Hi, I'm using limma to analyze an Operon Oligo data set. I would try to use in down stream part of the analysis a nestedF approach. The problem is that using > results<-decideTest(fit2,method="nestedF",adjust.method="fdr") I had this error message Errore: nestedF method can't handle NA p-values Can I use instead classifyTestsF? What is the difference? I will have with this function a way to obtain adjusted p.values Or otherwise what is the best solution to avoid NA P.values? Below, the R code of my analysis:what is wrong??? >Targets<-readTargets() >Targets file.name Cy5 Cy3 1 013.gpr nsM linf B 2 015.gpr UM linf B 3 018.gpr UM linf B 4 021.gpr UM linf B 5 022.gpr UM linf B 6 032.gpr nsM linf B 7 039.gpr UM linf B 8 047.gpr UM linf B 9 049.gpr nsM linf B 10 067.gpr sM linf B 11 068.gpr nsM linf B 12 079.gpr sM linf B 13 080.gpr nsM linf B 14 089.gpr linf B 15 098.gpr sM linf B 16 107.gpr sM linf B 17 119.gpr UM linf B 18 127.gpr sM linf B 19 128.gpr UM linf B 20 129.gpr nsM linf B 21 149.gpr UM linf B 22 164.gpr nsM linf B 23 181.gpr sM linf B 24 185.gpr sM linf B 25 186.gpr UM linf B 26 188.gpr nsM linf B 27 191.gpr UM linf B 28 195.gpr UM linf B 29 245.gpr UM linf B 30 257.gpr sM linf B 31 258.gpr nsM linf B 32 286.gpr nsM linf B 33 287.gpr sM linf B 34 288.gpr nsM linf B 35 304.gpr nsM linf B 36 305.gpr sM linf B 37 313.gpr nsM linf B 38 316.gpr sM linf B 39 318.gpr sM linf B 40 320.gpr sM linf B 41 323.gpr nsM linf B 42 325.gpr sM linf B 43 326.gpr nsM linf B 44 328.gpr UM linf B 45 329.gpr sM linf B 46 331.gpr UM linf B 47 332.gpr sM linf B 48 334.gpr sM linf B 49 337.gpr sM linf B 50 338.gpr sM linf B 51 340.gpr sM linf B 52 344.gpr UM linf B 53 345.gpr UM linf B 54 346.gpr nsM linf B 55 354.gpr UM linf B 56 369.gpr nsM linf B 57 378.gpr nsM linf B 58 382.gpr nsM linf B >RG<-read.maimages(Targets$file.name,source="genepix",wt.fun=wtflags(0 .1)) >MAmov<-normalizeWithinArrays(RG,bc.method="movingmin") >MAmovQ<-normalizeBetweenArrays(MA,method="quantile") >group<-factor(c("nsM",rep("UM",7),"nsM","sM","nsM","sM","nsM","NC",re p("sM",2),"UM","sM","UM","nsM","UM","nsM", rep("sM",2),"UM","nsM",rep("UM",3),"sM",rep("nsM",2),"sM",rep("nsM",2) ,"sM","nsM",rep("sM",3),"nsM","sM","nsM", "UM","sM","UM",rep("sM",5),rep("UM",2),"nsM","UM",rep("nsM",3)),levels =c("nsM","UM","sM","NC")) >design<-model.matrix(~0+group) >colnames(design)<-c("nsM","UM","sM","NC") >fit<-lmFit(MAmovQ,design,weights=MAmovQ$weights,ndups=1) >cont.matrix<-makeContrasts(UM-(nsM+sM),UM-nsM,UM-sM,nsM- sM,levels=design) > cont.matrix UM - (nsM + sM) UM - nsM UM - sM nsM - sM nsM -1 -1 0 1 UM 1 1 1 0 sM -1 0 -1 -1 NC 0 0 0 0 >fit2<-contrasts.fit(fit,cont.matrix) >results<-decideTests(fit2,method="nestedF",adjust.method="fdr",p.valu e=0.05)