Differential analysis of count data – the DESeq2

A basic task in the analysis of count data from RNA-Seq is the detection of differentially expressed genes. The count data are presented as a table which reports, for each sample, the number of sequence fragments that have been as-signed to each gene. Analogous data also arise for other assay types, including comparative ChIP-Seq, HiC, shRNA screening, mass spectrometry. An impor-tant analysis question is the quantification and statistical inference of systematic changes between conditions, as compared to within-condition variability. The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models; the estimates of dispersion and log-arithmic fold changes incorporate data-driven prior distributions 1. This vignette explains the use of the package and demonstrates typical work flows. 1Other Bioconductor packages with similar aims are edgeR, baySeq and DSS