Hello Moritz,

You may want to take a look at
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052263/ .  DeSantis et al.
(2009) identified different genomic profiles using  a Bayesian Latent Class
methodology.

Best,

James


On Sun, Jun 30, 2013 at 2:47 PM, Moritz Kebschull <moritz@kebschull.me>wrote:

> Dear list.
>
> I am looking at a dataset comprised of Affy images from disease-affected
> tissue samples that I am trying to cluster.
>
> The problem is that we have 2+ biopsies per study subject, and I am not
> sure how to best account for their dependency. In contrast to cancer
> samples, these biopsies differ to a certain extent in their disease
> severity, i.e. they are not perfect replicates, but share certain
> similarities since they are from the same person.
>
> I first tried to just cluster all available biopsies using
> ConsensusClusterPlus. However, this produced clusters of biopsies according
> to their disease severity - often with different samples from the same
> patient assigned to different clusters - and that´s not what I want. I am
> trying to identify different classes between subjects, not biopsies.
>
> For the diff exp analyses, we dealt with this issue by adding the patient
> as a random effect to the model. Could I do something similar using
> model-based clustering, perhaps also adding a variable for disease
> severity?
>
> As an alternative, I have explored aggregating all available samples per
> subject into one expression profile, and cluster the pattients using these
> aggregates. I am, however, not convinced that this is right, since this
> approach creates 'artificial' data.
>
> Does anyone have an idea?
>
> Many thanks,
>
> Moritz
>
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>
>
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>


-- 
*James C. Whanger*
*
*

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