Level 1: Gene Sets
ReactomeGSA 31
ReactomeGSA is an explanation extraction tool for comparative pathway-based gene set analysis. ReactomeGSA defines its gene sets from the pathways curated in the Reactome5 database, then conducts a comparative gene set analysis at a pathway level to explain and biologically ground the differences between omics datasets, making it a quintessential explanation extraction tool with some phenotype prediction applications.
ReactomeGSA performs a differential expression analysis on a pathway scale for five quantitative omics data types, including microarray intensities, transcriptomics counts (raw or normalized), proteomics (spectral counts or intensity based quantitative data). ReactomeGSA is also capable of analyzing single-cell RNAseq (scRNAseq) datasets by calculating the mean expression for genes in a cluster and using this as ‘pseudo-bulk’ RNAseq to describe the cluster. For the analysis the user selects an appropriate methodology depending on their datatype and computational capacity. ReactomeGSA currently accommodates three gene set analysis methodologies, PADOG32, Camera33, and ssGSEA via GSVA34. The results of the analysis are mapped to the complete pathway browser database, where the user can view the pathway-level enrichment scores in the hierarchical ‘tree-view’ which also descending into individual pathways to view the differential gene expression values mapped to the corresponding genes in each pathway.
To demonstrate the clinical applications of ReactomeGSA the authors conducted a comparative pathway analysis of tumor induced plasmablast-like B-cell (TIPB) signaling across five TCGA cancer cohorts. These included melanoma, breast cancer, ovarian cancer, lung adenocarcinoma, and lung squamous cell carcinoma. The authors compared TIPB-high vs -low in each cohort, in addition to some cross cohort comparisons. They found that pathway-based gene sets describing B-cell receptor signaling and apoptosis were enriched for TIPB-high melanoma and ovarian cancer samples, which they later correlated with improved survival in these groups. When compared to melanoma, lung adenocarcinoma samples with high TIPB retained a unique signaling phenotype. These samples exhibited downregulation of the pathway-based gene sets describing B-cell receptor signaling, NF-kB signaling, p53 associated DNA damage repair, cell cycle, and apoptosis.