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

1 2 3 |

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

`x` |
a |

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

This is a preliminary attempt 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.

The `GenePCA`

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

class.

Objects should be created using the `GenePCA`

generator function.

`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.

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

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

Kevin R. Coombes krc@silicovore.com

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
showClass("GenePCA")
## simulate samples from three 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)
``` |

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