> 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
>
>
>
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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 betw 
> een 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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>

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