RNA degradation plots

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James W. MacDonald 65k

@james-w-macdonald-5106

Last seen 1 hour ago

United States

One way is to simply color the lines using the col argument (e.g., plotAffyRNAdeg(AffyRNAdeg(abatch), col=1:14). You will only get 8 unique colors, but they recycle so you should be able to figure out which one is which. You could also add a legend (legend(x,y, lty=1, col=1:14, legend=list.celfiles())). 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 >>> <kfbargad@lg.ehu.es> 07/23/04 12:52PM >>> Dear users, I am working with 14 U133plus chips. I read in my data using ReadAffy() and it was a bit slow but worked fine after having increased the memory usage to 3000. I have tried to obtain some degradation plots and this time the computer crashes. Is AffyRNADeg that demanding? >Raw.Data <- ReadAffy() >deg <- AffyRNADeg(Raw.Data) I am running R 1.9.1 on a PC, 512megas RAM Also, how could I label the outcome lines of plotAffyRNAdeg so that I graphically know which chip is the odd one in the case there is one? Maybe use the "legend" function, but how? Thanks for your help Regards David _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor

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ADD COMMENT • link updated 19.8 years ago by Dipl.-Ing. Johannes Rainer ▴ 430 • written 19.8 years ago by James W. MacDonald 65k

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Hao Liu ▴ 130

@hao-liu-618

Last seen 9.6 years ago

Dear all: I read 12 cDNA experimental data into RGwt, when I use plotMA, only 1 shows up, how do I show others? I can't find documentation for it anywhere. Thanks Hao Liu, Ph. D

ADD COMMENT • link 19.8 years ago Hao Liu ▴ 130

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Dipl.-Ing. Johannes Rainer ▴ 430

@dipl-ing-johannes-rainer-846

Last seen 9.6 years ago

hi, we got a new affymetrix station and are now becoming a affymetrix core facility . as i am fairly new in the one color micro array field i wanted to know how other people work with affymetrix chips. at the moment i am normalizing the chips with RMA. i compared these results with MAS5 and GC-RMA background correction. from this comparsions it looked that RMA worked best (also with only two chips used in the comparsion), GC-RMA made some strange adjustements (i found genes down regulated after GC-RMA background correction, where they should (must) be up regulated). with MAS 5 i get to many regulated genes, to big variance in the low intensity range... so the method i am using now is RMA. now to the questions: a) quality control: how to define when a chip has not worked, when excluding a chip from the analyis? i am looking at the moment at the histogram (big signal range or not?) and at the 3' 5' ratio, but where is the limit for this range? when was the RNA degraded? b) RMA with a low number of chips, is this possible? i thins the more chips (biological replicates) i have the better the model fitting workes. c) defining regulated genes: i am currently using a M (fold change) cut off of 1 (2 fold), better solutions? thanks, jo

ADD COMMENT • link 19.8 years ago Dipl.-Ing. Johannes Rainer ▴ 430

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Hi, For the quality control Look at affyPLM package and plot the residual of a linear probe model : And look the "pseudo-image". If the residual are randomly distributed, when your hybridation is probably good. Hopes it's help. L. Buffat -----Message d'origine----- De?: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces@stat.math.ethz.ch] De la part de Dipl.-Ing. Johannes Rainer Envoy??: mardi 27 juillet 2004 08:36 ??: James MacDonald Cc?: bioconductor@stat.math.ethz.ch Objet?: [BioC] Affymetrix quality control and normalization hi, we got a new affymetrix station and are now becoming a affymetrix core facility . as i am fairly new in the one color micro array field i wanted to know how other people work with affymetrix chips. at the moment i am normalizing the chips with RMA. i compared these results with MAS5 and GC-RMA background correction. from this comparsions it looked that RMA worked best (also with only two chips used in the comparsion), GC-RMA made some strange adjustements (i found genes down regulated after GC-RMA background correction, where they should (must) be up regulated). with MAS 5 i get to many regulated genes, to big variance in the low intensity range... so the method i am using now is RMA. now to the questions: a) quality control: how to define when a chip has not worked, when excluding a chip from the analyis? i am looking at the moment at the histogram (big signal range or not?) and at the 3' 5' ratio, but where is the limit for this range? when was the RNA degraded? b) RMA with a low number of chips, is this possible? i thins the more chips (biological replicates) i have the better the model fitting workes. c) defining regulated genes: i am currently using a M (fold change) cut off of 1 (2 fold), better solutions? thanks, jo _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor

ADD REPLY • link 19.8 years ago laurent buffat ▴ 190

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