2014
DOI: 10.1186/gb-2014-15-2-r29
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voom: precision weights unlock linear model analysis tools for RNA-seq read counts

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 gene… Show more

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Cited by 4,775 publications
(4,310 citation statements)
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References 57 publications
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“…We estimated expression using STAR alignments in transcriptomic coordinates and the RSEM package 52 , and calculated differential expression using the limma package following voom transformation of the estimated counts 53,54 . Raw read counts for genes with counts per million (cpm) value >1 in at least three samples (15,729 genes) were used as inputs to tbe limma/voom pipeline and scale normalization of the RNA-seq read counts was performed using the TMM normalization method 55 .…”
Section: Methodsmentioning
confidence: 99%
“…We estimated expression using STAR alignments in transcriptomic coordinates and the RSEM package 52 , and calculated differential expression using the limma package following voom transformation of the estimated counts 53,54 . Raw read counts for genes with counts per million (cpm) value >1 in at least three samples (15,729 genes) were used as inputs to tbe limma/voom pipeline and scale normalization of the RNA-seq read counts was performed using the TMM normalization method 55 .…”
Section: Methodsmentioning
confidence: 99%
“…Counts were converted to log2 counts per million, quantile normalized and precision weighted with the voom function of the limma package. 52,53 A linear model was fitted to each gene, and empirical Bayes moderated t-statistics were used to assess differences in expression. 54 A false discovery rate cut-off of 0.15 was applied for calling differentially expressed genes.…”
Section: Methodsmentioning
confidence: 99%
“…The bioconductor package Limma was used for analysis of RNA-seq data (39,40). RNA-seq counts for 10 conditions (control þ 2 doses þ 2 timepoints for 2 cell lines) were preprocessed using the voom function (41), and linear models fit using the lmFit function. Specifically, a full model was fit (expression $ 0 þ Treatment.group þ cellline.group þ dose.group þ timepoint) and the coefficients extracted for the Treatment.group effect.…”
Section: Differential Gene Expression Analysismentioning
confidence: 99%