[BioC] [Fwd: result of linear model]

[Naomi Altman]

Please look at some of my previous comments on the interpretation of 
FDR.  If e.g. fewer than 1% of your genes are significant at p<.01, 
then the FDR is 100%.

--Naomi

At 09:01 AM 9/8/2009, Md.Mamunur Rashid wrote:


>-------- 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.
>
>regards,
>Md.Mamunur Rashid
>
>
>
>########   code #############
>
>
>## norm_object is the normalized object
>
>d_Matrix<- exprs(norm_object)
>probeList<- rownames(d_Matrix)
>library(illuminaHumanv3BeadID.db)
>
>## 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
>d_matrix<-d_matrix[idx,]
>
>
>## 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")
>
>
>
>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
>
>         *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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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111