Hi Weinong,
I would recommend using prcomp() for this. My general paradigm is as
follows:
pca <- prcomp(t(gene_matrix))
plot(pca$x[,1:2])
Notes:
1.) In general, matrices of gene data are usually samples in columns
and
genes in rows, which is the transpose of what prcomp() expects, so you
have to use t().
2.) Usually when I plot the results, I also use pch, col, xlab, ylab,
main, etc. to make the plotting symbols for each group different
shapes
and colors, add reasonable axis labels, a main title, etc. See ?par
for
other variables you can pass to plot.
3.) It is nice to follow up with legend() to add a nice legend to the
plot. People seem to like that stuff ;-D.
4.) The general recommendation is to set scale. = TRUE in the call to
prcomp. I'm not sure if it will make much difference with microarray
data because it is usually normalized anyway, so I usually don't
bother.
However, it is worth a try.
HTH,
Jim
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
>>> weinong han <hanweinong at="" yahoo.com=""> 06/18/05 2:10 AM >>>
Dear All,
Wish you have a nice weekend.
I want to run the two-dimensional principal-components analysis of
patient group using 174-gene signature set from Welch-T test to
separate the patient group, at the same time, I want to the plots of
PCA
results.
Anyone tried or not? please tell me the functions and scripts.
Any advice and suggestions will be much appreciated.
Thanks in advance.
Best Regards
Han Weinong
hanweinong at yahoo.com
---------------------------------
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Dear Weinong,
We have provided a simple package for running PCA or correspondence
analysis in the bioconductor package made4.
It will accept most bioconductor data classes, a matrix or data.frame.
To run a PCA
library(made4)
res<-ord(data, "pca")
plot(res)
This will give a plot of the eigenvalues, and the first two
eigenvectors
of the cases (arrays) and genes. Only the genes at the ends of the
axes
are labelled (to ease the visualisation). You can also use
plotarrays(res)
plotgenes(res)
To visualise the genes and arrays.
The package made4 required that ade4 is installed. If this is not, you
can install it using
install.packages("ade4")
Regards
Aedin
>>> weinong han <hanweinong at="" yahoo.com=""> 06/18/05 2:10 AM >>>
Dear All,
Wish you have a nice weekend.
I want to run the two-dimensional principal-components analysis of
patient group using 174-gene signature set from Welch-T test to
separate the patient group, at the same time, I want to the plots of
PCA
results.
Anyone tried or not? please tell me the functions and scripts.
Any advice and suggestions will be much appreciated.
Thanks in advance.
Best Regards
Han Weinong
hanweinong at yahoo.com
