Dear all, I need some help on matrix design and B statistics by using limma package. I want to compare gene expression in 2 groups of cDNA samples. The experiment compares 4 treated mice(#1,#2,#3,#4) and 4 control mice (#5,#6,#7,#8).
The target file is FileName Cy3 Cy5 mice1.spot Control_#5 Treat_#1 mice2.spot Treat_#1 Control_#5 mice3.spot Control_#6 Treat_#2 mice4.spot Treat_#3 Control_#7 mice5.spot Control_#8 Treat_#4 The first slide (mice1.spot) and the second slide(mice2.spot) are dye-swap. There is no common reference. There are 3 replicated spots of each gene on each array (384 genes in total). MA is an object of class marrayNorm, below is what I did. >design <- c(1,-1,1,-1,1) >cor <- duplicateCorrelation(MA,design,ndups=3) >cor$consensus.correlation [1] 0.506 >fit <- lmFit(MA,design,ndups=3,correlation=cor$consensus.correlation) >fit <- eBayes(fit) >topTable(fit,n=20,adjust="fdr") The result is, ID M A t P.Value B 348 -1.3 10.8 -3.98 0.577 -4.47 371 -1.91 11.5 -3.36 0.577 -4.47 172 -2.56 13.4 -3.36 0.577 -4.47 273 -0.98 10.3 -3.22 0.577 -4.48 ... It seems this is no evidence of differential expression. But if I use the first three slides to do analysis, design <- c(1,-1,1),the result is good, B>5, P.Value is very small. I am wondering if my design matrix is right? Many thanks in advance and best regards. Michelle ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html