Hi,

I'm dealing with quite an unusual study design. Originally (due to
unfortunate and inevitable circumstances) we had all "high_risk" and
"medium_risk" samples on batch 1 and "low_risk" samples on batch 2. Then we
discussed the batch effect and decided to re-hybridize some randomly
selected samples from each risk group on batch 3. The resulting study
design looks a bit like like this (in reality we have 30 - 45 samples in
each group and 16 samples re-hybridized but you'll get an idea):

Array  name        Batch        Covariate  1
sample_A        1        High_risk
sample_B        1        High_risk
sample_C        1        High_risk
sample_C_2     3        High_risk
sample_D        1        High_risk
sample_D_2    3        High_risk
sample_E        1        Medium_risk
sample_F        1        Medium_risk
sample_G        1        Medium_risk
sample_G_2     3        Medium_risk
sample_H        2        Low_risk
sample_I        2        Low_risk
sample_J        2        Low_risk
sample_J_2     3        Low_risk
sample_K        2        Low_risk
sample_K_2     3        Low_risk

For example Sample_C and Sample_C_2 are the same RNA sample and the only
difference between them is the batch (the same applies to D and D_2 etc.).
Such array pairs should be valuable for estimating batch effects. The
question is: Can ComBat utilize this information? Or can you recommend some
other batch correction method that could? For now, I've applied ComBat
after removing the replicated samples on batches 1 and 2 (C, D, G, J and K
in the above example) but this is certainly not an optimal solution.

Best regards,

Essi Laajala
PhD student in bioinformatics
Turku, Finland

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