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

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

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.

Value

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

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.

References