limma on RNASeq time-series with patient and batch as blocking factor
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Julia • 0
Last seen 27 days ago

Dear all,

I'm struggeling to find the correct approach to handling batch effects. I have a longitudinal RNA-Seq experiment where patient samples were measured in 3 batches: First the samples from the inital visit (visit 0, batch "baseline") were sequenced, some had to be re-sequenced due to QC issues (batch "baseline_reseq") and finally the follow-up samples from visits 1, 2, 3 came in and were sequenced in the third batch. Here is an example of the meta data matrix:

patient visit batch
s1 0 baseline
s2 0 baseline_resq
s1 1 follow-up
s3 0 baseline
s2 1 follow-up
s3 1 follow-up

For now we're only looking for differentailly expressed genes between visit 0 and visit 1.

I think I should fit a mixed model using the duplicateCorrelation function to account for the batch effects like in this example.

## Limma with batch as blocking factor
dge <- DGEList(counts=counts)
design.test <- data.frame(group=c(rep(1,2), rep(2,2), rep(3,2)), batch=c(1,2,1,2,1,2))
design <- model.matrix(~group, design.test)
dge <- calcNormFactors(dge)
v <- voom(dge, design, plot=FALSE)
corfit <- duplicateCorrelation(v, design, block=design.test$batch)

# re-run voom & dupCorr to get a more accurate fit (recommended [here][1])
v <- voom(dge, design, block=design.test$batch, correlation = corfit$consensus)
corfit <- duplicateCorrelation(v, design, block=design.test$batch)
fit <- lmFit(v, design, block=design.test$batch, correlation=corfit$consensus)
results <- topTable(fit, coef="TimepointT120",

But how do I account for the patient in this case? Would it make sense to combine patient and batch and use the combined factor as blocking factor?

Your advice is much appreciated!


duplicateCorrelation limma BatchEffect • 58 views

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