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.