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

Abstract

As the use of RNA-seq has popularized, there is an in-creasing consciousness of the importance of experi-mental design, bias removal, accurate quantification and control of false positives for proper data analy-sis. 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 mon-itor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false dis-coveries 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 differen-tial expression