plotCtPCA {HTqPCR} | R Documentation

## PCA for qPCR Ct values.

### Description

Perform and plot a principal component analysis for high-throughput qPCR data from any platform, for doing clustering.

### Usage

    plotCtPCA(q, s.names, f.names, scale = TRUE, features = TRUE, col, cex = c(1, 1))

### Arguments

`q` | a matrix or an object of class qPCRset containing Ct values.
`s.names` | character vector, names of samples. See details.
`f.names` | character vector, names of features. See details.
`scale` | logical, should the variables be scaled to have unit variance. Passed on to `prcomp`.
`features` | logical, should the features be plotted. See details.
`col` | vector, the colours to use for the samples if `features=FALSE`.
`cex` | vector of length 2, the expansion to use for features and samples respectively if `features=FALSE`.

### Details

Per default the sample names from the qPCRset are used, however the feature names are replaced by "*" to avoid cluttering the plot.

If `features=TRUE` then a biplot including all features is produced, with samples represented by vectors. I.e. both observations and variables are plotted, which can potentially be used to identify outliers among the features. For `features=FALSE` only the samples will be included in the plot. This might be more useful for clustering.

In case of high-throughput arrays, some samples may be all NAs. These are ignored during the PCA calculation.

### Value

A plot is created on the current graphics device.

### Note

This is still a work in progress, and the function is not particularly sophisticated. Suggestions/wishes are welcome though.

### Author(s)

Heidi Dvinge

### See Also

`prcomp`, `biplot`

### Examples

    # Load example data
    data(qPCRraw)
    # Plot
    plotCtPCA(qPCRraw)
    # Include feature names; make them smaller
    plotCtPCA(qPCRraw, f.names=featureNames(qPCRraw), cex=c(0.5,1))
    # Plot only the samples
    plotCtPCA(qPCRraw, features=FALSE)

[Package HTqPCR version 1.41.0 Index]