[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
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	 

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?

Zhuoxun Chen
R version 2.8.0 (2008-10-20) 
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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