limma (version 3.28.14)

lmscFit: Fit Linear Model to Individual Channels of Two-Color Data

Description

Fit a linear model to the individual log-intensities for each gene given a series of two-color arrays

Usage

lmscFit(object, design, correlation)

Arguments

object

an MAList object or a list from which M and A values may be extracted

design

a numeric matrix containing the design matrix for linear model in terms of the individual channels. The number of rows should be twice the number of arrays. The number of columns will determine the number of coefficients estimated for each gene.

correlation

numeric value giving the intra-spot correlation

Value

An object of class MArrayLM

Details

For two color arrays, the channels measured on the same set of arrays are correlated. The M and A however are uncorrelated for each gene. This function fits a linear model to the set of M and A-values for each gene after re-scaling the M and A-values to have equal variances. The input correlation determines the scaling required. The input correlation is usually estimated using intraspotCorrelation before using lmscFit.

Missing values in M or A are not allowed.

References

Smyth, GK (2005). Individual channel analysis of two-colour microarray data. Proceedings of the 55th Session of the International Statistics Institute, 5-12 April 2005, Sydney, Australia; Internatational Statistics Institute; Paper 116. http://www.statsci.org/smyth/pubs/ISI2005-116.pdf

Smyth, GK, and Altman, NS (2013). Separate-channel analysis of two-channel microarrays: recovering inter-spot information. BMC Bioinformatics 14, 165. http://www.biomedcentral.com/1471-2105/14/165

See Also

lm.fit.

An overview of methods for single channel analysis in limma is given by 07.SingleChannel.

Examples

## Not run: 
# # Subset of data from ApoAI case study in Limma User's Guide
# # Avoid non-positive intensities
# RG <- backgroundCorrect(RG,method="normexp")
# MA <- normalizeWithinArrays(RG)
# MA <- normalizeBetweenArrays(MA,method="Aq")
# targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
# targets.sc <- targetsA2C(targets)
# targets.sc$Target <- factor(targets.sc$Target,levels=c("Pool","WT","KO"))
# design <- model.matrix(~Target,data=targets.sc)
# corfit <- intraspotCorrelation(MA,design)
# fit <- lmscFit(MA,design,correlation=corfit$consensus)
# cont.matrix <- cbind(KOvsWT=c(0,-1,1))
# fit2 <- contrasts.fit(fit,cont.matrix)
# fit2 <- eBayes(fit2)
# topTable(fit2,adjust="fdr")
# ## End(Not run)