Differential analysis of count data – the DESeq2 package

A basic task in the analysis of count data from RNA-Seq is the detection of di↵erentially expressed genes. The count data are presented as a table which reports, for each sample, the number of sequence fragments that have been assigned to each gene. Analogous data also arise for other assay types, including comparative ChIP-Seq, HiC, shRNA screening, mass spectrometry. An important 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 di↵erential expression by use of negative binomial generalized linear models; the estimates of dispersion and logarithmic fold changes incorporate data-driven prior distributions 1 . This vignette explains the use of the package and demonstrates typical work flows. Another vignette, “Beginner’s guide to using the DESeq2 package”, covers similar material but at a slower pace, including the generation of count tables from FASTQ files.

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