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.
Perspectives
Pathway analysis has become an essential part of any Omics-based study, in no small part due to the early contribution of GSEA emerging as one of the leading tools in the field, originally designed for analysis of microarray data two decades ago. This method offers a variety of options, but guidelines on which ones to select are missing. In particular, we noticed a pressing need to develop rigorous benchmarks and provide practical guidelines for the use of GSEA in the context of RNA-Seq data. By filling that gap, we hope that our work will be found useful by practitioners and will also stimulate further efforts to advance pathway analysis methods.
Julián Candia
National Institute on Aging, USA
Read the Original
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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