Adjustable execution parameters for the scRNAbox pipeline
Introduction
Prior to running each Analytical Step of the scRNAbox pipeline, users are strongly encouraged to modify the execution parameters of the analysis using the adjustable, Step-specific parameters text files. Upon running the pipeline initiation Step (Step 0), adjustable text files for each Analytical Step will be automatically deposited in ~/working_directory/job_info/parameters
:
parameters
├── step1_par.txt
├── step2_par.txt
├── step3_par.txt
├── step4_par.txt
├── step5_par.txt
├── step6_par.txt
├── step7_par.txt
├── step8_contrast_celltype.txt
├── step8_contrast_genotype.txt
├── step8_contrast_pseudo_bulk.txt
└──step8_par.txt
To ensure replicability, a summary report file documents the execution parameters for each iteration of each Analytical Step, which is located in ~/working_directory/job_info/summary_report.txt
.
Note:
1) Parameters that require a character input (e.g. "Control 1") must be placed in quotations (" " or ' ').
2) Parameters that require a numerical input must not be placed in quotations (e.g. 0.50).
3) Parameters that require a "yes" or "no" answer are not case-sensitive.
Standard scRNAseq Analysis Track
Step 1: FASTQ to gene expression matrix
Parameter |
Default |
Description |
par_automated_library_prep |
No |
Whether or not to perform automated library prep. Alternatively, you may set this parameter to "no" and manually prepare the libraries. |
par_fastq_directory |
NULL |
Path to directory containing the FASTQ files. This directory should only contain FASTQ files for the experiment. |
par_sample_names |
NULL |
The sample names used to name the FASTQ files according to CellRanger nomeclature |
par_rename_samples |
Yes |
Whether or not you want to rename your samples. These names will be used to identify cells in the Seurat objects |
par_new_sample_names |
NULL |
New sample names. Make sure they are defined in the same order as 'par_sample_names' |
par_paired_end_seq |
Yes |
Whether or not paired-end sequencing was performed |
REF_DIR_GRCH |
NULL |
Path to reference genome for FASTQ alignment. 10X Genomics reference genomes are available for download. For more information see the 10X Genomics documentation. |
R1LENGTH |
NULL |
Minimum number of bases to retain for R1 sequence of gene expression |
MEMPERCORE |
30 |
For clusters whose job managers do not support memory requests, it is possible to request memory in the form of cores. This option will scale up the number of threads requested via the MRO_THREADS variable according to how much memory a stage requires when given to the ratio of memory on your nodes. |
Step 2: Create Seurat object and remove ambient RNA
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_ambient_RNA |
Yes |
Whether or not to correct the feature-barcode expression matrices for ambient RNA contamination |
par_count_matrices |
NULL |
If users skipped Step 1, the may provide the path to a directory that contains existing feature-barcode expression matrices to initiate the pipeline at Step 2 |
par_min.cells_L |
0 |
Only retain genes expressed in a minimum number of cells |
par_normalization.method |
LogNormalize |
Method to use for normalization |
par_scale.factor |
10000 |
Scale factor for scaling the data |
par_selection.method |
vst |
Method for choosing the top variable features |
par_nfeatures |
2500 |
Number of features to select as top variable features |
Step 3: Quality control and filtering
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_seurat_object |
NULL |
If users skipped Steps 1 and 2, the may provide the path to a directory that contains existing Seurat objects to initiate the pipeline at Step 3 |
par_nFeature_RNA_L |
NULL |
Only retain cells expressing a minimum number of genes |
par_nFeature_RNA_U |
NULL |
Only retain cells expressing a maximum number of genes |
par_nCount_RNA_L |
NULL |
Only retain cells with a minimum number of unique transcripts |
par_nCount_RNA_U |
NULL |
Only retain cells with a maximum number of unique transcripts |
par_mitochondria_percent_L |
NULL |
Only retain cells with a minimum percentage of mitochondrial genes |
