[BioC] Problem with p-values calculated by eBayes--corrected format
Chen, Zhuoxun
Zhuoxun_Chen at URMC.Rochester.edu
Fri Jan 9 18:21:07 CET 2009
Hi Bioconductors,
I am really sorry about sending this email again. I didn't know that the table on my email will be lost and reformat. I corrected the format now. Thank you for your patience.
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
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corfit <- duplicateCorrelation(Gvsn, design = design, ndups = 2, spacing = 1)
fit <- lmFit(Gvsn, design = design, ndups = 2, spacing = 1, correlation = corfit$consensus)
contrast.matrix <- makeContrasts(A-B, levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit3 <- eBayes(fit2)
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Then, I looked at the raw data; copy and paste onto Excel and did a simple t-test
A B
1 6.938162 7.093199
2 7.012382 8.05612
3 7.000305 6.999078
Avg 6.983616 7.382799
contrast 0.399182
p-value
one tailed, unequal variance, t-test=0.179333
one tailed, equal variance, t-test=0.151844
The p-value is NOT even close to 0.05. Then I looked at the contrast of fit3$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:
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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
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