Gene Set Scoring Module

Purpose

This module is based on AUCell, GSVA, IOBR and other R packages to perform multiple method scoring of custom gene set activity in bulk RNA-Seq data expression matrices, and visualize grouped heatmaps.

Supported gene set scoring algorithms:

• ssGSEA: Calculate enrichment scores based on cumulative distribution of gene expression ranking, supports single sample analysis

• GSVA: Kernel density estimation of gene expression distribution, outputs standardized enrichment scores

• PCA: Principal component analysis after gene expression standardization, takes first principal component score

• Z-score: Calculate the mean Z-score of genes within the gene set

• AUCell: Based on gene expression ranking, calculate the area under the curve (AUC) of gene sets in the top 5% highly expressed genes

Input File Examples

• expression Expression matrix file: Each row represents a gene name, each column represents a sample name, values are corresponding expression levels, tab-separated

GeneID	Sample1	Sample2	Sample3
GENE1	1.234	2.345	3.456
GENE2	4.567	5.678	6.789

• clinical Clinical information file: Each row represents a sample name, each column represents a clinical feature, tab-separated

SampleID	tissue_type.samples	age	gender
Sample1	Tumor	45	Male
Sample2	Normal	50	Female
Sample3	Tumor	55	Male

• gene_set Gene set file: Header is gene set name, each row represents one gene name

test_geneset
CD8A
CD8B
GZMA
GZMB

Running Method

SDAS bulkValidate geneSetScore --expression fpkm.txt --gene_set geneset.txt --clinical clinical.txt --group_col tissue_type.samples --group_type discrete --output result_dir

Input Parameter Description

Parameter	Required	Default	Description
--expression	Yes		Expression matrix file path. Tab-separated, rows: gene IDs, columns: sample IDs, values: FPKM/TPM etc., cannot be raw counts, cannot be log
--clinical	Yes		Clinical information file path. Tab-separated, rows: sample IDs, columns: clinical features
--group_col	Yes		Grouping column name (must exist in clinical information file)
--gene_set	Yes		Custom gene set file path. First row is gene set name (e.g. test_geneset) followed by one gene name per line
--output	Yes		Output directory path
--group_type	No	discrete	Grouping type: discrete/continuous, default discrete

• --group_type Grouping Type Description

  • Discrete grouping (discrete): Directly use categorical variables in clinical information for grouping, e.g.: Tumor vs Normal, Stage I vs Stage II vs Stage III

  • Continuous grouping (continuous): Divide continuous variables into three groups by quantiles: Low, Medium, High

  Quantiles: 0%, 30%, 70%, 100%

Output Results Display

Result File	Description
genescore_combine.txt	Combined results of all scoring methods
geneset_score_heatmap.png/pdf	Grouped heatmap of different scoring methods

• Gene set scoring result table: genescore_combine.txt Each row represents one sample, each column represents results of different scoring methods.

  SampleID	test_geneset_AUCell	test_geneset_GSVA	test_geneset_zscore	test_geneset_ssGSEA	test_geneset_PCA
  Sample1	0.123	0.456	9.065	9.065	9.065
  Sample2	0.234	0.567	0.0677	0.0677	0.0677

• Gene set scoring heatmap: geneset_score_heatmap.png/pdf Shows expression scores of specified gene sets for all samples.

Result Interpretation Description

• Gene set scoring result table: genescore_combine.txt

  • ​If using TCGA bulk transcriptome, prioritize GSVA or ssGSEA for interpretation, these two methods have robust results and can support complex pathways

  • If ​you need to preserve gene correlation, prioritize PCA for interpretation

  • If you want ​quick analysis, prioritize Z-score for interpretation