| printtipWeights {limma} | R Documentation |
Estimates relative quality weights for each sub-array in a multi-array experiment.
printtipWeights(object, design = NULL, weights = NULL, method = "genebygene", layout, maxiter = 50, tol = 1e-10, trace=FALSE)
object |
object of class numeric, matrix, MAList, marrayNorm,
or ExpressionSet containing log-ratios or log-values of
expression for a series of spotted 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". |
layout |
list specifying the dimensions of the spot matrix and the grid matrix. For details see PrintLayout-class. |
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 sub-array (print-tip group) is estimated by measuring how well the expression values for that sub-array follow the linear model.
The method described in Ritchie et al (2006) and implemented in
the arrayWeights function is adapted for this purpose.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 sub-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 the sub-array variances are converted to weights.
The data object object is interpreted as for lmFit.
In particular, the arguments design, weights and layout 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.
A matrix of sub-array weights which can be passed to lmFit.
Matthew Ritchie and Gordon Smyth
Ritchie, M. E., Diyagama, D., Neilson, van Laar, R., J., Dobrovic, A., Holloway, A., and Smyth, G. K. (2006). Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics 7, 261. http://www.biomedcentral.com/1471-2105/7/261/abstract
An overview of linear model functions in limma is given by 06.LinearModels.
library(sma)
# Subset of data from ApoAI case study in Limma User's Guide
data(MouseArray)
# Avoid non-positive intensities
RG <- backgroundCorrect(mouse.data, method="half")
MA <- normalizeWithinArrays(RG, mouse.setup)
MA <- normalizeBetweenArrays(MA, method="Aq")
targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
design <- modelMatrix(targets, ref="Pool")
subarrayw <- printtipWeights(MA, design, layout=mouse.setup)
fit <- lmFit(MA, design, weights=subarrayw)
fit2 <- contrasts.fit(fit, contrasts=c(-1,1))
fit2 <- eBayes(fit2)
# Use of sub-array weights increases the significance of the top genes
topTable(fit2)
# Create an image plot of sub-array weights from each array
zlim <- c(min(subarrayw), max(subarrayw))
par(mfrow=c(3,2), mai=c(0.1,0.1,0.3,0.1))
for(i in 1:6)
imageplot(subarrayw[,i], layout=mouse.setup, zlim=zlim, main=paste("Array", i))