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 17 articles )

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  1. Martin, Bryan D.; Witten, Daniela; Willis, Amy D.: Modeling microbial abundances and dysbiosis with beta-binomial regression (2020)
  2. S. Thomas Kelly; Michael A. Black: graphsim: An R package for simulating gene expression data from graph structures of biological pathways (2020) not zbMATH
  3. Wilson, Douglas R.; Jin, Chong; Ibrahim, Joseph G.; Sun, Wei: ICeD-t provides accurate estimates of immune cell abundance in tumor samples by allowing for aberrant gene expression patterns (2020)
  4. Jia, Gaoxiang; Wang, Xinlei; Li, Qiwei; Lu, Wei; Tang, Ximing; Wistuba, Ignacio; Xie, Yang: RCRnorm: an integrated system of random-coefficient hierarchical regression models for normalizing nanostring nCounter data (2019)
  5. Liang, Kun: Empirical Bayes analysis of RNA sequencing experiments with auxiliary information (2019)
  6. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  7. Xia, Yinglin; Sun, Jun; Chen, Ding-Geng: Statistical analysis of microbiome data with R (2018)
  8. Gunady, Mohamed K.; Cornwell, Steffen; Mount, Stephen M.; Bravo, Héctor Corrada: Yanagi: transcript segment library construction for RNA-seq quantification (2017)
  9. 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)
  10. Bonafede, Elisabetta; Picard, Franck; Robin, Stéphane; Viroli, Cinzia: Modeling overdispersion heterogeneity in differential expression analysis using mixtures (2016)
  11. 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)
  12. Ruddy, Sean; Johnson, Marla; Purdom, Elizabeth: Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (2016)
  13. Zhou, Yi-Hui: Pathway analysis for RNA-seq data using a score-based approach (2016)
  14. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  15. Lithio, Andrew; Nettleton, Dan: Hierarchical modeling and differential expression analysis for RNA-seq experiments with inbred and hybrid genotypes (2015)
  16. Liu, Fangfang; Wang, Chong; Liu, Peng: A semi-parametric Bayesian approach for differential expression analysis of RNA-seq data (2015)
  17. 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)