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Nam, Dougu
Statistical Genomics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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

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Title
Improving gene-set enrichment analysis of RNA-Seq data with small replicates
Author
Yoon, SoraKim, Seon-YoungNam, Dougu
Issue Date
201611
Publisher
PUBLIC LIBRARY SCIENCE
Citation
PLOS ONE, v.11, no.11, pp.e0165919 -
Abstract
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 (AbsFilterG- SEA) coded with C++ (Rcpp) is available from CRAN.
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DOI
http://dx.doi.org/10.1371/journal.pone.0165919
ISSN
1932-6203
Appears in Collections:
SLS_Journal Papers
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