On Thu, Sep 4, 2008 at 5:21 AM, Mark Cowley <m.cowley@garvan.org.au> wrote:

> Hi Abhilash,
>
> On 02/09/2008, at 11:09 PM, Abhilash Venu wrote:
>
>  Hi all,
>>
>> I am working on a single color expression data using limma. I would like
>> to
>> perform a cluster analysis after selecting the differentially genes based
>> on
>> the P value (say 0.001). As far as my knowledge is concerned I have to do
>> the sub setting of these selected genes on the normalized data (MA), to
>> retrieve the distribution across the samples.
>>
> That's correct



> Thank you Mark, But I am quite cinfused here. Because our colaborator has
> already performed single color in agilent platform, when I had performed
> cluster using the same method as I mentioned the color key has given
> positive values (as all the values are positive, if I chose values from MA).
> Our collaborator feels that this scenario is quite unusual because the green
> color usually represents down regulation. Could you suggest, how I should go
> about it?
>
>>
>> But I am wondering whether I can perform using the R script?
>>
> Can you elaborate on "using the R script"I was not sure about the R script
> for subsetting, so I performed using python.
>
>>
>> I would appreciate any help.
>>
> You need 2 things: the names of the DE genes, and the normalised data.
> Get the DE genes from your toptable, and the normalised data from within
> your MA object (hint: names(MA) ).
> Then sub-set the normalised data to just those rows from the DE genes, then
> perform cluster analysis. There are large number of ways of doing this. To
> get you started, have a look at heatmap.2 from the package gplots.
> others include the built in
> hclust( dist( yourDEdata ) )
>
> cheers,
> Mark
>
> -----------------------------------------------------
> Mark Cowley, BSc (Bioinformatics)(Hons)
>
> Peter Wills Bioinformatics Centre
> Garvan Institute of Medical Research, Sydney, Australia
> -----------------------------------------------------
>
>


-- 

Regards,
Abhilash

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