Short answer : It is unlikely that you will get a paper published whose results were based on unnormalised data. Long answer : There are many systematic and unsystematic variation in microarrays and it may be that in your case that some of these cancel out nicely before normalisation. To elaborate further on Sean's comments, let me give you an example. Suppose you did a batch of arrays and its dye-swaps on today with a particular PMT setting for scanning. 3 months later, you do another batch of arrays and dye-swaps but with a higher PMT settings. Assuming that all other experimental conditions are equal and of high quality, then the dye-swap pairs would be consistent regardless of batch. But all the intensity values of the 2nd batch would be much higher than the 1st batch because of the higher PMT settings and thus the two batches would not be comparable. In other words, the "center" of the two batches are different and normalisation would be useful. Regards, Adai On Tue, 2005-04-12 at 14:00 -0400, Sean Davis wrote: > On Apr 12, 2005, at 1:19 PM, vijayaraj nagarajan wrote: > > > dear friends > > i have situation, where i thought its ok for me not to > > do normalisation, i am afraid i may be wrong. i want > > your advice in this regard. > > > > we performed a wild type - mutant, dye-swap > > experiment. > > when we analysed the intensity values, they were > > consistant among the two experiment (dye-swap). ie., > > almost same values for mutants in both the experiments > > of the dye-swap. > > since the values are almost same, i thought there > > might not be any dye-bias, so i just went ahead, > > averaged the two values, found out their ratio and > > filtered genes with 2 fold change. > > > > If you don't normalize, then the "center" of your ratios may be > significantly off, not to mention the fact that the two arrays may have > different centers. So, at the very least, I think you have to do > within-arrays normalization of some kind. > > Sean > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >

On Apr 13, 2005, at 8:03 AM, Gorjanc Gregor wrote: > Hi! > > You might try analysis with and without normalization and take > a look at the results. If they say the same thing than I would > say, no it is not necessary to do normalization. > So, if the two results agree, then the results with normalization are correct; if not then the results with normalization are still correct. Sounds like we are pretty much stuck with normalization.... Sean >> dear friends >> i have situation, where i thought its ok for me not to >> do normalisation, i am afraid i may be wrong. i want >> your advice in this regard. >> >> we performed a wild type - mutant, dye-swap >> experiment. >> when we analysed the intensity values, they were >> consistant among the two experiment (dye-swap). ie., >> almost same values for mutants in both the experiments >> of the dye-swap. >> since the values are almost same, i thought there >> might not be any dye-bias, so i just went ahead, >> averaged the two values, found out their ratio and >> filtered genes with 2 fold change. >> >> so i have done this without normalisation. >> i am afraid, i might be wrong, my 2 fold chaging genes >> might be wrong... >> kindly give me your advice in this regard. >> i did analyse the data with limma, but the topTable >> genes there never correlates with my 2 fold genes. >> >> kindly correct me. >> thanks >> >> vijay >> graduate student >> department of biological sciences >> the university of southern mississippi >> MS, USA > > -- > Lep pozdrav / With regards, > Gregor Gorjanc > > -------------------------------------------------------------------- --- > - > University of Ljubljana > Biotechnical Faculty URI: http://www.bfro.uni-lj.si/MR/ggorjan > Zootechnical Department email: gregor.gorjanc <at> bfro.uni-lj.si > Groblje 3 tel: +386 (0)1 72 17 861 > SI-1230 Domzale fax: +386 (0)1 72 17 888 > Slovenia > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor

On Wed, 2005-04-13 at 14:29 +0200, Gorjanc Gregor wrote: <snip> > > So, if the two results agree, then the results with normalization are > > correct; if not then the results with normalization are still correct. > > Sounds like we are pretty much stuck with normalization.... > > > > Sean > Why should one do normalization if the results aren't different. But, in > that case it really does not matter and one can do it or not. Perhaps for the following reasons : 1) Generalisability and comparison of results with other datasets 2) Ease of programming and automation 3) Publish-ability 4) In case there were other biases that we were not aware of. How do you measure the agreement between two arrays may only show some aspects of the data. For example, the M-A plot can reveal some biases within an array that correlation between pairs will not. There are many clever people (most of them on this list) who have spent their time improving preprocessing algorithms. There must be a reason why they have spent their time and effort on normalisation. Regards, Adai

Gorjanc and Vijay This is a misconception as to why to normalize the data. It is not so that we can get "pleasing" results or agreement between analytic methods but because statistically it is the correct thing to do. If I use the wrong statistical test on a set of data (e.g. parametric tests on data that violates all the assumptions) and it gives the same result as an appropriate non-parametric analysis that does not make it "right" and ok to do again. It means I got lucky. If the analysis of non-normalized data is the same as of normalized data you are lucky not right. Sean is on target- if they agree normalize; if they do not agree normalize. I would add to that why bother analyzing the non-normalized data. Gordon Gordon A. Barr, Ph.D. Senior Research Scientist NYS Psychiatric Institute Columbia College of Physicians and Surgeons 212-543-5694 (V) 212-543-5467 (F) "There is no flag large enough to cover the shame of killing innocent people." -- Howard Zinn _____________________________________________________ This e-mail is confidential and may be privileged. Use or disclosure of it by anyone other than a designated addressee is unauthorized. If you are not an intended recipient, please delete this e-mail. On Apr 13, 2005, at 8:29 AM, Gorjanc Gregor wrote: >> -----Original Message----- >> From: Sean Davis [mailto:sdavis2@mail.nih.gov] >> Sent: sre 2005-04-13 14:16 >> To: Gorjanc Gregor >> Cc: bioconductor@stat.math.ethz.ch >> Subject: Re: [BioC] is-normalisation-really-required >> >> On Apr 13, 2005, at 8:03 AM, Gorjanc Gregor wrote: >> >>> Hi! >>> >>> You might try analysis with and without normalization and take >>> a look at the results. If they say the same thing than I would >>> say, no it is not necessary to do normalization. >>> >> >> So, if the two results agree, then the results with normalization are >> correct; if not then the results with normalization are still correct. >> Sounds like we are pretty much stuck with normalization.... >> >> Sean > Why should one do normalization if the results aren't different. But, > in > that case it really does not matter and one can do it or not. > > >>> dear friends >>> i have situation, where i thought its ok for me not to >>> do normalisation, i am afraid i may be wrong. i want >>> your advice in this regard. >>> >>> we performed a wild type - mutant, dye-swap >>> experiment. >>> when we analysed the intensity values, they were >>> consistant among the two experiment (dye-swap). ie., >>> almost same values for mutants in both the experiments >>> of the dye-swap. >>> since the values are almost same, i thought there >>> might not be any dye-bias, so i just went ahead, >>> averaged the two values, found out their ratio and >>> filtered genes with 2 fold change. >>> >>> so i have done this without normalisation. >>> i am afraid, i might be wrong, my 2 fold chaging genes >>> might be wrong... >>> kindly give me your advice in this regard. >>> i did analyse the data with limma, but the topTable >>> genes there never correlates with my 2 fold genes. >>> >>> kindly correct me. >>> thanks >>> >>> vijay >>> graduate student >>> department of biological sciences >>> the university of southern mississippi >>> MS, USA >> >> -- >> Lep pozdrav / With regards, >> Gregor Gorjanc >> >> ---------------------------------------------------------------------- >> - >> - >> University of Ljubljana >> Biotechnical Faculty URI: http://www.bfro.uni- lj.si/MR/ggorjan >> Zootechnical Department email: gregor.gorjanc <at> bfro.uni- lj.si >> Groblje 3 tel: +386 (0)1 72 17 861 >> SI-1230 Domzale fax: +386 (0)1 72 17 888 >> Slovenia >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor