Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. 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.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
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- Zhou, Yi-Hui: Pathway analysis for RNA-Seq data using a score-based approach (2016)
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- Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
- Liu, Fangfang; Wang, Chong; Liu, Peng: A semi-parametric Bayesian approach for differential expression analysis of RNA-seq data (2015)
- Nguyen, Yet; Nettleton, Dan; Liu, Haibo; Tuggle, Christopher K.: Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates (2015)