Perform principal components analysis on the genes (rows) from a microarray or proteomics experiment.
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A data matrix, with rows interpreted as genes and columns as samples
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.
GenePCA function constructs and returns a valid object of
Objects should be created using the
GenePCA generator function.
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
numeric vector of length N; the
amount of the total variance explained by each principal component.
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 [email protected]
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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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