voom(counts, design = NULL, lib.size = NULL, normalize.method = "none", plot = FALSE, span=0.5, ...)matrix containing raw counts, or an ExpressionSet containing raw counts, or a DGEList object.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.method argument of normalizeBetweenArrays when the data is single-channel.logical, should a plot of the mean-variance trend be displayed?lmFit.EList object with the following components:
countsvoom 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.
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
voom case study is given in the User's Guide.vooma is a similar function but for microarrays instead of RNA-seq.