Note: I should have said "paired t-test" and "paired Wilcoxon test" in my comments below. --Naomi At 10:44 AM 2/7/2005, Naomi Altman wrote: >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 > >_______________________________________________ >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