limma: vooma – R documentation

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limma

vooma

Convert Mean-Variance Trend to Observation-specific Precision Weights for Microarray Data

Description

Estimate the mean-variance relationship and use this to compute appropriate observational-level weights.

Usage

vooma(y, design=NULL, correlation, block=NULL, plot=FALSE, span=NULL)
voomaByGroup(y, group, design=NULL, correlation, block=NULL,
             plot=FALSE, span=NULL, col=NULL, lwd=1, alpha=0.5,
             pch=16, cex=0.3, legend="topright")

Arguments

`y`	a numeric `matrix`, `EList` object, or any object containing log-expression data that can be coerced to a matrix.
`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.
`block`	vector or factor specifying a blocking variable on the arrays. Has length equal to the number of arrays.
`correlation`	intra-block correlation
`span`	width of the smoothing window, as a proportion of the data set.
`plot`	`logical` value indicating whether a plot of mean-variance trend should be displayed.
`group`	categorical vector or factor giving group membership of columns of `y`.
`col`	vector of colors for plotting group trends
`lwd`	line width for plotting group trends
`pch`	plotting character. Default is integer code 16 which gives a solid circle. If a vector, then should be of length `nrow(y)`.
`cex`	numeric vector of plot symbol expansions. If a vector, then should be of length equal to number of groups.
`alpha`	transparancy of points, on scale from `0` for fully transparant to `1` for fully opaque.
`legend`	character string giving position to place legend.

Details

vooma is an acronym for mean-variance modelling at the observational level for arrays.

vooma estimates the mean-variance relationship in the data, and uses this to compute appropriate weights for each observation. This done by estimating a mean-variance trend, then interpolating this trend to obtain a precision weight (inverse variance) for each observation. The weights can then used by other functions such as lmFit to adjust for heteroscedasticity.

voomaByGroup estimates precision weights separately for each group. In other words, it allows for different mean-variance curves in different groups.

Value

An EList object with the following components:

`E`	numeric matrix of as input
`weights`	numeric matrix of weights
`design`	numeric matrix of experimental design
`genes`	dataframe of gene annotation, only if `counts` was a `DGEList` object

Author(s)

Charity Law and Gordon Smyth

References

Law, C. (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia. http://repository.unimelb.edu.au/10187/17598