What is it about?
Pathway enrichment analysis helps researchers gain biological insight into gene lists generated from genome-scale (omics) experiments. Gene Set Enrichment Analysis (GSEA) is a trailblazing method originally designed for analysis of microarray data, but nowadays widely used with other types of experimental data, particularly RNA-seq. Despite its broad adoption, modeling choices within GSEA, such as gene-set vs phenotype permutations (used to assess the statistical significance of a pathway’s enrichment) and weight parameters available for the Kolmogorov-Smirnov enrichment statistic, remain poorly understood. In this work, we quantitatively assess different GSEA modeling choices using curated RNA-seq-based benchmarks, thus filling the existing gap in current guidelines for this long established and popular pathway analysis tool.
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Why is it important?
Despite GSEA arguably remaining the most highly cited and widely used pathway analysis method, different parameter choices (e.g. enrichment statistic) and modalities (e.g gene-set vs phenotype permutations) had not been thoroughly assessed in the context of RNA-Seq input data. This work aims to fill this gap.
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This page is a summary of: Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks, PLoS ONE, May 2024, PLOS,
DOI: 10.1371/journal.pone.0302696.
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