par_mitochondria_percent_U |
NULL |
Only retain cells with a maximum percentage of mitochondrial genes |
par_ribosomal_percent_L |
NULL |
Only retain cells with a minimum percentage of ribosomal genes |
par_ribosomal_percent_U |
NULL |
Only retain cells with a maximum percentage of ribosomal genes |
par_remove_mitochondrial_genes |
Yes |
Whether or not to remove mitochondrial genes |
par_remove_ribosomal_genes |
Yes |
Whether or not to remove ribosomal genes |
par_remove_genes |
NULL |
If users want to remove specific genes from their data, they may define a list of gene identifiers |
par_regress_cell_cycle_genes |
Yes |
Whether or not to regress cell cycle genes |
par_normalization.method |
LogNormalize |
Method to use for normalization |
par_scale.factor |
10000 |
Scale factor for scaling the data |
par_selection.method |
vst |
Method for choosing the top variable features |
par_nfeatures |
2500 |
Number of features to select as top variable features |
par_top |
10 |
Number of most variable features to be reported in the csv file |
par_npcs_pca |
30 |
Total Number of principal components to compute and store for principal component analysis (PCA) |
Step 4: Doublet removal
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_RunUMAP_dims |
10 |
Number of dimensions to use as input features for uniform manifold approximation and projection (UMAP) |
par_RunUMAP_n.neighbors |
65 |
Number of neighboring points used in local approximations of manifold structure |
par_dropDN |
Yes |
Whether or not to remove predicted doublets from downstream analyses |
par_PCs |
20 |
The number of statistically significant principal components. Can be informed by elbow plot produced in Step 3 |
par_pN |
0.25 |
The number of artificial doublets to generate. DoubletFinderr is largely invariant to this parameter. We suggest keeping 0.25 |
par_sct |
FALSE |
Logical representing whether SCTransform was used during original Seurat object pre-processing |
par_sample_names |
NULL |
A list of sample names for each sample in the experiement, corresponding to the expected doublet rates listed in the parameter below. Sample names should be the same as those used to produce the samples_info folder during the setup procedures. |
par_expected_doublet_rate |
NULL |
A vector of expected doublet rates for each sample (e.g. for a 5% expected doublet rate, write 0.05). The expected doublet rates for each sample should be listed in the same order as the sample names in the above parameter. Make sure to have as many expected doublet rates listed as you have samples. |
Step 5: Integration and linear dimensional reduction
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_skip_integration |
No |
Whether or not to skip integration. This is applicable for experiments that comprises of only one sequencing run. |
par_DefaultAssay |
RNA |
The assay to perform normalization, scaling, and linear dimensiona reduction on. For most use cases this will be RNA. |
par_normalization.method |
LogNormalize |
Method to use for normalization |
par_scale.factor |
10000 |
Scale factor for scaling the data |
par_selection.method |
vst |
Method for detecting top variable features |
par_nfeatures |
2500 |
Number of features to select as top variable features |
par_FindIntegrationAnchors_dim |
25 |
Which dimensions to use from the canonical correlation analysis (CCA) to specify the neighbor search space |
par_RunPCA_npcs |
30 |
Total Number of principal components to compute and store for principal component analysis (PCA) |
par_RunUMAP_dims |
10 |
Number of dimensions to use as input features for uniform manifold approximation and projection (UMAP) |
par_RunUMAP_n.neighbors |
65 |
Number of neighboring points used in local approximations of manifold structure |
par_compute_jackstraw |
No |
Whether or not to perform JackStraw computation. This computation takes a long time. |
Step 6: Clustering
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_skip_integration |
No |
Whether or not the user skipped integration in Step 5 |
par_FindNeighbors_dims |
30 |
Number of dimensions from linear dimensional reduction used as input to identify neighbours. Can be informed by the elbow and Jackstraw plots produced in Step 5 |
