It is better to cc the mailing list as this kind of information would help others help solve your problem. [Press REPLY ALL instead of REPLY next time please]. -----Forwarded Message----- From: Hairong Wei <hwei@ms.soph.uab.edu> To: 'ramasamy@cancer.org.uk' <ramasamy@cancer.org.uk> Subject: RE: [BioC] RMA normalization Date: Fri, 10 Sep 2004 10:54:23 -0500 Go to: http://files.protsuggest.org/cgi- bin/biocond.cgi?search=rma%2C+normalization Then do a search with "tissue, rma, and normalization" If you find that my explanation is wrong, please let me know. Hairong -----Original Message----- From: Adaikalavan Ramasamy [mailto:ramasamy@cancer.org.uk] Sent: Friday, September 10, 2004 9:56 AM To: Hairong Wei Cc: BioConductor mailing list Subject: RE: [BioC] RMA normalization I was under the impression getting a sufficient mRNA from a single sample was difficult enough. Sorry, I do not think I can be of much help as I never encountered this sort of problem, perhaps due to my own inability to distinguish the terms mRNA, sample, tissue. But there are many other people on the list who have better appreciation of biology and hopefully one of them could advise you. Could you give us the link to this message you are talking about. On Fri, 2004-09-10 at 15:26, Hairong Wei wrote: > Dear Adai: > > Thanks for asking. I got this phrase from the messages stored in the > archive yesterday. My understand is that, suppose you have 100 arrays, and > 10 mRNA samples from 10 tissues. Each 10 arrays are hybridized with mRNAs > from the same tissue. When you run RMA algoritm, you run those arrays (10 > each time) that hybridized with mRNA from same tissue together rathan than > running 100 arrays together. After running RMA for each tissue, the scaling > is applied to arrays form different tissues. > > The reason for doing this is that it is not reasonable to assume that the > arrays from different have the same distribution. > > What is you idea to do background.correction and normalization of 100 arrays > across 10 tissues? > > Thank you very much in advance > > Hairong Wei, Ph.D. > Department of Biostatisitics > University of Alabama at Birmingham > Phone: 205-975-7762 > > > > -----Original Message----- > From: Adaikalavan Ramasamy [mailto:ramasamy@cancer.org.uk] > Sent: Thursday, September 09, 2004 5:09 PM > To: Hairong Wei > Cc: 'bioconductor@stat.math.ethz.ch' > Subject: Re: [BioC] RMA normalization > > > What do you mean by "normalization within tissue-of-origin" ? Can you > give us examples of these messages/papers/references discussing this. > > I often work with finding differentially expressed genes between two > phenotypes of the same type of cancer and tissue type. How would this > normalisation work then ? > > Regards, Adai > > > > On Thu, 2004-09-09 at 16:43, Hairong Wei wrote: > > I just started to work on low-level microarray data analysis and do not > have > > experience in using RMA algorithm. I am now in a situation where I have > to > > normalize a a few hundred of arrays across multiple tissues. I have seen > a > > few messages regarding the legitimacy of using quantile-quantile (Q-Q) > > method to normalize many arrays across multiple tissue types in the > > bioconductor archive. It seems that normalization within > tissue-of-origin > > was favored by some folks. Although I feel it is the approach I should > > take, I still hope to be more secure before I do it, just bacuse a lot of > > work will be done on the normalized data. > > > > Can anybody help by pointing out a few references that use Q-Q method > within > > or not within tissue-of-origin? For those who has done Q-Q within the > > tissue-of-origin, could you please give some comments or your feelings > > regarding Q-Q withn tissue-of-origin? > > > > Hairong > > > > . > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > >