[rmaexpress_help] Re: Batch effect produced when using RMA

  • From: Ben Bolstad <bmb@xxxxxxxxxxxxx>
  • To: rmaexpress_help@xxxxxxxxxxxxx
  • Date: Tue, 23 Jan 2007 19:54:12 -0800


An interesting question, and an issue I am well aware of. Hopefully,
your experiment is not such that the batches effect is not confounded
with the treatment effect. My instinct would be that it is still better
to process all 120 together rather than as 3 sets of 40 if you intend to
do an analysis involving all the samples.

As for dealing with the remaining batch effect:

One solution might be to include a batch effect parameter in your
subsequent analysis. 

Another that might be worth your time 

W. Evan Johnson , Cheng Li , and Ariel Rabinovic 
        Adjusting batch effects in microarray expression data using
        empirical Bayes methods 
        Biostatistics Advance Access published on January 1, 2007, DOI
        Biostat 8: 118-127.


I do have a probe-level normalization which does remove batch effects.
However, I have yet to publish on it and it will be some months yet
before it is incorporated into RMAExpress.



On Tue, 2007-01-23 at 19:08 -0500, Jun Ding wrote:
> Hi Dr. Bolstad,
> I have a question regarding how to use RMA correctly.
> We have data of 120 microarrays. But those 120 microarrays were not 
> done all together at one time. Actually, we collected 40 samples every 
> time and then went ahead to do microarrays on those 40 samples. So 
> basically we have 3 batches of microarrays (microarrays from the same 
> batch were done at the same time and there was a gap of several months 
> between two batches). I wonder in this case, when I use RMA, whether I 
> should analyze those 120 microarrays together or I should analyze each 
> batch of microarrays separately. I don't know the details of RMA, so I 
> really don't know which way I should take.
> I have tried to use RMA to analyze each batch of microarrays separately 
> and then combined them together. I used PCA (principal component 
> analysis) to do an unsupervised analysis and what I found was that the 
> first principal component could perfectly separate three batches. I 
> guess that means there is an obvious batch effect in the data after RMA.
> Look forward to getting your suggestions! Thanks a lot!
> Jun
> ----------------------------
> Jun Ding, Ph.D. student
> Department of Biostatistics
> University of Michigan
> Ann Arbor, MI, 48105
> ----------------------------

Other related posts: