GenePCA {ClassDiscovery} | R Documentation |
Perform principal components analysis on the genes (rows) from a microarray or proteomics experiment.
GenePCA(geneData)
## S4 method for signature 'GenePCA,missing'
plot(x, splitter=0)
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.
signature(x = GenePCA, y = missing)
: Plot the
genes in the space of the first two principal components.
Kevin R. Coombes krc@silicovore.com
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)