Cited 11 time in
- Title
- Improving gene-set enrichment analysis of RNA-seq data with small replicates
- Author(s)
- S Yoon; Seon-Young Kim; D Nam
- Bibliographic Citation
- PLoS One, vol. 11, no. 11, pp. e0165919-e0165919
- Publication Year
- 2016
- 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.
- ISSN
- 1932-6203
- Publisher
- Public Library of Science
- DOI
- http://dx.doi.org/10.1371/journal.pone.0165919
- Type
- Article
- Appears in Collections:
- 1. Journal Articles > Journal Articles
- Files in This Item:
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