Hi Tim,
Yes your right I need to fit binary model one for present and the other for
absent.
I have a dataset of 11 disease state arrays and 6 control arrays.This is an
affymetrix HG-U133 plus 2 which has 54675 probes.So from this I should get
the present probes with the average intensity across all the samples
which are above the threshold of where these 2 Gaussian PDF's. I am not a
biostatistician, so could you give me the rough code or place where I can
easily correlate with my data and fit accordingly to my data.
Thanks,
Viritha


On Wed, Apr 13, 2011 at 4:31 PM, Tim Triche, Jr. <tim.triche@gmail.com>wrote:

> what he said.  now it's a Bayesian mixture model.  ;-)
>
>
> On Wed, Apr 13, 2011 at 1:28 PM, Matthew McCall <mccallm@gmail.com> wrote:
>
>> Viritha,
>>
>> For present/absent calls on Affy arrays, you might want to look into
>> the barcode function in the frma package. This does something similar
>> to what you want to do but uses a huge database of arrays to estimate
>> the mixture distribution ahead of time.
>>
>> Best,
>> Matt
>>
>> On Wed, Apr 13, 2011 at 3:50 PM, viritha kaza <viritha.k@gmail.com>
>> wrote:
>> > Hi Group,
>> > I have GCRMA normalised Affymetrix dataset.With this I wanted to find
>> > present and absent probes using Gaussian mixture model,by creating 2
>> > Gaussian probability density function,one for present and the other for
>> > absent.The probes whose average intensity across all the samples, larger
>> > than threshold value where the 2 gaussian PDFs meet is to be considered
>> as
>> > present.
>> > To do the analysis I was suggested to use Mclust package. But could
>> anyone
>> > suggest some fuctions in the package that would be useful in this task
>> on
>> > which I could focus on.
>> > Thanks,
>> > Viritha
>> >
>> >        [[alternative HTML version deleted]]
>> >
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>>
>>
>>
>> --
>> Matthew N McCall, PhD
>> 112 Arvine Heights
>> Rochester, NY 14611
>> Cell: 202-222-5880
>>
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>
>
>
> --
>  If people do not believe that mathematics is simple, it is only because
> they do not realize how complicated life is.
> John von Neumann<http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Von_Neumann.html>
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>

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