{limma}R Documentation

Compute Contrasts from Linear Model Fit


Given a linear model fit to microarray data, compute estimated coefficients and standard errors for a given set of contrasts.

Usage, contrasts)


fit an MArrayLM object or a list object produced by the function lm.series or equivalent. Must contain components coefficients and stdev.unscaled.
contrasts numeric matrix with columns containing contrasts. May be a vector if there is only one contrast.


This function accepts input from any of the functions lmFit, lm.series, mrlm, gls.series or lmscFit. The function re-orientates the fitted model object from the coefficients of the original design matrix to any set of contrasts of the original coefficients. The coefficients, unscaled standard deviations and correlation matrix are re-calculated in terms of the contrasts.

The idea of this function is to fit a full-rank model using lmFit or equivalent, then use to obtain coefficients and standard errors for any number of contrasts of the coefficients of the original model. Unlike the design matrix input to lmFit, which normally has one column for each treatment in the experiment, the matrix contrasts may have any number of columns and these are not required to be linearly independent. Methods of assessing differential expression, such as eBayes or classifyTestsF, can then be applied to fitted model object.

Warning. For efficiency reasons, this function does not re-factorize the design matrix for each probe. A consequence is that, if the design matrix is non-orthogonal and the original fit included quality weights or missing values, then the unscaled standard deviations produced by this function are approximate rather than exact. The approximation is usually acceptable. The results are always exact if the original fit was a oneway model.


An list object of the same class as fit. This is a list with components components

coefficients numeric matrix containing the estimated coefficients for each contrast for each probe.
stdev.unscaled numeric matrix conformal with coef containing the unscaled standard deviations for the coefficient estimators.
cor.coefficients numeric correlation matrix for the estimated coefficients
... any other components input in fit


Gordon Smyth

See Also

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


#  Simulate gene expression data: 6 microarrays and 100 genes
#  with one gene differentially expressed in first 3 arrays
M <- matrix(rnorm(100*6,sd=0.3),100,6)
M[1,1:3] <- M[1,1:3] + 2
#  Design matrix corresponds to oneway layout, columns are orthogonal
design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
fit <- lmFit(M,design=design)
#  Would like to consider original two estimates plus difference between first 3 and last 3 arrays
contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
fit2 <-,contrast.matrix)
fit2 <- eBayes(fit2)
#  Large values of eb$t indicate differential expression
results <- classifyTestsF(fit2)

[Package limma version 2.4.7 Index]