Comparative evaluation of gene set analysis approaches for RNA-Seq data.

ABSTRACT:

Background

Over the last few years transcriptome sequencing (RNA-Seq) has almost completely taken over microarrays for high-throughput studies of gene expression. Currently, the most popular use of RNA-Seq is to identify genes which are differentially expressed between two or more conditions. Despite the importance of Gene Set Analysis (GSA) in the interpretation of the results from RNA-Seq experiments, the limitations of GSA methods developed for microarrays in the context of RNA-Seq data are not well understood.

Results

We provide a thorough evaluation of popular multivariate and gene-level self-contained GSA approaches on simulated and real RNA-Seq data. The multivariate approach employs multivariate non-parametric tests combined with popular normalizations for RNA-Seq da

SUBMITTER: Rahmatallah Y 

PROVIDER: S-EPMC4265362 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature