Combining multiple tools outperforms individual methods in gene set enrichment analyses

Abstract

Motivation: Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular dataset. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions. Results: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. EGSEA's gene set database contains around 25 000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse datasets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes. Availability and Implementation: EGSEA is available as an R package at http://www.bioconductor. org/packages/EGSEA/. The gene sets collections are available in the R package EGSEAdata from

Modular and efficient pre-processing of single-cell RNA-seq

“…Spearman and Pearson correlations were computed for the top 15 cluster marker genes. Gene Set Enrichment Analysis(GSEA) was performed on the top 20 cluster marker genes using the R package EGSEA 34 with the KEGG pathway gene sets. SingleR 35 was used to annotate cell types based on correlation profiles with bulk RNA-seq from 36 .…”

STAT3-dependent systems-level analysis revealsPDK4as an independent predictor of biochemical recurrence in prostate cancer

“…p-value ≤ 0.05, Table S1). Gene set testing using the Ensemble Of Gene Set Enrichment Analyses (EGSEA) method (Alhamdoosh et al, 2017) (Methods) showed gene sets directly associated with STAT3 signaling to be downregulated ( Figure S1A-B, Table S1).…”

“…For gene set testing of transcriptomic and proteomic data, the Ensemble Of Gene Set Enrichment Analyses (EGSEA) R package v.1.10.1 (Alhamdoosh et al, 2017), was used. EGSEA allows to use results from up to twelve Gene Set Enrichment (GSE)-algorithms, covering competitive and self-contained methods (Goeman and Bühlmann, 2007), to calculate collective gene set scores.…”

Tumor Evolution Decoder (TED): Unveiling Tumor Evolution Based on Mutation Profiles of Subclones or Single Cells

“…7), in which subclone 4 is an unobserved genome inferred by TED. For SNVs and CNAs on each evolution step, we selected the mutations in the Coding DNA Sequence (CDS), which were labeled as CDS-SNVs and CDS-CNAs, respectively, and then used the Bioconductor package EGSEA 35 to carry out Gene Set Enrichment Analyses (GSEA) for the genes that harbored these mutations. GSEA was done separately for the genes with CDS-SNVs and for the genes with CDS-CNAs.…”