[R] PCA on high dimentional data

[Stephen Sefick]

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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>
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>
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>
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