Improving gene-set enrichment analysis of RNA-seq data with small replicates

Cited 11 time in scopus
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dc.contributor.authorS Yoon-
dc.contributor.authorSeon-Young Kim-
dc.contributor.authorD Nam-
dc.description.abstractDeregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correlation in each gene-set. We demonstrate that incorporating the absolute gene statistic in one-tailed GSEA considerably improves the false-positive control and the overall discriminatory ability of the gene-permuting GSEA methods for RNA-seq data. To test the performance, a simulation method to generate correlated read counts within a gene-set was newly developed, and a dozen of currently available RNA-seq enrichment analysis methods were compared, where the proposed methods outperformed others that do not account for the inter-gene correlation. Analysis of real RNA-seq data also supported the proposed methods in terms of false positive control, ranks of true positives and biological relevance. An efficient R package (AbsFilterG- SEA) coded with C++ (Rcpp) is available from CRAN.-
dc.publisherPublic Library of Science-
dc.titleImproving gene-set enrichment analysis of RNA-seq data with small replicates-
dc.title.alternativeImproving gene-set enrichment analysis of RNA-seq data with small replicates-
dc.citation.titlePLoS One-
dc.contributor.affiliatedAuthorSeon-Young Kim-
dc.identifier.bibliographicCitationPLoS One, vol. 11, no. 11, pp. e0165919-e0165919-
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