voom: precision weights unlock linear model analysis tools for RNA-seq read counts

By Charity W. Law, Yunshun Chen, Wei Shi, Gordon K. Smyth. Genome Biology, 2014

5,697 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f85a6cb90dbe6f064e37b427a129e3976e87a86d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

Modelling dropouts for feature selection in scRNASeq experiments (2016, methodology): “The ground truth differentially expressed genes were obtained for each dataset using three standard analysis methods: DESeq2 [43], edgeR [44], and limma-voom [45].”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “Both observation-level (2,10,43) and sample-specific weights (11) can be used in an analysis.”
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features (2013, methodology): “, 2010) or limma (Law et al., 2013) that are designed to analyse next-gen read counts.”
Tximeta: Reference sequence checksums for provenance identification in RNA-seq (2019, methodology): “223 The SummarizedExperiment object, se, that is returned by tximeta can then be 224 passed to various downstream packages such as DESeq2, edgeR, limma-voom, or 225 fishpond, with example code in the tximeta package vignette [32, 34–37].”
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline (2016, methodology): “The RNA-seq pipelines of the limma package also offer excellent error rate control [13, 14].”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, methodology): “A key element of sleuth is borrowed from previous work 3,5 , namely shrinkage to stabilize variance estimates from few samples.”
Comprehensive molecular portraits of invasive lobular breast cancer (2015, methodology): “Differential expression analyses on RNA-seq data were performed using the limmavoom package (Law et al., 2014).”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, methodology): “The authors of edgeR [7] and limma-voom [12] suggest using a ﬁltering rule that removes genes with low scaled counts before statistical analysis [14].”
Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, methodology): “The code for the simulations and the plots of the results can be found at (https://github.com/ciaranlevans/rnaSeqAssumptions), and was adapted from the R code used in the simulations of Law et al. [34].”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Other approaches include data transformation methods that take into account the sampling variance of small read counts and create discrete gene expression distributions that can be analyzed by regular linear models [55].”
ACE-2 expression in the small airway epithelia of smokers and COPD patients: implications for COVID-19 (2020, methodology): “Limma voom[11] was applied to normalize the counts to log2 counts per million reads (CPM), which was used in the downstream analysis.”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, positive): “Other methods compared were the voom normalization method followed by linear modeling using the limma package [35] and the SAMseq permutation method of the samr package [23].”