Hello all, I'm analyzing a set of data that turns out to be a little unusual, but related to the recent discussions on what to do if you have a large (>40%) proportion of genes changing . I'd like some advice on my approach, particularly from the point of view of a manuscript reviewer... Here's the scenario: I get a set of 6 affymetrix chips to analyze, 2 treatments, 3 independent reps each. The QC on the chips is outstanding, the distributions of intensities within each set of reps are very similar, but the "Inf" treatment has slightly lower expression values overall than the "Non" treatment, based on boxplot() and hist(). I use GCRMA for preprocessing, and limma functions for the two-group comparison. Results: about half of the genes are differentially expressed at FDR=0.05, and twice as many are downregulated as upregulated. I am now worried about the normalization, because quantile normalization (and just about every other normalization method) assumes that only a small proportion of genes (~20% - 40% at most) are changing. So I ask the researcher if she would expect a large number of genes to be changing, and if most of them would be decreasing, and she says "yes, of course". Turns out her treatments on the cell line are mock-infected (control) and infected with a virus that takes over the cell completely to produce viral RNA and eventually kills the cell. The infected treatment was harvested right when the first cells started dying, so there should be broad-scale down-regulation of host mRNAs due to infection. This corresponds to the lower overall intensities in the "Inf" group; extraction efficiencies were equivalent for all the samples, and equal volumes of labeled RNA were hybridized to each chip, so I assume the remainder of the RNA in the "Inf" samples was viral. The viral RNA did not appear to have much effect on non-specific binding because MM distributions were extremely similar across all arrays, although again slightly lower for "Inf" replicates. What is the best way to normalize these data? Suggestions in the Bioconductor Archives for dealing with disparate groups mostly involved samples from different tissue types, and the consensus seemed to be to normalize within each group separately. However, there were cautions that the values across tissue types may not be comparable, and that scaling each array to the same mean/median intensity might be a good solution. However, in this case I don't think scaling is appropriate because there is reason to believe that the mean/median intensity is not the same between the treatments. I remember a paper discussing normalization assumptions that mentioned a case where programmed cell death was being assayed, and so most transcripts were going way down. However, I can't remember what they advised to do in this case, nor which paper it was - anyone know? This situation also turns out to be very similar to the spike-in experiment of Choe et al. (Genome Biology 2005, 6:R16) where they spiked in ~2500 RNA species at the same concentration for two groups(C and S), and another ~1300 RNA species at various concentrations, all higher in the S group; to make up for the difference in overall RNA concentration, they added an appropriate amount of unlabeled ploy(C) RNA to the C group. So in total, ~3800 RNA species were present of the ~14,000 probe sets on the Affy DrosGenome1 chip. Even though less than 10% of all the probe sets were changed, because they were all "up-regulated", the typical normalization routines resulted in apparent "down-regulation" of many probe sets that were spiked-in at the same level. Their solution was to normalize to the probe sets corresponding to the RNAs not changed, so they could evaluate variants of other pre-processing steps and analysis methods. Obviously, we cannot do this. There are only 4 external spike in controls, so I am hesitant to normalize to them as well. Here is what I propose to do to account for both a large proportion of genes changing, and most of them changing in one direction, along with justification that I hope is acceptable: Background correction was performed based on GC content of the probes (Wu et al. 2004). Because infection is expected to cause a large proportion of genes to change, normalization across all arrays could not be performed because most normalization methods assume that only a small fraction of genes are changing (refs). Instead, quantile normalization was performed separately for treatment group, as has been suggested for disparate samples such as different tissue types. Additionally, the amount of host RNA in the infected cells is expected to decrease, so both sets of arrays were not scaled to the same median but instead were left alone; in this experiment, the extremely high correlation and consistency of arrays values suggests that the arrays can be directly compared. What do you think? Would this past muster with you if you were the reviewer? Thanks, Jenny Jenny Drnevich, Ph.D. Functional Genomics Bioinformatics Specialist W.M. Keck Center for Comparative and Functional Genomics Roy J. Carver Biotechnology Center University of Illinois, Urbana-Champaign 330 ERML 1201 W. Gregory Dr. Urbana, IL 61801 USA ph: 217-244-7355 fax: 217-265-5066 e-mail: drnevich at uiuc.edu

