[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:
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 Roux, PhD
Gilad lab, Department of Human Genetics, University of Chicago
920 East 58th Street, CLSC 317, Chicago, IL 60637, USA
tel: +1-773-834-1984   fax: +1-773-834-8470

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