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[…] n-trivial mean-variance relationship, which commonly characterize such data. A simple, albeit imperfect, approach to ameliorate these effects is the application of a logarithmic transformation, making sure that zero counts are appropriately handled in order to avoid infinities. Two further options are power transformations, such as the Box-Cox transformation (), and rank-based transformations, such as the Blom transformation (), both of which aim to make the data more normal-like. While the aforementioned transformations are not specific to RNA-seq, variance-stabilizing approaches that explicitly model the mean-variance relationship in such data are provided by specialized software, such as DESeq2 (functions rlog and vst) () and limma (function voom) (). The practical advantage of using appropriate data transformations is the immediate availability of analytical methods, which (being built around the assumption of normally distributed data) are powered by a tractable mathematical theory., An alternative approach is to develop statistical methods that specifically address the discrete nature of digital expression data (; ). Explicitly modeling counts has been quite popular in the study of differential gene expression, and several methods have been developed for this purpose based on Poisson, Binomial and, especially, Negative Binomial distributions (; ; ). These models directly address non-normality (particularly at small count numb […]

Pipeline specifications

Software tools DESeq2, limma, voom