voom {limma}R Documentation

Transform RNA-Seq Data Ready for Linear Modelling


Transform count data to log2-counts per million (logCPM), estimate the mean-variance relationship and use this to compute appropriate observation-level weights. The data are then ready for linear modelling.


voom(counts, design = NULL, lib.size = NULL, normalize.method = "none",
     span = 0.5, plot = FALSE, save.plot = FALSE, ...)



a numeric matrix containing raw counts, or an ExpressionSet containing raw counts, or a DGEList object. Counts must be non-negative and NAs are not permitted.


design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates.


numeric vector containing total library sizes for each sample. If NULL and counts is a DGEList then, the normalized library sizes are taken from counts. Otherwise library sizes are calculated from the columnwise counts totals.


normalization method to be applied to the logCPM values. Choices are as for the method argument of normalizeBetweenArrays when the data is single-channel.


width of the lowess smoothing window as a proportion.


logical, should a plot of the mean-variance trend be displayed?


logical, should the coordinates and line of the plot be saved in the output?


other arguments are passed to lmFit.


This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.

voom is an acronym for mean-variance modelling at the observational level. The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation. Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend. This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance. The weights are then used in the linear modelling process to adjust for heteroscedasticity.

voom performs the following specific calculations. First, the counts are converted to logCPM values, adding 0.5 to all the counts to avoid taking the logarithm of zero. The matrix of logCPM values is then optionally normalized. The lmFit function is used to fit row-wise linear models. The lowess function is then used to fit a trend to the square-root-standard-deviations as a function of average logCPM. The trend line is then used to predict the variance of each logCPM value as a function of its fitted value, and the inverse variances become the estimated precision weights.


An EList object with the following components:


numeric matrix of normalized expression values on the log2 scale


numeric matrix of inverse variance weights


design matrix


numeric vector of total normalized library sizes


dataframe of gene annotation extracted from counts


if save.plot, list containing x and y coordinates for points in mean-variance plot


if save.plot, list containing coordinates of loess line in the mean-variance plot


Charity Law and Gordon Smyth


Law, CW, Chen, Y, Shi, W, Smyth, GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29. http://genomebiology.com/2014/15/2/R29

See Also

voomWithQualityWeights. vooma is similar to voom but for microarrays instead of RNA-seq.

A summary of functions for RNA-seq analysis is given in 11.RNAseq.

[Package limma version 3.34.5 Index]