title: RNA-Enrich: a cut-off free functional enrichment testing method for RNA-seq with improved detection power.
abstract: Tests for differential gene expression with RNA-seq data have a tendency to identify certain types of transcripts as significant, e.g. longer and highly-expressed transcripts. This tendency has been shown to bias gene set enrichment (GSE) testing, which is used to find over- or under-represented biological functions in the data. Yet, there remains a surprising lack of tools for GSE testing specific for RNA-seq. We present a new GSE method for RNA-seq data, RNA-Enrich, that accounts for the above tendency empirically by adjusting for average read count per gene. RNA-Enrich is a quick, flexible method and web-based tool, with 16 available gene annotation databases. It does not require a P-value cut-off to define differential expression, and works well even with small sample-sized experiments. We show that adjusting for read counts per gene improves both the type I error rate and detection power of the test.
  
  
  
  RNA-Enrich is available at http://lrpath.ncibi.org or from supplemental material as R code.
  
  
  
  sartorma@umich.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computational Medicine and Bioinformatics and.
authors.forename: Chee
authors.initials: C
authors.lastname: Lee
authors.affiliation: Department of Computational Medicine and Bioinformatics and.
authors.forename: Snehal
authors.initials: S
authors.lastname: Patil
authors.affiliation: Department of Computational Medicine and Bioinformatics and Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Maureen A
authors.initials: MA
authors.lastname: Sartor
mesh_terms: D020869:Gene Expression Profiling; D058977:Molecular Sequence Annotation; D012313:RNA; D017423:Sequence Analysis, RNA; D012984:Software
chemical_list: D012313:RNA