Steve,
 
Subtract the list of genes bounded by circles from the total.  You
should be able to build a list of probeids bounded by circles with:
 
>within<-c(alls.genes[[1]], alls.genes[[2]], alls.genes[[4]], etc...)
 
#subtract these from the input by:
 
>outside<-setdiff(rownames(temp.results),within)
 
In the future please remember to cc the BioC list.  Archiving solutions
to problems is one of the most valuable assets provided by BioC.  
 
Scott
 
 
 

Scott A. Ochsner, PhD
One Baylor Plaza BCM130, Houston, TX 77030
Voice: (713) 798-6227  Fax: (713) 790-1275 

 

________________________________

From: Stephen V. Su [mailto:svs8@yahoo.com] 
Sent: Friday, July 31, 2009 4:15 AM
To: Ochsner, Scott A
Subject: Re: [BioC] venn diagram


hey Scott,

thanx for the help.  I was able to retrieve the probe IDs following your
code.  However, I wasn't able to retrieve the probe IDs for those genes
residing outside the 3 circles- the genes that are not expressed
differently in the 3 conditions.  In a 3 circle vennDiagram, there are 8
numbers, 7 of which are bounded by circles for comparison to each other.
But the 8th number represents those genes not significantly expressed
differently in the 3 conditions.  I couldn't do:

alls.genes[[8]]
to retrieve probe IDs

it gave the response:

Error in alls.genes[[8]] : subscript out of bounds

how do i retrieve those genes?

thanx again for your insights,

Steve

________________________________

From: "Ochsner, Scott A" <sochsner@bcm.tmc.edu>
To: Stephen V. Su <svs8@yahoo.com>
Cc: bioconductor@stat.math.ethz.ch
Sent: Thursday, July 30, 2009 9:13:43 AM
Subject: RE: [BioC] venn diagram

Steve,

I don't remember exactly where I picked this bit of code up so I
apologize for not giving credit.  I'm almost sure I got it from looking
at the mail archives.  It uses the makeIndices function within
affycoretools.

>vennDiagram(temp.results)

# make an index of all the probeIds with a TRUE call.  As the default
for vennCounts() is "both" (and you have implicitly called vennCounts()
in your call to vennDiagram()), you need to use "both" in the
makeIndices call.

alls<-affycoretools:::makeIndices(temp.results,"both")


>length(which(alls[[3]]==T))

#extract from alls those genes in your venn diagram.

>alls.genes<-lapply(alls,function(x)row.names(temp.results)[x])

or lapply(alls,function(x)fit2$genes$Name[x])

#test to see if the lists of all.genes agree with the venn diagram

>length(alls.genes[[1]])
>length(alls.genes[[2]])
etc...

>alls.genes[[3]]
[1] "1417023_a_at" "1417561_at"  "1417580_s_at" "1417867_at"

Hope this helps,

Scott




Scott A. Ochsner, PhD
One Baylor Plaza BCM130, Houston, TX 77030
Voice: (713) 798-6227  Fax: (713) 790-1275 
-----Original Message-----
From: bioconductor-bounces@stat.math.ethz.ch
[mailto:bioconductor-bounces@stat.math.ethz.ch] On Behalf Of Stephen V.
Su
Sent: Thursday, July 30, 2009 1:13 AM
To: bioconductor@stat.math.ethz.ch
Subject: [BioC] venn diagram

To whom this may concern:

    I must first apologize for not being a bioinformaticist and really
only "pretend" to know how to use R to analyze microarray.  But i do
understand the concepts of Limma and when to apply it.  I think it is a
really powerful way to analyze matrix data.  So, if you don't mind, I
would like to ask you a question regarding Venn Diagrams.  I have
generated a RMA-processed expression set for 3 different conditions
(each condition has a minimum of 8 CEL files- Affy 0.5 genome rat chip),
fed it through limma to create a statistical computation of all the
genes that are differentially expressed and created a venn Diagram to
depict the results.  A general input functions of what i just wrote is
depicted here:


> fit <- lmFit(x, design)

> cont.matrix <- makeContrasts(x3Dvsinvivo = x3D - invivo,
+ x3Dvsx2D = x3D - x2D,
+ x2Dvsinvivo = x2D - invivo, levels = design)
> fit2 <- contrasts.fit(fit, cont.matrix)
> fit2 <- eBayes(fit2)
> results <- decideTests(fit2)
> vennDiagram(results)

My question is:  How do I extract out the genes that reside in each of
the quadrant in the Venn diagram?  Is there a way for me to say,
retrieve the 108 genes that are unique to condition A, 98 genes unique
to condition B and C, and so on ....

Your help is greatly appreciated and I thank-you in advance of your
considerations

Steve


      
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