R: Transform RNA-Seq Data Ready for Linear Modelling

voom {limma}

R Documentation

Transform RNA-Seq Data Ready for Linear Modelling

Description

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

Usage

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

Arguments

`counts`	a numeric `matrix` containing raw counts, or an `ExpressionSet` containing raw counts, or a `DGEList` object.
`design`	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.
`lib.size`	numeric vector containing total library sizes for each sample. If `NULL` and `counts` is a `DGEList` then, then normalized library sizes are taken from `counts`. Otherwise library sizes are calculated from the columnwise counts totals.
`normalize.method`	normalization method to be applied to the logCPM values. Choices are as for the `method` argument of `normalizeBetweenArrays` when the data is single-channel.
`plot`	`logical`, should a plot of the mean-variance trend be displayed?
`span`	width of the lowess smoothing window as a proportion.
`...`	other arguments are passed to `lmFit`.

Details

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.

In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess. The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag. The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag. Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays. Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation. This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.

Value

An EList object with the following components:

`E`	numeric matrix of normalized expression values on the log2 scale
`weights`	numeric matrix of inverse variance weights
`design`	design matrix
`lib.size`	numeric vector of total normalized library sizes
`genes`	dataframe of gene annotation extracted from `counts`

Author(s)

Charity Law and Gordon Smyth

References

Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.

Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts. Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia. http://www.statsci.org/smyth/pubs/VoomPreprint.pdf