par_FindNeighbors_k.param |
60 |
Defines k for the k-nearest neighbor algorithm |
par_FindNeighbors_prune.SNN |
1/15 |
Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the shared nearest-neighbour (SNN) construction |
par_FindClusters_resolution |
0, 0.05, 0.2, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0 |
Value of the clustering resolution parameter. You may provide multiple resolution values |
par_compute_ARI |
Yes |
Whether or not you want to compute the Adjusted Rand Index (ARI) between clusters at a given clustering resolution |
par_RI_reps |
100 |
Number of iterations for clustering the data at a given resolution in order to calculate the ARI |
Step 7: Cluster annotation
Annotation Method |
Parameter |
Default |
Description |
Method 1 |
par_run_find_marker |
Yes |
Whether or not to find marker genes for each cluster |
Method 1 |
par_run_enrichR |
Yes |
Whether or not to run gene set enrichment analysis (GSEA) on the marker genes for each cluster using the EnrichR tools. Note that the HPC must have access to the internet to run GSEA. |
Method 2 |
par_run_module_score |
Yes |
Whether or not to compute module score |
Method 3 |
par_run_reference |
Yes |
Whether or not to perform reference-based annotation |
Visualize features |
par_run_visualize_markers |
Yes |
Whether or not to Visualize select features |
Annotate |
par_run_annotate |
Yes |
Whether or not to Annotate |
General |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
General |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
General |
par_level_cluster |
integrated_snn_res.0.7 |
The cluster resolution that you want to use for downstream analyses. If you skipped integration in Step 5, use par_level_cluster='RNA_snn_res.0.7', if you want to proceed with a clustering resolution of 0.7 |
Method 1 |
par_top_sel |
5 |
Number of top markers to identify based on avg_log2FC |
Method 1 |
par_db |
Descartes_Cell_Types_and_Tissue_2021, CellMarker_Augmented_2021, Azimuth_Cell_Types_2021 |
Character vector of EnrichR databases that define cell types. The top marker genes for each cluster will be tested for enrichment across these databases. |
Method 2 |
par_module_score |
NULL |
Path defining the location of the directory that contains the csv file of the gene sets used to compute the module score |
Method 3 |
par_reference |
NULL |
Path defining the location of the reference Seurat object |
Method 3 |
par_reference_name |
Reference |
An arbitrary name for the reference object. This will be used to name the metadata slot. |
Method 3 |
par_level_celltype |
NULL |
The name of the metadata column in the reference Seurat object that defines cell types |
Method 3 |
par_FindTransferAnchors_dim |
10 |
Number of dimensions from linear dimensional reduction used to find transfer anchors between the reference and query Seurat objects |
Method 3 |
par_futureglobalsmaxSize |
50000 * 1024^2 |
This will increase your RAM usage so set this number mindfully |
Visualize features |
par_select_features_list |
NULL |
A list of features to visualize |
Visualize features |
par_select_features_csv |
NULL |
If you want to define multiple lists of features to visualize, you can do so with a csv file. The header should contain the list names and all features belonging to the same list should be in the same column. |
Annotate |
par_annotate_resolution |
NULL |
Which clustering resolution you want to annotate |
Annotate |
par_name_metadata |
clustering_label_1 |
The name of the metadata slot that will contain the annotations |
Annotate |
par_annotate_labels |
NULL |
A list of cluster labels. There must as many labels as clusters at the defined clustering resolution. Please refrain from using "_" when annotating. |
Step 8: Differential gene expression contrasts
Parameter |
Default |
Description (the cluster annotation method associated with the parameter is shown) |
par_run_add_metadata |
Yes |
Whether or not to add metadata to the Seurat object to facilitate differential gene expression contrasts. |
par_run_sample_sample_wilcoxon |
Yes |
