GeneSetCluster: a tool for summarizing and integrating gene-set analysis results
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GeneSetCluster: a tool for summarizing and integrating gene‑set analysis results Ewoud Ewing1* , Nuria Planell‑Picola2, Maja Jagodic1 and David Gomez‑Cabrero2,3 *Correspondence: [email protected] 1 Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden Full list of author information is available at the end of the article
Abstract Background: Gene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are overrepresented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNAsequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biologi‑ cal insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights. Results: We present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identi‑ fies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations. Conclusions: GeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://githu b.com/TranslationalBioinformaticsUnit Keywords: Data-mining, Gene-set enrichment, Clustering pathways, Overlapping pathways, Clustering gene-sets
Background Modern gene-set analysis (GSA) [1] are standard tools aimed to provide biological insights derived from the list of genes associated with a trait of interest. Tools such as Ingenuity Pathway Analysis (IPA) [2], GREAT [3], GSEA [4], among others, make use of curated collections of gene-sets such as Gene Ontology [5] or KEGG [6] to identify those relevant (statistically significant) gene-sets associated with the trait of interest. © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com
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