modelMatrix: Construct Design Matrix
In limma: Linear Models for Microarray Data

Description Usage Arguments Details Value Author(s) See Also Examples

Construct design matrix from RNA target information for a two colour microarray experiment.

1 2	modelMatrix(targets, parameters, ref, verbose=TRUE) uniqueTargets(targets)

`targets`	matrix or data.frame with columns `Cy3` and `Cy5` specifying which RNA was hybridized to each array
`parameters`	matrix specifying contrasts between RNA samples which should correspond to regression coefficients. Row names should correspond to unique RNA sample names found in `targets`.
`ref`	character string giving name of one of the RNA sources to be treated as reference. Exactly one argument of `parameters` or `ref` should be specified.
`verbose`	logical, if `TRUE` then unique names found in `targets` will be printed to standard output

This function computes a design matrix for input to lmFit when analysing two-color microarray experiments in terms of log-ratios.

If the argument ref is used, then the experiment is treated as a one-way layout and the coefficients measure expression changes relative to the RNA source specified by ref. The RNA source ref is often a common reference which appears on every array or is a control sample to which all the others are compared. There is no restriction however. One can choose ref to be any of the RNA sources appearing the Cy3 or Cy5 columns of targets.

If the parameters argument is set, then the columns of this matrix specify the comparisons between the RNA sources which are of interest. This matrix must be of size n by (n-1), where n is the number of unique RNA sources found in Cy3 and Cy5, and must have row names which correspond to the RNA sources.

modelMatrix produces a numeric design matrix with row names as in targets and column names as in parameters.

uniqueTargets produces a character vector of unique target names from the columns Cy3 and Cy5 of targets.

Gordon Smyth

model.matrix in the stats package.

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

targets <- cbind(Cy3=c("Ref","Control","Ref","Treatment"),Cy5=c("Control","Ref","Treatment","Ref"))
rownames(targets) <- paste("Array",1:4)

parameters <- cbind(C=c(-1,1,0),T=c(-1,0,1))
rownames(parameters) <- c("Ref","Control","Treatment")

modelMatrix(targets, parameters)
modelMatrix(targets, ref="Ref")

Found unique target names:
 Control Ref Treatment 
         C  T
Array 1  1  0
Array 2 -1  0
Array 3  0  1
Array 4  0 -1
Found unique target names:
 Control Ref Treatment 
        Control Treatment
Array 1       1         0
Array 2      -1         0
Array 3       0         1
Array 4       0        -1