Whether or to perform DGE contrasts between samples across all cells using the Wilcoxon method. |
par_run_sample_cell_wilcoxon |
Yes |
Whether or to perform DGE contrasts between samples stratified by cell type using the Wilcoxon method. |
par_run_pseudo_bulk |
Yes |
Whether or not to perform pseudo-bulk analysis |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_metadata |
NULL |
Path to a csv file defining new metadata that should be added to the Seurat object to facilitate DEG analysis. At least one column should contain "orig.ident". |
Cell Hashtag scRNAseq Analysis Track
Step 1: FASTQ to gene expression matrix
Parameter |
Default |
Description |
par_automated_library_prep |
Yes |
Whether or not to perform automated library prep. Alternatively, you may set this parameter to "no" and manually prepare the libraries. |
par_fastq_directory |
NULL |
Path to directory containing the FASTQ files. This directory should only contain FASTQ files for the experiment. |
par_RNA_run_names |
NULL |
The names of the sequencing runs for the RNA assay |
par_HTO_run_names |
NULL |
The names of the sequencing runs for the HTO assay |
par_seq_run_names |
NULL |
The user-selected name for the sequencing run. These names will be used to identify cells in the Seurat objects |
par_paired_end_seq |
Yes |
Whether or not paired-end sequencing was performed |
id |
NULL |
Barcode ID which will be used to track the feature counts |
name |
NULL |
The user-selected name for the barcode identifier |
read |
R2 |
Which RNA sequencing read contains the barcode sequence. This value Will be either R1 or R2. |
pattern |
NULL |
The pattern of the barcode identifiers |
sequence |
NULL |
The nucleotide sequence associated with the barcode identifier |
REF_DIR_GRCH |
NULL |
Path to reference genome for FASTQ alignment. 10X Genomics reference genomes are available for download. For more information see their documentation. |
R1LENGTH |
NULL |
Minimum number of bases to retain for R1 sequence of gene expression |
MEMPERCORE |
30 |
For clusters whose job managers do not support memory requests, it is possible to request memory in the form of cores. This option will scale up the number of threads requested via the MRO_THREADS variable according to how much memory a stage requires when given to the ratio of memory on your nodes. |
Step 2: Create Seurat object and remove ambient RNA
Parameter |
Default |
Description |
Save_RNA |
No |
Whether or not to export an RNA expression matrix |
Save_metadata |
No |
Whether or not to export a metadata dataframe |
par_ambient_RNA |
Yes |
Whether or not to correct the feature-barcode expression matrices for ambient RNA contamination |
par_count_matrices |
NULL |
If users skipped Step 1, the may provide the path to a directory that contains existing feature-barcode expression matrices to initiate the pipeline at Step 2 |
par_min.cells_L |
0 |
Only retain genes expressed in a minimum number of cells |
par_normalization.method |
LogNormalize |
Method to use for normalization |
par_scale.factor |
10000 |
Scale factor for scaling the data |
par_selection.method |
vst |
Method for choosing the top variable features |
par_nfeatures |
2500 |
Number of features to select as top variable features |
Step 3: Quality control and filtering
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_seurat_object |
NULL |
If users skipped Steps 1 and 2, the may provide the path to a directory that contains existing Seurat objects to initiate the pipeline at Step 3 |
par_nFeature_RNA_L |
NULL |
Only retain cells expressing a minimum number of genes |
par_nFeature_RNA_U |
NULL |
Only retain cells expressing a maximum number of genes |
par_nCount_RNA_L |
NULL |
Only retain cells with a minimum number of unique transcripts |
par_nCount_RNA_U |
NULL |
Only retain cells with a maximum number of unique transcripts |
par_mitochondria_percent_L |
NULL |
Only retain cells with a minimum percentage of mitochondrial genes |
par_mitochondria_percent_U |
NULL |
Only retain cells with a maximum percentage of mitochondrial genes |
par_ribosomal_percent_L |
NULL |
Only retain cells with a minimum percentage of ribosomal genes |
