[BioC] limma for identifying differentially expressed genes from illumina data

[Md.Mamunur Rashid]

Dear All,

I am working with a set of illumina microarray data (96 samples)  from three groups 
(i.e. group-1(X) group-2 (Y),  group-3(Z)). I have read the data using lumiR methoda 
and normalized the data using lumi Methods. Now I need to identify the differentially 
expressed genes by comparing each of these groups with each other. 
I am using linear model fit in limma package and topTable method to identify top N differentially
expressed genes.


1. When I am adjusting the p value  using "BH" method in the topTable method the adj.p.value is getting too high
   as a result none of the genes are getting selected with threshold p.value = 0.05 . 
2.* *The logfold change values are very low. 

I have tried comparing all the 3 combination and the situation is more or less similar.
Does this indicate that none of the genes are not differentially expressed at all!!!
(Which might be a odd) or I am doing something wrong???!!! 

Please I will really appreciate if any body can give any advice.

Thanks in advance.

regards,
Md. Mamunur Rashid

****************************************************
I have attached the code and the result below. 
******************************************************

## norm_object is the normalized object

d_Matrix <- exprs(norm_object)
probeList <- rownames(d_Matrix)

## 32 samples from each group without any pair 

sampleType <- c("X","X","Y","Y",..........96  samples.......... ,"Z","X","Y","Y","X","X","Z","I","X") 
design <- model.matrix(~0+sampleType)
colnames(design_norm_test) <- c('X','Y','Z')
fit1 <- lmFit(d_Matrix,design)
constrast.matrix <- makeContrasts (Y-X , Z-Y , Z-X, levels=design)
fit1_2 <- contrasts.fit(fit1,contrast.matrix)
fit1_2 <- eBayes(fit1_2)
topTable(fit1_2,coef=1, adjust="BH")

>

                ID       logFC  AveExpr         t      P.Value adj.P.Val
6284  ILMN_1111111  0.11999169 6.341387  4.828711 5.237786e-06 0.2325975  
12919 ILMN_2222222 -0.05966259 6.187268 -4.678886 9.532099e-06 0.2325975
6928  ILMN_3333333 -0.31283278 6.881315 -4.561366 1.513503e-05 0.2462115
42428 ILMN_4444444 -0.13036276 6.815443 -4.288051 4.321272e-05 0.3964163
36153 ILMN_5555555  0.25070344 6.487644  4.190735 6.220719e-05 0.3964163
36152 ILMN_6666666  0.21502145 6.470917  4.158153 7.019901e-05 0.3964163
28506 ILMN_7777777  0.13918530 6.616036  4.158140 7.020219e-05 0.3964163
11763 ILMN_8888888 -0.17331384 7.322021 -4.154668 7.110990e-05 0.3964163
38906 ILMN_9999999  0.05532714 6.224477  4.093623 8.903425e-05 0.3964163
4728  ILMN_0000000  0.05371882 6.177268  4.081921 9.293339e-05 0.3964163
           
	  B

12919 3.236579
6928  2.801832
42428 1.818263
36153 1.477781
36152 1.364969
28506 1.364927
11763 1.352940
38906 1.143329
4728  1.103392