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add info about input file, fix spelling #55

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7158982
add info about input file, fix spelling
cklamann Sep 13, 2024
64d3d3d
update readme
cklamann Sep 13, 2024
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -22,7 +22,7 @@ A **tutorial** outlining basic usage is available [here][tutorial].

## Requirements

Python 3.9 or 3.10 are required to run starling. If your current version of python is not one of these, we recommend using [pyenv](https://github.com/pyenv/pyenv) to install a compatible version alongside your current one. Alternately, you could use the Docker configuration described below.
Python 3.9 or above is required to run starling. If your current version of python is not one of these, we recommend using [pyenv](https://github.com/pyenv/pyenv) to install a compatible version alongside your current one. Alternately, you could use the Docker configuration described below.

## Installation

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39 changes: 21 additions & 18 deletions docs/source/tutorial/getting-started.ipynb
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Expand Up @@ -50,7 +50,6 @@
},
"outputs": [],
"source": [
"# pl.utilities.seed.seed_everything(10, workers=True)\n",
"seed_everything(10, workers=True)"
]
},
Expand All @@ -67,7 +66,7 @@
"id": "69ef8c1d",
"metadata": {},
"source": [
"The example below runs Kmeans with 10 clusters read from \"sample_input.h5ad\" object.\n"
"The example below runs Kmeans with 10 clusters read from \"sample_input.h5ad\" object."
]
},
{
Expand All @@ -91,8 +90,9 @@
"id": "52d3d9fb",
"metadata": {},
"source": [
"- Users might want to arcsinh protein expressions in \\*.h5ad (for example, 'sample_input.h5ad').\n",
"- The utility.py provides an easy setup of GMM, KM (Kmeans) or PG (PhenoGraph).\n",
"- The input anndata object should contain a cell-by-protein matrix of segmented single-cell expression profiles in the `.X` position. Optionally, cell size information can also be provided as a column of the `.obs` DataFrame. In this case `model_cell_size` should be set to `True` and the column specified in the `cell_size_col_name`argument.\n",
"- Users might want to arcsinh protein expressions in \\*.h5ad (for example, `sample_input.h5ad`).\n",
"- The `utility.py` provides an easy setup of GMM, KM (Kmeans) or PG (PhenoGraph).\n",
"- Default settings are applied to each method.\n",
"- k can be omitted when PG is used.\n"
]
Expand Down Expand Up @@ -132,32 +132,34 @@
"\n",
"- adata: annDATA object of the sample\n",
"- dist_option (default: 'T'): T for Student-T (df=2) and N for Normal (Gaussian)\n",
"- the proportion of anticipated segmentation error free cells (default: 0.6)\n",
"- singlet_prop (default: 0.6): the proportion of anticipated segmentation error free cells \n",
"- model_cell_size (default: 'Y'): Y for incoporating cell size in the model and N otherwise\n",
"- cell_size_col_name (default: 'area'): area is the column name in anndata.obs dataframe\n",
"- model_zplane_overlap (default: 'Y'): Y for modeling z-plane overlap when cell size is modelled and N otherwise\n",
" Note: if the user sets model_cell_size = 'N', then model_zplane_overlap is ignored\n",
"- model_regularizer (default: 1): Regularizier term impose on synthetic doublet loss (BCE)\n",
"- learning_rate (default: 1e-3): The learning rate of ADAM optimizer for STARLING\n",
"\n",
"Equivalent as the above example:\n",
"st = starling.ST(adata, 'T', 'Y', 'area', 'Y', 1, 1e-3)\n"
"Equivalent to the above example:\n",
"```python\n",
"st = starling.ST(adata, 'T', 'Y', 'area', 'Y', 1, 1e-3)\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "63939215",
"metadata": {},
"source": [
"## Setting trainning log\n"
"## Setting training log\n"
]
},
{
"cell_type": "markdown",
"id": "d721258f",
"metadata": {},
"source": [
"Once training starts, a new directory 'log' will created.\n"
"Once training starts, a new directory 'log' will be created."
]
},
{
Expand Down Expand Up @@ -237,7 +239,7 @@
"id": "3ba887b2",
"metadata": {},
"source": [
"## Appending STARLING results to annData object\n"
"## Appending STARLING results to the annData object\n"
]
},
{
Expand All @@ -258,7 +260,7 @@
"id": "a705d895",
"metadata": {},
"source": [
"## The following information can be retrived from annData object:\n",
"## The following information can be retrived from the annData object:\n",
"\n",
"- st.adata.varm['init_exp_centroids'] -- initial expression cluster centroids (P x C matrix)\n",
"- st.adata.varm['st_exp_centroids'] -- ST expression cluster centroids (P x C matrix)\n",
Expand All @@ -271,7 +273,8 @@
"- st.adata.obs['init_label'] -- initial assignments\n",
"- st.adata.obs['st_label'] -- ST assignments\n",
"- st.adata.obs['max_assign_prob'] -- ST max probabilites of assignments\n",
" - N: # of cells; C: # of clusters; P: # of proteins\n"
"\n",
"_N: # of cells; C: # of clusters; P: # of proteins_\n"
]
},
{
Expand Down Expand Up @@ -354,7 +357,7 @@
"id": "80e61208",
"metadata": {},
"source": [
"## Showing initial expression centriods:\n"
"## Showing initial expression centroids:\n"
]
},
{
Expand All @@ -364,7 +367,7 @@
"metadata": {},
"outputs": [],
"source": [
"## initial expression centriods (p x c) matrix\n",
"## initial expression centroids (p x c) matrix\n",
"pd.DataFrame(result.varm[\"init_exp_centroids\"], index=result.var_names)"
]
},
Expand All @@ -381,7 +384,7 @@
"id": "f0bc41a8",
"metadata": {},
"source": [
"## Showing Starling expression centriods:\n"
"## Showing Starling expression centroids:\n"
]
},
{
Expand All @@ -391,7 +394,7 @@
"metadata": {},
"outputs": [],
"source": [
"## starling expression centriods (p x c) matrix\n",
"## starling expression centroids (p x c) matrix\n",
"pd.DataFrame(result.varm[\"st_exp_centroids\"], index=result.var_names)"
]
},
Expand All @@ -400,7 +403,7 @@
"id": "a2cccf9d",
"metadata": {},
"source": [
"From here one could easily annotate cluster centriods to cell type.\n"
"From here one could easily annotate cluster centroids to cell type.\n"
]
},
{
Expand All @@ -427,7 +430,7 @@
"id": "b203933c",
"metadata": {},
"source": [
"Currently, we assign a cell label based on the maximum probability among all possible clusters. However, there could be mislabeled because maximum and second highest probabilies can be very close that the user might be interested.\n"
"Currently, we assign a cell label based on the maximum probability among all possible clusters. However, these could be mislabeled because maximum and second highest probabilies can be very close."
]
}
],
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