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

[Wei Shi]

Dear Md.Mamunur Rashid:

    Perhaps you can try filter out probes which do not express in all 
the arrays. You can use the detection p value to do this. The detection 
p value of a probe is the proportion of negative control probes which 
have intensities larger than that probe. You can filter out those probes 
which have detection p values larger than a cutoff (e.g. 0.01) in all 
the arrays. This might help you find some differentially expressed genes.

Cheers,
Wei

Md.Mamunur Rashid wrote:
> 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
>
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