voom: precision weights unlock linear model analysis tools for RNA-seq read counts

TLDR

The study introduces new linear modeling strategies for analyzing RNA‑seq read counts. The voom method estimates the mean‑variance relationship of log‑counts, assigns precision weights to each observation, and feeds them into the limma empirical‑Bayes analysis pipeline. Voom enables RNA‑seq analysts to use extensive microarray methodology, performs as well or better than count‑based methods in simulations, and is demonstrated in two case studies.

Abstract

Abstract New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.