Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.

Charity W Law,Wei Shi,Yunshun Chen,Gordon K Smyth

doi:10.1186/gb-2014-15-2-r29

Charity W Law, Wei Shi + Show 2 more

Open Access

https://doi.org/10.1186/gb-2014-15-2-r29

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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.

Highlights

Gene expression profiling is one of the most commonly used genomic techniques in biological research
The limma pipeline includes linear modeling to analyze complex experiments with multiple treatment factors, quantitative weights to account for variations in precision between different observations, and empirical Bayes statistical methods to borrow strength between genes
Borrowing information between genes is a crucial feature of the genome-wide statistical methods, as it allows for gene-specific variation while still providing reliable inference with small sample sizes

Summary

Introduction

Gene expression profiling is one of the most commonly used genomic techniques in biological research. For most of the past 16 years or more, DNA microarrays have been the premier technology for genome-wide gene expression experiments, and a large body of mature statistical methods and tools has been developed to analyze intensity data from microarrays. This includes methods for differential expression analysis [1,2,3], random effects [4,5], gene set enrichment [6], gene set testing [7,8] and so on. The normal-based empirical Bayes statistical procedures can adapt to different types of datasets and can provide exact type I error rate control even for experiments with a small number of replicate samples [3]

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