I would never normalize within treatment, as the normalization could cause the apparent differential expression. In the absence of any suitable between array method, I would use MAS5.0. Of course, the MM binding could also be affected if the virus affects RNAs that bind to the MM probes, but this still seems preferable to normalization within groups. --Naomi At 11:26 AM 1/20/2006, Jenny Drnevich wrote: >Hello all, > >I'm analyzing a set of data that turns out to be a little unusual, but >related to the recent discussions on what to do if you have a large (>40%) >proportion of genes changing . I'd like some advice on my approach, >particularly from the point of view of a manuscript reviewer... > >Here's the scenario: I get a set of 6 affymetrix chips to analyze, 2 >treatments, 3 independent reps each. The QC on the chips is outstanding, >the distributions of intensities within each set of reps are very similar, >but the "Inf" treatment has slightly lower expression values overall than >the "Non" treatment, based on boxplot() and hist(). I use GCRMA for >preprocessing, and limma functions for the two-group comparison. Results: >about half of the genes are differentially expressed at FDR=0.05, and twice >as many are downregulated as upregulated. I am now worried about the >normalization, because quantile normalization (and just about every other >normalization method) assumes that only a small proportion of genes (~20% - >40% at most) are changing. So I ask the researcher if she would expect a >large number of genes to be changing, and if most of them would be >decreasing, and she says "yes, of course". Turns out her treatments on the >cell line are mock-infected (control) and infected with a virus that takes >over the cell completely to produce viral RNA and eventually kills the >cell. The infected treatment was harvested right when the first cells >started dying, so there should be broad-scale down-regulation of host mRNAs >due to infection. This corresponds to the lower overall intensities in the >"Inf" group; extraction efficiencies were equivalent for all the samples, >and equal volumes of labeled RNA were hybridized to each chip, so I assume >the remainder of the RNA in the "Inf" samples was viral. The viral RNA did >not appear to have much effect on non-specific binding because MM >distributions were extremely similar across all arrays, although again >slightly lower for "Inf" replicates. > >What is the best way to normalize these data? Suggestions in the >Bioconductor Archives for dealing with disparate groups mostly involved >samples from different tissue types, and the consensus seemed to be to >normalize within each group separately. However, there were cautions that >the values across tissue types may not be comparable, and that scaling each >array to the same mean/median intensity might be a good solution. However, >in this case I don't think scaling is appropriate because there is reason >to believe that the mean/median intensity is not the same between the >treatments. I remember a paper discussing normalization assumptions that >mentioned a case where programmed cell death was being assayed, and so most >transcripts were going way down. However, I can't remember what they >advised to do in this case, nor which paper it was - anyone know? > >This situation also turns out to be very similar to the spike-in experiment >of Choe et al. (Genome Biology 2005, 6:R16) where they spiked in ~2500 RNA >species at the same concentration for two groups(C and S), and another >~1300 RNA species at various concentrations, all higher in the S group; to >make up for the difference in overall RNA concentration, they added an >appropriate amount of unlabeled ploy(C) RNA to the C group. So in total, >~3800 RNA species were present of the ~14,000 probe sets on the Affy >DrosGenome1 chip. Even though less than 10% of all the probe sets were >changed, because they were all "up-regulated", the typical normalization >routines resulted in apparent "down-regulation" of many probe sets that >were spiked-in at the same level. Their solution was to normalize to the >probe sets corresponding to the RNAs not changed, so they could evaluate >variants of other pre-processing steps and analysis methods. Obviously, we >cannot do this. There are only 4 external spike in controls, so I am >hesitant to normalize to them as well. > >Here is what I propose to do to account for both a large proportion of >genes changing, and most of them changing in one direction, along with >justification that I hope is acceptable: > >Background correction was performed based on GC content of the probes (Wu >et al. 2004). Because infection is expected to cause a large proportion of >genes to change, normalization across all arrays could not be performed >because most normalization methods assume that only a small fraction of >genes are changing (refs). Instead, quantile normalization was performed >separately for treatment group, as has been suggested for disparate samples >such as different tissue types. Additionally, the amount of host RNA in the >infected cells is expected to decrease, so both sets of arrays were not >scaled to the same median but instead were left alone; in this experiment, >the extremely high correlation and consistency of arrays values suggests >that the arrays can be directly compared. > > >What do you think? Would this past muster with you if you were the reviewer? > >Thanks, >Jenny > > > >Jenny Drnevich, Ph.D. > >Functional Genomics Bioinformatics Specialist >W.M. Keck Center for Comparative and Functional Genomics >Roy J. Carver Biotechnology Center >University of Illinois, Urbana-Champaign > >330 ERML >1201 W. Gregory Dr. >Urbana, IL 61801 >USA > >ph: 217-244-7355 >fax: 217-265-5066 >e-mail: drnevich at uiuc.edu > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111