, feature_name): + """Calculate total rank score for given weights and feature. + + Parameters + ---------- + w1 : float + Weight for the feature-based similarity (0-1) + feature_name : str + Name of the similarity feature to evaluate + + Returns + ------- + float + Total rank score (lower is better) + + """ + w2 = 1 - w1 + total_rank = 0 + for query_dataset, data in ans.items(): + df_A = data.copy() + # if feature_name == "bures": + df_A.loc[feature_name, :] = df_A.loc[feature_name, :].apply(convert_to_complex) + # print(df_A.loc[feature_name, :]) + df_A.loc['score_similarity', :] = w1 * df_A.loc[feature_name, :].values.astype(float) + w2 * df_A.loc[ + 'metadata_sim', :].values.astype(float) + if df_A.loc['score_similarity', :].isna().any(): + pass + df_A.loc['score_similarity', :] = df_A.loc['score_similarity', :].fillna(0) + max_idx = df_A.loc['score_similarity', :].idxmax() + max_B = df_A.loc[:, max_idx] + ranks = [] + for method in methods: + ranks.append(max_B.loc['rank_' + method]) + total_rank += np.sum(ranks) + return total_rank + + +if __name__ == "__main__": + pd.set_option('future.no_silent_downcasting', True) + wandb = try_import("wandb") + entity = "xzy11632" + project = "dance-dev" + # query_datasets = [ + # "c7775e88-49bf-4ba2-a03b-93f00447c958", + # "456e8b9b-f872-488b-871d-94534090a865", + # "738942eb-ac72-44ff-a64b-8943b5ecd8d9", + # # "a5d95a42-0137-496f-8a60-101e17f263c8", + # "71be997d-ff75-41b9-8a9f-1288c865f921" + # ] + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("--tissue", type=str, default="heart") + parser.add_argument("--reduce_error", action="store_true") + parser.add_argument("--in_query", action="store_true") + + args = parser.parse_args() + tissue = args.tissue + reduce_error = args.reduce_error + in_query = args.in_query + set_seed(42) + # conf_data = pd.read_csv(f"results/{tissue}_result.csv", index_col=0) + conf_data = pd.read_excel(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", sheet_name=tissue) + query_datasets = list(conf_data[conf_data["queryed"] == True]["dataset_id"]) + methods = ["cta_actinn", "cta_celltypist", "cta_scdeepsort", "cta_singlecellnet"] + feature_names = ["wasserstein", "Hausdorff", "chamfer", "energy", "sinkhorn2", "bures", "spectral", "mmd"] + import json + with open(SIMILARITYDIR / "configs/exclude_dataset.json", encoding='utf-8') as f: + exclude_dataset_json = json.load(f) + exclude_dataset = exclude_dataset_json[tissue] if tissue in exclude_dataset_json else [] + + ans = get_ans() + get_rank(reduce_error=reduce_error, in_query=in_query) + all_results = [] + # for query_dataset, data in ans.items(): + # data.to_csv(f"ranks/{tissue}/{query_dataset}_rank.csv") + for feature_name in feature_names: + w1_values = np.linspace(0, 1, 101) + results = [] + for w1 in w1_values: + total_rank = objective(w1, feature_name) + results.append({'feature_name': feature_name, 'w1': w1, 'total_rank': total_rank}) + all_results.extend(results) + # for w1 in w1_values: + # total_rank = objective(w1) + # results.append({'w1': w1, 'total_rank': total_rank}) + + results_df = pd.DataFrame(all_results) + results_df.to_csv(f"temp/{tissue}_results_df.csv") + best_result = results_df.loc[results_df['total_rank'].idxmin()] + + print('Best similarity feature:', best_result['feature_name']) + print('Best w1:', best_result['w1']) + print('Corresponding total rank:', best_result['total_rank']) + + import json + import os + result_path = SIMILARITYDIR / f"data/similarity_weights_results/{'reduce_error_' if reduce_error else ''}{'in_query_' if in_query else ''}sim_dict.json" + if not os.path.exists(result_path): + sim_dict = {} + else: + with open(result_path, encoding='utf-8') as f: + sim_dict = json.load(f) + sim_dict[tissue] = { + "feature_name": best_result['feature_name'], + "weight1": best_result['w1'], + "total rank": int(best_result['total_rank']) + } + with open(result_path, 'w', encoding='utf-8') as f: + json.dump(sim_dict, f, indent=4, ensure_ascii=False) diff --git a/examples/atlas/sc_similarity_examples/similarity/process_tissue_similarity_matrices.py b/examples/atlas/sc_similarity_examples/similarity/process_tissue_similarity_matrices.py new file mode 100644 index 00000000..19d3e8c7 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/similarity/process_tissue_similarity_matrices.py @@ -0,0 +1,146 @@ +import ast +import os + +import numpy as np +import pandas as pd + +from dance.settings import SIMILARITYDIR + + +def convert_to_complex(s): + """Convert string representations of complex numbers to float values. + + Parameters + ---------- + s : str or float + Input value to convert + + Returns + ------- + float + Real part of complex number or NaN if conversion fails + + """ + if isinstance(s, float) or isinstance(s, int): + return s + try: + s = ast.literal_eval(s) + return float(s.real) + except (ValueError, SyntaxError): + return np.nan + + +def convert_complex_value(x): + """Helper function to convert a single value.""" + if isinstance(x, str): + try: + complex_val = complex(x.strip('()')) + # If imaginary part is close to 0, return real part + if abs(complex_val.imag) < 1e-10: + return float(complex_val.real) + return complex_val + except ValueError: + return x + elif isinstance(x, complex): + # If imaginary part is close to 0, return real part + if abs(x.imag) < 1e-10: + return float(x.real) + return x + return x + + +def unify_complex_float_types_cell(df): + """Process by cell.""" + for col in df.columns: + for idx in df.index: + df.at[idx, col] = convert_complex_value(df.at[idx, col]) + return df + + +def unify_complex_float_types_row(df): + """Process by row.""" + for idx in df.index: + df.loc[idx] = df.loc[idx].apply(convert_complex_value) + return df + + +def unify_complex_float_types(df): + """Process by column.""" + for col in df.columns: + # Skip non-numeric columns + if not pd.api.types.is_numeric_dtype(df[col]): + continue + + # Check if contains complex numbers + has_complex = df[col].apply(lambda x: isinstance(x, complex)).any() + + if has_complex: + # Convert column to complex and process + df[col] = df[col].apply(convert_complex_value) + + return df + + +def process_excel_files(excel_files): + # List to store all data + all_data = [] + + for file_path in excel_files: + # Get filename (without extension) + file_name = os.path.splitext(os.path.basename(file_path))[0] + + # Read all sheets in Excel file + excel = pd.ExcelFile(file_path) + + # Process each sheet + for sheet_name in excel.sheet_names: + # Read data + df = pd.read_excel(file_path, sheet_name=sheet_name) + + # Transpose data + df_transposed = df.transpose() + + # Add filename and sheet name columns + df_transposed['file_name'] = file_name + df_transposed['sheet_name'] = sheet_name + + # Add data to list + all_data.append(df_transposed) + + # Merge all data + final_df = pd.concat(all_data, ignore_index=True) + + # Unify data types + final_df = unify_complex_float_types(final_df) + + # Save as CSV + output_path = os.path.join(os.path.dirname(excel_files[0]), 'combined_output.csv') + final_df.to_csv(output_path, encoding='utf-8-sig', index=True) + + return output_path + + +if __name__ == "__main__": + tissues = ["blood", "brain", "heart", "intestine", "kidney", "lung", "pancreas"] + for tissue in tissues: + file_path = SIMILARITYDIR / f"data/dataset_similarity/{tissue}_similarity.xlsx" + excel = pd.ExcelFile(file_path) + for sheet_name in excel.sheet_names: + df = pd.read_excel(file_path, sheet_name=sheet_name, index_col=0) + df = df[~df.index.duplicated(keep='last')] + # df=unify_complex_float_types_row(df) #Some complex numbers may lose precision, but it's not a big issue since only real parts are used for comparison + df = unify_complex_float_types_cell( + df + ) #Some complex numbers may lose precision, but it's not a big issue since only real parts are used for comparison + if os.path.exists(SIMILARITYDIR / f"data/new_sim/{tissue}_similarity.xlsx"): + mode = 'a' + if_sheet_exists = "replace" + else: + mode = 'w' + if_sheet_exists = None + with pd.ExcelWriter(SIMILARITYDIR / f"data/new_sim/{tissue}_similarity.xlsx", engine='openpyxl', mode=mode, + if_sheet_exists=if_sheet_exists) as writer: + df.to_excel(writer, sheet_name=sheet_name) + excel_files = [SIMILARITYDIR / f"data/new_sim/{tissue}_similarity.xlsx" for tissue in tissues] + output_file = process_excel_files(excel_files) + print(f"Combined data has been saved to: {output_file}") diff --git a/examples/atlas/sc_similarity_examples/visualization/__init__.py b/examples/atlas/sc_similarity_examples/visualization/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/atlas/sc_similarity_examples/visualization/visualize_atlas_performance.py b/examples/atlas/sc_similarity_examples/visualization/visualize_atlas_performance.py new file mode 100644 index 00000000..cb032f54 --- /dev/null +++ b/examples/atlas/sc_similarity_examples/visualization/visualize_atlas_performance.py @@ -0,0 +1,233 @@ +import argparse +import os +import re +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +from dance.settings import ATLASDIR, SIMILARITYDIR +from dance.utils import set_seed, try_import + +sys.path.append(str(SIMILARITYDIR)) +sys.path.append(str(ATLASDIR)) +import json + +import matplotlib.pyplot as plt +import seaborn as sns +from get_result_web import spilt_web +from similarity.process_tissue_similarity_matrices import convert_to_complex + +wandb = try_import("wandb") + +from dance.settings import entity, project + + +def get_accs(sweep): + """Extract test accuracies from a wandb sweep. + + Parameters + ---------- + sweep : wandb.Sweep + Sweep object containing multiple runs + + Returns + ------- + list + List of test accuracies from all runs + + """ + ans = [] + for run in sweep.runs: + if "test_acc" in run.summary: + ans.append(run.summary["test_acc"]) + return ans + + +# def get_runs(sweep_record): +# """Parse sweep URLs and collect all run results. + +# Parameters +# ---------- +# sweep_record : str +# String containing sweep URLs for different steps + +# Returns +# ------- +# list +# Combined list of test accuracies from all sweeps + +# """ +# step_links = {} +# pattern = r'(step\d+):((?:https?://[^|,]+(?:,)?)+)' +# matches = re.finditer(pattern, sweep_record) +# for match in matches: +# step = match.group(1) # e.g., 'step2' +# links_str = match.group(2) # e.g., 'https://...y31tzbnv' +# links = links_str.split(',') +# step_links[step] = links +# ans = [] +# for step, links in step_links.items(): +# for sweep_url in links: +# _, _, sweep_id = spilt_web(sweep_url) +# sweep = wandb.Api(timeout=1000).sweep(f"{entity}/{project}/{sweep_id}") +# ans += get_accs(sweep) +# return ans + + +def get_atlas_ans(query_dataset, method): + """Calculate atlas-based prediction accuracy for a given dataset and method. + + Parameters + ---------- + query_dataset : str + Dataset identifier + method : str + Method name to evaluate + + Returns + ------- + float + Predicted accuracy based on atlas similarity + + """ + data = pd.read_excel(SIMILARITYDIR / f"data/new_sim/{tissue}_similarity.xlsx", sheet_name=query_dataset[:4], + index_col=0) + weight1 = sim_dict[tissue]["weight1"] # Weight for feature-based similarity + weight2 = 1 - weight1 # Weight for metadata similarity + data.loc[feature_name, :] = data.loc[feature_name, :].apply(convert_to_complex) + weighted_sum = data.loc[feature_name, :] * weight1 + data.loc["metadata_sim", :] * weight2 + atlas_dataset_res = weighted_sum.idxmax() # Get most similar dataset + max_value = weighted_sum.max() + if method in data.index: + return data.loc[method, atlas_dataset_res], atlas_dataset_res + else: + return 0, "null" + + +def vis(data, target_value, title, ax): + """Create violin plot comparing distribution of accuracies with atlas prediction. + + Parameters + ---------- + data : list + List of accuracy values + target_value : float + Atlas-predicted accuracy value + title : str + Plot title + ax : matplotlib.axes.Axes + Axes object to plot on + + """ + # sns.boxplot(data=data, color='skyblue',ax=ax) + # if target_value is not np.nan: + # ax.axhline(y=target_value, color='red', linestyle='--', linewidth=2, label=f'atlas_value = {target_value}') + # ax.text(0, target_value + (max(data)-min(data))*0.01, f'{target_value}', color='red', ha='center',size=16) + + data = np.array(data) + data_df = pd.DataFrame({'test_acc': data}) + sns.violinplot(y='test_acc', data=data_df, inner=None, color='skyblue', ax=ax) + median = np.median(data) + ax.axhline(median, color='gray', linestyle='--', label=f'Median: {median:.1f}') + if np.isnan(target_value): + target_value = -0.01 + percentile = (np.sum(data < float(target_value)) / len(data)) * 100 + ax.scatter(0, float(target_value), color='red', s=100, zorder=5, + label=f'Specific Value: {target_value}\n({percentile:.1f} percentile)') + ax.set_title(str(title)) + ax.set_ylabel('test_acc') + ax.title.set_size(16) + ax.yaxis.label.set_size(14) + ax.tick_params(axis='both', which='major', labelsize=10) + ax.legend() + + +def get_runs(conf_data, query_dataset, method): + cache_file = SIMILARITYDIR / "cache/sweep_cache.json" + step_str = conf_data[conf_data["dataset_id"] == query_dataset][method].iloc[0] + step2_str = step_str.split("step2:")[1].split("|")[0] + _, _, sweep_id = spilt_web(step2_str) + if os.path.exists(cache_file): + with open(cache_file) as f: + sweep_cache = json.load(f) + else: + sweep_cache = {} + # print(sweep_id) + if sweep_id in sweep_cache: + return sweep_cache[sweep_id] + + sweep = wandb.Api(timeout=1000).sweep(f"{entity}/{project}/{sweep_id}") + runs = [] + for run in sweep.runs: + if "test_acc" in run.summary: + runs.append(run.summary["test_acc"]) + else: + runs.append(-0.01) + + sweep_cache[sweep_id] = runs + with open(cache_file, 'w') as f: + json.dump(sweep_cache, f) + return runs + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("--tissue", type=str, default="blood") + parser.add_argument("--reduce_error", action="store_true") + parser.add_argument("--in_query", action="store_true") + args = parser.parse_args() + tissue = args.tissue + reduce_error = args.reduce_error + in_query = args.in_query + set_seed(42) + # conf_data = pd.read_csv(f"results/{tissue}_result.csv", index_col=0) + conf_data = pd.read_excel(SIMILARITYDIR / "data/Cell Type Annotation Atlas.xlsx", sheet_name=tissue) + query_datasets = list(conf_data[conf_data["queryed"] == True]["dataset_id"]) + + methods = ["cta_actinn", "cta_celltypist", "cta_scdeepsort", "cta_singlecellnet"] + with open( + SIMILARITYDIR / + f"data/similarity_weights_results/{'reduce_error_' if reduce_error else ''}{'in_query_' if in_query else ''}sim_dict.json", + encoding='utf-8') as f: + sim_dict = json.load(f) + feature_name = sim_dict[tissue]["feature_name"] + """Visualization script for comparing model performance across different datasets + and methods. + + This script loads experiment results from wandb and compares them with atlas-based + predictions, generating violin plots to visualize the distribution of accuracies. + + """ + # ans_all=defaultdict(dict) + # for query_dataset in query_datasets: + # for method in methods: + # sweep_record=ground_truth_conf.loc[query_dataset,method] + # ans_all[query_dataset][method]=get_runs(sweep_record) + # with open("runs.json","w") as f: + # json.dump(ans_all,f) + + # with open("runs.json") as f: + # runs = json.load(f) + plt.style.use("default") + import json + with open(SIMILARITYDIR / "configs/exclude_dataset.json", encoding='utf-8') as f: + exclude_dataset_json = json.load(f) + exclude_dataset = exclude_dataset_json[tissue] if tissue in exclude_dataset_json else [] + # Generate visualization for each dataset + for query_dataset in query_datasets: + if query_dataset in exclude_dataset: + continue + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + axes = axes.flatten() + # Create subplot for each method + for i, method in enumerate(methods): + target_value, atlas_dataset = get_atlas_ans(query_dataset, method) + vis(get_runs(conf_data, query_dataset, method), target_value, f"{atlas_dataset}_{method}", axes[i]) + plt.tight_layout() + result_path = SIMILARITYDIR / f"data/imgs/{'reduce_error/' if reduce_error else ''}{'in_query/' if in_query else ''}{tissue}/{query_dataset}.png" + os.makedirs(os.path.dirname(result_path), exist_ok=True) + plt.savefig(result_path, dpi=300) + plt.show() diff --git a/examples/atlas/setup_run.py b/examples/atlas/setup_run.py new file mode 100644 index 00000000..06217714 --- /dev/null +++ b/examples/atlas/setup_run.py @@ -0,0 +1,86 @@ +import argparse +import os +import shutil +import sys + +import pandas as pd +import yaml + +from dance.settings import DANCEDIR +from dance.utils import logger + + +def load_commands(config_path): + """Load YAML configuration file containing command templates for different + algorithms.""" + with open(config_path, encoding='utf-8') as f: + return yaml.safe_load(f) + + +def load_run_configs(run_config_path): + """Load CSV file containing run configurations for different experiments.""" + return pd.read_csv(run_config_path) + + +def main(): + parser = argparse.ArgumentParser(description='Setup run parameters') + parser.add_argument('--config', type=str, default="config/run_config.csv", help='Run configuration CSV file') + + args = parser.parse_args() + + # Load configuration files + run_configs_df = load_run_configs(args.config) + commands_config = load_commands("config/commands.yaml") + + # Process each run configuration + for _, run in run_configs_df.iterrows(): + # Extract parameters for current run + algorithm_name = run['algorithm_name'] + dataset_id = run['dataset_id'] + species = run['species'] + tissue = run['tissue'] + filetype = run['filetype'] + count = run['count'] + device = run['device'] + + # Setup directory structure for the algorithm configuration + template_path = os.path.join("config/atlas_template_yamls", + f"{algorithm_name}/pipeline_params_tuning_config.yaml") + config_dir = f"{DANCEDIR}/examples/tuning/{algorithm_name}/{dataset_id}" + + # Create configuration directory if it doesn't exist + try: + os.makedirs(config_dir, exist_ok=False) + except FileExistsError: + logger.warning(f"Error: Directory {config_dir} already exists. Please remove it before running again.") + continue + + config_filename = f"pipeline_params_tuning_config.yaml" + config_path = os.path.join(config_dir, config_filename) + + # Copy configuration file + shutil.copy(template_path, config_path) + print(f"Template copied to {config_path}") + + # Validate algorithm exists in commands configuration + if algorithm_name not in commands_config.get("algorithms", {}): + print(f"Error: Command not found for algorithm '{algorithm_name}'. Please check commands.yaml file.") + continue + + # Format command template with run parameters + command_template = commands_config["algorithms"][algorithm_name]["command"] + run_command = command_template.format(dataset_id=dataset_id, species=species, tissue=tissue, filetype=filetype, + count=count, device=device) + + # Append generated command to run script + run_sh_path = f"{DANCEDIR}/examples/tuning/{algorithm_name}/run.sh" + with open(run_sh_path, "a", encoding='utf-8') as run_script: + run_script.write(f"{run_command}\n") + + print(f"Run command appended to {run_sh_path}: {run_command}") + + print("All run configurations have been processed.") + + +if __name__ == "__main__": + main() diff --git a/examples/atlas/sweep_results/Blood_ans.csv b/examples/atlas/sweep_results/Blood_ans.csv new file mode 100644 index 00000000..b65f532f --- /dev/null +++ b/examples/atlas/sweep_results/Blood_ans.csv @@ -0,0 +1,5066 @@ +Dataset_id,cta_celltypist,cta_celltypist_best_yaml,cta_celltypist_best_res,cta_celltypist_run_stats,cta_celltypist_check,cta_celltypist_step2_best_yaml,cta_celltypist_step2_best_res,cta_scdeepsort,cta_scdeepsort_best_yaml,cta_scdeepsort_best_res,cta_scdeepsort_run_stats,cta_scdeepsort_check,cta_scdeepsort_step2_best_yaml,cta_scdeepsort_step2_best_res,cta_singlecellnet,cta_singlecellnet_best_yaml,cta_singlecellnet_best_res,cta_singlecellnet_run_stats,cta_singlecellnet_check,cta_singlecellnet_step2_best_yaml,cta_singlecellnet_step2_best_res,cta_actinn,cta_actinn_best_yaml,cta_actinn_best_res,cta_actinn_run_stats,cta_actinn_check,cta_actinn_step2_best_yaml,cta_actinn_step2_best_res +84230ea4-998d-4aa8-8456-81dd54ce23af,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/4lhkspy4|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/v3i9xvkp,https://wandb.ai/xzy11632/dance-dev/sweeps/4kznqqwz,https://wandb.ai/xzy11632/dance-dev/sweeps/ntv3k0kl","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_cells + - min_counts + - max_counts + - max_cells + min_counts: 28 + min_cells: 0.01009194186360022 + max_counts: 0.9387079584289736 + max_cells: 0.9674538063359476 +- type: normalize + target: ScTransform + params: + processes_num: 8 + min_cells: 4 + gmean_eps: 1 + n_genes: 2153 + n_cells: 100 + bin_size: 778 + bw_adjust: 4.348275785008059 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 4617 + n_bins: 12 + flavor: seurat +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.7700534759358288,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/iprfvp38|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/sfsccz7u,https://wandb.ai/xzy11632/dance-dev/sweeps/sauccnk6,https://wandb.ai/xzy11632/dance-dev/sweeps/rxsuxkxm","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.7584670186042786,all_total_runs:600|all_finished_runs:510|step2_total_runs:540|step2_finished_runs:450|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:85.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO 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params: + min_val: 4 + max_val: 95 + mode: sum +- type: normalize + target: Log1P + params: + base: 9.417934904058548 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 9808 + span: 0.19479949862140847 +- type: feature.cell + target: WeightedFeatureSVD + params: + out: feature.cell + log_level: INFO + n_components: 505 + feat_norm_mode: l2 +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.2339999973773956,all_total_runs:210|all_finished_runs:175|step2_total_runs:150|step2_finished_runs:120|step3_0_total_runs:20|step3_0_finished_runs:19|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:17|finished_rate:83.33%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK 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FilterGenesPlaceHolder + params: {} +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2345000058412552 +738942eb-ac72-44ff-a64b-8943b5ecd8d9,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/pk55qst3|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/ikkth34p,https://wandb.ai/xzy11632/dance-dev/sweeps/33eivzhs,https://wandb.ai/xzy11632/dance-dev/sweeps/0iyvi4uj","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_cells + - min_counts + - min_cells + - max_counts + min_counts: 78 + min_cells: 0.010918863730631823 + max_counts: 0.9529126141448788 + max_cells: 0.9571952580883056 +- type: normalize + target: NormalizeTotal + params: + target_sum: 100000 + max_fraction: 0.5 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 8304 + top: false + mode: var +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.3030000030994415,all_total_runs:510|all_finished_runs:378|step2_total_runs:450|step2_finished_runs:356|step3_0_total_runs:20|step3_0_finished_runs:1|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:1|finished_rate:74.12%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + 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filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.338,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/tsfqsce4|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/qlbcth45,https://wandb.ai/xzy11632/dance-dev/sweeps/qsdgy6a1,https://wandb.ai/xzy11632/dance-dev/sweeps/uzqb64rs","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + 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feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4641327559947967,all_total_runs:510|all_finished_runs:337|step2_total_runs:450|step2_finished_runs:334|step3_0_total_runs:20|step3_0_finished_runs:1|step3_1_total_runs:20|step3_1_finished_runs:2|step3_2_total_runs:20|step3_2_finished_runs:0|finished_rate:66.08%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4641327559947967,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/7bo3ceh1|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/dtijfdv0,https://wandb.ai/xzy11632/dance-dev/sweeps/hdzrhugh,https://wandb.ai/xzy11632/dance-dev/sweeps/41dsr5zv","- type: filter.gene + target: FilterGenesPlaceHolder +- type: normalize + target: Log1P + params: + base: 2.104951608085805 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3803 + top: true + mode: cv +- type: feature.cell + target: WeightedFeaturePCA + params: + out: feature.cell + log_level: INFO + n_components: 127 + feat_norm_mode: minmax +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type 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target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.5187366167023555,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/rf2i7twp|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/85j5c24x,https://wandb.ai/xzy11632/dance-dev/sweeps/kwna8mqg,https://wandb.ai/xzy11632/dance-dev/sweeps/8tvxcuym","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.5251606106758118,all_total_runs:600|all_finished_runs:492|step2_total_runs:540|step2_finished_runs:450|step3_0_total_runs:20|step3_0_finished_runs:2|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:82.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.5251606106758118 diff --git a/examples/atlas/sweep_results/Brain_ans.csv b/examples/atlas/sweep_results/Brain_ans.csv new file mode 100644 index 00000000..56278b4e --- /dev/null +++ b/examples/atlas/sweep_results/Brain_ans.csv @@ -0,0 +1,5833 @@ +Dataset_id,cta_scdeepsort,cta_scdeepsort_best_yaml,cta_scdeepsort_best_res,cta_scdeepsort_run_stats,cta_scdeepsort_check,cta_scdeepsort_step2_best_yaml,cta_scdeepsort_step2_best_res,cta_celltypist,cta_celltypist_best_yaml,cta_celltypist_best_res,cta_celltypist_run_stats,cta_celltypist_check,cta_celltypist_step2_best_yaml,cta_celltypist_step2_best_res,cta_singlecellnet,cta_singlecellnet_best_yaml,cta_singlecellnet_best_res,cta_singlecellnet_run_stats,cta_singlecellnet_check,cta_singlecellnet_step2_best_yaml,cta_singlecellnet_step2_best_res,cta_actinn,cta_actinn_best_yaml,cta_actinn_best_res,cta_actinn_run_stats,cta_actinn_check,cta_actinn_step2_best_yaml,cta_actinn_step2_best_res 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max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",1.0 +22658f4f-9268-41ad-8828-cc53f4baa9fa,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/t0uj441u|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/bhl2shvy,https://wandb.ai/xzy11632/dance-dev/sweeps/ce1n3vno,https://wandb.ai/xzy11632/dance-dev/sweeps/u9cpiusu","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.7710000276565552,all_total_runs:182|all_finished_runs:122|step2_total_runs:150|step2_finished_runs:120|step3_0_total_runs:6|step3_0_finished_runs:0|step3_1_total_runs:20|step3_1_finished_runs:2|step3_2_total_runs:6|step3_2_finished_runs:0|finished_rate:67.03%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.7710000276565552,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/aio76f5y|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/4igc6uwy,https://wandb.ai/xzy11632/dance-dev/sweeps/c21dzyzr,https://wandb.ai/xzy11632/dance-dev/sweeps/tesqknws","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: 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target: FilterGenesPercentile + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4393939375877381,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/miscnge3|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/23lpopcy,https://wandb.ai/xzy11632/dance-dev/sweeps/b41qby5x,https://wandb.ai/xzy11632/dance-dev/sweeps/tyxa0naz","- type: filter.gene + target: FilterGenesPlaceHolder +- type: normalize + target: Log1P + params: + base: 9.242037197483024 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 107 + span: 0.4291184002623445 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig 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max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.3901515007019043 +f6d9f2ad-5ec7-4d53-b7f0-ceb0e7bcd181,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/kvawc25y|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/9sheweny,https://wandb.ai/xzy11632/dance-dev/sweeps/b0rongi3,https://wandb.ai/xzy11632/dance-dev/sweeps/ekmf487e","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_counts + - min_cells + - min_counts + - max_cells + min_counts: 263 + min_cells: 0.09338611959221152 + max_counts: 0.9103664063089278 + max_cells: 0.9776450063364456 +- type: normalize + target: Log1P + params: + base: 9.78567214435452 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: + min_disp: 0.42152838507294543 + max_disp: 90.25784555274645 + min_mean: 0.003754725333592855 + max_mean: 13.674551723139253 + n_bins: 13 + flavor: seurat +- type: feature.cell + target: WeightedFeatureSVD + params: + out: feature.cell + log_level: INFO + n_components: 484 + feat_norm_mode: minmax +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.5167151093482971,all_total_runs:210|all_finished_runs:176|step2_total_runs:150|step2_finished_runs:119|step3_0_total_runs:20|step3_0_finished_runs:18|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:83.81%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.5130813717842102,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/qphd8ocy|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/x4rsitxd,https://wandb.ai/xzy11632/dance-dev/sweeps/4f2tamuu,https://wandb.ai/xzy11632/dance-dev/sweeps/y585p279","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.5130813717842102,all_total_runs:510|all_finished_runs:400|step2_total_runs:450|step2_finished_runs:342|step3_0_total_runs:20|step3_0_finished_runs:19|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:78.43%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: NormalizePlaceHolder + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.5254360437393188 +f64e1be1-de15-4d27-8da4-82225cd4c035,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/4voqq7a0|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/6para2wq,https://wandb.ai/xzy11632/dance-dev/sweeps/cabldqak,https://wandb.ai/xzy11632/dance-dev/sweeps/s2xpuetf","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.6637080907821655,all_total_runs:196|all_finished_runs:147|step2_total_runs:150|step2_finished_runs:117|step3_0_total_runs:6|step3_0_finished_runs:0|step3_1_total_runs:20|step3_1_finished_runs:15|step3_2_total_runs:20|step3_2_finished_runs:15|finished_rate:75.00%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + 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SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.9419999718666076 diff --git a/examples/atlas/sweep_results/Heart_ans.csv b/examples/atlas/sweep_results/Heart_ans.csv new file mode 100644 index 00000000..d09699b0 --- /dev/null +++ b/examples/atlas/sweep_results/Heart_ans.csv @@ -0,0 +1,4771 @@ 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type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.6290000081062317,all_total_runs:196|all_finished_runs:120|step2_total_runs:150|step2_finished_runs:106|step3_0_total_runs:20|step3_0_finished_runs:7|step3_1_total_runs:20|step3_1_finished_runs:7|step3_2_total_runs:6|step3_2_finished_runs:0|finished_rate:61.22%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.6290000081062317,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/ugaq0qoa|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/ce8bglci,https://wandb.ai/xzy11632/dance-dev/sweeps/7rk5gmih,https://wandb.ai/xzy11632/dance-dev/sweeps/r3xtjpfd","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.6230000257492065,all_total_runs:600|all_finished_runs:478|step2_total_runs:540|step2_finished_runs:418|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:79.67%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + 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CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.3880000114440918,all_total_runs:196|all_finished_runs:154|step2_total_runs:150|step2_finished_runs:119|step3_0_total_runs:20|step3_0_finished_runs:19|step3_1_total_runs:20|step3_1_finished_runs:16|step3_2_total_runs:6|step3_2_finished_runs:0|finished_rate:78.57%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type 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FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.32499998807907104,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/k6sheszo|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/s5sq7wq1,https://wandb.ai/xzy11632/dance-dev/sweeps/v5th243l,https://wandb.ai/xzy11632/dance-dev/sweeps/8mczjqow","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + 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+ target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.329,all_total_runs:150|all_finished_runs:105|step2_total_runs:90|step2_finished_runs:66|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:0|finished_rate:70.00%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.329 diff --git a/examples/atlas/sweep_results/Intestine_ans.csv b/examples/atlas/sweep_results/Intestine_ans.csv new file mode 100644 index 00000000..4d0160e7 --- /dev/null +++ b/examples/atlas/sweep_results/Intestine_ans.csv @@ -0,0 +1,1258 @@ +Dataset_id,cta_scdeepsort,cta_scdeepsort_best_yaml,cta_scdeepsort_best_res,cta_scdeepsort_run_stats,cta_scdeepsort_check,cta_scdeepsort_step2_best_yaml,cta_scdeepsort_step2_best_res,cta_singlecellnet,cta_singlecellnet_best_yaml,cta_singlecellnet_best_res,cta_singlecellnet_run_stats,cta_singlecellnet_check,cta_singlecellnet_step2_best_yaml,cta_singlecellnet_step2_best_res,cta_actinn,cta_actinn_best_yaml,cta_actinn_best_res,cta_actinn_run_stats,cta_actinn_check,cta_actinn_step2_best_yaml,cta_actinn_step2_best_res,cta_celltypist,cta_celltypist_best_yaml,cta_celltypist_best_res,cta_celltypist_run_stats,cta_celltypist_check,cta_celltypist_step2_best_yaml,cta_celltypist_step2_best_res 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ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.4595,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/w85f0jr1|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/yvez2as1,https://wandb.ai/xzy11632/dance-dev/sweeps/3r5lt9pe,https://wandb.ai/xzy11632/dance-dev/sweeps/78288m6z","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: CellPCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.44999998807907104,all_total_runs:510|all_finished_runs:408|step2_total_runs:450|step2_finished_runs:349|step3_0_total_runs:20|step3_0_finished_runs:19|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:80.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.44999998807907104 +f7c1c579-2dc0-47e2-ba19-8165c5a0e353(Intestine),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/dedm26ib|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/765195mr,https://wandb.ai/xzy11632/dance-dev/sweeps/4f58apli,https://wandb.ai/xzy11632/dance-dev/sweeps/fvoqt6jw","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 7 + max_val: 96 + mode: cv +- type: normalize + target: ScaleFeature + params: + mode: l2 + eps: 0.3 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 8455 + top: false + mode: cv +- type: feature.cell + target: WeightedFeatureSVD + params: + out: feature.cell + log_level: INFO + n_components: 747 + feat_norm_mode: l2 +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.4900000095367432,all_total_runs:210|all_finished_runs:166|step2_total_runs:150|step2_finished_runs:113|step3_0_total_runs:20|step3_0_finished_runs:14|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:79.05%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD 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filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.481000006198883,all_total_runs:600|all_finished_runs:510|step2_total_runs:540|step2_finished_runs:450|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:85.00%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: 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config_dict: + label_channel: cell_type +",0.3653995394706726,all_total_runs:196|all_finished_runs:125|step2_total_runs:150|step2_finished_runs:86|step3_0_total_runs:20|step3_0_finished_runs:19|step3_1_total_runs:6|step3_1_finished_runs:0|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:63.78%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type 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ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.5601074546675622,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/w4ju135u|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/8plcumyk,https://wandb.ai/xzy11632/dance-dev/sweeps/67bwy8sx,https://wandb.ai/xzy11632/dance-dev/sweeps/ltrjfmqu","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: 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file mode 100644 index 00000000..1f9d473c --- /dev/null +++ b/examples/atlas/sweep_results/Kidney_ans.csv @@ -0,0 +1,4224 @@ +Dataset_id,cta_scdeepsort,cta_scdeepsort_best_yaml,cta_scdeepsort_best_res,cta_scdeepsort_run_stats,cta_scdeepsort_check,cta_scdeepsort_step2_best_yaml,cta_scdeepsort_step2_best_res,cta_singlecellnet,cta_singlecellnet_best_yaml,cta_singlecellnet_best_res,cta_singlecellnet_run_stats,cta_singlecellnet_check,cta_singlecellnet_step2_best_yaml,cta_singlecellnet_step2_best_res,cta_actinn,cta_actinn_best_yaml,cta_actinn_best_res,cta_actinn_run_stats,cta_actinn_check,cta_actinn_step2_best_yaml,cta_actinn_step2_best_res,cta_celltypist,cta_celltypist_best_yaml,cta_celltypist_best_res,cta_celltypist_run_stats,cta_celltypist_check,cta_celltypist_step2_best_yaml,cta_celltypist_step2_best_res 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HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2804999947547912,all_total_runs:600|all_finished_runs:487|step2_total_runs:540|step2_finished_runs:427|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:81.17%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2804999947547912,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/58296xo1|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/ckbfs7l7,https://wandb.ai/xzy11632/dance-dev/sweeps/ri8pts3d,https://wandb.ai/xzy11632/dance-dev/sweeps/8i66984j","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 5 + max_val: 98 + mode: sum +- type: normalize + target: Log1P + params: + base: 4.533975561756228 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 4165 + top: false + mode: cv +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.2465,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/zw08f3vc|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/zokwex9g,https://wandb.ai/xzy11632/dance-dev/sweeps/6w8eah70,https://wandb.ai/xzy11632/dance-dev/sweeps/rh47nlz0","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + 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/dev/null +++ b/examples/atlas/sweep_results/Lung_ans.csv @@ -0,0 +1,4468 @@ +Dataset_id,cta_scdeepsort,cta_scdeepsort_best_yaml,cta_scdeepsort_best_res,cta_scdeepsort_run_stats,cta_scdeepsort_check,cta_scdeepsort_step2_best_yaml,cta_scdeepsort_step2_best_res,cta_singlecellnet,cta_singlecellnet_best_yaml,cta_singlecellnet_best_res,cta_singlecellnet_run_stats,cta_singlecellnet_check,cta_singlecellnet_step2_best_yaml,cta_singlecellnet_step2_best_res,cta_actinn,cta_actinn_best_yaml,cta_actinn_best_res,cta_actinn_run_stats,cta_actinn_check,cta_actinn_step2_best_yaml,cta_actinn_step2_best_res,cta_celltypist,cta_celltypist_best_yaml,cta_celltypist_best_res,cta_celltypist_run_stats,cta_celltypist_check,cta_celltypist_step2_best_yaml,cta_celltypist_step2_best_res 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filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - max_counts + - max_cells + - min_cells + min_counts: 458 + min_cells: 0.011591892698839612 + max_counts: 0.9504786715733048 + max_cells: 0.968929807966842 +- type: normalize + target: Log1P + params: + base: 2.5831898659365935 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 1528 + span: 0.25048936376968134 +- type: feature.cell + target: WeightedFeaturePCA + params: + out: feature.cell + log_level: INFO + n_components: 757 + feat_norm_mode: standardize +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.1739999949932098,all_total_runs:600|all_finished_runs:472|step2_total_runs:540|step2_finished_runs:415|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:18|step3_2_total_runs:20|step3_2_finished_runs:19|finished_rate:78.67%,False,"- type: filter.gene + target: 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type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.15850000083446503,all_total_runs:510|all_finished_runs:419|step2_total_runs:450|step2_finished_runs:359|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:82.16%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.15850000083446503 +1e5bd3b8-6a0e-4959-8d69-cafed30fe814,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/gvzf9hvu|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/xn6gkyue,https://wandb.ai/xzy11632/dance-dev/sweeps/wohspkph,https://wandb.ai/xzy11632/dance-dev/sweeps/0es2sjqj","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 4 + max_val: 99 + mode: cv +- type: normalize + target: ScaleFeature + params: + mode: standardize + eps: 0.5 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3133 + n_bins: 25 + flavor: cell_ranger +- type: feature.cell + target: WeightedFeaturePCA + params: + out: feature.cell + log_level: INFO + n_components: 327 + feat_norm_mode: l2 +- type: graph.cell + 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params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.1545,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/rxpz0717|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/kwdb5hwh,https://wandb.ai/xzy11632/dance-dev/sweeps/8y8lxfu9,https://wandb.ai/xzy11632/dance-dev/sweeps/tc33wt4o","- type: filter.gene + target: FilterGenesPlaceHolder +- type: normalize + target: ScaleFeature + params: + mode: minmax + eps: 0.1 +- type: filter.gene + target: FilterGenesRegression + params: + method: scmap + num_genes: 4755 +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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normalize + target: NormalizePlaceHolder + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.14749999344348907 +486486d4-9462-43e5-9249-eb43fa5a49a6,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/z3e3gntq|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/eal8ymn1,https://wandb.ai/xzy11632/dance-dev/sweeps/hdzb2brg,https://wandb.ai/xzy11632/dance-dev/sweeps/du2ryve3","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: 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max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4345323741436004,all_total_runs:600|all_finished_runs:468|step2_total_runs:540|step2_finished_runs:421|step3_0_total_runs:20|step3_0_finished_runs:19|step3_1_total_runs:20|step3_1_finished_runs:13|step3_2_total_runs:20|step3_2_finished_runs:15|finished_rate:78.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4345323741436004,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/v0gc7aor|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/u7i7sus4,https://wandb.ai/xzy11632/dance-dev/sweeps/8cyvbkoa,https://wandb.ai/xzy11632/dance-dev/sweeps/xhuq33y2","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_counts + - max_cells + - min_cells + - min_counts + min_counts: 463 + min_cells: 0.049804811405829666 + max_counts: 0.9755914780222 + max_cells: 0.9940224942643062 +- type: normalize + target: ScTransform + params: + processes_num: 8 + min_cells: 10 + gmean_eps: 1 + n_genes: 1460 + n_cells: 10 + bin_size: 658 + bw_adjust: 3.5601682171616047 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 4623 + top: false + mode: var +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.3827338218688965,all_total_runs:493|all_finished_runs:377|step2_total_runs:450|step2_finished_runs:360|step3_0_total_runs:20|step3_0_finished_runs:2|step3_1_total_runs:3|step3_1_finished_runs:0|step3_2_total_runs:20|step3_2_finished_runs:15|finished_rate:76.47%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.35827338695526123 +a68b64d8-aee3-4947-81b7-36b8fe5a44d2(Lung),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/8vygl5n6|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/il88es7n,https://wandb.ai/xzy11632/dance-dev/sweeps/kyv0eh67,https://wandb.ai/xzy11632/dance-dev/sweeps/qx4x5rqz","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.3348017632961273,all_total_runs:210|all_finished_runs:127|step2_total_runs:150|step2_finished_runs:105|step3_0_total_runs:20|step3_0_finished_runs:1|step3_1_total_runs:20|step3_1_finished_runs:1|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:60.48%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc 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filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.3303964757709251,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/xn9x8pbm|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/g2cw98af,https://wandb.ai/xzy11632/dance-dev/sweeps/cuif1dsz,https://wandb.ai/xzy11632/dance-dev/sweeps/twq492am","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.3480176329612732,all_total_runs:510|all_finished_runs:383|step2_total_runs:450|step2_finished_runs:326|step3_0_total_runs:20|step3_0_finished_runs:17|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:75.10%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 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+ target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.2515000104904175,all_total_runs:210|all_finished_runs:172|step2_total_runs:150|step2_finished_runs:120|step3_0_total_runs:20|step3_0_finished_runs:14|step3_1_total_runs:20|step3_1_finished_runs:18|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:81.90%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type 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FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2419999986886978,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/mwjlq7a2|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/pt55s3l9,https://wandb.ai/xzy11632/dance-dev/sweeps/gk0raad1,https://wandb.ai/xzy11632/dance-dev/sweeps/sxex7w80","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_cells + - max_counts + - min_counts + - min_cells + min_counts: 455 + min_cells: 0.08766785970976693 + max_counts: 0.982343433796268 + max_cells: 0.9664774788562582 +- type: normalize + target: Log1P + params: + base: 7.459782027773534 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 9807 + top: true + mode: cv +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.226500004529953,all_total_runs:476|all_finished_runs:380|step2_total_runs:450|step2_finished_runs:360|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:3|step3_1_finished_runs:0|step3_2_total_runs:3|step3_2_finished_runs:0|finished_rate:79.83%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.22300000488758087 +2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Lung),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/x7ca9zjk|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/g69bgevs,https://wandb.ai/xzy11632/dance-dev/sweeps/lqjvl3t9,https://wandb.ai/xzy11632/dance-dev/sweeps/lcb0w1tc","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_cells + - min_cells + - max_counts + - min_counts + min_counts: 452 + min_cells: 0.03353995237571974 + max_counts: 0.9121476758403412 + max_cells: 0.99348026292544 +- type: normalize + target: ScaleFeature + params: + mode: minmax + eps: 0.3 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 6962 + top: true + mode: cv +- type: feature.cell + target: WeightedFeatureSVD 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params: {} +- type: normalize + target: NormalizePlaceHolder + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.15800000727176666 +4023a2bc-6325-47db-bfdf-9639e91042c2,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/hv9b5aer|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/clfj1oh7,https://wandb.ai/xzy11632/dance-dev/sweeps/myvaevvn,https://wandb.ai/xzy11632/dance-dev/sweeps/xkcoq40e","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 10 + max_val: 95 + mode: cv +- type: normalize + target: Log1P + params: + base: 1.8942568918726996 +- type: filter.gene + target: FilterGenesRegression + params: + method: scmap + num_genes: 1319 +- type: feature.cell + target: WeightedFeaturePCA + params: + out: 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+",0.4242424242424242,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/4zpkroz8|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/8ya78zvj,https://wandb.ai/xzy11632/dance-dev/sweeps/etsb2867,https://wandb.ai/xzy11632/dance-dev/sweeps/ul16ui5e","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: CellSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4848484992980957,all_total_runs:572|all_finished_runs:438|step2_total_runs:540|step2_finished_runs:435|step3_0_total_runs:20|step3_0_finished_runs:3|step3_1_total_runs:6|step3_1_finished_runs:0|step3_2_total_runs:6|step3_2_finished_runs:0|finished_rate:76.57%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: CellSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4848484992980957,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/l2wuxpnd|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/t17ir30r,https://wandb.ai/xzy11632/dance-dev/sweeps/boxtoi63,https://wandb.ai/xzy11632/dance-dev/sweeps/wu62crhr","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.5909090638160706,all_total_runs:510|all_finished_runs:413|step2_total_runs:450|step2_finished_runs:357|step3_0_total_runs:20|step3_0_finished_runs:16|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:80.98%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.5909090638160706 diff --git a/examples/atlas/sweep_results/Pancreas_ans.csv b/examples/atlas/sweep_results/Pancreas_ans.csv new file mode 100644 index 00000000..dbfec2b2 --- /dev/null +++ b/examples/atlas/sweep_results/Pancreas_ans.csv @@ -0,0 +1,2985 @@ +Dataset_id,cta_scdeepsort,cta_scdeepsort_best_yaml,cta_scdeepsort_best_res,cta_scdeepsort_run_stats,cta_scdeepsort_check,cta_scdeepsort_step2_best_yaml,cta_scdeepsort_step2_best_res,cta_singlecellnet,cta_singlecellnet_best_yaml,cta_singlecellnet_best_res,cta_singlecellnet_run_stats,cta_singlecellnet_check,cta_singlecellnet_step2_best_yaml,cta_singlecellnet_step2_best_res,cta_actinn,cta_actinn_best_yaml,cta_actinn_best_res,cta_actinn_run_stats,cta_actinn_check,cta_actinn_step2_best_yaml,cta_actinn_step2_best_res,cta_celltypist,cta_celltypist_best_yaml,cta_celltypist_best_res,cta_celltypist_run_stats,cta_celltypist_check,cta_celltypist_step2_best_yaml,cta_celltypist_step2_best_res +53d208b0-2cfd-4366-9866-c3c6114081bc(Pancreas),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/g7t7xwhn|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/cf0x6364,https://wandb.ai/xzy11632/dance-dev/sweeps/ol212cn4,https://wandb.ai/xzy11632/dance-dev/sweeps/i688o13x","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_cells + - min_cells + - min_counts + - max_counts + min_counts: 482 + min_cells: 0.08359667621587258 + max_counts: 0.984260099671448 + max_cells: 0.9831967316325464 +- type: normalize + target: ScaleFeature + params: + mode: minmax + eps: 0.1 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 4805 + span: 0.59331455825036 +- type: feature.cell + target: WeightedFeatureSVD + params: + out: feature.cell + log_level: INFO + n_components: 541 + feat_norm_mode: minmax +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.40299999713897705,all_total_runs:210|all_finished_runs:126|step2_total_runs:150|step2_finished_runs:99|step3_0_total_runs:20|step3_0_finished_runs:8|step3_1_total_runs:20|step3_1_finished_runs:10|step3_2_total_runs:20|step3_2_finished_runs:9|finished_rate:60.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.4020000100135803,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/gpj4i6jb|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/pqpwvbcl,https://wandb.ai/xzy11632/dance-dev/sweeps/hc0zyrsm,https://wandb.ai/xzy11632/dance-dev/sweeps/oqeg7gt6","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 1 + max_val: 95 + mode: cv +- type: normalize + target: NormalizeTotal + params: + target_sum: 1000 + max_fraction: 0.7 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3006 + span: 0.3081446146760134 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.4035,all_total_runs:150|all_finished_runs:91|step2_total_runs:90|step2_finished_runs:57|step3_0_total_runs:20|step3_0_finished_runs:9|step3_1_total_runs:20|step3_1_finished_runs:16|step3_2_total_runs:20|step3_2_finished_runs:9|finished_rate:60.67%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.402,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/22ozz7li|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/7af40uoa,https://wandb.ai/xzy11632/dance-dev/sweeps/go7b10ao,https://wandb.ai/xzy11632/dance-dev/sweeps/fogjho7h","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4149999916553498,all_total_runs:586|all_finished_runs:442|step2_total_runs:540|step2_finished_runs:402|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:6|step3_1_finished_runs:0|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:75.43%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4149999916553498,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/q7l6m2qp|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/5go16fns,https://wandb.ai/xzy11632/dance-dev/sweeps/i63ze0b1,https://wandb.ai/xzy11632/dance-dev/sweeps/e7g4eglj","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4020000100135803,all_total_runs:510|all_finished_runs:402|step2_total_runs:450|step2_finished_runs:342|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:78.82%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4020000100135803 +78f10833-3e61-4fad-96c9-4bbd4f14bdfa,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/ile10jfv|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/8tfuwq6k,https://wandb.ai/xzy11632/dance-dev/sweeps/cwadf7td,https://wandb.ai/xzy11632/dance-dev/sweeps/6kx7b6kc","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",1.0,all_total_runs:210|all_finished_runs:163|step2_total_runs:150|step2_finished_runs:119|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:13|step3_2_total_runs:20|step3_2_finished_runs:11|finished_rate:77.62%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",1.0,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/mjxq9wbz|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/mb64lqt6,https://wandb.ai/xzy11632/dance-dev/sweeps/gaeji3wq,https://wandb.ai/xzy11632/dance-dev/sweeps/jdxyftrd","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",1.0,all_total_runs:150|all_finished_runs:33|step2_total_runs:90|step2_finished_runs:16|step3_0_total_runs:20|step3_0_finished_runs:9|step3_1_total_runs:20|step3_1_finished_runs:0|step3_2_total_runs:20|step3_2_finished_runs:8|finished_rate:22.00%,True,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",1.0,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/t544qsq4|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/mdst6ozg,https://wandb.ai/xzy11632/dance-dev/sweeps/5wuust9u,https://wandb.ai/xzy11632/dance-dev/sweeps/0jz8lzw5","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",1.0,all_total_runs:600|all_finished_runs:455|step2_total_runs:540|step2_finished_runs:415|step3_0_total_runs:20|step3_0_finished_runs:17|step3_1_total_runs:20|step3_1_finished_runs:12|step3_2_total_runs:20|step3_2_finished_runs:11|finished_rate:75.83%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",1.0,,,,,,, +f7c1c579-2dc0-47e2-ba19-8165c5a0e353(Pancreas),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/alp04n2p|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/ki0b8xal,https://wandb.ai/xzy11632/dance-dev/sweeps/hzv5a32f,https://wandb.ai/xzy11632/dance-dev/sweeps/vvy7c4q9","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.25999999046325684,all_total_runs:210|all_finished_runs:121|step2_total_runs:150|step2_finished_runs:104|step3_0_total_runs:20|step3_0_finished_runs:7|step3_1_total_runs:20|step3_1_finished_runs:6|step3_2_total_runs:20|step3_2_finished_runs:4|finished_rate:57.62%,True,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.25999999046325684,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/szccibek|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/ncv8d9cl,https://wandb.ai/xzy11632/dance-dev/sweeps/9c2q16tc,https://wandb.ai/xzy11632/dance-dev/sweeps/t3jzvjsd","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - max_cells + - min_cells + - max_counts + min_counts: 290 + min_cells: 0.09769546522542771 + max_counts: 0.9875755318917276 + max_cells: 0.9902514987938889 +- type: normalize + target: ScaleFeature + params: + mode: l2 + eps: 0.3 +- type: filter.gene + target: FilterGenesRegression + params: + method: scmap + num_genes: 7878 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type 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FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2585000097751617,all_total_runs:600|all_finished_runs:457|step2_total_runs:540|step2_finished_runs:410|step3_0_total_runs:20|step3_0_finished_runs:16|step3_1_total_runs:20|step3_1_finished_runs:16|step3_2_total_runs:20|step3_2_finished_runs:15|finished_rate:76.17%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",1.0,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/ldwyub15|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/wbohhp4w,https://wandb.ai/xzy11632/dance-dev/sweeps/4m9kquzw,https://wandb.ai/xzy11632/dance-dev/sweeps/dy3sel7f","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",1.0,all_total_runs:600|all_finished_runs:432|step2_total_runs:540|step2_finished_runs:382|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:17|step3_2_total_runs:20|step3_2_finished_runs:13|finished_rate:72.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",1.0,,,,,,, +3294d050-6eeb-4a00-b24c-71aacc9b777f,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/rq1aqcks|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/w7fykvmx,https://wandb.ai/xzy11632/dance-dev/sweeps/7zg7srym,https://wandb.ai/xzy11632/dance-dev/sweeps/qgcvp9vy","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type 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params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByMeanAndDisp + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.4745,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/s0e382wf|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/hemo1t7y,https://wandb.ai/xzy11632/dance-dev/sweeps/x2gofgl5,https://wandb.ai/xzy11632/dance-dev/sweeps/9cwz7pvl","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 8 + max_val: 98 + mode: sum +- type: normalize + target: Log1P + params: + base: 6.12335875625052 +- type: filter.gene + target: FilterGenesRegression + params: + method: seurat3 + num_genes: 8561 +- type: feature.cell + target: GaussRandProjFeature + params: + out: feature.cell + log_level: INFO + n_components: 909 +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4634999930858612,all_total_runs:599|all_finished_runs:425|step2_total_runs:539|step2_finished_runs:367|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:19|finished_rate:70.95%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: CellSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4589999914169311,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/g85syyzg|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/cdggwljw,https://wandb.ai/xzy11632/dance-dev/sweeps/natn1hgy,https://wandb.ai/xzy11632/dance-dev/sweeps/b0eefc5r","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4670000076293946,all_total_runs:510|all_finished_runs:363|step2_total_runs:450|step2_finished_runs:341|step3_0_total_runs:20|step3_0_finished_runs:0|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:2|finished_rate:71.18%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4670000076293946 +2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Pancreas),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/ghnlphpt|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/qmwfsxt6,https://wandb.ai/xzy11632/dance-dev/sweeps/0bv97iz8,https://wandb.ai/xzy11632/dance-dev/sweeps/fhv7qkjx","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: NormalizePlaceHolder + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type 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filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2585000097751617,all_total_runs:601|all_finished_runs:487|step2_total_runs:541|step2_finished_runs:427|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:81.03%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.2585000097751617,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/tnlms8f7|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/37b6ag4h,https://wandb.ai/xzy11632/dance-dev/sweeps/drrj9j62,https://wandb.ai/xzy11632/dance-dev/sweeps/yrm085kp","- type: filter.gene + target: FilterGenesPlaceHolder +- type: normalize + target: ScaleFeature + params: + mode: minmax + eps: -1 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 8013 + top: false + mode: sum +- type: feature.cell + target: WeightedFeaturePCA + params: + out: feature.cell + log_level: INFO + n_components: 897 + feat_norm_mode: l2 +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type 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filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.4124999940395355,all_total_runs:210|all_finished_runs:141|step2_total_runs:150|step2_finished_runs:93|step3_0_total_runs:20|step3_0_finished_runs:12|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:17|finished_rate:67.14%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.4124999940395355,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/qmyvmdq1|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/repllvxm,https://wandb.ai/xzy11632/dance-dev/sweeps/1u1hsnc6,https://wandb.ai/xzy11632/dance-dev/sweeps/0romoagi","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_cells + - max_counts + - min_counts + - max_cells + min_counts: 142 + min_cells: 0.09808550094261272 + max_counts: 0.9702330969318216 + max_cells: 0.9570367158864392 +- type: normalize + target: ScaleFeature + params: + mode: l2 + eps: 0.7 +- type: filter.gene + target: FilterGenesRegression + params: + method: seurat3 + num_genes: 5791 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.4135,all_total_runs:150|all_finished_runs:112|step2_total_runs:90|step2_finished_runs:62|step3_0_total_runs:20|step3_0_finished_runs:14|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:16|finished_rate:74.67%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.4095,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/lzckrtvs|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/h108k8vo,https://wandb.ai/xzy11632/dance-dev/sweeps/yglgx70h,https://wandb.ai/xzy11632/dance-dev/sweeps/19oi9utb","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4135000109672546,all_total_runs:600|all_finished_runs:499|step2_total_runs:540|step2_finished_runs:442|step3_0_total_runs:20|step3_0_finished_runs:17|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:83.17%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4135000109672546,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/li0kaahk|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/mftedu6f,https://wandb.ai/xzy11632/dance-dev/sweeps/05zma5yg,https://wandb.ai/xzy11632/dance-dev/sweeps/3a1vt11n","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4115000069141388,all_total_runs:510|all_finished_runs:384|step2_total_runs:450|step2_finished_runs:325|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:19|finished_rate:75.29%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4115000069141388 +66d15835-5dc8-4e96-b0eb-f48971cb65e8,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/vqiycw97|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/mqxn1w2x,https://wandb.ai/xzy11632/dance-dev/sweeps/7xrgbuws,https://wandb.ai/xzy11632/dance-dev/sweeps/rfy7mvau","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.3791748583316803,all_total_runs:196|all_finished_runs:158|step2_total_runs:150|step2_finished_runs:119|step3_0_total_runs:6|step3_0_finished_runs:0|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:19|finished_rate:80.61%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: Log1P + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.3791748583316803,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/0tpkjxr3|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/dxjir4oa,https://wandb.ai/xzy11632/dance-dev/sweeps/ozctmfyc,https://wandb.ai/xzy11632/dance-dev/sweeps/ie0fgnbo","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.3595284872298624,all_total_runs:150|all_finished_runs:115|step2_total_runs:90|step2_finished_runs:60|step3_0_total_runs:20|step3_0_finished_runs:15|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:76.67%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesRegression + params: + num_genes: 3000 +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.3595284872298624,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/5i2bqatn|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/f3fnnke6,https://wandb.ai/xzy11632/dance-dev/sweeps/0nyyt1k7,https://wandb.ai/xzy11632/dance-dev/sweeps/oaa1e4hg","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4420432150363922,all_total_runs:600|all_finished_runs:494|step2_total_runs:540|step2_finished_runs:434|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:82.33%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: GaussRandProjFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.4420432150363922,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/p87lac44|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/lfrx75bu,https://wandb.ai/xzy11632/dance-dev/sweeps/9dafryxc,https://wandb.ai/xzy11632/dance-dev/sweeps/yh7p9cq3","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - max_cells + - min_cells + - max_counts + - min_counts + min_counts: 436 + min_cells: 0.008052271855593807 + max_counts: 0.9724095836117284 + max_cells: 0.968847446724503 +- type: normalize + target: Log1P + params: + base: 5.483824101447068 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 6222 + top: true + mode: var +- type: feature.cell + target: FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.3791748583316803,all_total_runs:493|all_finished_runs:344|step2_total_runs:450|step2_finished_runs:307|step3_0_total_runs:3|step3_0_finished_runs:0|step3_1_total_runs:20|step3_1_finished_runs:19|step3_2_total_runs:20|step3_2_finished_runs:18|finished_rate:69.78%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScTransform + params: + processes_num: 8 +- type: filter.gene + target: HighlyVariableGenesLogarithmizedByTopGenes + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + target: FeatureCellPlaceHolder +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.3634577691555023 +97a17473-e2b1-4f31-a544-44a60773e2dd(Pancreas),"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/5sc3ymja|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/tzb3gq58,https://wandb.ai/xzy11632/dance-dev/sweeps/epie6vgv,https://wandb.ai/xzy11632/dance-dev/sweeps/geh4eyae","- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.6768107414245605,all_total_runs:210|all_finished_runs:145|step2_total_runs:150|step2_finished_runs:120|step3_0_total_runs:20|step3_0_finished_runs:10|step3_1_total_runs:20|step3_1_finished_runs:3|step3_2_total_runs:20|step3_2_finished_runs:12|finished_rate:69.05%,False,"- type: filter.gene + target: FilterGenesPercentile + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeaturePCA +- type: graph.cell + target: CellFeatureGraph + params: + cell_feature_channel: feature.cell +- type: misc + target: SetConfig + params: + config_dict: + label_channel: cell_type +",0.6768107414245605,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/09vtppjh|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/l5fovbpb,https://wandb.ai/xzy11632/dance-dev/sweeps/utu1c0zt,https://wandb.ai/xzy11632/dance-dev/sweeps/689d3b91","- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_cells + - min_counts + - max_counts + - max_cells + min_counts: 327 + min_cells: 0.08986191604520012 + max_counts: 0.9467699188647029 + max_cells: 0.9545870083635444 +- type: normalize + target: NormalizeTotal + params: + target_sum: 1000000 + max_fraction: 0.05 +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 571 + top: true + mode: sum +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.6518563603164942,all_total_runs:150|all_finished_runs:116|step2_total_runs:90|step2_finished_runs:56|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:77.33%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: NormalizeTotal + params: {} +- type: filter.gene + target: FilterGenesNumberPlaceHolder + params: {} +- type: feature.cell + target: SCNFeature +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: SCNFeature + label_channel: cell_type +",0.6378575776019476,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/q75qr9j4|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/sk8ji4mk,https://wandb.ai/xzy11632/dance-dev/sweeps/bjg9ihgt,https://wandb.ai/xzy11632/dance-dev/sweeps/w24yl7xi","- type: filter.gene + target: FilterGenesPercentile + params: + min_val: 9 + max_val: 98 + mode: rv +- type: normalize + target: Log1P + params: + base: 9.215313360927013 +- type: filter.gene + target: FilterGenesNumberPlaceHolder +- type: feature.cell + target: WeightedFeaturePCA + params: + out: feature.cell + log_level: INFO + n_components: 323 + feat_norm_mode: standardize +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.6792452931404114,all_total_runs:600|all_finished_runs:486|step2_total_runs:540|step2_finished_runs:426|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:20|finished_rate:81.00%,False,"- type: filter.gene + target: FilterGenesScanpyOrder + params: + order: + - min_counts + - min_cells + - max_counts + - max_cells + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: HighlyVariableGenesRawCount + params: + n_top_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.6786366105079651,"step2:https://wandb.ai/xzy11632/dance-dev/sweeps/hikrp0lg|step3:https://wandb.ai/xzy11632/dance-dev/sweeps/uazhlu4v,https://wandb.ai/xzy11632/dance-dev/sweeps/wrbzgnc1,https://wandb.ai/xzy11632/dance-dev/sweeps/kvelln8e","- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.6755934357643127,all_total_runs:511|all_finished_runs:385|step2_total_runs:451|step2_finished_runs:327|step3_0_total_runs:20|step3_0_finished_runs:20|step3_1_total_runs:20|step3_1_finished_runs:20|step3_2_total_runs:20|step3_2_finished_runs:18|finished_rate:75.34%,False,"- type: filter.gene + target: FilterGenesPlaceHolder + params: {} +- type: normalize + target: ScaleFeature + params: {} +- type: filter.gene + target: FilterGenesTopK + params: + num_genes: 3000 +- type: feature.cell + params: + out: feature.cell + log_level: INFO + split_name: train + target: WeightedFeatureSVD +- type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +",0.6755934357643127 diff --git a/examples/atlas/upload_data.py b/examples/atlas/upload_data.py new file mode 100644 index 00000000..c8bd7baa --- /dev/null +++ b/examples/atlas/upload_data.py @@ -0,0 +1,126 @@ +"""Upload Atlas and Query datasets to Dropbox. + +This script handles the upload of single-cell RNA sequencing datasets to Dropbox. +It processes both atlas and query datasets, handling large (>10000 cells) and small datasets separately. +The script reads data from local h5ad files and uploads them to a specified Dropbox location. + +Required environment variables: + DROPBOX_ACCESS_TOKEN: Authentication token for Dropbox API + +Usage: + python upload_data.py --maindir --filedir + --tissues --dropbox_dest_path + +""" + +import argparse +import json +import os +import pathlib + +import pandas as pd +import scanpy as sc +from dotenv import load_dotenv + +from dance.atlas.data_dropbox_upload import get_ans, get_data + +if __name__ == "__main__": + # Load environment variables + load_dotenv() + + args = argparse.ArgumentParser() + args.add_argument("--maindir", type=str) + args.add_argument("--filedir", type=str) + args.add_argument("--tissues", type=str, nargs="+") + args.add_argument("--dropbox_dest_path", type=str) + args = args.parse_args() + + # Get access token from environment variables + ACCESS_TOKEN = os.getenv('DROPBOX_ACCESS_TOKEN') + if not ACCESS_TOKEN: + raise ValueError("DROPBOX_ACCESS_TOKEN environment variable not found!\n" + "Please set DROPBOX_ACCESS_TOKEN=your_token_here in .env file") + + MAINDIR = pathlib.Path(args.maindir) + FILEDIR = pathlib.Path(args.filedir) + tissues = args.tissues + DROPBOX_DEST_PATH = args.dropbox_dest_path + + def get_data(dataset_id, in_atlas=False, large=False): + """Load h5ad dataset from local path. + + Parameters + ---------- + dataset_id : str + Identifier for the dataset + in_atlas : bool + Whether dataset is from atlas (True) or query (False) + large : bool + Whether dataset is large (>10000 cells) requiring sampling + + Returns + ------- + AnnData + Loaded single cell data + Path + Local path to the data file + + """ + if large: + if in_atlas: + local_path = MAINDIR / f"sampled-10000/{tissue}/{dataset_id}.h5ad" + else: + local_path = FILEDIR / f"sampled-10000/{tissue}/{dataset_id}.h5ad" + else: + local_path = MAINDIR / f"{tissue}/{dataset_id}.h5ad" + data = sc.read_h5ad(local_path) + return data, local_path + + upload_results = [] + + # Load atlas and query results + with open(FILEDIR / "results/atlas_result.json") as f: + atlas_result = json.load(f) + with open(FILEDIR / "results/query_result.json") as f: + query_result = json.load(f) + + for tissue in tissues: + # Process atlas datasets + large_dataset_ids = atlas_result[tissue][0] + small_dataset_ids = atlas_result[tissue][1] + + # Upload large atlas datasets + for dataset_id in large_dataset_ids: + data, local_path = get_data(dataset_id=dataset_id, in_atlas=True, large=True) + upload_results.append( + get_ans(dataset_id=dataset_id, tissue=tissue, data=data, local_path=local_path, + ACCESS_TOKEN=ACCESS_TOKEN, DROPBOX_DEST_PATH=DROPBOX_DEST_PATH)) + + # Upload small atlas datasets + for dataset_id in small_dataset_ids: + data, local_path = get_data(dataset_id=dataset_id, in_atlas=True, large=False) + upload_results.append( + get_ans(dataset_id=dataset_id, tissue=tissue, data=data, local_path=local_path, + ACCESS_TOKEN=ACCESS_TOKEN, DROPBOX_DEST_PATH=DROPBOX_DEST_PATH)) + + # Process query datasets + large_query_ids = query_result[tissue][0] + small_query_ids = query_result[tissue][1] + + # Upload large query datasets + for dataset_id in large_query_ids: + data, local_path = get_data(dataset_id=dataset_id, in_atlas=False, large=True) + upload_results.append( + get_ans(dataset_id=dataset_id, tissue=tissue, data=data, local_path=local_path, + ACCESS_TOKEN=ACCESS_TOKEN, DROPBOX_DEST_PATH=DROPBOX_DEST_PATH)) + + # Upload small query datasets + for dataset_id in small_query_ids: + data, local_path = get_data(dataset_id=dataset_id, in_atlas=False, large=False) + upload_results.append( + get_ans(dataset_id=dataset_id, tissue=tissue, data=data, local_path=local_path, + ACCESS_TOKEN=ACCESS_TOKEN, DROPBOX_DEST_PATH=DROPBOX_DEST_PATH)) + + # Save upload results + output_filename = f"{','.join(tissues)}_scdeepsort.csv" + pd.DataFrame(upload_results).set_index("species").to_csv(output_filename) diff --git a/examples/config/cta_svm/main.py b/examples/config/cta_svm/main.py deleted file mode 100644 index 7d89fa8d..00000000 --- a/examples/config/cta_svm/main.py +++ /dev/null @@ -1,72 +0,0 @@ -import argparse -import pprint -from typing import get_args - -import numpy as np - -from dance import logger -from dance.datasets.singlemodality import CellTypeAnnotationDataset -from dance.modules.single_modality.cell_type_annotation.svm import SVM -from dance.pipeline import Pipeline -from dance.typing import LogLevel -from dance.utils import set_seed - -if __name__ == "__main__": - parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser.add_argument("--cache", action="store_true", help="Cache processed data.") - parser.add_argument("--dense_dim", type=int, default=400, help="dim of PCA") - parser.add_argument("--gpu", type=int, default=-1, help="GPU id, set to -1 for CPU") - parser.add_argument("--log_level", type=str, default="INFO", choices=get_args(LogLevel)) - parser.add_argument("--species", default="mouse") - parser.add_argument("--test_dataset", nargs="+", default=[2695], type=int, help="list of dataset id") - parser.add_argument("--tissue", default="Brain") # TODO: Add option for different tissue name for train/test - parser.add_argument("--train_dataset", nargs="+", default=[753, 3285], type=int, help="list of dataset id") - parser.add_argument("--seed", type=int, default=10) - parser.add_argument("--num_runs", type=int, default=1) - - args = parser.parse_args() - logger.setLevel(args.log_level) - logger.info(f"Running SVM with the following parameters:\n{pprint.pformat(vars(args))}") - - scores = [] - for seed in range(args.seed, args.seed + args.num_runs): - set_seed(seed) - # Initialize model and get model specific preprocessing pipeline - model = SVM(args, random_state=seed) # TODO: get useful args out - # preprocessing_pipeline = model.preprocessing_pipeline(n_components=args.dense_dim, log_level=args.log_level) - - # Load raw data - data = CellTypeAnnotationDataset(train_dataset=args.train_dataset, test_dataset=args.test_dataset, - species=args.species, tissue=args.tissue).load_data() - - # Construct preprocessing pipeline from config file - preprocessing_pipeline = Pipeline.from_config_file("preprocessing_config.yaml") - print(f"Loaded preprocessing config:\n{preprocessing_pipeline.to_yaml()}") - - # Apply preprocessing pipeline to data - preprocessing_pipeline(data) - - # Obtain training and testing data - x_train, y_train = data.get_train_data() - y_train_converted = y_train.argmax(1) # convert one-hot representation into label index representation - x_test, y_test = data.get_test_data() - - # Train and evaluate the model - model.fit(x_train, y_train_converted) - score = model.score(x_test, y_test) - scores.append(score) - print(f"{score=:.4f}") - print(f"SVM {args.species} {args.tissue} {args.test_dataset}:") - print(f"{scores}\n{np.mean(scores):.5f} +/- {np.std(scores):.5f}") -"""To reproduce SVM benchmarks, please refer to command lines below: - -Mouse Brain -$ python main.py --species mouse --tissue Brain --train_dataset 753 3285 --test_dataset 2695 - -Mouse Spleen -$ python main.py --species mouse --tissue Spleen --train_dataset 1970 --test_dataset 1759 - -Mouse Kidney -$ python main.py --species mouse --tissue Kidney --train_dataset 4682 --test_dataset 203 - -""" diff --git a/examples/config/cta_svm/preprocessing_config.yaml b/examples/config/cta_svm/preprocessing_config.yaml deleted file mode 100644 index a884be5c..00000000 --- a/examples/config/cta_svm/preprocessing_config.yaml +++ /dev/null @@ -1,34 +0,0 @@ -type: preprocessor -desc: >- - Preprocess scRNA-seq counts into dense features for SVM by taking the - weighted sum of the gene PCA components. -pipeline: - - type: feature.cell - target: WeightedFeaturePCA - # Scope will be set to _registry_ by default, so you can leave out the - # specification of scope as well. - # Setting scope to "_registry_" will trigger auto-scope-resolve and - # iteratively combines the type. In this case, it will be resolved to - # "_registry_.preprocessor.feature.cell". - # You can also specify the absolute scope of the target object, rather than - # the scope of the registry. For example, the absolute scope for - # "WeightedFeaturePCA" is "dance.transforms.feature.cell". - scope: _registry_ - params: - n_components: 400 - split_name: train - - type: misc - target: SetConfig - # Scope will be set to _registry_ by default, so you can leave out the - # specification of scope as well. - # Setting scope to "_registry_" will trigger auto-scope-resolve and - # iteratively combines the type. In this case, it will be resolved to - # "_registry_.preprocessor.misc". - # You can also specify the absolute scope of the target object, rather than - # the scope of the registry. For example, the absolute scope for - # "SetConfig" is "dance.transforms.misc". - scope: _registry_ - params: - config_dict: - feature_channel: WeightedFeaturePCA - label_channel: cell_type diff --git a/examples/dataset_server.json b/examples/dataset_server.json new file mode 100644 index 00000000..8d07f604 --- /dev/null +++ b/examples/dataset_server.json @@ -0,0 +1,112 @@ +{ + + "heart":{ + "cta_actinn": [ + "572f3f3e-d3e4-4d13-8e2b-88215e508481", + "fa27492b-82ff-4ab7-ac61-0e2b184eee67", + "f15e263b-6544-46cb-a46e-e33ab7ce8347", + "f7995301-7551-4e1d-8396-ffe3c9497ace", + "e6a11140-2545-46bc-929e-da243eed2cae", + "1062c0f2-2a44-4cf9-a7c8-b5ed58b4728d", + "1c739a3e-c3f5-49d5-98e0-73975e751201", + "1252c5fb-945f-42d6-b1a8-8a3bd864384b", + "a68b64d8-aee3-4947-81b7-36b8fe5a44d2", + "d567b692-c374-4628-a508-8008f6778f22", + "2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Heart)", + "65badd7a-9262-4fd1-9ce2-eb5dc0ca8039", + "f7c1c579-2dc0-47e2-ba19-8165c5a0e353", + "c5d88abe-f23a-45fa-a534-788985e93dad(Heart)", + "83b5e943-a1d5-4164-b3f2-f7a37f01b524", + "bdf69f8d-5a96-4d6f-a9f5-9ee0e33597b7", + "5a11f879-d1ef-458a-910c-9b0bdfca5ebf", + "1009f384-b12d-448e-ba9f-1b7d2ecfbb4e", + "9434b020-de42-43eb-bcc4-542b2be69015", + "d4e69e01-3ba2-4d6b-a15d-e7048f78f22e", + "4ed927e9-c099-49af-b8ce-a2652d069333", + "ed852810-a003-4386-9846-1638362cee39", + "f75f2ff4-2884-4c2d-b375-70de37a34507", + "97a17473-e2b1-4f31-a544-44a60773e2dd" + ] + , + "cta_celltypist": [ + "572f3f3e-d3e4-4d13-8e2b-88215e508481", + "fa27492b-82ff-4ab7-ac61-0e2b184eee67", + "f15e263b-6544-46cb-a46e-e33ab7ce8347", + "f7995301-7551-4e1d-8396-ffe3c9497ace", + "e6a11140-2545-46bc-929e-da243eed2cae", + "1062c0f2-2a44-4cf9-a7c8-b5ed58b4728d", + "1c739a3e-c3f5-49d5-98e0-73975e751201", + "1252c5fb-945f-42d6-b1a8-8a3bd864384b", + "a68b64d8-aee3-4947-81b7-36b8fe5a44d2", + "d567b692-c374-4628-a508-8008f6778f22", + "2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Heart)", + "65badd7a-9262-4fd1-9ce2-eb5dc0ca8039", + "f7c1c579-2dc0-47e2-ba19-8165c5a0e353", + "c5d88abe-f23a-45fa-a534-788985e93dad(Heart)", + "83b5e943-a1d5-4164-b3f2-f7a37f01b524", + "bdf69f8d-5a96-4d6f-a9f5-9ee0e33597b7", + "5a11f879-d1ef-458a-910c-9b0bdfca5ebf", + "1009f384-b12d-448e-ba9f-1b7d2ecfbb4e", + "9434b020-de42-43eb-bcc4-542b2be69015", + "d4e69e01-3ba2-4d6b-a15d-e7048f78f22e", + "4ed927e9-c099-49af-b8ce-a2652d069333", + "ed852810-a003-4386-9846-1638362cee39", + "f75f2ff4-2884-4c2d-b375-70de37a34507", + "97a17473-e2b1-4f31-a544-44a60773e2dd" + ], + "cta_scdeepsort": [ + "572f3f3e-d3e4-4d13-8e2b-88215e508481", + "fa27492b-82ff-4ab7-ac61-0e2b184eee67", + "f15e263b-6544-46cb-a46e-e33ab7ce8347", + "f7995301-7551-4e1d-8396-ffe3c9497ace", + "e6a11140-2545-46bc-929e-da243eed2cae", + "1062c0f2-2a44-4cf9-a7c8-b5ed58b4728d", + "1c739a3e-c3f5-49d5-98e0-73975e751201", + "1252c5fb-945f-42d6-b1a8-8a3bd864384b", + "a68b64d8-aee3-4947-81b7-36b8fe5a44d2", + "d567b692-c374-4628-a508-8008f6778f22", + "2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Heart)", + "65badd7a-9262-4fd1-9ce2-eb5dc0ca8039", + "f7c1c579-2dc0-47e2-ba19-8165c5a0e353", + "c5d88abe-f23a-45fa-a534-788985e93dad(Heart)", + "83b5e943-a1d5-4164-b3f2-f7a37f01b524", + "bdf69f8d-5a96-4d6f-a9f5-9ee0e33597b7", + "5a11f879-d1ef-458a-910c-9b0bdfca5ebf", + "1009f384-b12d-448e-ba9f-1b7d2ecfbb4e", + "9434b020-de42-43eb-bcc4-542b2be69015", + "d4e69e01-3ba2-4d6b-a15d-e7048f78f22e", + "4ed927e9-c099-49af-b8ce-a2652d069333", + "ed852810-a003-4386-9846-1638362cee39", + "f75f2ff4-2884-4c2d-b375-70de37a34507", + "97a17473-e2b1-4f31-a544-44a60773e2dd" + ] + , + "cta_singlecellnet": [ + "572f3f3e-d3e4-4d13-8e2b-88215e508481", + "fa27492b-82ff-4ab7-ac61-0e2b184eee67", + "f15e263b-6544-46cb-a46e-e33ab7ce8347", + "f7995301-7551-4e1d-8396-ffe3c9497ace", + "e6a11140-2545-46bc-929e-da243eed2cae", + "1062c0f2-2a44-4cf9-a7c8-b5ed58b4728d", + "1c739a3e-c3f5-49d5-98e0-73975e751201", + "1252c5fb-945f-42d6-b1a8-8a3bd864384b", + "a68b64d8-aee3-4947-81b7-36b8fe5a44d2", + "d567b692-c374-4628-a508-8008f6778f22", + "2adb1f8a-a6b1-4909-8ee8-484814e2d4bf(Heart)", + "65badd7a-9262-4fd1-9ce2-eb5dc0ca8039", + "f7c1c579-2dc0-47e2-ba19-8165c5a0e353", + "c5d88abe-f23a-45fa-a534-788985e93dad(Heart)", + "83b5e943-a1d5-4164-b3f2-f7a37f01b524", + "bdf69f8d-5a96-4d6f-a9f5-9ee0e33597b7", + "5a11f879-d1ef-458a-910c-9b0bdfca5ebf", + "1009f384-b12d-448e-ba9f-1b7d2ecfbb4e", + "9434b020-de42-43eb-bcc4-542b2be69015", + "d4e69e01-3ba2-4d6b-a15d-e7048f78f22e", + "4ed927e9-c099-49af-b8ce-a2652d069333", + "ed852810-a003-4386-9846-1638362cee39", + "f75f2ff4-2884-4c2d-b375-70de37a34507", + "97a17473-e2b1-4f31-a544-44a60773e2dd" + ] + } + +} diff --git a/examples/multi_modality/joint_embedding/dcca.py b/examples/multi_modality/joint_embedding/dcca.py index 73668160..da092b93 100644 --- a/examples/multi_modality/joint_embedding/dcca.py +++ b/examples/multi_modality/joint_embedding/dcca.py @@ -45,11 +45,12 @@ def parameter_setting(): parser.add_argument("--anneal_epoch", "-ae", type=int, default=200, help="Anneal epoch") parser.add_argument("--epoch_per_test", "-ept", type=int, default=5, help="Epoch per test") parser.add_argument("--max_ARI", "-ma", type=int, default=-200, help="initial ARI") - parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2") - parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("-t", "--subtask", default="openproblems_2022_multi_atac2gex") + parser.add_argument("-device", "--device", default="cuda:5") parser.add_argument("--final_rate", type=float, default=1e-4) parser.add_argument("--scale_factor", type=float, default=4) - + parser.add_argument("--span", type=float, default=0.3) + parser.add_argument("--selection_threshold", type=int, default=3000) return parser @@ -65,13 +66,15 @@ def parameter_setting(): args.lr2 = 0.005 args.flr2 = 0.0005 - dataset = JointEmbeddingNIPSDataset(args.subtask, root="./data/joint_embedding", preprocess="feature_selection") + dataset = JointEmbeddingNIPSDataset(args.subtask, root="./data/joint_embedding", preprocess="feature_selection", + span=args.span, selection_threshold=args.selection_threshold) data = dataset.load_data() le = preprocessing.LabelEncoder() labels = le.fit_transform(data.mod["test_sol"].obs["cell_type"]) data.mod["mod2"].obsm["size_factors"] = np.sum(data.mod["mod2"].X.todense(), 1) / 100 - data.mod["mod1"].obsm["size_factors"] = data.mod["mod1"].obs["size_factors"] + # data.mod["mod1"].obsm["size_factors"] = data.mod["mod1"].obs["size_factors"] + data.mod["mod1"].obsm["size_factors"] = np.sum(data.mod["mod1"].X.todense(), 1) / 100 data.mod["mod1"].obsm["labels"] = labels data.set_config(feature_mod=["mod1", "mod2", "mod1", "mod2", "mod1", "mod2"], label_mod="mod1", @@ -131,7 +134,7 @@ def parameter_setting(): adata = adata[adata_sol.obs_names] adata_sol.obsm['X_emb'] = adata.X score = metrics.labeled_clustering_evaluate(adata, adata_sol) - score.update(metrics.integration_openproblems_evaluate(adata_sol)) + # score.update(metrics.integration_openproblems_evaluate(adata_sol)) score.update({ 'seed': args.seed + k, 'subtask': args.subtask, diff --git a/examples/multi_modality/joint_embedding/jae.py b/examples/multi_modality/joint_embedding/jae.py index cca32808..7504f2e7 100644 --- a/examples/multi_modality/joint_embedding/jae.py +++ b/examples/multi_modality/joint_embedding/jae.py @@ -10,8 +10,11 @@ if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2", - choices=["openproblems_bmmc_cite_phase2", "openproblems_bmmc_multiome_phase2"]) + parser.add_argument( + "-t", "--subtask", default="openproblems_2022_multi_atac2gex", choices=[ + "openproblems_bmmc_cite_phase2", "openproblems_bmmc_multiome_phase2", "GSE140203_BRAIN_atac2gex", + "GSE140203_SKIN_atac2gex", "openproblems_2022_multi_atac2gex" + ]) parser.add_argument("-d", "--data_folder", default="./data/joint_embedding") parser.add_argument("-pre", "--pretrained_folder", default="./data/joint_embedding/pretrained") parser.add_argument("-csv", "--csv_path", default="decoupled_lsi.csv") @@ -21,7 +24,8 @@ parser.add_argument("-bs", "--batch_size", default=128, type=int) parser.add_argument("-nm", "--normalize", default=1, type=int, choices=[0, 1]) parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") - + parser.add_argument("--span", type=float, default=0.3) + parser.add_argument("--preprocess", default="aux") args = parser.parse_args() device = args.device @@ -30,7 +34,8 @@ rndseed = args.seed set_seed(rndseed) - dataset = JointEmbeddingNIPSDataset(args.subtask, root=args.data_folder, preprocess="aux", normalize=True) + dataset = JointEmbeddingNIPSDataset(args.subtask, root=args.data_folder, preprocess=args.preprocess, normalize=True, + span=args.span) data = dataset.load_data() data.set_config( @@ -39,6 +44,15 @@ feature_channel=["X_pca", "X_pca"], label_channel=["cell_type", "batch_label", "phase_labels", "S_scores", "G2M_scores"], ) + if args.preprocess != "aux": + cell_type_labels = data.data['test_sol'].obs["cell_type"].to_numpy() + cell_type_labels_unique = list(np.unique(cell_type_labels)) + c_labels = np.array([cell_type_labels_unique.index(item) for item in cell_type_labels]) + data.data['mod1'].obsm["cell_type"] = c_labels + data.data["mod1"].obsm["S_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["G2M_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["batch_label"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["phase_labels"] = np.zeros(data.data['mod1'].shape[0]) (X_mod1_train, X_mod2_train), (cell_type, batch_label, phase_label, S_score, G2M_score) = data.get_train_data(return_type="torch") (X_mod1_test, X_mod2_test), (cell_type_test, _, _, _, _) = data.get_test_data(return_type="torch") @@ -61,7 +75,7 @@ print(embeds) score = model.score(X_test, test_id, labels, metric="clustering") - score.update(model.score(X_test, test_id, labels, adata_sol=adata_sol, metric="openproblems")) + # score.update(model.score(X_test, test_id, labels, adata_sol=adata_sol, metric="openproblems")) score.update({ 'seed': args.seed + k, 'subtask': args.subtask, diff --git a/examples/multi_modality/joint_embedding/scmogcn.py b/examples/multi_modality/joint_embedding/scmogcn.py index 0ed73f3f..51e556c2 100644 --- a/examples/multi_modality/joint_embedding/scmogcn.py +++ b/examples/multi_modality/joint_embedding/scmogcn.py @@ -11,8 +11,11 @@ if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2", - choices=["openproblems_bmmc_cite_phase2", "openproblems_bmmc_multiome_phase2"]) + parser.add_argument( + "-t", "--subtask", default="openproblems_2022_multi_atac2gex", choices=[ + "openproblems_bmmc_cite_phase2", "openproblems_bmmc_multiome_phase2", "GSE140203_BRAIN_atac2gex", + "GSE140203_SKIN_atac2gex", "openproblems_2022_multi_atac2gex" + ]) parser.add_argument("-d", "--data_folder", default="./data/joint_embedding") parser.add_argument("-pre", "--pretrained_folder", default="./data/joint_embedding/pretrained") parser.add_argument("-csv", "--csv_path", default="decoupled_lsi.csv") @@ -24,7 +27,8 @@ parser.add_argument("-bs", "--batch_size", default=512, type=int) parser.add_argument("-nm", "--normalize", default=1, type=int, choices=[0, 1]) parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") - + parser.add_argument("--span", type=float, default=0.3) + parser.add_argument("--preprocess", default="aux") args = parser.parse_args() device = args.device @@ -33,7 +37,8 @@ rndseed = args.seed set_seed(rndseed) - dataset = JointEmbeddingNIPSDataset(args.subtask, root=args.data_folder, preprocess="aux", normalize=True) + dataset = JointEmbeddingNIPSDataset(args.subtask, root=args.data_folder, preprocess=args.preprocess, normalize=True, + span=args.span) data = dataset.load_data() train_size = len(data.get_split_idx("train")) @@ -45,6 +50,15 @@ feature_channel=["X_pca", "X_pca"], label_channel=["cell_type", "batch_label", "phase_labels", "S_scores", "G2M_scores"], ) + if args.preprocess != "aux": + cell_type_labels = data.data['test_sol'].obs["cell_type"].to_numpy() + cell_type_labels_unique = list(np.unique(cell_type_labels)) + c_labels = np.array([cell_type_labels_unique.index(item) for item in cell_type_labels]) + data.data['mod1'].obsm["cell_type"] = c_labels + data.data["mod1"].obsm["S_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["G2M_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["batch_label"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["phase_labels"] = np.zeros(data.data['mod1'].shape[0]) (x_mod1, x_mod2), (cell_type, batch_label, phase_label, S_score, G2M_score) = data.get_data(return_type="torch") phase_score = torch.cat([S_score[:, None], G2M_score[:, None]], 1) test_id = np.arange(x_mod1.shape[0]) @@ -68,7 +82,7 @@ embeds = model.predict(test_id).cpu().numpy() print(embeds) score = model.score(test_id, labels, metric="clustering") - score.update(model.score(test_id, labels, adata_sol=adata_sol, metric="openproblems")) + # score.update(model.score(test_id, labels, adata_sol=adata_sol, metric="openproblems")) score.update({ 'seed': args.seed + k, 'subtask': args.subtask, diff --git a/examples/multi_modality/joint_embedding/scmvae.py b/examples/multi_modality/joint_embedding/scmvae.py index 65464c0f..c638f35b 100644 --- a/examples/multi_modality/joint_embedding/scmvae.py +++ b/examples/multi_modality/joint_embedding/scmvae.py @@ -32,11 +32,12 @@ def parameter_setting(): parser.add_argument("--epoch_per_test", "-ept", type=int, default=1, help="Epoch per test, must smaller than max iteration.") parser.add_argument("--max_ARI", "-ma", type=int, default=-200, help="initial ARI") - parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2") - parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("-t", "--subtask", default="openproblems_2022_multi_atac2gex") + parser.add_argument("-device", "--device", default="cuda:4") parser.add_argument("--final_rate", type=float, default=1e-4) parser.add_argument("--scale_factor", type=float, default=4) - + parser.add_argument("--span", type=float, default=0.3) + parser.add_argument("--selection_threshold", type=int, default=3000) return parser @@ -46,7 +47,8 @@ def parameter_setting(): set_seed(args.seed) assert args.max_iteration > args.epoch_per_test - dataset = JointEmbeddingNIPSDataset(args.subtask, root="./data/joint_embedding", preprocess="feature_selection") + dataset = JointEmbeddingNIPSDataset(args.subtask, root="./data/joint_embedding", preprocess="feature_selection", + span=args.span, selection_threshold=args.selection_threshold) data = dataset.load_data() le = preprocessing.LabelEncoder() @@ -121,7 +123,7 @@ def parameter_setting(): embeds = model.predict(x_test, y_test).cpu().numpy() print(embeds.shape) score = model.score(x_test, y_test, labels) - score.update(model.score(x_test, y_test, labels, adata_sol=data.data['test_sol'], metric="openproblems")) + # score.update(model.score(x_test, y_test, labels, adata_sol=data.data['test_sol'], metric="openproblems")) score.update({ 'seed': args.seed + k, 'subtask': args.subtask, diff --git a/examples/readme.md b/examples/readme.md new file mode 100644 index 00000000..a7b97cae --- /dev/null +++ b/examples/readme.md @@ -0,0 +1,286 @@ +# How to Add New Algorithms to the Auto-Search Framework + +This document explains how to integrate new algorithms into the project's automatic search framework. + +## Implementation Requirements + +### 1. Inherit Base Classes + +New algorithms should inherit from appropriate base classes in `dance.modules.base`: + +```python +from dance.modules.base import ( + BaseClusteringMethod, # Clustering algorithms + BaseClassificationMethod, # Classification algorithms + BaseRegressionMethod, # Regression algorithms + TorchNNPretrain # If pretraining is needed +) + +class YourMethod(BaseClusteringMethod): + """Your method description.""" + pass +``` + +### 2. Implement Required Interfaces + +All algorithms must implement these core interfaces: + +```python +def preprocessing_pipeline(**kwargs) -> BaseTransform: + """Define data preprocessing pipeline""" + ... + +def fit(self, x, y=None, **kwargs): + """Train the model + + Parameters + ---------- + x : array-like + Input features + y : array-like, optional + Labels (required for supervised learning) + """ + ... + +def predict(self, x): + """Predict results + + Parameters + ---------- + x : array-like + Input features + + Returns + ------- + array-like + Prediction results + """ + ... +``` + +The base class provides a default implementation of the `score()` method, which: + +1. Calls `predict()` to get prediction results +1. Calculates scores using predefined evaluation metrics + - Clustering algorithms: ARI (Adjusted Rand Index) + - Classification algorithms: Accuracy + - Regression algorithms: MSE (Mean Squared Error) + +## Directory Structure + +New algorithms should follow this directory structure: + +```plaintext + examples/tuning/ + └── [task_name]_[algorithm_name]/ + ├── main.py # Main execution file + └── [dataset_name]/ # Dataset related configurations + ├── pipeline_params_tuning_config.yaml + └── config_yamls + ├── 0_test_acc_params_tuning_config.yaml + ├── 1_test_acc_params_tuning_config.yaml + └── 2_test_acc_params_tuning_config.yaml +``` + +## Integration Steps + +### 1. Create Algorithm Directory + +Create a new algorithm directory under `examples/tuning/`, named as `[task_name]_[algorithm_name]`. + +### 2. Implement Main Execution File + +Create `main.py` in the algorithm directory with these key components: + +1. **Parameter Configuration** + +```python +parser = argparse.ArgumentParser() +# Add necessary parameters +parser.add_argument("--data_dir", default="../temp_data"，help="Directory path containing the input data files") +parser.add_argument("--dataset", type=str, choices=[...]，help="Dataset name") +parser.add_argument("--tune_mode", default="pipeline_params", + choices=["pipeline", "params", "pipeline_params"]， help="Tuning mode: 'pipeline' for pipeline tuning only, 'params' for parameter tuning only, 'pipeline_params' for both") +parser.add_argument("--sweep_id", type=str, default=None，help="Existing sweep ID to resume. If None, creates a new sweep") +parser.add_argument("--count", type=int, default=2，help="Number of times to run the sweep agent") +parser.add_argument( + "--summary_file_path", + default="results/pipeline/best_test_acc.csv", + type=str, + help="Path to save the summary results file" + ) +parser.add_argument( + "--root_path", + default=str(Path(__file__).resolve().parent), + type=str, + help="Root directory path for saving results and configuration files" + ) +# ... other model-specific parameters ... +args = parser.parse_args() +``` + +2. **Evaluation Function Definition** + +```python +def evaluate_pipeline(tune_mode, pipeline_planer): + # Initialize wandb + wandb.init(settings=wandb.Settings(start_method='thread')) + + # Load data according to the task + dataloader = TaskDataset(args.data_dir, args.dataset) + data = dataloader.load_data(cache=args.cache) + + # Apply preprocessing pipeline + kwargs = {tune_mode: dict(wandb.config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) + preprocessing_pipeline(data) + + # Get processed data + inputs, y = data.get_data(return_type="default") + + # Initialize and train model + model = YourModel(model_params) + model.fit(inputs, y) + + # Evaluate and log results + score = model.score(None, y) + wandb.log({"acc": score}) + wandb.finish() +``` + +3. **Main Program Flow** + +```python +if __name__ == "__main__": + # Initialize pipeline planer + pipeline_planer = PipelinePlaner.from_config_file( + f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + + # Run hyperparameter search + entity, project, sweep_id = pipeline_planer.wandb_sweep_agent( + evaluate_pipeline, + sweep_id=args.sweep_id, + count=args.count + ) + + # Save results + save_summary_data( + entity, project, sweep_id, + summary_file_path=args.summary_file_path, + root_path=file_root_path + ) + if args.tune_mode == "pipeline" or args.tune_mode == "pipeline_params": + #generate step3_default_params.yaml + get_step3_yaml(result_load_path=f"{args.summary_file_path}", step2_pipeline_planer=pipeline_planer, + conf_load_path=f"{Path(args.root_path).resolve().parent}/step3_default_params.yaml", + root_path=file_root_path, required_funs=["SaveRaw", "UpdateRaw", "NeighborGraph", "SetConfig"], + required_indexes=[2, 5, sys.maxsize - 1, sys.maxsize], metric="acc") + if args.tune_mode == "pipeline_params": + #run step3 + run_step3(file_root_path, evaluate_pipeline, tune_mode="params", step2_pipeline_planer=pipeline_planer) +``` + +### 3. Configuration File Setup + +Create corresponding configuration files in the dataset directory to guide the hyperparameter search process. + +#### Configuration File Types + +- `pipeline_params_tuning_config.yaml`: Main configuration file for joint search +- `config_yamls/*.yaml`: Parameter search configuration files automatically generated by the system + +#### Search Modes Explanation + +The system supports three search modes (specified by `tune_mode`): + +1. **pipeline mode** + + - Only searches for optimal preprocessing pipeline combinations + - Uses `pipeline_tuning_config.yaml` + +1. **params mode** + + - Only searches for optimal model parameter combinations + - Uses `params_tuning_config.yaml` + +1. **pipeline_params mode** + + - Performs two-stage joint search + - First stage: searches for optimal preprocessing pipeline + - Second stage: searches for optimal model parameters based on the best pipeline + - System automatically generates parameter search config files (e.g., `config_yamls/0_test_acc_params_tuning_config.yaml`) + +Configuration file example: + +```yaml +# pipeline_params_tuning_config.yaml +type: preprocessor +tune_mode: pipeline_params +pipeline_tuning_top_k: 3 #topk for pipeline tuning to use parameter tuning +parameter_tuning_freq_n: 20 #frequency for parameter tuning +pipeline: + - type: filter.gene + include: + - FilterGenesPercentile + - FilterGenesScanpyOrder + - FilterGenesPlaceHolder + default_params: + FilterGenesScanpyOrder: + order: ["min_counts", "min_cells", "max_counts", "max_cells"] + min_counts: 0.01 + max_counts: 0.99 + min_cells: 0.01 + max_cells: 0.99 + - type: feature.cell + include: + - WeightedFeaturePCA + - WeightedFeatureSVD + - CellPCA + - CellSVD + - GaussRandProjFeature # Registered custom preprocessing func + - FeatureCellPlaceHolder + params: + out: feature.cell + log_level: INFO + default_params: + WeightedFeaturePCA: + split_name: train + WeightedFeatureSVD: + split_name: train + - type: misc + target: SetConfig + params: + config_dict: + feature_channel: feature.cell + label_channel: cell_type +wandb: + entity: xxxxx + project: xxxxx + method: grid #try grid to provide a comprehensive search + metric: + name: acc # val/acc + goal: maximize +``` + +### 4. Run Tests + +After integration, test using these commands: + +```bash + # Search preprocessing pipeline only + python main.py --tune_mode pipeline + # Search model parameters only + python main.py --tune_mode params + # Joint search + python main.py --tune_mode pipeline_params +``` + +## Notes + +1. Ensure the model implements `fit()` and `score()` interfaces +1. wandb configuration should correspond to model parameters +1. Recommend testing on small datasets first + +## Examples + +Refer to `examples/tuning/cluster_graphsc` and `examples/tuning/cta_celltypist` implementations. diff --git a/examples/result_analysis/get_important_pattern.py b/examples/result_analysis/get_important_pattern.py new file mode 100644 index 00000000..3db84b95 --- /dev/null +++ b/examples/result_analysis/get_important_pattern.py @@ -0,0 +1,384 @@ +# metric_name = "test_acc" +# ascending = False +import argparse +import itertools +import pathlib +from collections import Counter +from copy import deepcopy +from itertools import combinations +from os import X_OK +from pathlib import Path +from venv import logger + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import scikit_posthocs as sp +import seaborn as sns +import shapiq +from mlxtend.frequent_patterns import apriori +from mlxtend.preprocessing import TransactionEncoder +from networkx import parse_adjlist +from scipy import cluster, stats +from scipy.stats import pointbiserialr +from sklearn.compose import ColumnTransformer +from sklearn.ensemble import RandomForestRegressor +from sklearn.model_selection import GridSearchCV, KFold, LeaveOneOut, cross_val_score +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import OneHotEncoder +from typing_extensions import deprecated + + +#use get_important_pattern_sweep.py +#Negative pattern, just need to change the order +def get_important_pattern(test_accs, ascending, vis=True, alpha=0.05, title=""): + """Identify important patterns in test accuracies using statistical tests. + + Given multiple groups of test accuracies, this function performs Kruskal-Wallis test followed by + Dunn's post-hoc test to identify statistically significant differences between groups. The results + are then used to rank the groups based on their relative performance. + + Parameters + ---------- + test_accs + List of test accuracy groups to compare. + ascending + Boolean indicating whether to sort results in ascending order. + vis + Whether to visualize the results using box plots. + alpha + Significance level for statistical tests. + title + Title for the visualization plot. + + Returns + ------- + list + List of ranks indicating the relative importance of each group. + + """ + + if vis: + fig = plt.figure(figsize=(12, 4)) + sns.boxplot(data=test_accs) + plt.xticks(list(range(len(test_accs))), [f"{i}" for i in range(len(test_accs))]) + plt.title(title) + plt.show() + _, p_value = stats.kruskal(*test_accs) + if p_value < alpha: + medians = [np.median(group) for group in test_accs] + data = test_accs + p_values_matrix = sp.posthoc_dunn(a=data, p_adjust="bonferroni") + sorted_indices = np.argsort(np.argsort([-x for x in medians] if ascending else medians)) + ranks = { + index: { + "rank": rank, + "before": None, + "after": [], + "real_rank": rank + } + for index, rank in enumerate(sorted_indices) + } + for (rank1, rank2) in combinations(range(max(sorted_indices) + 1), 2): + for idx1 in [index for index, value in ranks.items() if value["rank"] == rank1]: + for idx2 in [index for index, value in ranks.items() if value["rank"] == rank2]: + if p_values_matrix.iloc[idx1, idx2] > alpha: + if ranks[idx2]["before"] is None: + ranks[idx1]["after"].append(idx2) + ranks[idx2]["before"] = idx1 + + def change_real_rank(rank_item, real_rank): + rank_item["real_rank"] = real_rank + for idx in rank_item["after"]: + change_real_rank(ranks[idx], real_rank) + + for rank_item in ranks.values(): + if rank_item["before"] is None: + for idx in rank_item["after"]: + change_real_rank(ranks[idx], rank_item["real_rank"]) + return [v["real_rank"] for k, v in ranks.items()] + else: + if vis: + print("No significant differences found between the groups.") + return [] + + +def are_all_elements_same_direct(list_2d): + first_element = None + for sublist in list_2d: + for element in sublist: + if first_element is None: + first_element = element + elif element != first_element: + return False + return True if first_element is not None else True + + +def get_frequent_itemsets(step2_data, metric_name, ascending, threshold_per=0.1, multi_mod=False): + """Extract frequent patterns from top performing pipeline configurations. + + Given a DataFrame containing pipeline configurations and their performance metrics, this function + identifies frequent patterns in the top performing configurations using the Apriori algorithm. + + Parameters + ---------- + step2_data + DataFrame containing pipeline configurations and metrics. + metric_name + Name of the performance metric to optimize. + ascending + Boolean indicating whether to sort in ascending order. + threshold_per + Percentage of top configurations to consider. + multi_mod + Whether to use multiple modalities (not implemented). + + Returns + ------- + list + List of dictionaries containing frequent itemsets and their support values. + + """ + if multi_mod: + raise NotImplementedError("need multimod") + threshold = int(len(step2_data) * threshold_per) + step2_data.loc[:, metric_name] = step2_data.loc[:, metric_name].astype(float) + df_sorted = step2_data.sort_values(metric_name, ascending=ascending) + top_10_percent = df_sorted.head(threshold) + columns = sorted([col for col in step2_data.columns if col.startswith("pipeline")]) + transactions = top_10_percent[columns].values.tolist() + te = TransactionEncoder() + te_ary = te.fit(transactions).transform(transactions) + df = pd.DataFrame(te_ary, columns=te.columns_) + frequent_itemsets = apriori(df, use_colnames=True, min_support=0.3) + # print(frequent_itemsets) + # rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.5) + frequent_itemsets['itemsets'] = frequent_itemsets['itemsets'].apply(lambda x: tuple(x)) + return frequent_itemsets.to_dict(orient='records') + + +def get_significant_top_n_zscore(data, n=3, threshold=1.0, ascending=False): + if not data: + return [] + n = max(1, n) + mean = np.mean(data) + std = np.std(data) + if std == 0: + return sorted(data, reverse=not ascending)[:n] + z_scores = [(x, (x - mean) / std) for x in data] + significant_values = [x for x, z in z_scores if z > threshold] + significant_values_sorted = sorted(significant_values, reverse=not ascending) + if len(significant_values_sorted) < n: + remaining = sorted(data, reverse=not ascending)[:n - len(significant_values_sorted)] + significant_values_sorted.extend(remaining) + return significant_values_sorted[:n] + + +def get_test_acc_and_names(step2_data, metric_name): + + def replace_nan_in_2d(lst): #nan should be an extreme value rather than being directly deleted + return [[np.nan if item == 'NaN' else item for item in sublist] for sublist in lst] + + columns = sorted([col for col in step2_data.columns if col.startswith("pipeline")]) + test_accs = [] + test_acc_names = [] + for r in range(1, len(columns) + 1): + for com in itertools.combinations(columns, r): + test_accs_arrays = [] + groups = step2_data.groupby(by=list(com)) + if len(groups) == 1: + continue + for g in groups: + test_accs_arrays.append({"name": g[0], metric_name: list(g[1][metric_name])}) + test_accs += [i[metric_name] for i in test_accs_arrays] + test_acc_names += [i["name"] for i in test_accs_arrays] + # if are_all_elements_same_direct(test_accs): + # continue + test_accs = replace_nan_in_2d(test_accs) + return test_accs, test_acc_names + + +@deprecated("not used") +def get_com_all(step2_data, metric_name, ascending, vis=True, alpha=0.05): + ans_all = [] + test_accs, test_acc_names = get_test_acc_and_names(step2_data, metric_name) + final_ranks = get_important_pattern(test_accs, ascending, alpha=alpha, title="all_pattern", vis=vis) + if len(final_ranks) > 0: + max_rank = max(final_ranks) + max_rank_count = final_ranks.count(max_rank) + if max_rank_count < len(final_ranks) / 2: + for index, (test_acc_name, rank) in enumerate(zip(test_acc_names, final_ranks)): + if rank == max_rank: + if vis: + print(f"index={index},name={test_acc_name},rank={rank}") + ans_all.append(test_acc_name if isinstance(test_acc_name, tuple) else (test_acc_name, )) + return ans_all + + +def get_significant_items(data): + abs_values = np.abs(list(data.values())) + percentile = 60 + threshold = np.percentile(abs_values, percentile) + significant_items = {k: v for k, v in data.items() if abs(v) >= threshold} + return significant_items + + +def get_forest_model_pattern(step2_data, metric_name): + """Analyze feature importance using Random Forest and SHAP values. + + Given pipeline configurations and their performance metrics, this function trains a Random Forest model + and uses SHAP values to identify important feature interactions. It also computes point-biserial + correlations to validate the importance of identified patterns.The reason for using the random forest model can be found at https://docs.wandb.ai/guides/app/features/panels/parameter-importance/ + For calculations of arbitrary-order Shapley interactions, see https://github.com/mmschlk/shapiq + + Parameters + ---------- + step2_data + DataFrame containing pipeline configurations and metrics. + metric_name + Target metric to predict. + + Returns + ------- + dict + Dictionary containing: + - Important feature interactions and their SHAP values + - Point-biserial correlation statistics + - Best model parameters and MSE + + """ + columns = sorted( + [col for col in step2_data.columns if (col.startswith("pipeline") or col.startswith("run_kwargs_pipeline"))]) + X = step2_data.loc[:, columns] + y = step2_data.loc[:, metric_name] + preprocessor = ColumnTransformer(transformers=[('onehot', OneHotEncoder(drop='first', handle_unknown='ignore'), + columns) # drop='first' to prevent dummy variable trap + ]) + pipeline = Pipeline(steps=[('preprocessor', preprocessor), + ('regressor', + RandomForestRegressor(n_estimators=100, max_depth=5, min_samples_split=2, + min_samples_leaf=1, random_state=42))]) + + param_grid = { + 'regressor__n_estimators': [10, 50, 100, 200], + 'regressor__max_depth': [3, 5, 7], + 'regressor__min_samples_split': [2, 5], + 'regressor__min_samples_leaf': [1, 2] + } + loo = LeaveOneOut() + + grid_search = GridSearchCV( + estimator=pipeline, + param_grid=param_grid, + cv=loo, + scoring='neg_mean_squared_error', + n_jobs=-1, + verbose=1, + refit=True # Ensure the best model is retrained on all data + ) + grid_search.fit(X, y) + best_pipeline = grid_search.best_estimator_ + model = best_pipeline.named_steps['regressor'] + X_preprocessed = best_pipeline.named_steps['preprocessor'].transform(X) + feature_names = best_pipeline.named_steps['preprocessor'].get_feature_names_out(columns) + logger.info(f"X.columns={X.columns}") + logger.info(f"feature_names={feature_names}") + explainer = shapiq.TreeExplainer( + model=model, index="k-SII", max_order=3 + ) # Consider why there are no negative values, possibly to prevent cancellation of positive and negative values + list_of_interaction_values = explainer.explain_X(X_preprocessed.toarray(), n_jobs=96, random_state=42) + plt.cla() + ax = shapiq.plot.bar_plot(list_of_interaction_values, feature_names=feature_names, max_display=None, show=False, + need_abbreviate=False) + ax.yaxis.get_major_locator().MAXTICKS = 1000000 + plt.show() + rects = ax.containers[0] + yticklabels = ax.get_yticklabels() # Need to verify if labels and rectangles overlap + shap_ans = {} + for rect, label in zip(rects, yticklabels): + xy = rect.get_xy() + height = rect.get_height() + width = rect.get_width() + k = label.get_text() + v = width + if k in shap_ans: + raise RuntimeError("Features should not be repeated") + shap_ans[k] = v + + ans = get_significant_items(shap_ans) # Check if it's really a pattern, the results seem not good, need to verify + preprocessed_df = pd.DataFrame(X_preprocessed.toarray(), index=X.index, + columns=best_pipeline.named_steps['preprocessor'].get_feature_names_out(columns)) + preprocessed_df[metric_name] = step2_data[metric_name] + preprocessed_df_copy = deepcopy(preprocessed_df) + real_ans = {} + new_columns = {} + for k, v in ans.items(): + feature_name = k.split(' x ') + one_col = f"{','.join(feature_name)}__all__one" + new_columns[one_col] = preprocessed_df_copy[feature_name].eq(1).all(axis=1) + # method='pearson' + # pearson_corr = preprocessed_df_copy.loc[:,one_col].corr(preprocessed_df_copy.loc[:,metric_name], method=method) + r_pb, p_value = pointbiserialr(new_columns[one_col].astype('category'), preprocessed_df_copy.loc[:, + metric_name]) + real_ans[k] = {"shapiq": v, "pointbiserialr": {"r_pb": r_pb, "p_value": p_value}} + preprocessed_df_copy = pd.concat([preprocessed_df_copy, pd.DataFrame(new_columns)], axis=1) + real_ans["best_params"] = grid_search.best_params_ + real_ans["best_mse"] = -grid_search.best_score_ + return real_ans + + +def summary_pattern(data_path, metric_name, ascending, alpha=0.05, vis=False): + step2_origin_data = pd.read_csv(data_path) + step2_data = step2_origin_data.dropna() + com_ans = get_com_all(step2_data, metric_name, ascending, vis=vis, alpha=alpha) + apr_ans = get_frequent_itemsets(step2_data, metric_name, ascending) + return list(set(com_ans) & set(apr_ans)) + + +# def list_files(directory,file_name="best_test_acc.csv",save_path="summary_file"): +# ans=[] +# path = Path(directory) +# for file_path in path.rglob('*'): +# if file_path.is_file(): +# if file_path.name==file_name: +# algorithm,dataset=file_path.relative_to(directory).parts[:2] +# ans.append({"algorithm":algorithm,"dataset":dataset,"summary_pattern":summary_pattern(file_path)}) +# pd.DataFrame(ans).to_csv(save_path) +def list_files(directories, metric_name, ascending, file_name="best_test_acc.csv", alpha=0.05, vis=False): + ans_all = [] + for directory in directories: + path = Path(directory) + for file_path in path.rglob('*'): + if file_path.is_file(): + if file_path.name == file_name: + print(file_path) + dataset = file_path.parent + method = file_path.parent.parent + ans = summary_pattern(file_path, metric_name, ascending, alpha=alpha, vis=vis) + with open(Path(file_path.parent.resolve(), "pipeline_summary_pattern.txt"), 'w') as f: + f.write(str(ans)) + ans_all.append({"dataset": dataset, "method": method, "ans": ans}) + return ans_all + + +# if __name__ == "__main__": +# directories = [] +# parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) +# parser.add_argument("task", default="cluster") +# parser.add_argument("metric_name", default="acc") +# parser.add_argument("ascending", default=False) +# args = parser.parse_args() +# task = args.task +# metric_name = args.metric_name +# ascending = args.ascending +# file_root = Path(__file__).resolve().parent.parent / "tuning" +# for path in file_root.iterdir(): +# if path.is_dir(): +# if str(path.name).startswith(task): +# directories.append(path) +# ans_all = list_files(directories, metric_name, ascending) +# df = pd.DataFrame(ans_all) +# pivot_df = df.pivot(index="dataset", columns="method", values="ans") +# pivot_df.to_csv(f"{task}_pattern.csv") + +# # print(summary_pattern("/home/zyxing/dance/examples/tuning/cta_actinn/328_138/results/pipeline/best_test_acc.csv",alpha=0.3,vis=True)) diff --git a/examples/result_analysis/get_important_pattern_sweep.py b/examples/result_analysis/get_important_pattern_sweep.py new file mode 100644 index 00000000..04ab85f3 --- /dev/null +++ b/examples/result_analysis/get_important_pattern_sweep.py @@ -0,0 +1,235 @@ +import argparse +import json +import sys +from pathlib import Path +from turtle import pos + +import pandas as pd +import requests +from get_important_pattern import get_com_all, get_forest_model_pattern, get_frequent_itemsets +from numpy import choose + +from dance.settings import ATLASDIR + +sys.path.append(str(ATLASDIR)) +from get_result_web import spilt_web + +from dance.pipeline import flatten_dict +from dance.utils import try_import + +# Define basic configuration parameters +entity = "xzy11632" +project = "dance-dev" +# # List of tasks to analyze +# # tasks = ["cell type annotation new", "clustering", "imputation_new", "spatial domain", "cell type deconvolution"] +# tasks = ['joint embedding'] +# # Corresponding metrics for each task +# # mertic_names = ["test_acc", "acc", "MRE", "ARI", "MSE"] +# mertic_names = ["ARI"] +# ascendings = [False] +# # Whether higher values are better for each metric +# # ascendings = [False, False, True, False, True] +metrics_dict = [{ + "task": "cell type annotation new", + "metric": "test_acc", + "ascending": False +}, { + "task": "clustering", + "metric": "acc", + "ascending": False +}, { + "task": "imputation_new", + "metric": "MRE", + "ascending": True +}, { + "task": "spatial domain", + "metric": "ARI", + "ascending": False +}, { + "task": "cell type deconvolution", + "metric": "MSE", + "ascending": True +}, { + "task": "joint embedding", + "metric": "ARI", + "ascending": False +}] +tasks = [d["task"] for d in metrics_dict] +mertic_names = [d["metric"] for d in metrics_dict] +ascendings = [d["ascending"] for d in metrics_dict] + +multi_mod = False +if multi_mod: + raise NotImplementedError("multi mod") + +parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) +parser.add_argument("--positive", action='store_true') +parser.add_argument("--only_apr", action='store_true') +parser.add_argument("--choose_tasks", nargs="+", default=tasks) +args = parser.parse_args() +choose_tasks = args.choose_tasks +positive = args.positive +only_apr = args.only_apr +if not positive: + assert only_apr + ascendings = [not item for item in ascendings] +file_root = Path(__file__).resolve().parent +prefix = f'https://wandb.ai/{entity}/{project}' +runs_sum = 0 +wandb = try_import("wandb") + + +def get_additional_sweep(sweep_id): + """Recursively retrieve all related sweep IDs from a given sweep. + + Given a sweep ID, this function recursively finds all related sweep IDs by examining the command + arguments of the runs within each sweep. It handles cases where sweeps may have prior runs or + additional sweep references. + + Parameters + ---------- + sweep_id : str + The initial sweep ID to start the search from. + + Returns + ------- + list + A list containing all related sweep IDs, including the input sweep_id. + + """ + sweep = wandb.Api().sweep(f"{entity}/{project}/{sweep_id}") + additional_sweep_ids = [sweep_id] + #last run command + run = next((t_run for t_run in sweep.runs if t_run.state == "finished"), None) + if run is None: # check summary data count, note aznph5wt, quantities may be inconsistent + return additional_sweep_ids + run_id = run.id + web_abs = requests.get(f"https://api.wandb.ai/files/{run.entity}/{run.project}/{run_id}/wandb-metadata.json") + args = dict(web_abs.json())["args"] + for i in range(len(args)): + if args[i] == '--additional_sweep_ids': + if i + 1 < len(args): + additional_sweep_ids += get_additional_sweep(args[i + 1]) + return additional_sweep_ids + + +def summary_pattern(step2_origin_data, metric_name, ascending, alpha=0.05, vis=False): + """Analyze patterns in pipeline configurations and their impact on performance + metrics. + + This function examines the relationship between pipeline configurations and their corresponding + performance metrics. It handles missing values differently based on whether higher or lower + metric values are better, and can optionally visualize the results. + + Parameters + ---------- + step2_origin_data : pd.DataFrame + DataFrame containing pipeline configurations and their results. + metric_name : str + Name of the performance metric to analyze. + ascending : bool + Whether higher metric values indicate better performance. + alpha : float, optional + Significance level for statistical tests, by default 0.05. + vis : bool, optional + Whether to generate visualizations, by default False. + + Returns + ------- + dict + A dictionary containing either: + - Error message if all metric values are NaN + - Pattern analysis results including forest model and/or APR analysis + + """ + columns = sorted([ + col for col in step2_origin_data.columns + if (col.startswith("pipeline") or col.startswith("run_kwargs_pipeline")) + ]) + step2_data = step2_origin_data.loc[:, columns + [metric_name]] + # com_ans = get_com_all(step2_data, metric_name, ascending, vis=vis, alpha=alpha) + step2_data[metric_name] = step2_data[metric_name].astype(float) + if not ascending: + min_metric = step2_data[metric_name].min() + if pd.isna(min_metric): + return { + "error": + f"All {metric_name} values are NaN and the minimum cannot be calculated. Please check your data." + } + step2_data[metric_name] = step2_data[metric_name].fillna(0) #if ascending=False + else: + max_metric = step2_data[metric_name].max() + if pd.isna(max_metric): + return { + "error": + f"All {metric_name} values are NaN and the maximum cannot be calculated. Please check your data." + } + print(f"\nmax {metric_name}:{max_metric}") + buffer_percentage = 0.2 # 20% + replacement = max_metric * (1 + buffer_percentage) + step2_data[metric_name] = step2_data[metric_name].fillna(replacement) + apr_ans = get_frequent_itemsets(step2_data, metric_name, ascending) + if positive and not only_apr: + return {"forest_model": get_forest_model_pattern(step2_data, metric_name), "apr_ans": apr_ans} + else: + return {"apr_ans": apr_ans} + + +if __name__ == "__main__": + start = False + ans_all = [] + for i, task in enumerate(tasks): + # Skip tasks not in choose_tasks list + if task not in choose_tasks: + continue + + # Read and preprocess results from Excel file + data = pd.read_excel(file_root / "results.xlsx", sheet_name=task, dtype=str) + data = data.ffill().set_index(['Methods']) + + # Iterate through each method and dataset combination + for row_idx in range(data.shape[0]): + for col_idx in range(data.shape[1]): + # Extract metadata + method = data.index[row_idx] + dataset = data.columns[col_idx] + value = data.iloc[row_idx, col_idx] + step_name = data.iloc[row_idx]["Unnamed: 1"] + if method == "Scmvae" and dataset == "Dataset3:openproblems_2022_multi_atac2gex": + start = True + if not start: + continue + # if method != "Scgnn2": + # continue + if isinstance(value, str) and value.startswith(prefix) and ( + str(step_name).lower() == "step2" or str(step_name).lower() == "step 2"): #TODO add step3 + sweep_url = value + else: + continue + _, _, sweep_id = spilt_web(sweep_url) + sweep_ids = get_additional_sweep(sweep_id) + summary_data = [] + for sweep_id in sweep_ids: + sweep = wandb.Api().sweep(f"{entity}/{project}/{sweep_id}") + for run in sweep.runs: + result = dict(run.summary._json_dict).copy() + result.update(run.config) + result.update({"id": run.id}) + summary_data.append(flatten_dict(result)) # get result and config + ans = pd.DataFrame(summary_data).set_index(["id"]) + ans.sort_index(axis=1, inplace=True) + ans_single = { + "task": task, + "dataset": dataset, + "method": method, + "pattern": summary_pattern(ans, mertic_names[i], ascendings[i]) + } + with open( + f"dance_auto_preprocess/patterns/{'only_apr_' if only_apr else ''}{'neg_' if not positive else ''}{task}_{dataset}_{method}_pattern.json", + "w") as f: + json.dump(ans_single, f, indent=2) + ans_all.append(ans_single) + print(dataset) + print(method) + with open(f"pattern.json", "w") as f: + json.dump(ans_all, f, indent=2) diff --git a/examples/result_analysis/get_num.py b/examples/result_analysis/get_num.py new file mode 100644 index 00000000..2432ae38 --- /dev/null +++ b/examples/result_analysis/get_num.py @@ -0,0 +1,49 @@ +"""Count the total number of experiment runs across different tasks in W&B project. + +This script analyzes experiment results stored in a W&B project by: +1. Reading task data from Excel sheets +2. Extracting sweep URLs for each task +3. Querying W&B API to count runs in each sweep +4. Computing the total number of experimental runs + +Parameters +---------- +None + +Returns +------- +int + Total number of runs across all tasks and sweeps + +""" + +import sys +from pathlib import Path + +import pandas as pd + +sys.path.append("..") +import urllib + +from get_result_web import spilt_web + +from dance.utils import try_import + +wandb = try_import("wandb") +entity = "xzy11632" +project = "dance-dev" +tasks = ["cell type annotation new", "clustering", "imputation_new", "spatial domain", "cell type deconvolution"] +file_root = Path(__file__).resolve().parent +prefix = 'https://wandb.ai/xzy11632/dance-dev' + +runs_sum = 0 + +for task in tasks: + data = pd.read_excel(file_root / "results.xlsx", sheet_name=task, dtype=str) + matched_list = data.applymap(lambda x: x if isinstance(x, str) and x.startswith(prefix) else None).stack().tolist() + for sweep_url in matched_list: + _, _, sweep_id = spilt_web(sweep_url) + print(sweep_id) + sweep = wandb.Api().sweep(f"{entity}/{project}/{sweep_id}") + runs_sum += (len(sweep.runs)) +print(runs_sum) diff --git a/examples/single_modality/clustering/graphsc.py b/examples/single_modality/clustering/graphsc.py index b1d72cef..af2c7576 100644 --- a/examples/single_modality/clustering/graphsc.py +++ b/examples/single_modality/clustering/graphsc.py @@ -37,7 +37,7 @@ parser.add_argument("-data", "--dataset", default="10X_PBMC", choices=["10X_PBMC", "mouse_bladder_cell", "mouse_ES_cell", "worm_neuron_cell"]) parser.add_argument("--seed", type=int, default=0, help="Initial seed random, offset for each repeatition") - parser.add_argument("--num_runs", type=int, default=1, help="Number of repetitions") + parser.add_argument("--num_runs", type=int, default=5, help="Number of repetitions") parser.add_argument("--cache", action="store_true", help="Cache processed data.") args = parser.parse_args() aris = [] diff --git a/examples/single_modality/imputation/graphsci.py b/examples/single_modality/imputation/graphsci.py index c107614d..be3a272b 100644 --- a/examples/single_modality/imputation/graphsci.py +++ b/examples/single_modality/imputation/graphsci.py @@ -32,7 +32,7 @@ parser.add_argument("--cache", action="store_true", help="Cache processed data.") parser.add_argument("--mask", type=bool, default=True, help="Mask data for validation.") parser.add_argument("--seed", type=int, default=0, help="Initial seed random, offset for each repeatition") - parser.add_argument("--num_runs", type=int, default=1, help="Number of repetitions") + parser.add_argument("--num_runs", type=int, default=5, help="Number of repetitions") params = parser.parse_args() print(vars(params)) rmses = [] diff --git a/examples/tuning/cta_actinn/main.py b/examples/tuning/cta_actinn/main.py index 506f71b9..230b3295 100644 --- a/examples/tuning/cta_actinn/main.py +++ b/examples/tuning/cta_actinn/main.py @@ -30,7 +30,7 @@ parser.add_argument("--num_epochs", type=int, default=50, help="Number of epochs") parser.add_argument("--print_cost", action="store_true", help="Print cost when training") parser.add_argument("--species", default="mouse") - parser.add_argument("--test_dataset", nargs="+", default=[1759], help="List of testing dataset ids.") + parser.add_argument("--test_dataset", nargs="+", default=[], help="List of testing dataset ids.") parser.add_argument("--tissue", default="Spleen") parser.add_argument("--train_dataset", nargs="+", default=[1970], help="List of training dataset ids.") parser.add_argument("--valid_dataset", nargs="+", default=None, help="List of valid dataset ids.") @@ -41,19 +41,21 @@ parser.add_argument("--sweep_id", type=str, default=None) parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + parser.add_argument("--filetype", default="csv") args = parser.parse_args() logger.setLevel(args.log_level) logger.info(f"\n{pprint.pformat(vars(args))}") file_root_path = Path( args.root_path, "_".join([ "-".join([str(num) for num in dataset]) - for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] if dataset is not None + for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] + if (dataset is not None and dataset != []) ])).resolve() logger.info(f"\n files is saved in {file_root_path}") pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") logger.setLevel(args.log_level) - logger.info(f"Running SVM with the following parameters:\n{pprint.pformat(vars(args))}") + logger.info(f"Running ACTINN with the following parameters:\n{pprint.pformat(vars(args))}") def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): wandb.init(settings=wandb.Settings(start_method='thread')) @@ -68,7 +70,7 @@ def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer) # Load data and perform necessary preprocessing data = CellTypeAnnotationDataset(train_dataset=args.train_dataset, test_dataset=args.test_dataset, valid_dataset=args.valid_dataset, data_dir="./temp_data", tissue=args.tissue, - species=args.species).load_data() + species=args.species, filetype=args.filetype).load_data() print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") preprocessing_pipeline(data) diff --git a/examples/tuning/cta_celltypist/main.py b/examples/tuning/cta_celltypist/main.py index d3539816..58870699 100644 --- a/examples/tuning/cta_celltypist/main.py +++ b/examples/tuning/cta_celltypist/main.py @@ -12,7 +12,7 @@ from dance import logger from dance.datasets.singlemodality import CellTypeAnnotationDataset from dance.modules.single_modality.cell_type_annotation.celltypist import Celltypist -from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.pipeline import Pipeline, PipelinePlaner, get_step3_yaml, run_step3, save_summary_data from dance.typing import LogLevel from dance.utils import set_seed @@ -25,7 +25,7 @@ help="Whether to refine the predicted labels via majority voting after over-clustering.") parser.add_argument("--n_jobs", type=int, help="Number of jobs", default=10) parser.add_argument("--species", default="mouse", type=str) - parser.add_argument("--test_dataset", nargs="+", default=[1759], help="List of testing dataset ids.") + parser.add_argument("--test_dataset", nargs="+", default=[], help="List of testing dataset ids.") parser.add_argument("--tissue", default="Spleen", type=str) parser.add_argument("--train_dataset", nargs="+", default=[1970], help="List of training dataset ids.") parser.add_argument("--valid_dataset", nargs="+", default=None, help="List of valid dataset ids.") @@ -38,33 +38,44 @@ parser.add_argument("--sweep_id", type=str, default=None) parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + parser.add_argument("--filetype", default="csv") args = parser.parse_args() logger.setLevel(args.log_level) logger.info(f"Running Celltypist with the following parameters:\n{pprint.pformat(vars(args))}") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" + # os.environ["WANDB_AGENT_DISABLE_FLAPPING"]="true" file_root_path = Path( args.root_path, "_".join([ "-".join([str(num) for num in dataset]) - for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] if dataset is not None + for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] + if (dataset is not None and dataset != []) ])).resolve() logger.info(f"\n files is saved in {file_root_path}") MAINDIR = Path(__file__).resolve().parent pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") - os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): wandb.init(settings=wandb.Settings(start_method='thread')) set_seed(args.seed) + if "run_kwargs" in pipeline_planer.config and tune_mode == "params": + wandb_config = dict(wandb.config) + config = {'pipeline': wandb_config["run_kwargs"], "type": "preprocessor"} + preprocessing_pipeline = Pipeline(config) + else: + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb.config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) # Initialize model and get model specific preprocessing pipeline model = Celltypist(majority_voting=args.majority_voting) # Prepare preprocessing pipeline and apply it to data - kwargs = {tune_mode: dict(wandb.config)} - preprocessing_pipeline = pipeline_planer.generate(**kwargs) + # kwargs = {tune_mode: dict(wandb.config)} + # preprocessing_pipeline = pipeline_planer.generate(**kwargs) # Load data and perform necessary preprocessing data = CellTypeAnnotationDataset(train_dataset=args.train_dataset, test_dataset=args.test_dataset, species=args.species, tissue=args.tissue, valid_dataset=args.valid_dataset, - data_dir="../temp_data").load_data() + data_dir="../temp_data", filetype=args.filetype).load_data() print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") preprocessing_pipeline(data) diff --git a/examples/tuning/cta_scdeepsort/main.py b/examples/tuning/cta_scdeepsort/main.py index 86cc5d52..85fe697e 100644 --- a/examples/tuning/cta_scdeepsort/main.py +++ b/examples/tuning/cta_scdeepsort/main.py @@ -1,5 +1,6 @@ import argparse import gc +import os import pprint import sys from pathlib import Path @@ -29,10 +30,10 @@ parser.add_argument("--n_epochs", type=int, default=100, help="number of training epochs") parser.add_argument("--n_layers", type=int, default=1, help="number of hidden gcn layers") parser.add_argument("--species", default="mouse", type=str) - parser.add_argument("--test_dataset", nargs="+", type=int, default=[1759], help="Testing dataset IDs") + parser.add_argument("--test_dataset", nargs="+", type=int, default=[], help="Testing dataset IDs") parser.add_argument("--test_rate", type=float, default=0.2) parser.add_argument("--tissue", default="Spleen", type=str) - parser.add_argument("--train_dataset", nargs="+", type=int, default=[1970], help="List of training dataset ids.") + parser.add_argument("--train_dataset", nargs="+", default=[1970], help="List of training dataset ids.") parser.add_argument("--valid_dataset", nargs="+", default=None, help="List of valid dataset ids.") parser.add_argument("--weight_decay", type=float, default=5e-4, help="Weight for L2 loss") parser.add_argument("--seed", type=int, default=42) @@ -42,17 +43,20 @@ parser.add_argument("--sweep_id", type=str, default=None) parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) - + parser.add_argument("--filetype", default="csv") args = parser.parse_args() logger.setLevel(args.log_level) + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" logger.info(f"Running ScDeepSort with the following parameters:\n{pprint.pformat(vars(args))}") file_root_path = Path( args.root_path, "_".join([ "-".join([str(num) for num in dataset]) - for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] if dataset is not None + for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] + if (dataset is not None and dataset != []) ])).resolve() logger.info(f"\n files is saved in {file_root_path}") pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): wandb.init(settings=wandb.Settings(start_method='thread')) @@ -61,7 +65,7 @@ def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer) # Load data and perform necessary preprocessing data = CellTypeAnnotationDataset(species=args.species, tissue=args.tissue, test_dataset=args.test_dataset, train_dataset=args.train_dataset, valid_dataset=args.valid_dataset, - data_dir="../temp_data").load_data() + data_dir="../temp_data", filetype=args.filetype).load_data() # Prepare preprocessing pipeline and apply it to data kwargs = {tune_mode: dict(wandb.config)} preprocessing_pipeline = pipeline_planer.generate(**kwargs) diff --git a/examples/tuning/cta_singlecellnet/main.py b/examples/tuning/cta_singlecellnet/main.py index 892c6633..bfe8bb88 100644 --- a/examples/tuning/cta_singlecellnet/main.py +++ b/examples/tuning/cta_singlecellnet/main.py @@ -11,7 +11,7 @@ from dance import logger from dance.datasets.singlemodality import CellTypeAnnotationDataset from dance.modules.single_modality.cell_type_annotation.singlecellnet import SingleCellNet -from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.pipeline import Pipeline, PipelinePlaner, get_step3_yaml, run_step3, save_summary_data from dance.typing import LogLevel from dance.utils import set_seed @@ -28,10 +28,9 @@ parser.add_argument("--num_trees", type=int, default=1000) parser.add_argument("--species", default="mouse", type=str) parser.add_argument("--stratify", type=bool, default=True) - parser.add_argument("--test_dataset", type=int, nargs="+", default=[1759], - help="List testing training dataset ids.") + parser.add_argument("--test_dataset", nargs="+", default=[], help="List testing training dataset ids.") parser.add_argument("--tissue", default="Spleen", type=str) - parser.add_argument("--train_dataset", type=int, nargs="+", default=[1970], help="List of training dataset ids.") + parser.add_argument("--train_dataset", nargs="+", default=[1970], help="List of training dataset ids.") parser.add_argument("--valid_dataset", nargs="+", default=None, help="List of valid dataset ids.") parser.add_argument("--seed", type=int, default=10) @@ -40,13 +39,15 @@ parser.add_argument("--sweep_id", type=str, default=None) parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + parser.add_argument("--filetype", default="csv") args = parser.parse_args() logger.setLevel(args.log_level) logger.info(f"{pprint.pformat(vars(args))}") file_root_path = Path( args.root_path, "_".join([ "-".join([str(num) for num in dataset]) - for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] if dataset is not None + for dataset in [args.train_dataset, args.valid_dataset, args.test_dataset] + if (dataset is not None and dataset != []) ])).resolve() logger.info(f"\n files is saved in {file_root_path}") pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") @@ -55,16 +56,24 @@ def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): wandb.init(settings=wandb.Settings(start_method='thread')) set_seed(args.seed) - + if "run_kwargs" in pipeline_planer.config and tune_mode == "params": + wandb_config = dict(wandb.config) + config = {'pipeline': wandb_config["run_kwargs"], "type": "preprocessor"} + preprocessing_pipeline = Pipeline(config) + + else: + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb.config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) # Initialize model and get model specific preprocessing pipeline model = SingleCellNet(num_trees=args.num_trees) # Load data and perform necessary preprocessing data = CellTypeAnnotationDataset(train_dataset=args.train_dataset, test_dataset=args.test_dataset, species=args.species, tissue=args.tissue, valid_dataset=args.valid_dataset, - data_dir="../temp_data").load_data(cache=args.cache) - kwargs = {tune_mode: dict(wandb.config)} - preprocessing_pipeline = pipeline_planer.generate(**kwargs) + data_dir="../temp_data", filetype=args.filetype).load_data(cache=args.cache) + # kwargs = {tune_mode: dict(wandb.config)} + # preprocessing_pipeline = pipeline_planer.generate(**kwargs) print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") preprocessing_pipeline(data) diff --git a/examples/tuning/domain_stagate/main.py b/examples/tuning/domain_stagate/main.py index caf9d012..d89b07ea 100644 --- a/examples/tuning/domain_stagate/main.py +++ b/examples/tuning/domain_stagate/main.py @@ -30,6 +30,7 @@ parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) parser.add_argument("--data_dir", type=str, default='../temp_data', help='test directory') parser.add_argument("--sample_file", type=str, default=None) + parser.add_argument("--device", type=str, default="cpu", help="Computation device") parser.add_argument('--additional_sweep_ids', action='append', type=str, help='get prior runs') os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" args = parser.parse_args() @@ -58,7 +59,7 @@ def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer) edge_list_array = np.vstack(np.nonzero(adj)) # Train and evaluate model - model = Stagate([x.shape[1]] + args.hidden_dims) + model = Stagate([x.shape[1]] + args.hidden_dims, device=args.device) score = model.fit_score((x, edge_list_array), y, epochs=args.epochs, random_state=args.seed) wandb.log({"ARI": score}) gc.collect() diff --git a/examples/tuning/get_important_pattern.py b/examples/tuning/get_important_pattern.py deleted file mode 100644 index 39fcfb2f..00000000 --- a/examples/tuning/get_important_pattern.py +++ /dev/null @@ -1,146 +0,0 @@ -import itertools -import pathlib -from itertools import combinations -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import scikit_posthocs as sp -import seaborn as sns -from mlxtend.frequent_patterns import apriori -from mlxtend.preprocessing import TransactionEncoder -from networkx import parse_adjlist -from scipy import stats - -metric_name = "acc" -ascending = False - - -def get_important_pattern(test_accs, vis=True, alpha=0.8, title=""): - medians = [np.median(group) for group in test_accs] - _, p_value = stats.kruskal(*test_accs) - if vis: - fig = plt.figure(figsize=(12, 4)) - sns.boxplot(data=test_accs) - plt.xticks(list(range(len(test_accs))), [f"{i}" for i in range(len(test_accs))]) - plt.title(title) - plt.show() - if p_value < alpha: - data = test_accs - p_values_matrix = sp.posthoc_dunn(a=data) - sorted_indices = np.argsort(np.argsort(medians * -1 if ascending else medians)) - ranks = { - index: { - "rank": rank, - "before": None, - "after": [], - "real_rank": rank - } - for index, rank in enumerate(sorted_indices) - } - for (rank1, rank2) in combinations(range(max(sorted_indices) + 1), 2): - for idx1 in [index for index, value in ranks.items() if value["rank"] == rank1]: - for idx2 in [index for index, value in ranks.items() if value["rank"] == rank2]: - if p_values_matrix.iloc[idx1, idx2] > alpha: - if ranks[idx2]["before"] is None: - ranks[idx1]["after"].append(idx2) - ranks[idx2]["before"] = idx1 - - def change_real_rank(rank_item, real_rank): - rank_item["real_rank"] = real_rank - for idx in rank_item["after"]: - change_real_rank(ranks[idx], real_rank) - - for rank_item in ranks.values(): - if rank_item["before"] is None: - for idx in rank_item["after"]: - change_real_rank(ranks[idx], rank_item["real_rank"]) - return [v["real_rank"] for k, v in ranks.items()] - else: - if vis: - print("No significant differences found between the groups.") - return [] - - -def get_com(step2_data, r=2, alpha=0.8, columns=None, vis=True): - ans = [] - for com in itertools.combinations(columns, r): - test_accs_arrays = [] - for g in step2_data.groupby(by=list(com)): - test_accs_arrays.append({"name": g[0], metric_name: list(g[1][metric_name])}) - test_accs = [i[metric_name] for i in test_accs_arrays] - test_acc_names = [i["name"] for i in test_accs_arrays] - final_ranks = get_important_pattern(test_accs, alpha=alpha, title=" ".join(list(com)), vis=vis) - if len(final_ranks) > 0: - max_rank = max(final_ranks) - max_rank_count = final_ranks.count(max_rank) - if max_rank_count < len(final_ranks) / 2: - for index, (test_acc_name, rank) in enumerate(zip(test_acc_names, final_ranks)): - if rank == max_rank: - if vis: - print(f"index={index},name={test_acc_name},rank={rank}") - ans.append(test_acc_name if isinstance(test_acc_name, tuple) else (test_acc_name, )) - return ans - - -def get_frequent_itemsets(step2_data, threshold_per=0.1): - threshold = int(len(step2_data) * threshold_per) - df_sorted = step2_data.sort_values(metric_name, ascending=ascending) - top_10_percent = df_sorted.head(threshold) - columns = sorted([col for col in step2_data.columns if col.startswith("pipeline")]) - transactions = top_10_percent[columns].values.tolist() - te = TransactionEncoder() - te_ary = te.fit(transactions).transform(transactions) - df = pd.DataFrame(te_ary, columns=te.columns_) - frequent_itemsets = apriori(df, min_support=0.3, use_colnames=True) - # print(frequent_itemsets) - # rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.5) - return [tuple(a) for a in frequent_itemsets["itemsets"]] - - -def get_com_all(step2_data, vis=True, alpha=0.8): - ans = [] - columns = sorted([col for col in step2_data.columns if col.startswith("pipeline")]) - for i in range(1, len(columns)): - ans += get_com(step2_data, i, columns=columns, vis=vis, alpha=alpha) - return ans - - -def summary_pattern(data_path, alpha=0.8, vis=False): - step2_origin_data = pd.read_csv(data_path) - step2_data = step2_origin_data.dropna() - com_ans = get_com_all(step2_data, vis=vis, alpha=alpha) - apr_ans = get_frequent_itemsets(step2_data) - return list(set(com_ans) & set(apr_ans)) - - -# def list_files(directory,file_name="best_test_acc.csv",save_path="summary_file"): -# ans=[] -# path = Path(directory) -# for file_path in path.rglob('*'): -# if file_path.is_file(): -# if file_path.name==file_name: -# algorithm,dataset=file_path.relative_to(directory).parts[:2] -# ans.append({"algorithm":algorithm,"dataset":dataset,"summary_pattern":summary_pattern(file_path)}) -# pd.DataFrame(ans).to_csv(save_path) -def list_files(directories, file_name="best_test_acc.csv", alpha=0.8, vis=False): - for directory in directories: - path = Path(directory) - for file_path in path.rglob('*'): - if file_path.is_file(): - if file_path.name == file_name: - print(file_path) - with open(Path(file_path.parent.resolve(), "pipeline_summary_pattern.txt"), 'w') as f: - f.write(str(summary_pattern(file_path, alpha=alpha, vis=vis))) - - -if __name__ == "__main__": - directories = [] - for path in Path('/home/zyxing/dance/examples/tuning').iterdir(): - if path.is_dir(): - if str(path.name).startswith("cluster"): - directories.append(path) - list_files(directories) - - # print(summary_pattern("/home/zyxing/dance/examples/tuning/cta_scdeepsort/328_138/results/pipeline/best_test_acc.csv",alpha=0.3,vis=True)) diff --git a/examples/tuning/joint_embedding_dcca/main.py b/examples/tuning/joint_embedding_dcca/main.py new file mode 100644 index 00000000..42465bcb --- /dev/null +++ b/examples/tuning/joint_embedding_dcca/main.py @@ -0,0 +1,226 @@ +import argparse +import gc +import os +import pprint +import sys +from copy import deepcopy +from pathlib import Path + +import anndata as ad +import numpy as np +import pandas as pd +import scipy +import torch +import torch.utils.data as data_utils +import wandb +from sklearn import preprocessing + +import dance.utils.metrics as metrics +from dance import logger +from dance.datasets.multimodality import JointEmbeddingNIPSDataset +from dance.modules.multi_modality.joint_embedding.dcca import DCCA +from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.utils import set_seed + + +def parameter_setting(): + parser = argparse.ArgumentParser(description="Single cell Multi-omics data analysis") + + parser.add_argument("--latent_fusion", "-olf1", type=str, default="First_simulate_fusion.csv", + help="fusion latent code file") + parser.add_argument("--latent_1", "-ol1", type=str, default="scRNA_latent_combine.csv", + help="first latent code file") + parser.add_argument("--latent_2", "-ol2", type=str, default="scATAC_latent.csv", help="seconde latent code file") + parser.add_argument("--denoised_1", "-od1", type=str, default="scRNA_seq_denoised.csv", + help="outfile for denoised file1") + parser.add_argument("--normalized_1", "-on1", type=str, default="scRNA_seq_normalized_combine.tsv", + help="outfile for normalized file1") + parser.add_argument("--denoised_2", "-od2", type=str, default="scATAC_seq_denoised.csv", + help="outfile for denoised file2") + + parser.add_argument("--workdir", "-wk", type=str, default="./new_test/", help="work path") + parser.add_argument("--outdir", "-od", type=str, default="./new_test/", help="Output path") + + parser.add_argument("--lr", type=float, default=1E-3, help="Learning rate") + parser.add_argument("--weight_decay", type=float, default=1e-6, help="weight decay") + parser.add_argument("--eps", type=float, default=0.01, help="eps") + + parser.add_argument("--batch_size", "-b", type=int, default=64, help="Batch size") + + parser.add_argument("--seed", type=int, default=1, help="Random seed for repeat results") + parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") + parser.add_argument("--latent", "-l", type=int, default=10, help="latent layer dim") + parser.add_argument("--max_epoch", "-me", type=int, default=10, help="Max epoches") + parser.add_argument("--max_iteration", "-mi", type=int, default=3000, help="Max iteration") + parser.add_argument("--anneal_epoch", "-ae", type=int, default=200, help="Anneal epoch") + parser.add_argument("--epoch_per_test", "-ept", type=int, default=5, help="Epoch per test") + parser.add_argument("--max_ARI", "-ma", type=int, default=-200, help="initial ARI") + parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2") + parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("--final_rate", type=float, default=1e-4) + parser.add_argument("--scale_factor", type=float, default=4) + + parser.add_argument("--cache", action="store_true", help="Cache processed data.") + parser.add_argument("--tune_mode", default="pipeline_params", choices=["pipeline", "params", "pipeline_params"]) + parser.add_argument("--count", type=int, default=2) + parser.add_argument("--sweep_id", type=str, default=None) + parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) + parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + return parser + + +if __name__ == "__main__": + parser = parameter_setting() + args = parser.parse_args() + + args.sf1 = 5 + args.sf2 = 1 + args.cluster1 = args.cluster2 = 4 + args.lr1 = 0.01 + args.flr1 = 0.001 + args.lr2 = 0.005 + args.flr2 = 0.0005 + + res = None + logger.info(f"\n{pprint.pformat(vars(args))}") + file_root_path = Path(args.root_path, args.subtask).resolve() + logger.info(f"\n files is saved in {file_root_path}") + pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" + + def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): + wandb.init(settings=wandb.Settings(start_method='thread')) + set_seed(args.seed) + # model = DCCA(layer_e_1=[Nfeature1, 128], hidden1_1=128, Zdim_1=4, layer_d_1=[4, 128], hidden2_1=128, + # layer_e_2=[Nfeature2, 1500, 128], hidden1_2=128, Zdim_2=4, layer_d_2=[4], hidden2_2=4, args=args, + # Type_1="NB", Type_2="Bernoulli", ground_truth1=torch.cat([train_labels, test_labels]), cycle=1, + # attention_loss="Eucli") # yapf: disable + wandb_config = wandb.config + if "run_kwargs" in pipeline_planer.config: + if any(d == dict(wandb.config["run_kwargs"]) for d in pipeline_planer.config.run_kwargs): + wandb_config = wandb_config["run_kwargs"] + else: + wandb.log({"skip": 1}) + wandb.finish() + return + try: + dataset = JointEmbeddingNIPSDataset(args.subtask, root="./data/joint_embedding") + data = dataset.load_data() + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb_config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) + print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") + preprocessing_pipeline(data) + le = preprocessing.LabelEncoder() + labels = le.fit_transform(data.mod["test_sol"].obs["cell_type"]) + data.mod["mod2"].obsm["size_factors"] = np.sum(data.mod["mod2"].X.todense() if scipy.sparse.issparse(data.mod["mod2"].X) else data.mod["mod2"].X, 1) / 100 + # data.mod["mod1"].obsm["size_factors"] = data.mod["mod1"].obs["size_factors"] + data.mod["mod1"].obsm["size_factors"] = np.sum(data.mod["mod1"].X.todense() if scipy.sparse.issparse(data.mod["mod1"].X) else data.mod["mod1"].X, 1) / 100 + data.mod["mod1"].obsm["labels"] = labels + + # data.set_config(feature_mod=["mod1", "mod2", "mod1", "mod2", "mod1", "mod2"], label_mod="mod1", + # feature_channel_type=["layers", "layers", None, None, "obsm", "obsm"], + # feature_channel=["counts", "counts", None, None, "size_factors", + # "size_factors"], label_channel="labels") + # TODO Feels like counts in layers should be raw + # TODO Indeed feels like counts in layers should be raw, not sure how big the reverse impact would be + (x_train, y_train, x_train_raw, y_train_raw, x_train_size, + y_train_size), train_labels = data.get_train_data(return_type="torch") + (x_test, y_test, x_test_raw, y_test_raw, x_test_size, + y_test_size), test_labels = data.get_test_data(return_type="torch") + train_idx=data.get_split_idx("train") + test_idx=data.get_split_idx("test") + Nfeature1 = x_train.shape[1] + Nfeature2 = y_train.shape[1] + + device = torch.device(args.device) + train = data_utils.TensorDataset(x_train.float(), x_train_raw, x_train_size.float(), y_train.float(), y_train_raw, + y_train_size.float()) + + train_loader = data_utils.DataLoader(train, batch_size=args.batch_size, shuffle=True) + + test = data_utils.TensorDataset(x_test.float(), x_test_raw, x_test_size.float(), y_test.float(), y_test_raw, + y_test_size.float()) + + test_loader = data_utils.DataLoader(test, batch_size=args.batch_size, shuffle=False) + + total = data_utils.TensorDataset( + torch.cat([x_train, x_test]).float(), torch.cat([x_train_raw, x_test_raw]), + torch.cat([x_train_size, x_test_size]).float(), + torch.cat([y_train, y_test]).float(), torch.cat([y_train_raw, y_test_raw]), + torch.cat([y_train_size, y_test_size]).float()) + + total_loader = data_utils.DataLoader(total, batch_size=args.batch_size, shuffle=False) + model = DCCA(layer_e_1=[Nfeature1, 128], hidden1_1=128, Zdim_1=50, layer_d_1=[50, 128], hidden2_1=128, + layer_e_2=[Nfeature2, 1500, 128], hidden1_2=128, Zdim_2=50, layer_d_2=[50], hidden2_2=50, + args=args, ground_truth1=torch.cat([train_labels, test_labels]), Type_1="NB", Type_2="Bernoulli", + cycle=1, attention_loss="Eucli").to(device) + model.to(device) + model.fit(train_loader, test_loader, total_loader, "RNA") + + emb1, emb2 = model.predict(total_loader) + embeds = np.concatenate([emb1, emb2], 1) + print(embeds) + + adata = ad.AnnData( + X=embeds, + obs=data.mod["mod1"].obs, + ) + adata_sol = data.mod["test_sol"] + adata = adata[adata_sol.obs_names] + adata_sol.obsm['X_emb'] = adata.X + score = metrics.labeled_clustering_evaluate(adata, adata_sol) + # score.update(metrics.integration_openproblems_evaluate(adata_sol)) + # score.update({ + # 'seed': args.seed + k, + # 'subtask': args.subtask, + # 'method': 'dcca', + # }) + + # if res is not None: + # res = res.append(score, ignore_index=True) + # else: + # for s in score: + # score[s] = [score[s]] + # res = pd.DataFrame(score) + score["ARI"]=score["dance_ari"] + del score["dance_ari"] + wandb.log(score.copy()) + wandb.finish() + finally: + # del data,model,adata_sol,adata,embeds,emb1, emb2,total_loader,total,test_loader,test,train_loader,train,Nfeature2,Nfeature1 + # del x_train, y_train, x_train_raw, y_train_raw, x_train_size,y_train_size,train_labels,x_test, y_test, x_test_raw, y_test_raw, x_test_size,y_test_size, test_labels + # del labels,le,dataset,score + # variables_to_delete=["data","model","adata_sol","adata","embeds","emb1", "emb2","total_loader","total,test_loader","test,train_loader","train","Nfeature2","Nfeature1","x_train", "y_train", "x_train_raw", "y_train_raw", "x_train_size","y_train_size","train_labels","x_test", "y_test"," x_test_raw", y_test_raw, x_test_size,y_test_size, test_labels,labels,le,dataset,score] + locals_keys=list(locals().keys()) + for var in locals_keys: + try: + exec(f"del {var}") + logger.info(f"Deleted '{var}'") + except NameError: + logger.info(f"Variable '{var}' does not exist, continuing...") + torch.cuda.empty_cache() + gc.collect() + # This is mainly caused by these commands not being executed when errors occur, I think + + + entity, project, sweep_id = pipeline_planer.wandb_sweep_agent( + evaluate_pipeline, sweep_id=args.sweep_id, count=args.count) #Score can be recorded for each epoch + save_summary_data(entity, project, sweep_id, summary_file_path=args.summary_file_path, root_path=file_root_path) + if args.tune_mode == "pipeline" or args.tune_mode == "pipeline_params": + get_step3_yaml(result_load_path=f"{args.summary_file_path}", step2_pipeline_planer=pipeline_planer, + conf_load_path=f"{Path(args.root_path).resolve().parent}/step3_default_params.yaml", + root_path=file_root_path, required_funs=["AlignMod","FilterCellsCommonMod","FilterCellsCommonMod","SetConfig"], + required_indexes=[2,11,14,sys.maxsize], metric="ARI") + if args.tune_mode == "pipeline_params": + run_step3(file_root_path, evaluate_pipeline, tune_mode="params", step2_pipeline_planer=pipeline_planer) + +"""To reproduce DCCA on other samples, please refer to command lines belows: + +GEX-ADT: +$ python dcca.py --subtask openproblems_bmmc_cite_phase2 --device cuda + +GEX-ATAC: +$ python dcca.py --subtask openproblems_bmmc_multiome_phase2 --device cuda + +""" diff --git a/examples/tuning/joint_embedding_jae/main.py b/examples/tuning/joint_embedding_jae/main.py new file mode 100644 index 00000000..0c5d283b --- /dev/null +++ b/examples/tuning/joint_embedding_jae/main.py @@ -0,0 +1,151 @@ +import argparse +import gc +import os +import pprint +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +import wandb + +from dance import logger +from dance.datasets.multimodality import JointEmbeddingNIPSDataset +from dance.modules.multi_modality.joint_embedding.jae import JAEWrapper +from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.utils import set_seed + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-t", "--subtask", default="openproblems_bmmc_cite_phase2", choices=[ + "GSE140203_BRAIN_atac2gex", "GSE140203_SKIN_atac2gex", "openproblems_bmmc_cite_phase2", + "openproblems_bmmc_multiome_phase2", "openproblems_2022_multi_atac2gex" + ]) + parser.add_argument("-d", "--data_folder", default="./data/joint_embedding") + parser.add_argument("-pre", "--pretrained_folder", default="./data/joint_embedding/pretrained") + parser.add_argument("-csv", "--csv_path", default="decoupled_lsi.csv") + parser.add_argument("-seed", "--seed", default=1, type=int) + parser.add_argument("-cpu", "--cpus", default=1, type=int) + parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("-bs", "--batch_size", default=128, type=int) + parser.add_argument("-nm", "--normalize", default=1, type=int, choices=[0, 1]) + parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") + parser.add_argument("--preprocess", type=str, default=None) + + parser.add_argument("--cache", action="store_true", help="Cache processed data.") + parser.add_argument("--tune_mode", default="pipeline_params", choices=["pipeline", "params", "pipeline_params"]) + parser.add_argument("--count", type=int, default=2) + parser.add_argument("--sweep_id", type=str, default=None) + parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) + parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + + args = parser.parse_args() + + device = args.device + pre_normalize = bool(args.normalize) + torch.set_num_threads(args.cpus) + rndseed = args.seed + set_seed(rndseed) + + res = None + logger.info(f"\n{pprint.pformat(vars(args))}") + file_root_path = Path(args.root_path, args.subtask).resolve() + logger.info(f"\n files is saved in {file_root_path}") + pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" + + def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): + wandb.init(settings=wandb.Settings(start_method='thread')) + set_seed(args.seed) + wandb_config = wandb.config + if "run_kwargs" in pipeline_planer.config: + if any(d == dict(wandb.config["run_kwargs"]) for d in pipeline_planer.config.run_kwargs): + wandb_config = wandb_config["run_kwargs"] + else: + wandb.log({"skip": 1}) + wandb.finish() + return + try: + dataset = JointEmbeddingNIPSDataset(args.subtask, root=args.data_folder, preprocess=args.preprocess) + data = dataset.load_data() + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb_config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) + print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") + preprocessing_pipeline(data) + if args.preprocess != "aux": + cell_type_labels = data.data['test_sol'].obs["cell_type"].to_numpy() + cell_type_labels_unique = list(np.unique(cell_type_labels)) + c_labels = np.array([cell_type_labels_unique.index(item) for item in cell_type_labels]) + data.data['mod1'].obsm["cell_type"] = c_labels + data.data["mod1"].obsm["S_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["G2M_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["batch_label"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["phase_labels"] = np.zeros(data.data['mod1'].shape[0]) + (X_mod1_train, X_mod2_train), (cell_type, batch_label, phase_label, S_score, + G2M_score) = data.get_train_data(return_type="torch") + (X_mod1_test, X_mod2_test), (cell_type_test, _, _, _, _) = data.get_test_data(return_type="torch") + X_train = torch.cat([X_mod1_train, X_mod2_train], dim=1) + phase_score = torch.cat([S_score[:, None], G2M_score[:, None]], 1) + X_test = torch.cat([X_mod1_test, X_mod2_test], dim=1) + X_test = torch.cat([X_train, X_test]).float().to(device) + test_id = np.arange(X_test.shape[0]) + labels = torch.cat([cell_type, cell_type_test]).numpy() + adata_sol = data.data['test_sol'] # [data._split_idx_dict['test']] + + model = JAEWrapper(args, num_celL_types=int(cell_type.max() + 1), num_batches=int(batch_label.max() + 1), + num_phases=phase_score.shape[1], num_features=X_train.shape[1]) + model.fit(X_train, cell_type, batch_label, phase_score, max_epochs=50) + + embeds = model.predict(X_test, test_id).cpu().numpy() + print(embeds) + + score = model.score(X_test, test_id, labels, metric="clustering") + # score.update(model.score(X_test, test_id, labels, adata_sol=adata_sol, metric="openproblems")) + score.update({ + 'subtask': args.subtask, + 'method': 'jae', + }) + score["ARI"] = score["dance_ari"] + del score["dance_ari"] + wandb.log(score) + wandb.finish() + finally: + # del data,model,adata_sol,adata,embeds,emb1, emb2,total_loader,total,test_loader,test,train_loader,train,Nfeature2,Nfeature1 + # del x_train, y_train, x_train_raw, y_train_raw, x_train_size,y_train_size,train_labels,x_test, y_test, x_test_raw, y_test_raw, x_test_size,y_test_size, test_labels + # del labels,le,dataset,score + # variables_to_delete=["data","model","adata_sol","adata","embeds","emb1", "emb2","total_loader","total,test_loader","test,train_loader","train","Nfeature2","Nfeature1","x_train", "y_train", "x_train_raw", "y_train_raw", "x_train_size","y_train_size","train_labels","x_test", "y_test"," x_test_raw", y_test_raw, x_test_size,y_test_size, test_labels,labels,le,dataset,score] + locals_keys = list(locals().keys()) + for var in locals_keys: + try: + exec(f"del {var}") + logger.info(f"Deleted '{var}'") + + except NameError: + logger.info(f"Variable '{var}' does not exist, continuing...") + torch.cuda.empty_cache() + gc.collect() + + entity, project, sweep_id = pipeline_planer.wandb_sweep_agent( + evaluate_pipeline, sweep_id=args.sweep_id, count=args.count) #Score can be recorded for each epoch + save_summary_data(entity, project, sweep_id, summary_file_path=args.summary_file_path, root_path=file_root_path) + if args.tune_mode == "pipeline" or args.tune_mode == "pipeline_params": + get_step3_yaml(result_load_path=f"{args.summary_file_path}", step2_pipeline_planer=pipeline_planer, + conf_load_path=f"{Path(args.root_path).resolve().parent}/step3_default_params.yaml", + root_path=file_root_path, + required_funs=["AlignMod", "FilterCellsCommonMod", "FilterCellsCommonMod", + "SetConfig"], required_indexes=[2, 11, 14, sys.maxsize], + metric="ARI") # need to delete required_funs and required_indexes + if args.tune_mode == "pipeline_params": + run_step3(file_root_path, evaluate_pipeline, tune_mode="params", step2_pipeline_planer=pipeline_planer) +"""To reproduce JAE on other samples, please refer to command lines belows: + +GEX-ADT: +$ python jae.py --subtask openproblems_bmmc_cite_phase2 --device cuda + +GEX-ATAC: +$ python jae.py --subtask openproblems_bmmc_multiome_phase2 --device cuda + +""" diff --git a/examples/tuning/joint_embedding_scmogcn/main.py b/examples/tuning/joint_embedding_scmogcn/main.py new file mode 100644 index 00000000..d860be0e --- /dev/null +++ b/examples/tuning/joint_embedding_scmogcn/main.py @@ -0,0 +1,176 @@ +import argparse +import gc +import os +import pprint +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +import wandb + +from dance import logger +from dance.datasets.multimodality import JointEmbeddingNIPSDataset +from dance.modules.multi_modality.joint_embedding.scmogcn import ScMoGCNWrapper +from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.transforms.graph.cell_feature_graph import CellFeatureBipartiteGraph +from dance.utils import set_seed + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-t", "--subtask", default="openproblems_bmmc_cite_phase2", choices=[ + "GSE140203_BRAIN_atac2gex", "openproblems_bmmc_cite_phase2", "openproblems_bmmc_multiome_phase2", + "GSE140203_SKIN_atac2gex", "openproblems_2022_multi_atac2gex" + ]) + parser.add_argument("-d", "--data_folder", default="./data/joint_embedding") + parser.add_argument("-pre", "--pretrained_folder", default="./data/joint_embedding/pretrained") + parser.add_argument("-csv", "--csv_path", default="decoupled_lsi.csv") + parser.add_argument("-l", "--layers", default=3, type=int, choices=[3, 4, 5, 6, 7]) + parser.add_argument("-dis", "--disable_propagation", default=0, type=int, choices=[0, 1, 2]) + parser.add_argument("-seed", "--seed", default=1, type=int) + parser.add_argument("-cpu", "--cpus", default=1, type=int) + parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("-bs", "--batch_size", default=512, type=int) + parser.add_argument("-nm", "--normalize", default=1, type=int, choices=[0, 1]) + parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") + parser.add_argument("--preprocess", type=str, default=None) + + parser.add_argument("--cache", action="store_true", help="Cache processed data.") + parser.add_argument("--tune_mode", default="pipeline_params", choices=["pipeline", "params", "pipeline_params"]) + parser.add_argument("--count", type=int, default=2) + parser.add_argument("--sweep_id", type=str, default=None) + parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) + parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + + args = parser.parse_args() + + device = args.device + pre_normalize = bool(args.normalize) + torch.set_num_threads(args.cpus) + rndseed = args.seed + set_seed(rndseed) + + res = None + logger.info(f"\n{pprint.pformat(vars(args))}") + file_root_path = Path(args.root_path, args.subtask).resolve() + logger.info(f"\n files is saved in {file_root_path}") + pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" + os.environ["CUDA_LAUNCH_BLOCKING"] = "1" + os.environ["WANDB_AGENT_DISABLE_FLAPPING"] = "True" + + def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): + wandb.init(settings=wandb.Settings(start_method='thread')) + set_seed(args.seed) + wandb_config = wandb.config + if "run_kwargs" in pipeline_planer.config: + if any(d == dict(wandb.config["run_kwargs"]) for d in pipeline_planer.config.run_kwargs): + wandb_config = wandb_config["run_kwargs"] + else: + wandb.log({"skip": 1}) + wandb.finish() + return + try: + dataset = JointEmbeddingNIPSDataset(args.subtask, root=args.data_folder, preprocess=args.preprocess) + data = dataset.load_data() + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb_config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) + print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") + preprocessing_pipeline(data) + # train_idx=list(set(data.mod["meta1"].obs_names) & set(data.mod["mod1"].obs_names)) + train_name = [item for item in data.mod["mod1"].obs_names if item in data.mod["meta1"].obs_names] + train_idx = [data.mod["mod1"].obs_names.get_loc(name) for name in train_name] + test_idx = list({i for i in range(data.mod["mod1"].shape[0])}.difference(set(train_idx))) + + # train_size=data.mod["meta1"].shape[0] + # test_size=data.mod["mod1"].shape[0]-train_size + data.set_split_idx("train", train_idx) + data.set_split_idx("test", test_idx) + if args.preprocess != "aux": + cell_type_labels = data.data['test_sol'].obs["cell_type"].to_numpy() + cell_type_labels_unique = list(np.unique(cell_type_labels)) + c_labels = np.array([cell_type_labels_unique.index(item) for item in cell_type_labels]) + data.data['mod1'].obsm["cell_type"] = c_labels + data.data["mod1"].obsm["S_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["G2M_scores"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["batch_label"] = np.zeros(data.data['mod1'].shape[0]) + data.data["mod1"].obsm["phase_labels"] = np.zeros(data.data['mod1'].shape[0]) + + # train_size = len(data.get_split_idx("train")) + # In theory, meta1 should include all content from the first half of mod1, the order might have been shuffled during processing + data = CellFeatureBipartiteGraph(cell_feature_channel="feature.cell", mod="mod1")(data) + data = CellFeatureBipartiteGraph(cell_feature_channel="feature.cell", mod="mod2")(data) + # data.set_config( + # feature_mod=["mod1", "mod2"], + # label_mod=["mod1", "mod1", "mod1", "mod1", "mod1"], + # feature_channel=["X_pca", "X_pca"], + # label_channel=["cell_type", "batch_label", "phase_labels", "S_scores", "G2M_scores"], + # ) + (x_mod1, x_mod2), (cell_type, batch_label, phase_label, S_score, + G2M_score) = data.get_data(return_type="torch") + phase_score = torch.cat([S_score[:, None], G2M_score[:, None]], 1) + test_id = np.arange(x_mod1.shape[0]) + labels = cell_type.numpy() + adata_sol = data.data['test_sol'] # [data._split_idx_dict['test']] + model = ScMoGCNWrapper(args, num_celL_types=int(cell_type.max() + 1), + num_batches=int(batch_label.max() + 1), num_phases=phase_score.shape[1], + num_features=x_mod1.shape[1] + x_mod2.shape[1]) + model.fit( + g_mod1=data.data["mod1"].uns["g"], + g_mod2=data.data["mod2"].uns["g"], + train_size=train_idx, + cell_type=cell_type, + batch_label=batch_label, + phase_score=phase_score, + ) + + embeds = model.predict(test_id).cpu().numpy() + score = model.score(test_id, labels, metric="clustering") + # score.update(model.score(test_id, labels, adata_sol=adata_sol, metric="openproblems")) + score.update({ + 'subtask': args.subtask, + 'method': 'scmogcn', + }) + + score["ARI"] = score["dance_ari"] + del score["dance_ari"] + wandb.log(score) + wandb.finish() + finally: + # del data,model,adata_sol,adata,embeds,emb1, emb2,total_loader,total,test_loader,test,train_loader,train,Nfeature2,Nfeature1 + # del x_train, y_train, x_train_raw, y_train_raw, x_train_size,y_train_size,train_labels,x_test, y_test, x_test_raw, y_test_raw, x_test_size,y_test_size, test_labels + # del labels,le,dataset,score + # variables_to_delete=["data","model","adata_sol","adata","embeds","emb1", "emb2","total_loader","total,test_loader","test,train_loader","train","Nfeature2","Nfeature1","x_train", "y_train", "x_train_raw", "y_train_raw", "x_train_size","y_train_size","train_labels","x_test", "y_test"," x_test_raw", y_test_raw, x_test_size,y_test_size, test_labels,labels,le,dataset,score] + locals_keys = list(locals().keys()) + for var in locals_keys: + try: + exec(f"del {var}") + logger.info(f"Deleted '{var}'") + except NameError: + logger.info(f"Variable '{var}' does not exist, continuing...") + torch.cuda.empty_cache() + gc.collect() + + entity, project, sweep_id = pipeline_planer.wandb_sweep_agent( + evaluate_pipeline, sweep_id=args.sweep_id, count=args.count) #Score can be recorded for each epoch + save_summary_data(entity, project, sweep_id, summary_file_path=args.summary_file_path, root_path=file_root_path) + if args.tune_mode == "pipeline" or args.tune_mode == "pipeline_params": + get_step3_yaml(result_load_path=f"{args.summary_file_path}", step2_pipeline_planer=pipeline_planer, + conf_load_path=f"{Path(args.root_path).resolve().parent}/step3_default_params.yaml", + root_path=file_root_path, + required_funs=["AlignMod", "FilterCellsCommonMod", "FilterCellsCommonMod", + "SetConfig"], required_indexes=[2, 11, 14, sys.maxsize], metric="ARI") + if args.tune_mode == "pipeline_params": + run_step3(file_root_path, evaluate_pipeline, tune_mode="params", step2_pipeline_planer=pipeline_planer) +"""To reproduce scMoGCN on other samples, please refer to command lines belows: + +GEX-ADT: +$ python scmogcn.py --subtask openproblems_bmmc_cite_phase2 --device cuda + +GEX-ATAC: +$ python scmogcn.py --subtask openproblems_bmmc_multiome_phase2 --device cuda + +""" diff --git a/examples/tuning/joint_embedding_scmvae/main.py b/examples/tuning/joint_embedding_scmvae/main.py new file mode 100644 index 00000000..5c1e264c --- /dev/null +++ b/examples/tuning/joint_embedding_scmvae/main.py @@ -0,0 +1,215 @@ +import argparse +import gc +import os +import pprint +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +import torch.utils.data as data_utils +import wandb +from sklearn import preprocessing + +from dance import logger +from dance.datasets.multimodality import JointEmbeddingNIPSDataset +from dance.modules.multi_modality.joint_embedding.scmvae import scMVAE +from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.transforms.preprocess import calculate_log_library_size +from dance.utils import set_seed + + +def parameter_setting(): + parser = argparse.ArgumentParser(description="Single cell Multi-omics data analysis") + + parser.add_argument("--workdir", "-wk", type=str, default="./new_test", help="work path") + parser.add_argument("--outdir", "-od", type=str, default="./new_test", help="Output path") + + parser.add_argument("--lr", type=float, default=1E-3, help="Learning rate") + parser.add_argument("--weight_decay", type=float, default=1e-6, help="weight decay") + parser.add_argument("--eps", type=float, default=0.01, help="eps") + parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") + + parser.add_argument("--batch_size", "-b", type=int, default=64, help="Batch size") + parser.add_argument('-seed', '--seed', type=int, default=1, help='Random seed for repeat results') + parser.add_argument("--latent", "-l", type=int, default=10, help="latent layer dim") + parser.add_argument("--max_epoch", "-me", type=int, default=25, help="Max epoches") + parser.add_argument("--max_iteration", "-mi", type=int, default=3000, help="Max iteration") + parser.add_argument("--anneal_epoch", "-ae", type=int, default=200, help="Anneal epoch") + parser.add_argument("--epoch_per_test", "-ept", type=int, default=1, + help="Epoch per test, must smaller than max iteration.") + parser.add_argument("--max_ARI", "-ma", type=int, default=-200, help="initial ARI") + parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2") + parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("--final_rate", type=float, default=1e-4) + parser.add_argument("--scale_factor", type=float, default=4) + + parser.add_argument("--cache", action="store_true", help="Cache processed data.") + parser.add_argument("--tune_mode", default="pipeline_params", choices=["pipeline", "params", "pipeline_params"]) + parser.add_argument("--count", type=int, default=2) + parser.add_argument("--sweep_id", type=str, default=None) + parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) + parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + + return parser + + +if __name__ == "__main__": + parser = parameter_setting() + args = parser.parse_args() + assert args.max_iteration > args.epoch_per_test + device = torch.device(args.device) + args.lr = 0.001 + args.anneal_epoch = 200 + res = None + logger.info(f"\n{pprint.pformat(vars(args))}") + file_root_path = Path(args.root_path, args.subtask).resolve() + logger.info(f"\n files is saved in {file_root_path}") + pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" + + def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): + wandb.init(settings=wandb.Settings(start_method='thread')) + set_seed(args.seed) + wandb_config = wandb.config + try: + wandb_config = wandb.config + if "run_kwargs" in pipeline_planer.config: + if any(d == dict(wandb.config["run_kwargs"]) for d in pipeline_planer.config.run_kwargs): + wandb_config = wandb_config["run_kwargs"] + else: + wandb.log({"skip": 1}) + wandb.finish() + return + dataset = JointEmbeddingNIPSDataset(args.subtask, root="./data/joint_embedding") + data = dataset.load_data() + + le = preprocessing.LabelEncoder() + labels = le.fit_transform(data.mod["test_sol"].obs["cell_type"]) + data.mod["mod1"].obsm["labels"] = labels + + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb_config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) + print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") + preprocessing_pipeline(data) + # train_name=[item for item in data.mod["mod1"].obs_names if item in data.mod["meta1"].obs_names] + # train_idx= [data.mod["mod1"].obs_names.get_loc(name) for name in train_name] + # test_idx=list(set([i for i in range(data.mod["mod1"].shape[0])]).difference(set(train_idx))) + # data.set_split_idx("train",train_idx) + # data.set_split_idx("test",test_idx) + + (x_train, y_train, x_train_raw, y_train_raw), _ = data.get_train_data(return_type="torch") + (x_test, y_test, x_test_raw, y_test_raw), labels = data.get_test_data(return_type="torch") + + train_size = len(x_train) + test_size = len(x_test) + train_idx = np.arange(train_size) + test_idx = np.arange(test_size) + train_size + + # x_train,y_train,x_test,y_test,labels=torch.nan_to_num(x_train),torch.nan_to_num(y_train),torch.nan_to_num(x_test),torch.nan_to_num(y_test),torch.nan_to_num(labels) + lib_mean1, lib_var1 = calculate_log_library_size(np.concatenate([x_train.numpy(), x_test.numpy()])) + lib_mean2, lib_var2 = calculate_log_library_size(np.concatenate([y_train.numpy(), y_test.numpy()])) + lib_mean1 = torch.from_numpy(lib_mean1) + lib_var1 = torch.from_numpy(lib_var1) + lib_mean2 = torch.from_numpy(lib_mean2) + lib_var2 = torch.from_numpy(lib_var2) + + Nfeature1 = x_train.shape[1] + Nfeature2 = y_train.shape[1] + + temp = lib_mean1[train_idx] + train = data_utils.TensorDataset(x_train, lib_mean1[train_idx], lib_var1[train_idx], lib_mean2[train_idx], + lib_var2[train_idx], y_train) + + valid = data_utils.TensorDataset(x_test, lib_mean1[test_idx], lib_var1[test_idx], lib_mean2[test_idx], + lib_var2[test_idx], y_test) + + total = data_utils.TensorDataset(torch.cat([x_train, x_test]), torch.cat([y_train, y_test])) + total = data_utils.TensorDataset(torch.cat([x_train, x_test]), torch.cat([y_train, y_test])) + + total_loader = data_utils.DataLoader(total, batch_size=args.batch_size, shuffle=False) + total_loader = data_utils.DataLoader(total, batch_size=args.batch_size, shuffle=False) + + x_test = torch.cat([x_train, x_test]) + y_test = torch.cat([y_train, y_test]) + labels = torch.from_numpy(le.fit_transform(data.mod["test_sol"].obs["cell_type"]) + ) # This might be problematic, likely due to dimensionality reduction issues + model = scMVAE( + encoder_1=[Nfeature1, 1024, 128, 128], + hidden_1=128, + Z_DIMS=22, + decoder_share=[22, 128, 256], + share_hidden=128, + decoder_1=[128, 128, 1024], + hidden_2=1024, + encoder_l=[Nfeature1, 128], + hidden3=128, + encoder_2=[Nfeature2, 1024, 128, 128], + hidden_4=128, + encoder_l1=[Nfeature2, 128], + hidden3_1=128, + decoder_2=[128, 128, 1024], + hidden_5=1024, + drop_rate=0.1, + log_variational=True, + Type="ZINB", + device=device, + n_centroids=22, + penality="GMM", + model=1, + ) + model.to(device) + model.init_gmm_params(total_loader) + model.fit(args, train, valid, args.final_rate, args.scale_factor, device) + # embeds = model.predict(x_test, y_test).cpu().numpy() + score = model.score(x_test, y_test, labels) + # score.update(model.score(x_test, y_test, labels, adata_sol=data.data['test_sol'], metric="openproblems")) + score["ARI"] = score["dance_ari"] + del score["dance_ari"] + wandb.log(score) + wandb.finish() + finally: + locals_keys = list(locals().keys()) + for var in locals_keys: + try: + exec(f"del {var}") + logger.info(f"Deleted '{var}'") + except NameError: + logger.info(f"Variable '{var}' does not exist, continuing...") + torch.cuda.empty_cache() + gc.collect() + # score.update({ + # 'seed': args.seed + k, + # 'subtask': args.subtask, + # 'method': 'scmvae', + # }) + + # if res is not None: + # res = res.append(score, ignore_index=True) + # else: + # for s in score: + # score[s] = [score[s]] + # res = pd.DataFrame(score) + + entity, project, sweep_id = pipeline_planer.wandb_sweep_agent( + evaluate_pipeline, sweep_id=args.sweep_id, count=args.count) #Score can be recorded for each epoch + save_summary_data(entity, project, sweep_id, summary_file_path=args.summary_file_path, root_path=file_root_path) + if args.tune_mode == "pipeline" or args.tune_mode == "pipeline_params": + get_step3_yaml(result_load_path=f"{args.summary_file_path}", step2_pipeline_planer=pipeline_planer, + conf_load_path=f"{Path(args.root_path).resolve().parent}/step3_default_params.yaml", + root_path=file_root_path, + required_funs=["AlignMod", "FilterCellsCommonMod", "FilterCellsCommonMod", + "SetConfig"], required_indexes=[2, 11, 14, sys.maxsize], metric="ARI") + if args.tune_mode == "pipeline_params": + run_step3(file_root_path, evaluate_pipeline, tune_mode="params", step2_pipeline_planer=pipeline_planer) +"""To reproduce scMVAE on other samples, please refer to command lines belows: + +GEX-ADT: +$ python scmvae.py --subtask openproblems_bmmc_cite_phase2 --device cuda + +GEX-ATAC: +$ python scmvae.py --subtask openproblems_bmmc_multiome_phase2 --device cuda + +""" diff --git a/examples/tuning/predict_modality_babel/main.py b/examples/tuning/predict_modality_babel/main.py new file mode 100644 index 00000000..97062150 --- /dev/null +++ b/examples/tuning/predict_modality_babel/main.py @@ -0,0 +1,114 @@ +import argparse +import logging +import os +import sys +from pathlib import Path + +import pandas as pd +import torch +import wandb + +from dance import logger +from dance.datasets.multimodality import ModalityPredictionDataset +from dance.modules.multi_modality.predict_modality.babel import BabelWrapper +from dance.pipeline import PipelinePlaner, get_step3_yaml, run_step3, save_summary_data +from dance.utils import set_seed + +if __name__ == "__main__": + OPTIMIZER_DICT = { + "adam": torch.optim.Adam, + "rmsprop": torch.optim.RMSprop, + } + parser = argparse.ArgumentParser() + parser.add_argument("-t", "--subtask", default="openproblems_bmmc_cite_phase2_rna") + parser.add_argument("-device", "--device", default="cuda") + parser.add_argument("-cpu", "--cpus", default=1, type=int) + parser.add_argument("-seed", "--seed", default=1, type=int) + parser.add_argument("--runs", type=int, default=1, help="Number of repetitions") + parser.add_argument("-m", "--model_folder", default="./models") + parser.add_argument("--outdir", "-o", default="./logs", help="Directory to output to") + parser.add_argument("--lossweight", type=float, default=1., help="Relative loss weight") + parser.add_argument("--lr", "-l", type=float, default=0.01, help="Learning rate") + parser.add_argument("--batchsize", "-b", type=int, default=64, help="Batch size") + parser.add_argument("--hidden", type=int, default=64, help="Hidden dimensions") + parser.add_argument("--earlystop", type=int, default=20, help="Early stopping after N epochs") + parser.add_argument("--naive", "-n", action="store_true", help="Use a naive model instead of lego model") + parser.add_argument("--resume", action="store_true") + parser.add_argument("--max_epochs", type=int, default=500) + + parser.add_argument("--cache", action="store_true", help="Cache processed data.") + parser.add_argument("--tune_mode", default="pipeline_params", choices=["pipeline", "params", "pipeline_params"]) + parser.add_argument("--count", type=int, default=2) + parser.add_argument("--sweep_id", type=str, default=None) + parser.add_argument("--summary_file_path", default="results/pipeline/best_test_acc.csv", type=str) + parser.add_argument("--root_path", default=str(Path(__file__).resolve().parent), type=str) + args = parser.parse_args() + args.resume = True + + torch.set_num_threads(args.cpus) + args.outdir = os.path.abspath(args.outdir) + os.makedirs(args.model_folder, exist_ok=True) + os.makedirs(args.outdir, exist_ok=True) + # Specify output log file + fh = logging.FileHandler(f"{args.outdir}/training_{args.subtask}_{args.seed}.log", "w") + fh.setLevel(logging.INFO) + logger.addHandler(fh) + file_root_path = Path(args.root_path, args.subtask).resolve() + logger.info(f"\n files is saved in {file_root_path}") + pipeline_planer = PipelinePlaner.from_config_file(f"{file_root_path}/{args.tune_mode}_tuning_config.yaml") + os.environ["WANDB_AGENT_MAX_INITIAL_FAILURES"] = "2000" + for arg in vars(args): + logger.info(f"Parameter {arg}: {getattr(args, arg)}") + + def evaluate_pipeline(tune_mode=args.tune_mode, pipeline_planer=pipeline_planer): + wandb.init(settings=wandb.Settings(start_method='thread')) + rndseed = args.seed + set_seed(rndseed) + dataset = ModalityPredictionDataset(args.subtask, preprocess=None) + data = dataset.load_data() + # Prepare preprocessing pipeline and apply it to data + kwargs = {tune_mode: dict(wandb.config)} + preprocessing_pipeline = pipeline_planer.generate(**kwargs) + print(f"Pipeline config:\n{preprocessing_pipeline.to_yaml()}") + preprocessing_pipeline(data) + + # Obtain training and testing data + x_train, y_train = data.get_train_data(return_type="torch") + x_test, y_test = data.get_test_data(return_type="torch") + x_train, y_train, x_test, y_test = x_train.float(), y_train.float(), x_test.float(), y_test.float() + # Train and evaluate the model + # Just realized some algorithms can do dimensionality reduction while others cannot, so it depends on the algorithm + model = BabelWrapper(args, dim_in=x_train.shape[1], dim_out=y_train.shape[1]) + model.fit(x_train, y_train, val_ratio=0.15) + wandb.log({'rmse': model.score(x_test, y_test)}) + wandb.finish() + + entity, project, sweep_id = pipeline_planer.wandb_sweep_agent( + evaluate_pipeline, sweep_id=args.sweep_id, count=args.count) #Score can be recorded for each epoch + save_summary_data(entity, project, sweep_id, summary_file_path=args.summary_file_path, root_path=file_root_path) + if args.tune_mode == "pipeline" or args.tune_mode == "pipeline_params": + get_step3_yaml(result_load_path=f"{args.summary_file_path}", step2_pipeline_planer=pipeline_planer, + conf_load_path=f"{Path(args.root_path).resolve().parent}/step3_default_params.yaml", + root_path=file_root_path, + required_funs=["AlignMod", "FilterCellsCommonMod", "FilterCellsCommonMod", + "SetConfig"], required_indexes=[2, 11, 14, sys.maxsize], metric="ARI") + if args.tune_mode == "pipeline_params": + run_step3(file_root_path, evaluate_pipeline, tune_mode="params", step2_pipeline_planer=pipeline_planer) +"""To reproduce BABEL on other samples, please refer to command lines belows: + +GEX to ADT (subset): +$ python babel.py --subtask openproblems_bmmc_cite_phase2_rna_subset --device cuda + +GEX to ADT: +$ python babel.py --subtask openproblems_bmmc_cite_phase2_rna --device cuda + +ADT to GEX: +$ python babel.py --subtask openproblems_bmmc_cite_phase2_mod2 --device cuda + +GEX to ATAC: +$ python babel.py --subtask openproblems_bmmc_multiome_phase2_rna --device cuda + +ATAC to GEX: +$ python babel.py --subtask openproblems_bmmc_multiome_phase2_mod2 --device cuda + +""" diff --git a/examples/tuning/step3_default_params.yaml b/examples/tuning/step3_default_params.yaml index 2a010328..aaca441b 100644 --- a/examples/tuning/step3_default_params.yaml +++ b/examples/tuning/step3_default_params.yaml @@ -54,6 +54,8 @@ pipeline: values: [null, 1e3, 1e4, 1e5, 1e6] max_fraction: values: [0.01, 0.05, 0.5, 0.7, 1.0] + - type: normalize + target: tfidfTransform - type: normalize target: NormalizePlaceHolder - type: filter.gene @@ -217,19 +219,21 @@ pipeline: - [max_genes, max_counts, min_counts, min_genes] - [max_genes, max_counts, min_genes, min_counts] min_counts: - min: 3 - max: 1000 + min: 0.0 # Change occurs when joint embedding + max: 0.05 min_genes: min: 0.0 max: 0.05 max_counts: - min: 10000 - max: 100000 + min: 0.95 + max: 1.0 max_genes: min: 0.95 max: 1.0 - type: filter.cell target: FilterCellsPlaceHolder + - type: filter.cell + target: FilterCellsCommonMod - type: feature.cell target: CellPCA params: @@ -246,6 +250,14 @@ pipeline: n_components: min: 100 max: 1000 + - type: feature.cell + target: SparsePCA + params: + out: feature.cell + params_to_tune: + n_components: + min: 100 + max: 1000 - type: feature.cell target: WeightedFeaturePCA params: diff --git a/requirements.txt b/requirements.txt index c58e5232..0e92f42a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,6 +11,7 @@ numpy==1.26.4 omegaconf==2.3.0 opencv-python==4.9.0.80 openpyxl==3.1.2 +pot==0.9.4 pandas==2.2.2 # pandas==1.5.3 # scib 1.1.4 requires pandas<2 pyro-ppl==1.9.0 @@ -26,3 +27,5 @@ threadpoolctl==3.5.0 tifffile==2024.2.12 torchnmf==0.3.5 tqdm==4.66.2 +anndata==0.10.8 +wandb==0.16.3 diff --git a/tests/atlas/test_anndata_similarity.py b/tests/atlas/test_anndata_similarity.py new file mode 100644 index 00000000..083177d6 --- /dev/null +++ b/tests/atlas/test_anndata_similarity.py @@ -0,0 +1,110 @@ +# test_anndata_similarity.py + +import numpy as np +import pytest +import scanpy as sc +from anndata import AnnData +from scipy import sparse + +from dance.atlas.sc_similarity.anndata_similarity import AnnDataSimilarity + + +@pytest.fixture +def test_data(): + """Create test AnnData objects.""" + n_cells = 20 + n_genes = 40 + + X1 = np.random.negative_binomial(n=5, p=0.3, size=(n_cells, n_genes)) + X2 = np.random.negative_binomial(n=5, p=0.3, size=(n_cells, n_genes)) + + X1[np.random.random(X1.shape) > 0.5] = 0 + X2[np.random.random(X2.shape) > 0.5] = 0 + X1 = sparse.csr_matrix(X1) + X2 = sparse.csr_matrix(X2) + + obs1 = { + 'celltype': ['type1'] * n_cells, + 'nnz': [X1[i].count_nonzero() for i in range(n_cells)], + 'n_measured_vars': [n_genes] * n_cells, + 'assay': ['assay1'] * n_cells, + 'tissue': ['tissue1'] * n_cells + } + + obs2 = { + 'celltype': ['type2'] * n_cells, + 'nnz': [X2[i].count_nonzero() for i in range(n_cells)], + 'n_measured_vars': [n_genes] * n_cells, + 'assay': ['assay2'] * n_cells, + 'tissue': ['tissue2'] * n_cells + } + + adata1 = AnnData(X1, obs=obs1) + adata2 = AnnData(X2, obs=obs2) + + # Perform standard single-cell data preprocessing + for adata in [adata1, adata2]: + sc.pp.normalize_total(adata, target_sum=1e4) + sc.pp.log1p(adata) + + return adata1, adata2 + + +def test_similarity_computation(test_data): + """Test the correctness of similarity computation.""" + adata1, adata2 = test_data + + # Initialize similarity calculator + similarity_calculator = AnnDataSimilarity(adata1, adata2, sample_size=2, init_random_state=42, n_runs=1) + + # Calculate similarity + similarity_matrices = similarity_calculator.get_similarity_matrix_A2B(methods=[ + 'wasserstein', 'Hausdorff', 'chamfer', 'energy', 'sinkhorn2', 'bures', 'spectral', 'common_genes_num', + 'metadata_sim', 'mmd' + ]) + for k, v in similarity_matrices.items(): + print(k, v) + + # Verify result format and basic properties + assert isinstance(similarity_matrices, dict) + assert all(0 <= v <= 1 for k, v in similarity_matrices.items() if k not in ['common_genes_num', 'bures']) + + # Verify specific metrics + assert similarity_matrices['common_genes_num'] >= 0 # At least 0 common genes + + +def test_preprocess(test_data): + """Test data preprocessing functionality.""" + adata1, adata2 = test_data + + calculator = AnnDataSimilarity(adata1, adata2) + calculator.preprocess() + + # Verify gene filtering + assert len(calculator.common_genes) > 0 + assert all(gene in calculator.origin_adata1.var_names for gene in calculator.common_genes) + assert all(gene in calculator.origin_adata2.var_names for gene in calculator.common_genes) + + +def test_sample_cells(test_data): + """Test cell sampling functionality.""" + adata1, adata2 = test_data + sample_size = 1 + + calculator = AnnDataSimilarity(adata1, adata2, sample_size=sample_size) + calculator.adata1 = calculator.origin_adata1.copy() + calculator.adata2 = calculator.origin_adata2.copy() + calculator.sample_cells(random_state=42) + + # Verify sample size + assert calculator.sampled_adata1.n_obs == sample_size + assert calculator.sampled_adata2.n_obs == sample_size + + +@pytest.mark.xfail(raises=ValueError, strict=True) +def test_invalid_method(test_data): + """Test invalid similarity computation method.""" + adata1, adata2 = test_data + calculator = AnnDataSimilarity(adata1, adata2) + + calculator.compute_similarity(random_state=42, methods=['invalid_method']) diff --git a/tests/atlas/test_atlas.py b/tests/atlas/test_atlas.py new file mode 100644 index 00000000..6faaf9c5 --- /dev/null +++ b/tests/atlas/test_atlas.py @@ -0,0 +1,70 @@ +"""Test suite for the Atlas similarity calculation functionality. + +This test verifies that the main function correctly returns: +1. The most similar dataset from the atlas +2. Its corresponding configuration settings +3. The similarity score + +""" + +import json +import sys + +import pandas as pd + +from dance.settings import ATLASDIR, DANCEDIR, SIMILARITYDIR + +sys.path.append(str(ATLASDIR)) +from demos.main import main + +from dance import logger + + +def test_main(): + # Construct test parameters with a sample Brain tissue dataset + class Args: + tissue = "Brain" + data_dir = str(DANCEDIR / "examples/tuning/temp_data/train/human") + source_file = "human_Brain364348b4-bc34-4fe1-a851-60d99e36cafa_data" + + args = Args() + logger.info(f"testing main with args: {args}") + source_id = "3643" + + # Execute main function with test parameters + ans_file, ans_conf, ans_value = main(args) + + # Verify return value types and ranges + assert isinstance(ans_file, str), "ans_file should be a string type" + assert isinstance(ans_value, float), "ans_value should be a float type" + assert 0 <= ans_value <= 1, "Similarity value should be between 0 and 1" + + # Verify configuration dictionary structure and content + expected_methods = ["cta_celltypist", "cta_scdeepsort", "cta_singlecellnet", "cta_actinn"] + assert isinstance(ans_conf, dict), "ans_conf should be a dictionary type" + assert set(ans_conf.keys()) == set(expected_methods), "ans_conf should contain all expected methods" + assert all(isinstance(v, str) for v in ans_conf.values()), "All configuration values should be string type" + + # Verify consistency with Excel spreadsheet results + data = pd.read_excel(SIMILARITYDIR / f"data/new_sim/{args.tissue.lower()}_similarity.xlsx", sheet_name=source_id, + index_col=0) + reduce_error = False + in_query = True + # Read weights + with open( + SIMILARITYDIR / + f"data/similarity_weights_results/{'reduce_error_' if reduce_error else ''}{'in_query_' if in_query else ''}sim_dict.json", + encoding='utf-8') as f: + sim_dict = json.load(f) + feature_name = sim_dict[args.tissue.lower()]["feature_name"] + w1 = sim_dict[args.tissue.lower()]["weight1"] + w2 = 1 - w1 + + # Calculate similarity in Excel + data.loc["similarity"] = data.loc[feature_name] * w1 + data.loc["metadata_sim"] * w2 + expected_file = data.loc["similarity"].idxmax() + expected_value = data.loc["similarity", expected_file] + + # Verify result consistency with Excel + assert abs(ans_value - expected_value) < 1e-4, "Calculated similarity value does not match Excel value" + assert ans_file == expected_file, "Selected most similar dataset does not match Excel result" diff --git a/tests/test_get_result_web.py b/tests/test_get_result_web.py new file mode 100644 index 00000000..2096e17e --- /dev/null +++ b/tests/test_get_result_web.py @@ -0,0 +1,154 @@ +import sys +from pathlib import Path +from unittest.mock import patch + +import pandas as pd +import pytest + +from dance.settings import DANCEDIR + +sys.path.append(str(DANCEDIR)) +from examples.atlas.get_result_web import check_exist, check_identical_strings, spilt_web, write_ans + + +# Test check_identical_strings function +def test_check_identical_strings(): + # Test case for identical strings + assert check_identical_strings(["test", "test", "test"]) == "test" + + # Test case for empty list + with pytest.raises(ValueError, match="The list is empty"): + check_identical_strings([]) + + # Test case for different strings + with pytest.raises(ValueError, match="Different strings found"): + check_identical_strings(["test1", "test2"]) + + +# Test spilt_web function +def test_spilt_web(): + # Test valid URL + url = "https://wandb.ai/user123/project456/sweeps/abc789" + result = spilt_web(url) + assert result == ("user123", "project456", "abc789") + + # Test invalid URL + invalid_url = "https://invalid-url.com" + assert spilt_web(invalid_url) is None + + +# Test check_exist function +def test_check_exist(tmp_path): + # Create temporary test directory + results_dir = tmp_path / "results" / "params" + results_dir.mkdir(parents=True) + + # Test empty directory + assert check_exist(str(tmp_path)) is False + + # Create test files + (results_dir / "file1.txt").touch() + (results_dir / "file2.txt").touch() + + # Test case with multiple files + assert check_exist(str(tmp_path)) is True + + +# Create test fixed data +@pytest.fixture +def sample_df(): + return pd.DataFrame({"id": ["run1", "run2", "run3"], "metric": [0.8, 0.9, 0.7]}) + + +# use mock to simulate wandb API +@pytest.fixture +def mock_wandb(mocker): + mock_api = mocker.patch("wandb.Api") + # set mock return values + return mock_api + + +# Add a mock fixture to simulate ATLASDIR +@pytest.fixture(autouse=True) +def mock_settings(tmp_path, monkeypatch): + """Mock ATLASDIR and METADIR settings for tests.""" + # Create temporary directory + mock_atlas_dir = tmp_path / "atlas" + mock_meta_dir = tmp_path / "meta" + mock_atlas_dir.mkdir(parents=True) + mock_meta_dir.mkdir(parents=True) + + # Set environment variables + monkeypatch.setenv("ATLAS_DIR", str(mock_atlas_dir)) + monkeypatch.setenv("META_DIR", str(mock_meta_dir)) + + # If import directly from dance.settings, also replace these values + monkeypatch.setattr("examples.atlas.get_result_web.ATLASDIR", mock_atlas_dir) + monkeypatch.setattr("examples.atlas.get_result_web.METADIR", mock_meta_dir) + + return mock_atlas_dir + + +def test_write_ans(mock_settings): + sweep_results_dir = mock_settings / "sweep_results" + sweep_results_dir.mkdir(parents=True) + output_file = sweep_results_dir / "heart_ans.csv" + + # Create initial data + existing_data = pd.DataFrame({ + 'Dataset_id': ['dataset1', 'dataset2'], + 'cta_actinn': ['url1', 'url2'], + 'cta_actinn_best_yaml': ['yaml1', 'yaml2'], + 'cta_actinn_best_res': [0.8, 0.7] + }) + existing_data.to_csv(output_file) + + # Test data: include lower score and higher score + new_data = pd.DataFrame({ + 'Dataset_id': ['dataset1', 'dataset2'], + 'cta_actinn': ['url1_new', 'url2_new'], + 'cta_actinn_best_yaml': ['yaml1_new', 'yaml2_new'], + 'cta_actinn_best_res': [0.9, 0.6] # dataset1 higher score, dataset2 lower score + }) + + write_ans("heart", new_data, output_file) + + # Verify results + result_df = pd.read_csv(output_file) + + # Verify high score update success + dataset1_row = result_df[result_df['Dataset_id'] == 'dataset1'].iloc[0] + assert dataset1_row['cta_actinn_best_res'] == 0.9 + assert dataset1_row['cta_actinn'] == 'url1_new' + assert dataset1_row['cta_actinn_best_yaml'] == 'yaml1_new' + + # Verify low score remains unchanged + dataset2_row = result_df[result_df['Dataset_id'] == 'dataset2'].iloc[0] + assert dataset2_row['cta_actinn_best_res'] == 0.7 + assert dataset2_row['cta_actinn'] == 'url2' + assert dataset2_row['cta_actinn_best_yaml'] == 'yaml2' + + +# Test completely new data write (file does not exist) +def test_write_ans_new_file(mock_settings): + # Use mock_settings instead of creating new temporary directory + sweep_results_dir = mock_settings / "sweep_results" + sweep_results_dir.mkdir(parents=True) + output_file = sweep_results_dir / "new_heart_ans.csv" + + new_data = pd.DataFrame({ + 'Dataset_id': ['dataset1', 'dataset2'], + 'cta_actinn': ['url1', 'url2'], + 'cta_actinn_best_yaml': ['yaml1', 'yaml2'], + 'cta_actinn_best_res': [0.8, 0.9] + }) + + # Test write to new file + + # Verify file is created and contains correct data + write_ans("heart", new_data, output_file) + assert output_file.exists() + + written_df = pd.read_csv(output_file, index_col='Dataset_id') + assert len(written_df) == 2 + assert all(written_df.index == ['dataset1', 'dataset2']) diff --git a/tests/test_mudata_to_anndata.py b/tests/test_mudata_to_anndata.py new file mode 100644 index 00000000..1949eb75 --- /dev/null +++ b/tests/test_mudata_to_anndata.py @@ -0,0 +1,99 @@ +import anndata +import mudata +import numpy as np +import pytest + +from dance.data.base import Data +from dance.utils.wrappers import add_mod_and_transform + + +@pytest.fixture +def mock_mudata(): + """Create a MuData object for testing.""" + adata1 = anndata.AnnData(X=np.array([[1, 2], [3, 4]])) + adata2 = anndata.AnnData(X=np.array([[5, 6], [7, 8]])) + return Data(data=mudata.MuData({'mod1': adata1, 'mod2': adata2})) + + +@add_mod_and_transform +class SampleClass: + """Example class for testing the decorator.""" + + def __init__(self, x=10, **kwargs): + self.x = x + + def __call__(self, data, *args, **kwargs): + # Multiply the data by self.x + if isinstance(data.data, anndata.AnnData): + data.data.X = data.data.X * self.x + return data + + +def test_class_init_with_mod(): + """Test class initialization with mod parameter.""" + obj = SampleClass(x=10, mod="mod1") + assert obj.x == 10 + assert obj.mod == "mod1" + + +def test_class_init_without_mod(): + """Test class initialization without mod parameter.""" + obj = SampleClass(x=10) + assert obj.x == 10 + assert obj.mod is None + + +def test_class_call_with_anndata(): + """Test calling with AnnData object.""" + obj = SampleClass(x=3) + original_data = np.array([[1, 2], [3, 4]]) + adata = Data(data=anndata.AnnData(X=original_data.copy())) + result = obj(adata) + # Verify data is multiplied by x=3 + assert np.array_equal(result.data.X, original_data * 3) + + +def test_class_call_with_mudata_and_mod(mock_mudata): + """Test calling with MuData object and mod parameter to verify that only the + specified modality is modified.""" + obj = SampleClass(x=2, mod="mod1") + # Store original data for both modalities + original_mod1 = mock_mudata.data.mod["mod1"].X.copy() + original_mod2 = mock_mudata.data.mod["mod2"].X.copy() + + obj(mock_mudata) + + # Verify mod1 data is multiplied by x=2 + assert np.array_equal(mock_mudata.data.mod["mod1"].X, original_mod1 * 2) + # Verify mod2 data remains unchanged + assert np.array_equal(mock_mudata.data.mod["mod2"].X, original_mod2) + + +def test_class_call_with_mudata_without_mod(mock_mudata): + """Test calling with MuData object but without mod parameter.""" + obj = SampleClass(x=10) + result = obj(mock_mudata) + assert result is mock_mudata + + +def test_class_call_with_mudata_invalid_mod(mock_mudata): + """Test using invalid mod parameter.""" + obj = SampleClass(x=10, mod="invalid_mod") + with pytest.raises(KeyError): + obj(mock_mudata) + + +def test_decorator_preserves_metadata(): + """Test if the decorator preserves the original class metadata.""" + assert hasattr(SampleClass, 'add_mod_and_transform') + assert SampleClass.__init__.__doc__ == SampleClass.__init__.__wrapped__.__doc__ + assert SampleClass.__call__.__doc__ == SampleClass.__call__.__wrapped__.__doc__ + + +def test_class_call_with_additional_args(mock_mudata): + """Test calling with additional arguments.""" + obj = SampleClass(x=10, mod="mod1") + original_data = mock_mudata.data.mod["mod1"].copy() + obj(mock_mudata, extra_arg="test") + # Verify original data is modified correctly + assert np.array_equal(mock_mudata.data.mod["mod1"].X, original_data.X * 10)