thank you naomi, this is exactly what i've done til now, first i have normalized each patient seperatly (or some patients together if i had three chips of one patient (as RMA works not so good with a small uneven number of chips)), and then when we finished to do the chips from all patients i normalized them all togehter. in the first analysis i searched for the genes that were regulated (bigger or smaller then M=1 (M=-1 respectively)) in most patients. the genes found by this method are fairly the same like those i got when normalizing all patients together and doing a wilcoxon test. that's all fine, but my boss was surprised to see that our replicates (=quality controls) looked better in the "single patient normalization way" than after normalizing all patients toghether. well, thanks for your answer, i think i will normalize all chips together and my boss has to accept that our controls are not as nice as they should be... thanks, jo Quoting Naomi Altman <naomi@stat.psu.edu>: > Dear Johannes, > Actually, technical replication is of little interest when you have > biological replication. If I understand your experiment, you have 40 > patients, each measured at 2 times. > > Because of the pairing, you have several options for appropriate > normalization and analysis: > > 1) Normalize the before and after for each patient together, and analyze M. > > You could use either RMA, or a simpler M vs A loess for this. > > 2) Normalize all the arrays together and then compute M for each patient. > > I would use RMA or gcRMA for this. > > In either case, I would simply use limma with the contrast > rep(1,npatients) since this gives the t-test for before-after which > seems to be of most interest. Limma has an advantage over ordinary > t-tests in that it combines some information across genes. However, > I expect it to be very similar to ordinary t-tests (or Wilcoxon > tests) because you have a fairly large sample size. Any of these > methods are appropriate. > > Incidentally, the technical reps are interesting for quality control, > but should not be included in this analysis. > > --Naomi > > At 09:48 AM 2/7/2005, Dipl.-Ing. Johannes Rainer wrote: >> thanks arne >> >> i have no replicates, affymetrix is still a little bit expensive ;) >> . all our chips were made by ourself and by looking at the >> histograms of the raw values there are no differences at all. in the >> whole experiment we made also two replicates, one with the same RNA, >> but different amount before amplification (one time 5 mug, the >> second time 1 mug) and the second replicate is RNA from the same >> patient, same time point, but the RNA was extracted by two different >> people not at the same time. if i normalize only those replicated >> chips i see nearly no differences between them (with a M (log2 >> regulation value) cut off of M=1 i get about 30 probe sets that >> differ), but when i normalize all 80 chips of all patients together >> the replicated chips show more differences... in my opinion i have >> to normalize all patient chips together, exspecially if i want to do >> for example a wilcox between all 0 hour and 6 hours chips. >> can you tell me a little bit more about the linear model you have >> used to merge the results? >> >> regards, jo >> >> >> Quoting Arne.Muller@sanofi-aventis.com: >> >>> Dear Johannes, >>> >>> I've a study with 84 affy chip to characterize a dose effect of a >>> drug. The study was conducted in 3 different laboratories. There >>> are strong differences betweent the laboratories and I've RMA >>> normalized per laboratory and then merged the results in a single >>> linear moel including the laboratory as an additional factor. Maybe >>> you can make the patient or source of RNA a random factor in a mixe >>> effects model - if you've replication per patient. >>> >>> Just looking at those genes with a significant dose effect I did >>> not find much differences between normalizing all chips together and >>> normalizing per laboratory. >>> >>> regards, >>> >>> Arne >>> >>> >>>> -----Original Message----- >>>> From: bioconductor-bounces@stat.math.ethz.ch >>>> [mailto:bioconductor-bounces@stat.math.ethz.ch]On Behalf Of Dipl.-Ing. >>>> Johannes Rainer >>>> Sent: 07 February 2005 10:13 >>>> To: bioconductor@stat.math.ethz.ch >>>> Subject: [BioC] RMA normalization,which samples should be normalized >>>> together >>>> >>>> >>>> hi, >>>> we are interested in the response of patients to a special treatment, >>>> so we have patient samples before and after treatment. i have >>>> normalized this samples in different ways using RMA. As RMA tries to >>>> detect and correct probe effects by looking at the expresison >>>> levels of >>>> the probes across all chips it is not surprising that the outcome of >>>> the analysis differs depending on which chips i normalize together. >>>> It is clear that i have to normalize all patient samples >>>> together if i >>>> want to compare the expression values of the genes (lets say using >>>> statistical tests). i am also analyzing the chips using the 'old >>>> fashioned way' by using M and A values and i suppose it is not >>>> problematic at all to compare M values of lets say patient 1, 6 hours >>>> sample against 0 hours sample with those from patient 2, also 6 hours >>>> versus 0 hours where the chips from the two patients were NOT >>>> normalized together. >>>> >>>> -now my question is if someone else has experience in what samples >>>> could and should be normalized together with RMA. I saw that ther are >>>> (big) differences in the regulation (M) values if i normalize two >>>> different patients together compared with the values that i >>>> get when i >>>> normalize only samples from the same patients together. >>>> >>>> thanks in advance >>>> >>>> _______________________________________________ >>>> 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 > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Bioinformatics Consulting Center > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > > ----- End forwarded message -----