sc-ssGSEA 1.0.0

A Python package for performing gene set enrichment analysis on single cell clusters.

NOTE: The method for this module is still under active development, not finalized, and should not be used.

scGSEA

Description: scGSEA is an extension of ssGSEA designed to improve the assessment of pathway activity in single-cell data by addressing sparsity and ensuring stable enrichment scoring. This module is intended to be used subsequent to Seurat.Clustering module and the user can supply the Seurat RDS file.

Authors: John Jun; UCSD - Mesirov Lab, UCSD

Contact: Forum Link.

Summary

scGSEA is an extension of ssGSEA tailored for single-cell data analysis. It addresses the challenges of sparsity and unreliable enrichment scoring by employing specialized normalization methods and scoring metrics. By utilizing scGSEA, scientists can explore and interpret pathway activity and functional alterations within heterogeneous populations of cells, thereby advancing our understanding of complex biological systems.

Parameters

Name	Description	Default Value
input_file *	File to be read in RDS format
chip_file	Chip file used for conversion to gene symbols
gene_set_database_file *	Gene set data in GMT format
output_file_name *	The basename to use for output file	scGSEA_scores

* required

Input Files

1. input_file This is the Seurat RDS file from the Seurat.Clustering module.
2. chip_file
   This parameter’s drop-down allows you to select CHIP files from the Molecular Signatures Database (MSigDB) on the GSEA website. This drop-down provides access to only the most current version of MSigDB. You can also upload your own gene set file(s) in CHIP format.
3. gene_set_database_file
   • This parameter’s drop-down allows you to select gene sets from the Molecular Signatures Database (MSigDB) on the GSEA website. This drop-down provides access to only the most current version of MSigDB. You can also upload your own gene set file(s) in GMT format.
   • If you want to use files from an earlier version of MSigDB you will need to download them from the archived releases on the website.
4. output_file_name
   The prefix used for the name of the output GCT and CSV file. If unspecified, output prefix will be set to scGSEA_scores. The output CSV and GCT files will contain the projection of input dataset onto a space of gene set enrichments scores.
5. cluster_data_label
   The name of the metadata label within the input Seurat object. This label will be used to access the annotations utilized for aggregating cells. The default value for this parameter is seurat_clusters, which is the metadata label for cluster annotations generated upon running Seurat.Clustering module. Use the default value when using the RDS file generated from the Seurat.Clustering module.

Output Files

1. <output_file_name>.csv
   This is a gene set by cell cluster data consisted of scGSEA scores.
2. <output_file_name>.gct
   This is a gene set by cell cluster data consisted of scGSEA scores. The HeatmapViewer module can accept this file as input for generating heatmap visualizations.
3. cluster_expression.csv
   This is a gene by cell cluster data consisted of normalized gene expression level.
4. stdout.txt
   This is standard output from the script.

For more details, please refer to the full documentation.