Thanks Mark and Sean.

It is working fine.

Abhilash

On Fri, Sep 5, 2008 at 9:37 AM, Mark Cowley <m.cowley@garvan.org.au> wrote:

>
> On 05/09/2008, at 5:06 AM, Sean Davis wrote:
>
>> On Thu, Sep 4, 2008 at 10:59 AM, Abhilash Venu <abhivenu@gmail.com>
>> wrote:
>>
>>> 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?
>>>>
>>> Part of this confusion stems from your non-standard use of 'MA' (I've
> checked your past posts to work this out), since 'MA' implies two-colour
> data, where the M-values, which are the ratios are the quantity of interest.
> You are dealing with single colour data, so I assume that in your use of MA
> you need to be referring to the A-values, but i'm not sure how limma deals
> with this in the way that you have used it. My clear preference is when you
> are dealing with single colour data is not to use 2-colour data objects.
> However, I assume that you have been able to identify and subset this data
> in order to have sent your previous reply to the list, so lets move on.
>
> back to your confusion: your collaborator is right. the vast majority of
> clustering is used to show RELATIVE expression, not absolute expression.
> If you 'mean correct' your absolute expression data, you will convert it to
> ratios, and then the heatmap.2 might give you a sensible picture.
>
> I agree with Sean (which I seem to be doing a lot recently) in that you
> need to improve your basic R usage, and the links that Sean provided are a
> great place to start, as is R for beginners by Paradis.
>
> cheers, Mark
>
>
>
>>>>
>> Did you use heatmap.2 to do the heatmap?  If so, there is an argument
>> "scale" that might be useful.  For ALL functions that are new, I would
>> advise reading the whole help page, as there is often very useful
>> information there.
>>
>>
>>>>> 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.
>>>>
>>>
>> You can try help.search('subset'), as a start.  RSiteSearch is also
>> useful for searching for answers.
>>
>> You will likely benefit from reading:
>>
>> http://cran.r-project.org/doc/manuals/R-intro.html
>>
>> And potentially from:
>>
>>
>> http://biostat-09.berkeley.edu/~bullard/courses/T-berkeley-08/resources/R_intro_easy.pdf<http://biostat-09.berkeley.edu/%7Ebullard/courses/T-berkeley-08/resources/R_intro_easy.pdf>
>>
>>
>>>>> 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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>>>
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>>>
>


-- 

Regards,
Abhilash

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