Hi everyone, I am new to analysis of microarrays, so I'm sorry if the answers to my questions should be obvious. But I would really appreciate any inputs... I should also mention that I am a biology grad student and not a statistician. 1) My question is regarding the normalization procedures for Affymetrix U133 arrays. It seems like the best way to normalize arrays is by using the RMA method (better than dChip or Affymetrix's Tukey's biweight?) I would like to use quantile normalization between arrays, so I have been using Bolstad's RMAExpress to analyze my .CEL files and then examining the residual images. If I encounter some horribly-looking arrays, is it wise to leave those arrays out of the subsequent analysis? or is there some way of removing the outliers (like dChip) and then apply the RMA procedure again? Or is that unnecessary? 2) Is there some sort of guideline to determine if the RNA was of low quality (due to experimenter's error etc) or if the labelling/hybridization was done incorrectly? 3) What is the difference between limma and RMA? Are there any publications discussing the merit of one method over the other? Thank you very much, Edmund Chang Graduate student- Physiology University of Illinois, Urbana-Champaign ecc0101@yahoo.com 217-333-7836

Hi Edmund, 1.) For outlier probes, rma should not be affected because it is using a robust model fit. For outlier arrays (e.g., those that appear completely different on a density plot), I dont' think you can do much except re-run that array. I have never been able to get reasonable results from 'obvious outlier' chips. I realize that the term 'obvious outlier' is non-scientific in the extreme, but given the amount of data, most tests designed to determine if the distributions are different will reject the null hypothesis for all the chips in a given set, so they are not very useful in this context. Another good way to detect outlier chips is to use the residual plots in affyPLM. Any chip with large residuals is not being fit by the model very well. 2.) We primarily use the Agilent Bioanalyzer 2100 to check the mRNA quality prior to putting it on a chip. After the fact, you can use the AffyRNAdeg() and plotAffyRNAdeg() functions to see if there is a problem chip. Again, this is more of an 'eyeballometric' test, but I have found it useful in the past. 3.) rma is a (set of) functions to convert probe-level data into gene-level expression values, whereas limma is a package designed to fit ANOVA models to microarray data. They are not used for the same thing, so there is no reason to compare. In fact, you can (and I often do) use rma to compute expression values, followed by limma to detect differentially expressed genes. HTH, Jim James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 >>> <echang4@life.uiuc.edu> 06/01/04 09:07PM >>> Hi everyone, I am new to analysis of microarrays, so I'm sorry if the answers to my questions should be obvious. But I would really appreciate any inputs... I should also mention that I am a biology grad student and not a statistician. 1) My question is regarding the normalization procedures for Affymetrix U133 arrays. It seems like the best way to normalize arrays is by using the RMA method (better than dChip or Affymetrix's Tukey's biweight?) I would like to use quantile normalization between arrays, so I have been using Bolstad's RMAExpress to analyze my .CEL files and then examining the residual images. If I encounter some horribly-looking arrays, is it wise to leave those arrays out of the subsequent analysis? or is there some way of removing the outliers (like dChip) and then apply the RMA procedure again? Or is that unnecessary? 2) Is there some sort of guideline to determine if the RNA was of low quality (due to experimenter's error etc) or if the labelling/hybridization was done incorrectly? 3) What is the difference between limma and RMA? Are there any publications discussing the merit of one method over the other? Thank you very much, Edmund Chang Graduate student- Physiology University of Illinois, Urbana-Champaign ecc0101@yahoo.com 217-333-7836 _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor