[BioC] strange layering in PCA after removeBatchEffect()
Julien Roux
jroux at uchicago.edu
Tue Nov 26 11:35:30 CET 2013
Dear all,
After using limma function removeBatchEffect() on RNA-seq data, I
observe a strange behavior when I use PCA to visualize my data. Here are
some more details:
# dge is my DGEList object with RNA-seq count data
y <- predFC(dge, prior.count=2)
# When I run a PCA on this matrix, I can observe that PCs 1 and 2 are
highly correlated with 2 technical variables (here variables 2 and 3)
that I wich to remove. The main effect is in variable 1
y.corrected <- removeBatchEffect(y, batch=var2, batch2=var3,
design=model.matrix(~ var1))
# I then run a centered and scaled PCA on this matrix
pca1 <- prcomp(t(y.corrected[apply(y.corrected, 1, sd) > 0, ]), scale = T)
When I plot the PCA scores, I observe that the different samples are
scattered on discrete layers on PC1:
https://dl.dropboxusercontent.com/u/828794/PCA_removeBatchEffect.pdf
This is something unexpected as it does not correlate with any technical
or biological variable...
Didi you observe this behavior before? Do you have an idea about what
could cause this pattern?
Thanks for your input
Julien
--
Julien Roux, PhD
Gilad lab, Department of Human Genetics, University of Chicago
http://giladlab.uchicago.edu/
920 East 58th Street, CLSC 317, Chicago, IL 60637, USA
tel: +1-773-834-1984 fax: +1-773-834-8470
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