Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.
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.
School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Sora
S
Yoon
Medical Genomics Research Center, Korea Research Institute of Bioscience & Biotechnology, Daejeon, Republic of Korea.|Department of Bioinformatics, University of Science and Technology, Daejeon, Republic of Korea.
Seon-Young
SY
Kim
School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.|Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Dougu
D
Nam
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods*; D059467:Transcriptome