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

Charity W Law12, Yunshun Chen12, Wei Shi13 and Gordon K Smyth14*

Author Affiliations

1 Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia

2 Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia

3 Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria 3010, Australia

4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia

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Genome Biology 2014, 15:R29  doi:10.1186/gb-2014-15-2-r29

Published: 3 February 2014

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.