Dear Han Weinong, What makes you think there is something wrong with your data? You appear to have no statistically significant results. How to deal with this has come up previously on this list. e.g. Toptable sorts the genes in order of most likely to be differentially expressed - you could use real time PCR on some of the genes at the top of the list. --Naomi Altman At 08:53 PM 9/4/2005, weinong han wrote: >Hi. List, > >17 samples(3 normal samples, 14 NPC tumor samples from different >patients) > >were used in my Affymetrix microarray experiments. The small size > >microarrays were recommmended to be analyzed using LIMMA. After >moderated > >t statistic, I found the results were not so nice. please see >attachment. > > > >What is wrong with my data? How to do next? > > > >Any advice and suggestions will be much appreciated. > > > >I am looking forward to your response > > > > > > >Best Regards > >Han Weinong >hanweinong at yahoo.com > >__________________________________________________ > > > > > > dir() > [1] "G05.CEL" "G09.CEL" "G10.CEL" "G12.CEL" "G15.CEL" > [6] "G19.CEL" "GF.CEL" "GM.CEL" "H044.CEL" "H05.CEL" >[11] "H07.CEL" "H10.CEL" "H11.CEL" "H14.CEL" "hgu133acdf" >[16] "N01.CEL" "N02.CEL" "N03.CEL" > > library(limma) > > library(affy) >Loading required package: Biobase >Loading required package: tools >Welcome to Bioconductor > Vignettes contain introductory material. To view, > simply type: openVignette() > For details on reading vignettes, see > the openVignette help page. >Loading required package: reposTools > > Data <- ReadAffy() > > eset <- rma(Data) >Background correcting >Normalizing >Calculating Expression > > pData(eset) > sample >G05.CEL 1 >G09.CEL 2 >G10.CEL 3 >G12.CEL 4 >G15.CEL 5 >G19.CEL 6 >GF.CEL 7 >GM.CEL 8 >H044.CEL 9 >H05.CEL 10 >H07.CEL 11 >H10.CEL 12 >H11.CEL 13 >H14.CEL 14 >N01.CEL 15 >N02.CEL 16 >N03.CEL 17 > > tissue <- > c("C","C","C","C","C","C","C","C","C","C","C","C","C","C","N","N","N") > > design <- model.matrix(~factor(tissue)) > > colnames(design) <- c("C", "CvsN") > > design > C CvsN >1 1 0 >2 1 0 >3 1 0 >4 1 0 >5 1 0 >6 1 0 >7 1 0 >8 1 0 >9 1 0 >10 1 0 >11 1 0 >12 1 0 >13 1 0 >14 1 0 >15 1 1 >16 1 1 >17 1 1 >attr(,"assign") >[1] 0 1 >attr(,"contrasts") >attr(,"contrasts")$"factor(tissue)" >[1] "contr.treatment" > > > > fit <-lmFit(eset,design) > > fit <-eBayes(fit) > > options(digits=2) > > topTable(fit,coef=2,n=50,adjust="fdr") > ID M A t P.Value B >22193 78047_s_at 0.60 7.3 5.3 0.82 -3.4 >2594 203065_s_at -1.26 6.7 -5.0 0.82 -3.5 >10680 211245_x_at 0.58 4.9 4.7 1.00 -3.6 >17919 218554_s_at 0.59 4.7 4.5 1.00 -3.6 >9431 209945_s_at -0.67 6.1 -4.5 1.00 -3.6 >4556 205029_s_at 3.09 3.6 4.4 1.00 -3.6 >4557 205030_at 3.58 4.6 4.3 1.00 -3.6 >5845 206319_s_at 0.82 4.0 4.3 1.00 -3.7 >21838 36019_at 0.67 6.7 4.2 1.00 -3.7 >5209 205682_x_at 0.61 4.8 4.2 1.00 -3.7 >6791 207266_x_at -0.95 7.8 -4.0 1.00 -3.7 >21916 38447_at 0.66 7.3 4.0 1.00 -3.7 >21914 38340_at 0.59 6.3 3.9 1.00 -3.8 >16241 216871_at 0.59 3.4 3.9 1.00 -3.8 >982 201454_s_at -0.65 6.2 -3.9 1.00 -3.8 >22024 46256_at 0.62 7.2 3.9 1.00 -3.8 >7489 207978_s_at 0.47 4.3 3.8 1.00 -3.8 >4452 204925_at 0.48 5.0 3.8 1.00 -3.8 >7121 207600_at 0.48 5.5 3.7 1.00 -3.8 >12443 213060_s_at 1.41 6.0 3.7 1.00 -3.8 >1619 202091_at 0.51 3.3 3.7 1.00 -3.8 >9890 210412_at 0.53 3.5 3.6 1.00 -3.8 >21922 38707_r_at 0.45 7.8 3.6 1.00 -3.9 >2715 203187_at 0.59 5.8 3.6 1.00 -3.9 >3354 203827_at -0.99 5.5 -3.6 1.00 -3.9 >5340 205813_s_at 0.52 5.8 3.5 1.00 -3.9 >2445 202916_s_at -0.61 6.1 -3.5 1.00 -3.9 >18810 219446_at -0.68 5.9 -3.5 1.00 -3.9 >14010 214632_at -0.54 4.2 -3.4 1.00 -3.9 >2915 203388_at 0.46 6.2 3.4 1.00 -3.9 >21936 396_f_at 0.70 7.7 3.4 1.00 -3.9 >16292 216922_x_at 0.61 3.8 3.4 1.00 -3.9 >13378 213999_at 0.44 4.5 3.4 1.00 -3.9 >9642 210158_at 0.58 4.4 3.4 1.00 -3.9 >19117 219753_at 0.65 5.6 3.4 1.00 -3.9 >10820 211405_x_at 0.53 5.3 3.4 1.00 -3.9 >19242 219878_s_at -0.58 4.5 -3.4 1.00 -3.9 >3275 203748_x_at -0.90 7.9 -3.4 1.00 -3.9 >16554 217187_at 0.58 5.7 3.4 1.00 -3.9 >8627 209133_s_at 0.54 4.7 3.3 1.00 -3.9 >17983 218618_s_at -1.15 8.0 -3.3 1.00 -3.9 >20977 221615_at 0.50 3.7 3.3 1.00 -3.9 >18562 219198_at 0.54 5.7 3.3 1.00 -3.9 >19513 220149_at 0.58 4.8 3.3 1.00 -3.9 >1770 202242_at 1.04 5.4 3.3 1.00 -3.9 >10081 210616_s_at -0.56 8.4 -3.3 1.00 -3.9 >17995 218630_at 0.37 5.4 3.3 1.00 -3.9 >3018 203491_s_at -0.67 5.1 -3.3 1.00 -3.9 >10823 211410_x_at 0.56 5.3 3.3 1.00 -3.9 >16351 216981_x_at 0.57 6.3 3.3 1.00 -3.9 > > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor 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

See this thread: Re: [BioC] adjusted p-values for large number of genes... At 08:53 PM 9/4/2005, weinong han wrote: >Hi. List, > >17 samples(3 normal samples, 14 NPC tumor samples from different >patients) > >were used in my Affymetrix microarray experiments. The small size > >microarrays were recommmended to be analyzed using LIMMA. After >moderated > >t statistic, I found the results were not so nice. please see >attachment. > > > >What is wrong with my data? How to do next? > > > >Any advice and suggestions will be much appreciated. > > > >I am looking forward to your response > > > > > > >Best Regards > >Han Weinong >hanweinong at yahoo.com > >__________________________________________________ > > > > > > dir() > [1] "G05.CEL" "G09.CEL" "G10.CEL" "G12.CEL" "G15.CEL" > [6] "G19.CEL" "GF.CEL" "GM.CEL" "H044.CEL" "H05.CEL" >[11] "H07.CEL" "H10.CEL" "H11.CEL" "H14.CEL" "hgu133acdf" >[16] "N01.CEL" "N02.CEL" "N03.CEL" > > library(limma) > > library(affy) >Loading required package: Biobase >Loading required package: tools >Welcome to Bioconductor > Vignettes contain introductory material. To view, > simply type: openVignette() > For details on reading vignettes, see > the openVignette help page. >Loading required package: reposTools > > Data <- ReadAffy() > > eset <- rma(Data) >Background correcting >Normalizing >Calculating Expression > > pData(eset) > sample >G05.CEL 1 >G09.CEL 2 >G10.CEL 3 >G12.CEL 4 >G15.CEL 5 >G19.CEL 6 >GF.CEL 7 >GM.CEL 8 >H044.CEL 9 >H05.CEL 10 >H07.CEL 11 >H10.CEL 12 >H11.CEL 13 >H14.CEL 14 >N01.CEL 15 >N02.CEL 16 >N03.CEL 17 > > tissue <- > c("C","C","C","C","C","C","C","C","C","C","C","C","C","C","N","N","N") > > design <- model.matrix(~factor(tissue)) > > colnames(design) <- c("C", "CvsN") > > design > C CvsN >1 1 0 >2 1 0 >3 1 0 >4 1 0 >5 1 0 >6 1 0 >7 1 0 >8 1 0 >9 1 0 >10 1 0 >11 1 0 >12 1 0 >13 1 0 >14 1 0 >15 1 1 >16 1 1 >17 1 1 >attr(,"assign") >[1] 0 1 >attr(,"contrasts") >attr(,"contrasts")$"factor(tissue)" >[1] "contr.treatment" > > > > fit <-lmFit(eset,design) > > fit <-eBayes(fit) > > options(digits=2) > > topTable(fit,coef=2,n=50,adjust="fdr") > ID M A t P.Value B >22193 78047_s_at 0.60 7.3 5.3 0.82 -3.4 >2594 203065_s_at -1.26 6.7 -5.0 0.82 -3.5 >10680 211245_x_at 0.58 4.9 4.7 1.00 -3.6 >17919 218554_s_at 0.59 4.7 4.5 1.00 -3.6 >9431 209945_s_at -0.67 6.1 -4.5 1.00 -3.6 >4556 205029_s_at 3.09 3.6 4.4 1.00 -3.6 >4557 205030_at 3.58 4.6 4.3 1.00 -3.6 >5845 206319_s_at 0.82 4.0 4.3 1.00 -3.7 >21838 36019_at 0.67 6.7 4.2 1.00 -3.7 >5209 205682_x_at 0.61 4.8 4.2 1.00 -3.7 >6791 207266_x_at -0.95 7.8 -4.0 1.00 -3.7 >21916 38447_at 0.66 7.3 4.0 1.00 -3.7 >21914 38340_at 0.59 6.3 3.9 1.00 -3.8 >16241 216871_at 0.59 3.4 3.9 1.00 -3.8 >982 201454_s_at -0.65 6.2 -3.9 1.00 -3.8 >22024 46256_at 0.62 7.2 3.9 1.00 -3.8 >7489 207978_s_at 0.47 4.3 3.8 1.00 -3.8 >4452 204925_at 0.48 5.0 3.8 1.00 -3.8 >7121 207600_at 0.48 5.5 3.7 1.00 -3.8 >12443 213060_s_at 1.41 6.0 3.7 1.00 -3.8 >1619 202091_at 0.51 3.3 3.7 1.00 -3.8 >9890 210412_at 0.53 3.5 3.6 1.00 -3.8 >21922 38707_r_at 0.45 7.8 3.6 1.00 -3.9 >2715 203187_at 0.59 5.8 3.6 1.00 -3.9 >3354 203827_at -0.99 5.5 -3.6 1.00 -3.9 >5340 205813_s_at 0.52 5.8 3.5 1.00 -3.9 >2445 202916_s_at -0.61 6.1 -3.5 1.00 -3.9 >18810 219446_at -0.68 5.9 -3.5 1.00 -3.9 >14010 214632_at -0.54 4.2 -3.4 1.00 -3.9 >2915 203388_at 0.46 6.2 3.4 1.00 -3.9 >21936 396_f_at 0.70 7.7 3.4 1.00 -3.9 >16292 216922_x_at 0.61 3.8 3.4 1.00 -3.9 >13378 213999_at 0.44 4.5 3.4 1.00 -3.9 >9642 210158_at 0.58 4.4 3.4 1.00 -3.9 >19117 219753_at 0.65 5.6 3.4 1.00 -3.9 >10820 211405_x_at 0.53 5.3 3.4 1.00 -3.9 >19242 219878_s_at -0.58 4.5 -3.4 1.00 -3.9 >3275 203748_x_at -0.90 7.9 -3.4 1.00 -3.9 >16554 217187_at 0.58 5.7 3.4 1.00 -3.9 >8627 209133_s_at 0.54 4.7 3.3 1.00 -3.9 >17983 218618_s_at -1.15 8.0 -3.3 1.00 -3.9 >20977 221615_at 0.50 3.7 3.3 1.00 -3.9 >18562 219198_at 0.54 5.7 3.3 1.00 -3.9 >19513 220149_at 0.58 4.8 3.3 1.00 -3.9 >1770 202242_at 1.04 5.4 3.3 1.00 -3.9 >10081 210616_s_at -0.56 8.4 -3.3 1.00 -3.9 >17995 218630_at 0.37 5.4 3.3 1.00 -3.9 >3018 203491_s_at -0.67 5.1 -3.3 1.00 -3.9 >10823 211410_x_at 0.56 5.3 3.3 1.00 -3.9 >16351 216981_x_at 0.57 6.3 3.3 1.00 -3.9 > > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor 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