Problem with p-values calculated by eBayes
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@chen-zhuoxun-3131
Last seen 10.2 years ago
> Hi Bioconductors, > > > > > I have a very weird problem with the statistics with my microarray > data. I would like to ask for your help. > > > > I am running a microarray with 16 groups, 3 samples/group. On my > genechip, every probe is spotted 2 times. > > > > By comparing two groups (let¡¯s say A and B), I came across a gene th > at is very significant by running the following codes, with a p-valu > e= 0.001669417 > > > > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --------------------------------------------------------------------- > > corfit <- duplicateCorrelation(Gvsn, design = design, ndups = 2, > spacing = 1) > > fit <- lmFit(Gvsn, design = design, ndups = 2, spacing = 1, > correlation = corfit$consensus) > > contrast.matrix <- makeContrasts(A-B, levels=design) > > fit2 <- contrasts.fit(fit, contrast.matrix) > > fit3 <- eBayes(fit2) > > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --------------------------------------------------------------------- > > > > Then, I looked at the raw data; copy and paste onto Excel and did a > simple t-test > > > > > > A > > B > > 1 > > 6.938162 > > 7.093199 > > 2 > > 7.012382 > > 8.05612 > > 3 > > 7.000305 > > 6.999078 > > > > > > > > Avg > > 6.983616 > > 7.382799 > > contrast > > 0.399182 > > > > p-value > > > > > > one tailed, unequal variance, t-test > > 0.179333 > > > > one tailed, equal variance, t-test > > 0.151844 > > > > The p-value is NOT even close to 0.05. Then I looked at the contrast > of fit3$coefficient, it is 0.399182, which indicates the data input > for the codes are correct. > > > > I don¡¯t understand why it has such a huge difference on p-value betw > een those two methods. Could somebody please help me with it? > > > > Thanks, > > Zhuoxun Chen > > > > > > SessionInfo: > > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > ---------------------------------------------------------------------- > > R version 2.8.0 (2008-10-20) > > i386-pc-mingw32 > > > > locale: > > LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States. > 1252;LC_MONETARY=English_United States. > 1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 > > > > attached base packages: > > [1] grid splines tools stats graphics grDevices > utils datasets methods base > > > > other attached packages: > > [1] gplots_2.6.0 gdata_2.4.2 > gtools_2.5.0 org.Hs.eg.db_2.2.6 GSEABase_1.4.0 > > [6] PGSEA_1.10.0 Ruuid_1.20.0 > Rgraphviz_1.20.2 XML_1.94-0.1 bioDist_1.14.0 > > [11] GOstats_2.8.0 Category_2.8.0 > genefilter_1.22.0 survival_2.34-1 RBGL_1.18.0 > > [16] annotate_1.20.0 xtable_1.5-4 > graph_1.20.0 eArrayCanary.db_1.0.0 annaffy_1.14.0 > > [21] KEGG.db_2.2.5 GO.db_2.2.5 > RSQLite_0.7-1 DBI_0.2-4 AnnotationDbi_1.4.0 > > [26] statmod_1.3.6 RODBC_1.2-3 > RColorBrewer_1.0-2 vsn_3.8.0 affy_1.20.0 > > [31] Biobase_2.2.0 lattice_0.17-15 limma_2.16.3 > > > > loaded via a namespace (and not attached): > > [1] affyio_1.10.1 cluster_1.11.11 preprocessCore_1.4.0 > > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --- > --------------------------------------------------------------------- > > [[alternative HTML version deleted]]
Microarray GO probe Microarray GO probe • 679 views
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Entering edit mode
@chen-zhuoxun-3131
Last seen 10.2 years ago
Hi Bioconductors, I have a very weird problem with the statistics with my microarray data. I would like to ask for your help. I am running a microarray with 16 groups, 3 samples/group. On my genechip, every probe is spotted 2 times. By comparing two groups (let’s say A and B), I came across a gene that is very significant by running the following codes, with a p-value= 0.001669417 -------------------------------------------------------------- ---------------------------------------------- corfit <- duplicateCorrelation(Gvsn, design = design, ndups = 2, spacing = 1) fit <- lmFit(Gvsn, design = design, ndups = 2, spacing = 1, correlation = corfit$consensus) contrast.matrix <- makeContrasts(A-B, levels=design) fit2 <- contrasts.fit(fit, contrast.matrix) fit3 <- eBayes(fit2) -------------------------------------------------------------- ---------------------------------------------- Then, I looked at the raw data; copy and paste onto Excel and did a simple t-test A B 1 6.938162 7.093199 2 7.012382 8.05612 3 7.000305 6.999078 Avg 6.983616 7.382799 contrast 0.399182 p-value one tailed, unequal variance, t-test 0.179333 one tailed, equal variance, t-test 0.151844 The p-value is NOT even close to 0.05. Then I looked at the contrast of fit3$coefficient, it is 0.399182, which indicates the data input for the codes are correct. I don’t understand why it has such a huge difference on p-value between those two methods. Could somebody please help me with it? Thanks, Zhuoxun Chen SessionInfo: -------------------------------------------------------------- ---------------------------------------------------------------------- - R version 2.8.0 (2008-10-20) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] grid splines tools stats graphics grDevices utils datasets methods base other attached packages: [1] gplots_2.6.0 gdata_2.4.2 gtools_2.5.0 org.Hs.eg.db_2.2.6 GSEABase_1.4.0 [6] PGSEA_1.10.0 Ruuid_1.20.0 Rgraphviz_1.20.2 XML_1.94-0.1 bioDist_1.14.0 [11] GOstats_2.8.0 Category_2.8.0 genefilter_1.22.0 survival_2.34-1 RBGL_1.18.0 [16] annotate_1.20.0 xtable_1.5-4 graph_1.20.0 eArrayCanary.db_1.0.0 annaffy_1.14.0 [21] KEGG.db_2.2.5 GO.db_2.2.5 RSQLite_0.7-1 DBI_0.2-4 AnnotationDbi_1.4.0 [26] statmod_1.3.6 RODBC_1.2-3 RColorBrewer_1.0-2 vsn_3.8.0 affy_1.20.0 [31] Biobase_2.2.0 lattice_0.17-15 limma_2.16.3 loaded via a namespace (and not attached): [1] affyio_1.10.1 cluster_1.11.11 preprocessCore_1.4.0 -------------------------------------------------------------- ---------------------------------------------------------------------- [[alternative HTML version deleted]]
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