modelMatrix {limma} | R Documentation

## Construct Design Matrix

### Description

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

### Usage

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

### Arguments

`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

### Details

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.

### Value

`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`.

### Author(s)

Gordon Smyth

### See Also

`model.matrix` in the stats package.

An overview of linear model functions in limma is given by 5.LinearModels.

### Examples

    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")

[Package limma version 1.9.6 Index]