Usage
voomWithQualityWeights(counts, design=NULL, lib.size=NULL, normalize.method="none",
plot=FALSE, span=0.5, var.design=NULL, method="genebygene", maxiter=50,
tol=1e-10, trace=FALSE, replace.weights=TRUE, col=NULL, ...)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, the 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 and sample-specific weights be displayed?
span
width of the lowess smoothing window as a proportion.
var.design
design matrix for the variance model. Defaults to the sample-specific model (i.e. each sample has a distinct variance) when NULL.
method
character string specifying the estimating algorithm to be used. Choices
are "genebygene" and "reml".
maxiter
maximum number of iterations allowed.
tol
convergence tolerance.
trace
logical variable. If true then output diagnostic information
at each iteration of the '"reml"' algorithm, or at every 1000th iteration of the
"genebygene" algorithm.
replace.weights
logical variable. If TRUE then the weights in the voom object will be replaced with
the combined voom and sample-specific weights and the EList object from voom is returned.
If FALSE, then a matrix of combined weights is returned. col
colours to use in the barplot of sample-specific weights (only used if plot=TRUE). If NULL, bars are plotted in grey.
...
other arguments are passed to lmFit.