Hello Sabrina, The function "fdr.adjust" with "resamp" option gives the matrix showing the cutoff values of x-critical at different target fdrs. It is investigator's decision to choose the cutoff fdr - the function fdr.adjust will give you a "guideline" corresponding to the chosen fdr. You should select the genes based on the lpe result whose z-value is higher than z-critical (at your chosen fdr). For fdr computation, the null distribution is derived from random sampling within each bin (refer to my recent paper (Jul 05) in BMC- Bioinformatics). Just FYI, I updated the LPE package (version 1.3.0) at bioconductor some time ago - it is still in the Bioc-devel - changes will show in the released version next month with the release of new Bioc version. Have attached the new version here. Best, Nitin ______________________ Nitin Jain, PhD <nitin.jain at="" pfizer.com=""> Non Clinical Statistics Pfizer, Inc. (Groton, CT) Bldg: 260, # 1451 Ph: (860) 686-2526 (Office) Fax: (860) 686-6170 -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of Sabrina Carpentier Sent: Tuesday, August 30, 2005 10:13 AM To: Bioconductor at stat.math.ethz.ch Subject: [BioC] incoherence with LPE Dear users, I use the LPE package to detect differentially expressed genes but I'm pretty surprised that I didn't find the same genes according the order of my groups >library(LPE) >ind_g1<-c(2,4:6) >ind_g2<-c(1,3,7) >group1<-data[,g1] >group2<-data[,g2] # Finding the baseline distribution of condition 1 and 2. >var.1 <- baseOlig.error(group1, q=0.01) >var.2 <- baseOlig.error(group2, q=0.01) >lpe.result <- lpe(group1,group2, var.1, var.2,probe.set.name=rownames(data)) >finalresult<-fdr.adjust(lpe.result,adjp="resamp") >finalresult target.fdr z.critical [1,] 0.001 5.606208 [2,] 0.010 5.606208 [3,] 0.020 5.606208 [4,] 0.030 5.606208 [5,] 0.040 4.720516 [6,] 0.050 4.123739 [7,] 0.060 2.823956 [8,] 0.070 2.593902 [9,] 0.080 2.534597 [10,] 0.090 2.482537 [11,] 0.100 2.375929 [12,] 0.150 2.015756 [13,] 0.200 1.630128 [14,] 0.500 1.500434 >lpe.inv<-lpe(group2,group1,var.2,var.1,probe.set.name=rownames(data)) >final.inv<- fdr.adjust(lpe.inv, adjp="resamp") >final.inv target.fdr z.critical [1,] 0.001 3.096275 [2,] 0.010 2.793027 [3,] 0.020 2.593902 [4,] 0.030 2.482537 [5,] 0.040 2.482537 [6,] 0.050 2.368713 [7,] 0.060 2.368713 [8,] 0.070 2.216946 [9,] 0.080 2.091183 [10,] 0.090 1.774938 [11,] 0.100 1.639910 [12,] 0.150 1.547681 [13,] 0.200 1.537522 [14,] 0.500 1.459746 When I change the order of my groups, I didn't find the same z-critical. Do you think that's right? Thanks Sabrina Sabrina Carpentier Service Bioinformatique Institut Curie - Bat. Trouillet Rossignol (4e ?tage) 26 rue d'Ulm - 75248 Paris Cedex 5 - FRANCE Tel : +33 1 42 34 65 21 [[alternative HTML version deleted]] LEGAL NOTICE Unless expressly stated otherwise, this message is confidential and may be privileged. It is intended for the addressee(s) only. Access to this E-mail by anyone else is unauthorized. If you are not an addressee, any disclosure or copying of the contents of this E-mail or any action taken (or not taken) in reliance on it is unauthorized and may be unlawful. If you are not an addressee, please inform the sender immediately.