tests/test05-spca.R

In ClassDiscovery: Classes and Methods for "Class Discovery" with Microarrays or Proteomics

library(ClassDiscovery)
suppressWarnings( RNGversion("3.5.3") )
set.seed(316912)
# simulate datda from three different groups
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)
kind <- factor(rep(c('red', 'green', 'blue'), each=10))

# perform PCA
spc <- SamplePCA(dd, splitter=kind)

# plot the results
plot(spc, col=levels(kind))

# mark the group centers
x1 <- predict(spc, matrix(apply(d1, 1, mean), ncol=1))
points(x1[1], x1[2], col='red', cex=2)
x2 <- predict(spc, matrix(apply(d2, 1, mean), ncol=1))
points(x2[1], x2[2], col='green', cex=2)
x3 <- predict(spc, matrix(apply(d3, 1, mean), ncol=1))
points(x3[1], x3[2], col='blue', cex=2)

# check out the variances
screeplot(spc)

# cleanup
rm(d1, d2, d3, dd,kind, spc, x1, x2, x3)