Hey Jeremy,

There are a couple things you can do. First off, how bad are the plots? Feel free to send them to me. In practice, the plots actually have to be REALLY bad, such as highly skewed or bi-modal to really make a difference. If your mean plots are fairly symmetric, using the parametric adjustment is probably fine.

For parallelization, the easiest thing to do--because your 450K dataset is so large--is to randomly chop your dataset into 10 segments and then running them separately. This might do the trick and get you down to hours or days, and likely won't impact the results much (and plus you can check it by running it a couple times). Finally, the non-parametric iterations likely can be implemented in an "apply" function in R (and then in parallel using the 'snow' package or something) instead of the loop, but it would be fairly involved (lots of manipulation of matrices and indexing), which is why I haven't already done it.

Anyway, hope this helps!

Evan


On Sep 10, 2013, at 10:43 AM, Jeremy Rosenblum wrote:

I am trying to normalize between 2 Illumina 450K methylation arrays (~480000 probes/sample for 16 samples).  The density plots (prior.plots=T) of the 2 arrays is such that I need to use ComBat in the nonparametric mode (red and black do not overlay well), which as expected took a very long time (1 month).  Is there a way to parallelize ComBat so that it can run faster?

Thanks

Jeremy Rosenblum, MD

Assistant Professor of Pediatrics
Pediatric Hematology/Oncology
Children's Hospital at Montefiore
Office:  (718)-741-2342
Lab: (718)-678-1162
Fax:  (718)-920-6506



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