[BioC] Venn Diagrams in Limma

Jason Skelton jps at sanger.ac.uk
Tue Oct 28 12:23:05 MET 2003

Hi Gordon

Gordon Smyth wrote:

> Venn diagrams are by their nature simply overall counts. But you can 
> easily identify the relevant genes from the 'classification' object 
> from which the Venn diagram is computed.

Not sure how this is done, the object I have created in ClassifyTests 
has classification (0 and 1's) and Fstats associated with it
am I looking in the wrong place ?  I have a list of genes made from 
uniquegenelist is this the list I'm wanting to access ?
My VennCounts object is:

     DIF1 DIF2 DIF3 Counts
[1,]    0    0    0   1925
[2,]    0    0    1     18
[3,]    0    1    0     26
[4,]    0    1    1      5
[5,]    1    0    0     70
[6,]    1    0    1     13
[7,]    1    1    0     90
[8,]    1    1    1     59
[1] "VennCounts"

could you give an example of how to associate each of the 8 counts with 
genes from the uniquegenelist (OR other)

I'd also like to be able to use the false discovery rate option that can 
beused in classifyTestsP method for my venn diagram but I also want to 
use data that I've received from makeContrasts and contrast.fit which I 
can only use in classifyTests but I can't use "fdr" in that function....

Is there a particular reason why I can't do this ?

Yes there is. classifyTests() uses a method intended to control false 
discovery rate across contrasts. This is not compatible with the simple 
p.adjust() approach to controling FDR across genes. I suggest that you 
simply find what unadjusted p-value corresponds to your desired FDR 
level and enter that to classifyTests().

OK that makes more sense now thanks.

I have yet more questions about the heatdiagram option
My gene names are very long 28+ characters long is there a heatdiagram 
specific command that alters the size of the box that the heatmap covers ?
So that genes appearing on the plot are not truncated (I've tried some 
of the R commands for Margins etc but they don't want to work with high 
level plots)

Also in the heatdiagram you can specify EB$t and fit$coef
e.g. heatdiagram(newDIF123SEPcontrastfitEB$t, 
newDIF123SEPcontrastsfit$coef etc)
you can specify the critical.primary argument  which  is the critical 
value "above" which genes are considered significant.


If you want to use EB$p.value fit$coef
e.g. heatdiagram(newDIF123SEPcontrastfitEB$p.value, 
newDIF123SEPcontrastsfit$coef etc)
you can't specify the critical.primary because you need to specify a 
maximum value e.g(0.05) rather than minimum as your cutoff

Thanks for you help


Jason Skelton
Pathogen Microarrays
Wellcome Trust Sanger Institute
CB10 1SA

Tel +44(0)1223 834244 Ext 7123
Fax +44(0)1223 494919

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