[rmaexpress_help] Re: Batch effect produced when using RMA

  • From: Jun Ding <junding@xxxxxxxxx>
  • To: rmaexpress_help@xxxxxxxxxxxxx, Ben Bolstad <bmb@xxxxxxxxxxxxx>
  • Date: Thu, 25 Jan 2007 00:00:23 -0500

Hi Dr. Bolstad,
Thank you very much for your quick and detailed suggestions! It is surely very helpful to me.
Just a little follow-up. Yesterday I told you that I used RMA to analyze each batch individually and then when I combined them together, the PCA plot perfectly separated three batches of samples. I tried to calculate the mean of genes' expression for each sample (i.e. the mean of ~54,000 transcripts of each sample). It turns out that within each batch, the means of samples are very close to each other, however, across batches, means are significantly different. I guess this would be the reason that the first principal component would perfectly separate three batches of samples. Meanwhile, the variances of genes' expression for samples are pretty similar to each other across batches. Are all these what you expect?
I will try to use RMA to analyze all 120 samples together and let you know what happens. 

Thanks!

Jun
 ----------------------------
Jun Ding, Ph.D. student
Department of Biostatistics
University of Michigan
Ann Arbor, MI, 48105
----------------------------


Quoting Ben Bolstad <bmb@xxxxxxxxxxxxx>:


Jun,

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
       10.1093/biostatistics/kxj037.
       Biostat 8: 118-127.

http://biostatistics.oxfordjournals.org/cgi/content/abstract/8/1/118


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.

Best,

Ben

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
----------------------------

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