[BioC] significance of "wrong" clustering of differential genes

[Naomi Altman]

The heatmap did not come through (to me).  However, clustering is 
highly dependent on the choice of distance measure.

--Naomi

At 09:57 AM 11/13/2006, Benjamin Otto wrote:
>Hi,
>
>
>
>Please imagine the following situation:
>
>For two sample sets (set1, set2) the most differentially expressed genes are
>identified by limma. The p.value correction would be "holm". Afterwards a
>heatmap is printed for these genes. The procedure would look like:
>
>
>
> >  f <- factor(as.character(pheno[,marker]))
>
> > design <- model.matrix(~f)
>
> > fit <- eBayes(lmFit(eSet,design))
>
> > tab <- topTable(fit, coef=2, number=nrow(eSet), adjust.method="holm")
>
> > selected <- tab$adj.P.Val < 0.01 & abs(tab$M) >= 1
>
> > ## print a heatmap for eSet[selected,]
>
>
>
>
>
>What can  lead to a misclassification in the clustering, say one sample of
>set1 is clustered together with set2? Afterall according to the workflow I
>have explicitly been searching for the genes which should discriminate
>between the two sets! However the expression values displayed in the heatmap
>assume, that this samle IS more similar to the "wrong" set than to the true
>one. (have a look at the jpg)
>
>Is it possible, that this sample is always treated as outlier in the
>significance calculations?
>
>And if it is so, then: Is it sensible to take such a misclassification as
>kind of significane?
>
>Regards
>
>
>
>Benjamin
>
>
>
>
>
>--
>Benjamin Otto
>Universitaetsklinikum Eppendorf Hamburg
>Institut fuer Klinische Chemie
>Martinistrasse 52
>20246 Hamburg
>
>
>
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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