Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.

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PloS one, ISSN: 1932-6203, Vol: 11, Issue: 11, Page: e0165919

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10.1371/journal.pone.0165919; 10.1371/journal.pone.0165919.g003; 10.1371/journal.pone.0165919.g001; 10.1371/journal.pone.0165919.t001; 10.1371/journal.pone.0165919.g002
Sora Yoon; Seon-Young Kim; Dougu Nam; Dongmei Li
Public Library of Science (PLoS); Figshare; PUBLIC LIBRARY SCIENCE
Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Biochemistry; Genetics; Molecular Biology; Cancer; Inorganic Chemistry; Space Science; 59999 Environmental Sciences not elsewhere classified; 69999 Biological Sciences not elsewhere classified; 19999 Mathematical Sciences not elsewhere classified; 80699 Information Systems not elsewhere classified; gene-permuting gsea methods; gene-set enrichment analysis; inter-gene correlation; rna-seq data; small replicates deregulated pathways; gene-permuting gsea method; rna-seq enrichment analysis methods; cran
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Deregulated 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 (AbsFilterGSEA) coded with C++ (Rcpp) is available from CRAN.