par_ribosomal_percent_U |
NULL |
Only retain cells with a maximum percentage of ribosomal genes |
par_remove_mitochondrial_genes |
Yes |
Whether or not to remove mitochondrial genes |
par_remove_ribosomal_genes |
Yes |
Whether or not to remove ribosomal genes |
par_remove_genes |
NULL |
If users want to remove specific genes from their data, they may define a list of gene identifiers |
par_regress_cell_cycle_genes |
Yes |
Whether or not to regress cell cycle genes |
par_normalization.method |
LogNormalize |
Method to use for normalization |
par_scale.factor |
10000 |
Scale factor for scaling the data |
par_selection.method |
vst |
Method for choosing the top variable features |
par_nfeatures |
2500 |
Number of features to select as top variable features |
par_top |
10 |
Number of most variable features to be reported in the csv file |
par_npcs_pca |
30 |
Total Number of principal components to compute and store for principal component analysis (PCA) |
Step 4: Doublet removal
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_normalization.method |
CLR |
Method for normalizing the HTO assay |
par_scale.factor |
1000 |
Scale factor for scaling the HTO assay |
par_selection.method |
vst |
Method for selecting the most variable features in the HTO assay |
par_nfeatures |
5 |
Number of features to select as top variable features for the HTO assay. This value is dependent on the number of sample specific barcodes used in the experiment |
par_dims_umap |
5 |
Number of dimensions to use as input features for uniform manifold approximation and projection (UMAP) of HTO assay |
par_n.neighbor |
65 |
Number of neighboring points to use in local approximations of manifold structure |
par_dimensionality_reduction |
Yes |
Whether or not to perform linear dimensionality reduction on the HTO assay |
par_npcs_pca |
30 |
Total Number of principal components to compute and store for principal component analysis (PCA) of HTO assay |
par_dropDN |
Yes |
Whether or not to remove predicted doublets and negatives from downstream analyses |
par_label_dropDN |
Doublet, Negative |
Labels used to identify doublet and negative droplets |
par_quantile |
0.9 |
The quantile to use for droplet classification using MULTIseqDemux |
par_autoThresh |
TRUE |
Whether or not to perform automated threshold finding to define the best quantile for droplet classification using MULTIseqDemux |
par_maxiter |
5 |
Maximum number of iterations to use if autoThresh = TRUE |
par_RidgePlot_ncol |
3 |
Number of columns used to display RidgePlots, which visualizes the enrichment of barcode labels across samples |
par_old_antibody_label |
NULL |
If you wish to rename the barcode labels, first list the existing barcode labels in this parameter. old antibody labels can be identified in the "_old_antibody_label_MULTIseqDemuxHTOcounts" file produced by running Step 4 msd |
par_new_antibody_label |
NULL |
If you wish to rename the barcode labels, list the new labels corresponding to the old labels listed in the parameter above |
Step 5: Integration and linear dimensional reduction
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_skip_integration |
No |
Whether or not to skip integration. This is applicable for experiments that comprises of only one sequencing run. |
par_FindIntegrationAnchors_dim |
25 |
Which dimensions to use from the canonical correlation analysis (CCA) to specify the neighbor search space |
par_DefaultAssay |
RNA |
The assay to perform normalization, scaling, and linear dimensiona reduction on. For most use cases this will be RNA. |
par_normalization.method |
LogNormalize |
Method to use for normalization |
par_scale.factor |
1000 |
Scale factor for scaling the data |
par_selection.method |
vst |
Method for detecting top variable features |
par_nfeatures |
2500 |
Number of features to select as top variable features |
par_RunUMAP_n.neighbors |
65 |
Number of neighboring points used in local approximations of manifold structure |
par_RunPCA_npcs |
30 |
Total Number of principal components to compute and store for principal component analysis (PCA) |
par_RunUMAP_dims |
10 |
