GenePCA {ClassDiscovery} | R Documentation |

## The GenePCA Class

### Description

Perform principal components analysis on the genes (rows) from a
microarray or proteomics experiment.

### Usage

GenePCA(geneData)
## S4 method for signature 'GenePCA, missing':
plot(x, splitter=0)

### Arguments

`geneData` |
A data matrix, with rows interpreted as genes and
columns as samples |

`x` |
a `GenePCA` object |

`splitter` |
A logical vector classifying the genes. |

### Details

This is a preliminary attenpt at a class for principal components
analysis of genes, parallel to the `SamplePCA`

class for
samples. The interface will (one hopes) improve markedly in the next
version of the library.

### Value

The `GenePCA`

function construcs and returns a valid object of
the `GenePCA`

class.

### Objects from the Class

Objects should be created using the `GenePCA`

function.

### Slots

`scores`

:- A
`matrix`

of size PxN, where P is the
number of rows and N the number fo columns in the input,
representing the projections of the input rows onto the first N
principal components.
`variances`

:- A
`numeric`

vector of length N; the
amount of the total variance explained by each principal component.
`components`

:- A
`matrix`

of size NxN containing
each of the first P principal components as columns.

### Methods

- plot
`signature(x = GenePCA, y = missing)`

: Plot the
genes in the space of the first two principal components.

### Author(s)

Kevin R. Coombes <kcoombes@mdanderson.org>

### See Also

`SamplePCA`

, `princomp`

### Examples

# simulate samples from thre different groups, with generic genes
d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
dd <- cbind(d1, d2, d3)
# perform PCA in gene space
gpc <- GenePCA(dd)
# plot the results
plot(gpc)
# cleanup
rm(d1, d2, d3, dd, gpc)

[Package

*ClassDiscovery* version 2.5.0

Index]