[BioC] [Fwd: result of linear model]

Md.Mamunur Rashid mamunur.rashid at kcl.ac.uk
Tue Sep 8 15:01:33 CEST 2009

-------- Original Message --------
Subject: 	result of linear model
Date: 	Tue, 8 Sep 2009 13:59:41 +0100
From: 	Md.Mamunur Rashid <mamunur.rashid at kcl.ac.uk>
To: 	smyth at wehi.edu.au <smyth at wehi.edu.au>, 
bioconductor at stat.math.ethz.ch <bioconductor at stat.math.ethz.ch>

Dear Gordon,

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)). 32 samples from each group.

I have read the data using lumiR method and processed the data using lumi Methods(lumiExpresso).

Now I need to identify the differentially expressed genes by comparing each of these groups
with each other. I am using linear model in limma package and topTable method to identify
top N differentially expressed genes. Below is the code that I have used to design the test
in linear model. If you look at the result of the top-table all the adjusted p-values are very
high. as a result none of the genes passes through the cut-off p-value 0.05. I have tried all
the tree combination and in all cases adjusted p-values are.

Now I have tested the same code on another set of data that has 20 samples that has been analyzed
before (i.e I have the results before hand). In that case also adjusted p-values are very high but genes in
the top-table are correct( i.e matches with the result of the previous analysis).

so in this situation, I will be really grateful if you can give me some suggestion on

1. Is there anything wrong in my code which is making the adjusted p-value so high.???
2. Or it might be a problem in the data pre-process phase.???

*** I have attached the code , the result and the array-weight in a text file.
     Please have a look.

thanks in advance. If anybody else want to have a suggestion, you are most welcome.

Md.Mamunur Rashid

########   code #############

## norm_object is the normalized object

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

## filtering out the un-annotated genes

x<- illuminaHumanv3BeadIDSYMBOL
annotated_ids<- mappedkeys(x)
d_Matrix<- exprs(norm_object)
probeList<- rownames(d_Matrix)
idx<- probeList %in% annotated_ids

## 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")
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")

Result of the toptable method:

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


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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