Hi!

I'm writing with a few questions about applying ComBat (sva package) to a
set of ~50 samples run on the the Illumina Infinium HumanMethylation450
BeadChip array (~450,000 DNA methylation data points).

There is a large amount of variation in my data due to both the batch the
samples were run in (3 different batches), in addition to the position they
were located on the chip - specifically the row (6 different rows), but not
the column. The chips are set up in a 6 row * 2 column format like this:


sample 01   sample 02
sample 03   sample 04
sample 05   sample 06
sample 07   sample 08
sample 09   sample 10
sample 11   sample 12


I read Dr. Evan Johnson's suggestions to someone else with this
"2-batch-effect-variable" problem in the ComBat google group (
https://groups.google.com/forum/#!topic/combat-user-forum/PcTxNlaUmAI). He
had 2 good suggestions:

   1. Combine the two batch variables into one, if 3-4 reps are left in
   each batch
   2. Use ComBat twice, adjusting for the first batch using the second
   batch as a covariate, and then adjust for the second batch.

I cannot go with the first suggestion because combining the 2 batch
variables would create 18 batch categories (3 batches * 6 rows), and I
would not have enough replicates per batch category.

So I tried the second option - applying ComBat twice. I first corrected for
row and then took the row-corrected data and applied ComBat again,
correcting for batch. It seems to have worked & the correlation of my
technical replicates improves. I am seeking advice on two points:

   1. The google group post is now a few years old, is it still thought
   that the step-wise correction is a valid approach?
   2. Row would be better treated as a continuous adjustment variable than
   a factor. In the version of sva that I am using (3.0.2) I believe that only
   factors adjustment variables are supported. I have seen mention in a few
   forums that there might be an update to ComBat to adjust for a numeric
   batch variable, is one available?

Thank you in advanced for your help!

Magda Price,
University of British Columbia

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