By doing PCA you are trying to find a lower dimensional representation
of the major variation structure in your data. You get PC* to represent
the "new" data. If you want to know what loads on the axes then you
need to look at the loadings. These are the link between the original
data and the "new" data. Maybe you need to read up on what PCA does?
Or, maybe I am misunderstanding your question...
FWIW
Stephen
On Sat 10 Dec 2011 09:56:35 AM CST, mail me wrote:
Hi:
I have a large dataset mydata, of 1000 rows and 1000 columns. The rows
have gene names and columns have condition names (cond1, cond2, cond3,
etc).
mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="")
I applied PCA as follows:
data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE);
Now i get 1000 PCs and i choose first three PCs and make a new data frame
new_data_frame<- cbind(data_after_pca$x[,1], data_after_pca$x[,2],
data_after_pca$x[,3]);
After the PCA, in the new_data_frame, i loose the previous cond1,
cond2, cond3 labels, and instead have PC1, PC2, PC3 as column names.
My question is, is there any way I can map the PC1, PC2, PC3 to the
original conditions, so that i can still have a reference to original
condition labels after PCA?
Thanks:
deb
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--
Stephen Sefick
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Auburn University
Biological Sciences
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Let's not spend our time and resources thinking about things that are so little
or so large that all they really do for us is puff us up and make us feel like
gods. We are mammals, and have not exhausted the annoying little problems of
being mammals.
-K. Mullis
"A big computer, a complex algorithm and a long time does not equal science."
-Robert Gentleman
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