vooma: Convert Mean-Variance Trend to Observation-specific Precision...

In richierocks/limma2: Linear Models 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)

Arguments

y	numeric matrix, EList object, or any similar object containing 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

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

See Also

voom

richierocks/limma2 documentation built on May 27, 2019, 8:47 a.m.