modelMatrix {limma} | R Documentation |
Construct design matrix from RNA target information for a two colour microarray experiment.
modelMatrix(targets, parameters, ref, verbose=TRUE) uniqueTargets(targets)
targets |
matrix or data.frame with columns |
parameters |
matrix specifying contrasts between RNA samples which should correspond to regression coefficients.
Row names should correspond to unique RNA sample names found in |
ref |
character string giving name of one of the RNA sources to be treated as reference.
Exactly one argument of |
verbose |
logical, if |
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")