Dear Mamun: Your code looks ok. I would suggest you do some quality checking for your raw data. For example, you can use boxplot to show the intensity distribution for each arrays and visually inspect the difference between your arrays. You have got quite a few arrays which should come from different chips. It might be worthwhile to check if their is a chip effect there. Or if your data were obtained from multiple batches, you should check whether there is a batch effect. You can also use the arrayWeights() function in limma to examine the relative quality weights for each array. Hope this helps. Cheers, Wei Md.Mamunur Rashid wrote: > Hi, > Thanks for your email and please accept my apology for late reply as I was away for few days. > I have tried filtering out the un-annotated genes which resulted to 28000 genes left for contrast. > But still I could not find any improvement in the result. In the > toptable() or decideTests() method when I am using "BH" as an adjusting method for p.value, > all the adjusted p.vales are changing to very high values ranging from 0.20 - 0.85. As a result > none of the genes can pass the cutoff p-value = 0.05. > > I am quite new to micro array analysis. I am attaching my code and result(at the bottom) here. > I will be really grateful if you can have a look,whether I have made any serious mistakes. > > thanks in advance. > > regards, > Mamun > > > > > > > > > On 08/28/2009 08:49 AM, J.Oosting@lumc.nl wrote: >> With Illumina arrays I usually filter out all the un-annotated probes >> before doing the statistical analysis. Especially in the 48k versions of >> these arrays about half the probes are not annotated, and for most >> purposes these are not interesting. >> >> Furthermore you should have a look at the quality of the arrays. >> -The average expression seems a bit low, but this could also be caused >> by the normalization method. >> -Check if the order of samples in the lumi object is the same as you >> define in the script. It is usually better to incorporate information >> like this in the phenoData slot of your object, and pull it out when >> defining groups for the analysis. >> >> >> Jan >> >> In your case filtering the probes would look something like this: >> >> library(illuminaHumanv3BeadID.db) >> # get annotations >> 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 >> design <-model.matrix(~0+pData(norm_object)$sampleType) >> colnames(design) <- 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") >> >> >> >>> 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. >>>> >>>> **************************************************** >>>> 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 >>>> > [[alternative HTML version deleted]]