Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package

Nucleic Acids Res. 2015 Dec 2;43(21):e140. doi: 10.1093/nar/gkv711. Epub 2015 Jul 16.


As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Line
  • Data Interpretation, Statistical
  • Gene Expression Profiling / standards*
  • Humans
  • Male
  • Prostatic Neoplasms / genetics
  • Quality Control
  • Sequence Analysis, RNA / standards*
  • Software*