If you really have good replicability, you should be able to use a gene-by-gene 2-sample t-test. --Naomi At 03:26 AM 2/19/2004, Matthew Hannah wrote: >Thanks for the responses. > >I used SAM by calling it on the exprsSet at was suggested. (It also worked >on table made from a text file output from rma). Anyway when I did the >analysis I got some 'interesting' results (see bottom of mail). Basically >unpaired the data gave me 2500 genes delta > 2 with a FDR of 0.003, and >paired (the U/T reps were conducted on the same plant batch) 4000 > 2 with >FDR of 0.001. However if I permutate the input data (see ex. 3 below) then >it just returns zeros. I guess this could be due to the coarseness of the >comparisons as suggested but I'll just give a few more details of my data >to see what people think. > >Basically the data is highly reproducable between biological replicates but >there is a big 'treatment' effect (this is what we want!?). For example Rsq >for within replicate x-y scatter plots of GCRMA data are 0.97 - 0.99, whilst >for the 3 U-T comparisons the values are 0.92-0.93. > >So as I interpret things then as soon as you permutate the data you get very >different data sets being mixed, massively increasing the variance and so few >significant changes are detected, hence a very low FDR. If you input the data >already permutated then some of the permutations of this data have loads of >sig changes (as they represent the correct data order) and so FDR is huge and >SAM returns all 0's. > >So where does this leave us, not using a test because the data is too 'good' >seems abit strange. But equallly not knowing how reliable it is is also not >good. > >Also on a more general note, when you get to this stage with so many changes >(1 rep U-T comparison with GCRMA - 5000 1.5x and 2500 2x changes) is the data >violating the assumption for the normalisation that most genes remain >unchanged? > >I'll investigate the limma package. > >Thanks >Matt > > > cl = c(0,0,0,1,1,1) > > rmasam <- sam(rma, cl) >SAM Analysis for the two class unpaired case. > >s0 = 0.0695 (The 15 % quantile of the s values.) > >SAM Analysis for a set of delta: > delta p0 false called FDR >1 0.2 0.723 9638 13270 0.525 >2 0.4 0.723 3951 9543 0.299 >3 0.6 0.723 1634 7480 0.158 >4 0.8 0.723 643 6068 0.077 >5 1.0 0.723 286 5155 0.040 >6 1.2 0.723 131 4394 0.022 >7 1.4 0.723 64 3764 0.012 >8 1.6 0.723 35 3259 0.008 >9 1.8 0.723 18 2846 0.005 >10 2.0 0.723 10 2478 0.003 > > > cl = c(1,2,3,-1,-2,-3) > > rmasamp <- sam(rma, cl) >SAM Analysis for the two class paired case. > >s0 = 0.0733 (The 45 % quantile of the s values.) > >SAM Analysis for a set of delta: > delta p0 false called FDR >1 0.2 0.52 9631 17275 0.290 >2 0.4 0.52 2378 13276 0.093 >3 0.6 0.52 695 10684 0.034 >4 0.8 0.52 257 8922 0.015 >5 1.0 0.52 127 7664 0.009 >6 1.2 0.52 53 6575 0.004 >7 1.4 0.52 28 5652 0.003 >8 1.6 0.52 14 4985 0.001 >9 1.8 0.52 9 4370 0.001 >10 2.0 0.52 5 3867 0.001 > > > cl = c(0,1,0,1,0,1) > > rmasamperm <- sam(rma, cl) >SAM Analysis for the two class unpaired case. > >s0 = 0.0549 (The 5 % quantile of the s values.) > >SAM Analysis for a set of delta: > delta p0 false called FDR >1 0.2 1 0 0 0 >2 0.4 1 0 0 0 >3 0.6 1 0 0 0 >4 0.8 1 0 0 0 >5 1.0 1 0 0 0 >6 1.2 1 0 0 0 >7 1.4 1 0 0 0 >8 1.6 1 0 0 0 >9 1.8 1 0 0 0 >10 2.0 1 0 0 0 > >_______________________________________________ >Bioconductor mailing list >Bioconductor@stat.math.ethz.ch >https://www.stat.math.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