Easy and efficient ensemble gene set testing with EGSEA.
AuthorAlhamdoosh, M; Law, CW; Tian, L; Sheridan, JM; Ng, M; Ritchie, ME
PublisherF1000 Research Ltd
University of Melbourne Author/sLaw, Charity; Sheridan, Julie; Ritchie, Matthew; Tian, Luyi; Ritchie, Matthew
AffiliationMedical Biology (W.E.H.I.)
School of Mathematics and Statistics
Document TypeJournal Article
CitationsAlhamdoosh, M., Law, C. W., Tian, L., Sheridan, J. M., Ng, M. & Ritchie, M. E. (2017). Easy and efficient ensemble gene set testing with EGSEA.. F1000Res, 6, pp.2010-. https://doi.org/10.12688/f1000research.12544.1.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5747338
Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.
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