Question: limma for identifying differentially expressed genes from illumina data

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Md.Mamunur Rashid •

**260**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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modified 10.1 years ago
by
Wei Shi •

**3.2k**• written 10.1 years ago by Md.Mamunur Rashid •**260**