Number of dimensions to use as input features for uniform manifold approximation and projection (UMAP) |
par_compute_jackstraw |
No |
Whether or not to perform JackStraw computation. This computation takes a long time. |
Step 6: Clustering
Parameter |
Default |
Description |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_skip_integration |
No |
Whether or not the user skipped integration in Step 5 |
par_FindNeighbors_dims |
30 |
Number of dimensions from linear dimensional reduction used as input to identify neighbours. Can be informed by the elbow and Jackstraw plots produced in Step 5 |
par_RunUMAP_dims |
10 |
Number of dimensions to use as input features for uniform manifold approximation and projection (UMAP) |
par_FindNeighbors_k.param |
60 |
Defines k for the k-nearest neighbor algorithm |
par_FindNeighbors_prune.SNN |
1/15 |
Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the shared nearest-neighbour (SNN) construction |
par_FindClusters_resolution |
0, 0.05, 0.2, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0 |
Value of the clustering resolution parameter. You may provide multiple resolution values |
par_compute_ARI |
Yes |
Whether or not you want to compute the Adjusted Rand Index (ARI) between clusters at a given clustering resolution |
par_RI_reps |
100 |
Number of iterations for clustering the data at a given resolution in order to calculate the ARI |
Step 7: Cluster annotation
Annotation Method |
Parameter |
Default |
Description |
Method 1 |
par_run_find_marker |
Yes |
Whether or not to find marker genes for each cluster |
Method 1 |
par_run_enrichR |
Yes |
Whether or not to run gene set enrichment analysis (GSEA) on the marker genes for each cluster using the EnrichR tools. Note that the HPC must have access to the internet to run GSEA. |
Method 2 |
par_run_module_score |
Yes |
Whether or not to compute module score |
Method 3 |
par_run_reference |
Yes |
Whether or not to perform reference-based annotation |
Visualize features |
par_run_visualize_markers |
Yes |
Whether or not to Visualize select features |
Annotate |
par_run_annotate |
Yes |
Whether or not to Annotate |
General |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
General |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
General |
par_level_cluster |
integrated_snn_res.0.7 |
The cluster resolution that you want to use for downstream analyses. If you skipped integration in Step 5, use par_level_cluster='RNA_snn_res.0.7', if you want to proceed with a clustering resolution of 0.7 |
Method 1 |
par_top_sel |
5 |
Number of top markers to identify based on avg_log2FC |
Method 1 |
par_db |
Descartes_Cell_Types_and_Tissue_2021, CellMarker_Augmented_2021, Azimuth_Cell_Types_2021 |
Character vector of EnrichR databases that define cell types. The top marker genes for each cluster will be tested for enrichment across these databases. |
Method 2 |
par_module_score |
NULL |
Path defining the location of the directory that contains the csv file of the gene sets used to compute the module score |
Method 3 |
par_reference |
NULL |
Path defining the location of the reference Seurat object |
Method 3 |
par_reference_name |
Reference |
An arbitrary name for the reference object. This will be used to name the metadata slot. |
Method 3 |
par_level_celltype |
NULL |
The name of the metadata column in the reference Seurat object that defines cell types |
Method 3 |
par_FindTransferAnchors_dim |
10 |
Number of dimensions from linear dimensional reduction used to find transfer anchors between the reference and query Seurat objects |
Method 3 |
par_futureglobalsmaxSize |
50000 * 1024^2 |
This will increase your RAM usage so set this number mindfully |
Visualize features |
par_select_features_list |
NULL |
A list of features to visualize |
Visualize features |
par_select_features_csv |
NULL |
If you want to define multiple lists of features to visualize, you can do so with a csv file. The header should contain the list names and all features belonging to the same list should be in the same column. |
Annotate |
par_annotate_resolution |
NULL |
Which clustering resolution you want to annotate |
Annotate |
par_name_metadata |
clustering_label_1 |
The name of the metadata slot that will contain the annotations |
Annotate |
par_annotate_labels |
NULL |
A list of cluster labels. There must as many labels as clusters at the defined clustering resolution. Please refrain from using "_" when annotating. |
Step 8: Differential gene expression contrasts
Parameter |
Default |
Description (the cluster annotation method associated with the parameter is shown) |
par_run_add_metadata |
Yes |
Whether or not to add metadata to the Seurat object to facilitate differential gene expression contrasts. |
par_run_sample_sample_wilcoxon |
Yes |
Whether or to perform DGE contrasts between samples across all cells using the Wilcoxon method. |
par_run_sample_cell_wilcoxon |
Yes |
Whether or to perform DGE contrasts between samples stratified by cell type using the Wilcoxon method. |
par_run_pseudo_bulk |
Yes |
Whether or not to perform pseudo-bulk analysis |
par_save_RNA |
No |
Whether or not to export an RNA expression matrix |
par_save_metadata |
No |
Whether or not to export a metadata dataframe |
par_metadata |
NULL |
Path to a csv file defining new metadata that should be added to the Seurat object to facilitate DEG analysis. At least one column should contain "MULTI_ID_Lables". |
Differential Gene Expression Contrast Matrices
Sample-sample contrasts
To perform sample-sample contrasts, users must fill in the step8_contrast_genotype.txt
file located in ~/working_directory/job_info/parameters
. The sample-sample contrasts matrix contains the following columns:
- contast_name: An abritrary name for the contrast
- meta_data_variable: The metadata slot containing the Sample IDs defined in group1 and group2
- group1: A list of sample IDs to be contrasted against the sample IDs listed in group2
- group2:A list of sample IDs to be contrasted against the sample IDs listed in group1
Multiple contrasts can be defined in the same file. In addition, multiple samples can be listed under group1 and group 2. For example:
contrast_name meta_data_variable group1 group2
design1 orig.ident Control1,Control2,Control3 Case1,Case2,Case3
design2 orig.ident Control1 Case1,Case2,Case3
design3 DiseaseStatus HC Case
Sample-cell contrasts
To perform sample-cell contrasts, users must fill in the step8_contrast_celltype.txt
file located in ~/working_directory/job_info/parameters
. The sample-cell contrasts matrix contains the following columns:
- contast_name: An abritrary name for the contrast
- meta_data_celltype: The metadata slot containing the cell type annotations
- cell_type: The cell type used for differential gene expression
- meta_data_variable: The metadata slot containing the Sample IDs defined in group1 and group2
- group1: A list of sample IDs to be contrasted against the sample IDs listed in group2
- group2:A list of sample IDs to be contrasted against the sample IDs listed in group1
Multiple contrasts can be defined in the same file. In addition, multiple samples can be listed under group1 and group 2. For example:
contrast_name meta_data_celltype cell_type meta_data_variable group1 group2
design1 clustering_1 Oligodendrocytes orig.ident Control1,Control2,Control3 Case1,Case2,Case3
design1 clustering_1 Oligodendrocytes orig.ident Control1 Case1,Case2,Case3
design2 clustering_2 Microglia DiseaseStatus HC PD
Pseudo-bulk contrasts
To perform pseudo-bulk contrasts, users must fill in the step8_contrast_pseudo_bulk.txt
file located in ~/working_directory/job_info/parameters
. The default pseudo-bulk contrasts matrix contains the following columns:
- ContrastName: An abritrary name for the contrast
- CellType: The metadata slot containing the cell type annotations. Pseudo-bulk DGE analysis will be performed on all cell types defined.
- MainContrast: The metadata slot defining the main variables for the contrast (e.g. Case or Control)
- SampleID: The metadata slot containing the sample IDs.
Only one contrast can can be defined in the same file. Pseudo-bulk analysis will not work without >1 Sample for each group defined in the MainContrast. For example:
ContrastName CellType MainContrast SampleID
Pseudo_design1 clustering_1 DiseaseStatus orig.ident