Dear all, I'm analysing data single color data (only intensities at F532) using limma. I have 14 arrays in one group and 5 in the other group. I want to compare them so my targets file is as follows FileName Mal Sain M2.gpr 1 0 M3.gpr 1 0 M4.gpr 1 0 M5.gpr 1 0 M6.gpr 1 0 M7.gpr 1 0 M8.gpr 1 0 M9.gpr 1 0 M10.gpr 1 0 M11.gpr 1 0 M12.gpr 1 0 M13.gpr 1 0 M14.gpr 1 0 M15.gpr 1 0 S1.gpr 0 1 S2.gpr 0 1 S3.gpr 0 1 S4.gpr 0 1 S5.gpr 0 1 I use some correction and normalisation methods as recommanded in limma user guide. Afterthat, I make my design and as I have 2 technical replicate on each array, I wanted to compute duplicatecorrelation coefficient with spacing=500 ( ref. .GAL file as follows : ATF 1 52 5 Type=GenePix ArrayList V1.0 BlockCount=48 BlockType=0 URL=http://genome-www4.stanford.edu/cgi-bin/SGD/locus.pl?locus=[ID] "Block1=2645, 2295, 100, 12, 300, 8, 300" "Block2=7145, 2295, 100, 12, 300, 8, 300" "Block3=11555, 2235, 100, 12, 300, 8, 300" "Block4=16055, 2235, 100, 12, 300, 8, 300" The  corfit$consensus is not good (0,36). Do I still use this parameters for lmfit??? Anyway, the more strange is with or without this parameters I have p-adjust value very very low. first line of my result : Block Row Column ID Name X.Intercept. factor.pData.population.sain AveExpr F P.Value adj.P.Val 39 2 10 42554 hsa-miR-923 13.4125293049973 -0.0517387609947947 13.3989138415776 25264.4487822867 1.20563134868457e-63 1.40456052121752e-60 Is anybody help me and what is wrong. As you can see in UNFILTERED section, I have less than 50% genes for the analysis. Could it be the explanation of these strange result?? Thanks in advance My srcipt: library(limma) myFilter = function(X) { H_threshold=2 okFLAG = X$Flags > -49; okm1 =abs(X[,"F532 Median"]-X[,"F532 Mean"]) okm2 = 0.5*(X[,"F532 Median"]+X[,"F532 Mean"]) okH = ((okm1)/okm2) <h_threshold as.numeric(okflag="" &="" okh)}="" targets="readTargets(file=" targets.txt","="" path="NULL," sep="\t" )="" e="read.maimages(targets,source=" genepix",wt.fun="myFilter,columns=list"" (e="F532 Mean" ,eb="B532 Mean" ,names="Name" ))="" #taux="" de="" filtrage="" unfiltered="apply(E$weights,MARGIN=2,FUN=mean)" round(unfiltered,2)="" # ="" m2  ="" m3  ="" m4  ="" m5  ="" m6  ="" m7  ="" m8  ="" m9 ="" m10 ="" m11 ="" m12 ="" m13 ="" m14 ="" m15="" s1  ="" s2="" #0.41="" 0.43="" 0.34="" 0.29="" 0.51="" 0.35="" 0.48="" 0.35="" 0.29="" 0.56="" 0.38="" 0.56="" 0.61="" 0.52="" 0.48="" 0.34="" # ="" s3  ="" s4  ="" s5="" #0.36="" 0.46="" 0.47="" #boite="" a="" moustache="" avant="" correction="" boxplot(as.data.frame(e$e),main="Mean intensities" )="" #correction="" background="" -="" subtract="" esub="" <-="" backgroundcorrect(e,="" method="subtract" ,offset="0)" boxplot(as.data.frame(esub$e),="" main="Mean intensities - subtract" )="" #correction="" background="" -="" minimum="" emin="" <-="" backgroundcorrect(e,="" method="minimum" ,offset="1)" boxplot(as.data.frame(emin$e),="" main="Mean intensities - minimum" )="" #correction="" background="" -="" normexp="" enorm="" <-="" backgroundcorrect(e,="" method="normexp" ,offset="1)" boxplot(as.data.frame(enorm$e),="" main="Mean intensities - normexp" )="" #normalisation="" quantile="" norme="" <-="" normalizebetweenarrays(enorm,method="quantile" )="" boxplot(as.data.frame(norme$e),="" main="Normalized intensities" )="" pdata="" <-="" data.frame(population="c('mal','mal'," 'mal',="" 'mal','mal','mal','mal','mal','mal','mal','mal','mal','mal','mal','sai="" n','sain','sain','sain','sain'))="" #="" create="" design="" matrix="" design="" <-="" model.matrix(~factor(pdata$population))="" #="" in="" my="" .gpr="" files="" all="" mirnas="" contain="" the="" string="" "mir"="" in="" their="" name="" #="" so="" the="" grep="" function="" can="" be="" used="" to="" extract="" all="" of="" these,="" removing="" #="" all="" control="" signals="" and="" printing="" buffers="" etc.="" #="" you="" need="" to="" check="" your="" .gpr="" files="" to="" find="" which="" signals="" you="" should="" extract.="" mirs="" <-="" c(grep("-mir-",="" norme$genes$name),="" grep("-let-",="" norme$genes$name))="" norme.final="" <-="" norme[mirs,="" ]="" norme.final="" <-norme.final[order(norme.final$genes$name),="" ]="" norme.final$genes$name="" #="" calculate="" duplicate="" correlation="" between="" the="" 2="" replicates="" on="" each="" array="" corfit="" <-="" duplicatecorrelation(norme.final,="" design,="" ndups="2,spacing=500,)" corfit$consensus="" #="" show="" a="" boxplot="" of="" the="" correlations="" boxplot(tanh(corfit$atanh.correlations))="" #="" fit="" the="" linear="" model,="" including="" info="" on="" duplicates="" and="" correlation="" fit="" <-="" lmfit(norme.final,="" design,="" ndups="2," correlation="corfit$consensus)" #fit="" <-="" lmfit(norme.final,="" design)="" #="" calculate="" values="" using="" ebayes="" ebayes="" <-="" ebayes(fit)="" #="" output="" a="" list="" of="" top="" differnetially="" expressed="" genes...="" result="topTable(ebayes,number=4608," p="0.05,adjust" =="" "bh")="" genes="result$Status==" genes""="" write.table(result,"gene.txt",quote="FALSE,sep=" \t",row.names="FALSE,"" col.names="TRUE)" mikel="" [[alternative="" html="" version="" deleted]]="" <="" div="">