arrayWeights {limma}R Documentation

Array Quality Weights


Estimates relative quality weights for each array in a multi-array experiment with replication.


arrayWeights(object, design = NULL, weights = NULL, method = "genebygene", maxiter = 50, tol = 1e-10, trace=FALSE)
arrayWeightsSimple(object, design = NULL, maxiter = 100, tol = 1e-10, trace=FALSE)


object object of class numeric, matrix, MAList, marrayNorm, exprSet or PLMset containing log-ratios or log-values of expression for a series of microarrays.
design the design matrix of the microarray experiment, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates.
weights optional numeric matrix containing prior weights for each spot.
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 '"reml"' algorithm.


The relative reliability of each array is estimated by measuring how well the expression values for that array follow the linear model.

A heteroscedastic model is fitted to the expression values for each gene by calling the function lm.wfit. The dispersion model is fitted to the squared residuals from the mean fit, and is set up to have array specific coefficients, which are updated in either full REML scoring iterations, or using an efficient gene-by-gene update algorithm. The final estimates of these array variances are converted to weights.

The arguments design and weights will be extracted from the data object if available and do not normally need to be set explicitly in the call; if any of these are set in the call then they will over-ride the slots or components in the data object.

If object is a PLMset, then expression values will be taken from the slot chip.coefs and weights will be computed from se.chip.coefs. If object is an exprSet, then expression values will be taken from the exprs slot, but weights will not be computed.

arrayWeightsSimple is a fast version of arrayWeights with method="reml", no prior weights and no missing values.


A vector of array weights.


Matthew Ritchie

See Also

An overview of linear model functions in limma is given by 06.LinearModels.


## Not run: 
array.wts <- arrayWeights(MA, design)
w <- MA$weights
if(is.null(w)) w <- array(1,dim(MA))
w <- matvec(w,array.wts)
fit.wts <- lmFit(MA, design, weights=w)
## End(Not run)

[Package limma version 2.4.7 Index]