voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Top Cited Papers
Open Access
- 1 January 2014
- journal article
- Published by Springer Science and Business Media LLC in Genome Biology
- Vol. 15 (2), R29
- https://doi.org/10.1186/gb-2014-15-2-r29
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.Keywords
This publication has 52 references indexed in Scilit:
- Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per geneGenome Biology, 2013
- Landscape of transcription in human cellsNature, 2012
- The developmental transcriptome of Drosophila melanogasterNature, 2010
- Understanding mechanisms underlying human gene expression variation with RNA sequencingNature, 2010
- RNA-Seq: a revolutionary tool for transcriptomicsNature Reviews Genetics, 2009
- Mapping and quantifying mammalian transcriptomes by RNA-SeqNature Methods, 2008
- The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurementsNature Biotechnology, 2006
- Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profilesProceedings of the National Academy of Sciences of the United States of America, 2005
- X-inactivation profile reveals extensive variability in X-linked gene expression in femalesNature, 2005
- The male-specific region of the human Y chromosome is a mosaic of discrete sequence classesNature, 2003