Hi,

I`m trying to find significant DE genes using the contrast (-1,.5,.5).
I want to test {cond1-mean(cond2,cond3)}
I created a data-set where all conditions have exact same values, divided
into 3 conditions and 3 replicates/condition.

Here is the data I`m using.

150    150    150    150    150    150    150    150    150
510    510    510    510    510    510    510    510    510
550    550    550    550    550    550    550    550    550
500    500    500    500    500    500    500    500    500
501    501    501    501    501    501    501    501    501
505    505    505    505    505    505    505    505    505
50    50    50    50    50    50    50    50    50


If the run the following code, I get very significant p-values (in lrt
table) and all genes are declared to be up-regulated.
Am I interpreting the output incorrectly or do I need to set up the
contrast in a different way?

  h <- read.delim("test10.txt")
  h <-h[rowSums(h)>0,]
   grp <- factor(c(1,1,1,2,2,2,3,3,3))
   design <- model.matrix(~grp)
   h <- DGEList(counts=h,lib.size=colSums(h),group=grp)
  h <- calcNormFactors(h)
  h <- estimateGLMCommonDisp(h, design, verbose=TRUE)
  h <- estimateGLMTrendedDisp(h, design)
  h <- estimateGLMTagwiseDisp(h, design)
 fit <- glmFit(h, design)
  lrt <- glmLRT(fit,contrast=c(-1,0.5,0.5))
  summary(de <- decideTestsDGE(lrt, p=0.05, adjust="BH"))

   [,1]
-1    0
0     0
1     6





> sessionInfo()
R version 2.15.2 (2012-10-26)
Platform: i386-w64-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United
States.1252

attached base packages:
[1] splines   stats     graphics  grDevices utils     datasets  methods
base

other attached packages:
 [1] epicalc_2.15.1.0        nnet_7.3-5
MASS_7.3-22             survival_2.36-14        foreign_0.8-51
edgeR_3.0.4
 [7] limma_3.14.3            goseq_1.10.0
geneLenDataBase_0.99.10 BiasedUrn_1.04          GenomicFeatures_1.10.1
AnnotationDbi_1.20.3
[13] Biobase_2.18.0          GenomicRanges_1.10.5
IRanges_1.16.4          BiocGenerics_0.4.0      BiocInstaller_1.8.3

loaded via a namespace (and not attached):
 [1] biomaRt_2.14.0     Biostrings_2.26.2  bitops_1.0-5
BSgenome_1.26.1    DBI_0.2-5          grid_2.15.2        lattice_0.20-10
Matrix_1.0-10
 [9] mgcv_1.7-22        nlme_3.1-105       parallel_2.15.2
RCurl_1.95-3       Rsamtools_1.10.2   RSQLite_0.11.2     rtracklayer_1.18.1
stats4_2.15.2
[17] tools_2.15.2       XML_3.95-0.1       zlibbioc_1.4.0

Thanks,
Al

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