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.

References in zbMATH (referenced in 9 articles )

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  1. Lun, Aaron T.L.; Smyth, Gordon K.: No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data (2017)
  2. Bonafede, Elisabetta; Picard, Franck; Robin, Stéphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  3. Phipson, Belinda; Lee, Stanley; Majewski, Ian J.; Alexander, Warren S.; Smyth, Gordon K.: Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression (2016)
  4. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  5. Zhou, Yi-Hui: Pathway analysis for RNA-Seq data using a score-based approach (2016)
  6. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  7. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  8. Liu, Fangfang; Wang, Chong; Liu, Peng: A semi-parametric Bayesian approach for differential expression analysis of RNA-seq data (2015)
  9. 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)