log transform in RMA normalization
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Jack Luo ▴ 440
@jack-luo-4241
Last seen 10.0 years ago
I am trying to understand the details of the RMA algorithm in terms of log transform. The probe level data obtained from ReadAffy is obviously not log transformed, so in the 3 steps: 1. background subtraction: 2. quantile normalization: 3. median polish summarization. should the algorithm work on raw data (without log transform) or log transformed data for these steps? I was trying to judge from the output data, but realize that it is not obvious to figure out because the following two options both give log transformed output: (take quantile normalization as example) A. quantile normalization on raw data and then log transform B. quantile normalization on log transformed data Thanks, -Jack [[alternative HTML version deleted]]
Normalization probe Normalization probe • 6.5k views
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snijesh ▴ 200
@snijesh-20358
Last seen 5 months ago
India

RMA is a three step algorithm. It is using "background noise removal" which is in fact some convolution algorithm that produce lower values than signal values itself. Then it does quantile normalization and log2 transformation. You can then subtract two values (from two arrays) and obtain a guess of the log fold change.

If you somehow shuffle three steps of RMA, you get slightly different results. Commercial softwares often implements RMA directly using code of R. Irizarry from Bioconductor. Thus the implementations of RMA should be the same and they probably do not shuffle anything.

RMA algorithm is defined as a 3-step process of:

1. background correction
2. quantile normalisation
3. log2 transformation

If you drift from this, then it is not RMA.

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Last seen 7.1 years ago
the log transformation occurs at the summarization step. i.e. both background correction and quantile normalization occur on the natural scale. Ben On Jan 24, 2013, at 6:43 AM, Jack Luo <jluo.rhelp at="" gmail.com=""> wrote: > I am trying to understand the details of the RMA algorithm in terms of log > transform. The probe level data obtained from ReadAffy is obviously not log > transformed, so in the 3 steps: > > 1. background subtraction: > 2. quantile normalization: > 3. median polish summarization. > > should the algorithm work on raw data (without log transform) or log > transformed data for these steps? I was trying to judge from the output > data, but realize that it is not obvious to figure out because the > following two options both give log transformed output: > (take quantile normalization as example) > > A. quantile normalization on raw data and then log transform > B. quantile normalization on log transformed data > > Thanks, > > -Jack > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor