[BioC] Statistics for Diagnostic Microarrays

[michael watson (IAH-C)]

Of course I agree - things are certainly not clear cut in this area!  
However, I would like to see the simpler problem of normalisation for
diagnostic arrays solved first :-)

-----Original Message-----
From: A.J. Rossini [mailto:rossini at blindglobe.net] 
Sent: 08 July 2004 13:49
To: michael watson (IAH-C)
Cc: Adaikalavan Ramasamy; BioConductor mailing list
Subject: Re: [BioC] Statistics for Diagnostic Microarrays



Sure, but then you've got a high-dimensional "diagnostic statistics"
problem; these are still not fully worked out, though see Margaret
Pepe's recent book on the topic for a start.

best,
-tony


"michael watson (IAH-C)" <michael.watson at bbsrc.ac.uk> writes:

> Actually, a lot of the work for pattern recognition is already there -

> from classical statistics and from use with proteomics data:
>
>
> -----Original Message-----
> From: Adaikalavan Ramasamy [mailto:ramasamy at cancer.org.uk]
> Sent: 08 July 2004 13:37
> To: michael watson (IAH-C)
> Cc: BioConductor mailing list
> Subject: Re: [BioC] Statistics for Diagnostic Microarrays
>
>
> Dear Mick,
>
> I think there is a gold field of opportunities for statistics in this 
> field. With more and more companies advertising disease-specific 
> chips, there are still questions to be answers, namely :
>
> a) gene selection : Only several hundreds or thousands of genes are 
> going to be selected for their discriminating ability.
>
> b) normalisation  : The assumption that majority (90-95%) of the genes

> unchanged will not hold here. If you are going to use "housekeeping" 
> genes, which ones to use and how to use them. So far, the main 
> normalisation methods (justifiably) ignore housekeeping genes as they 
> vary from sample to sample.
>
> c) multiple spots : If you are going to spot, say 2000 genes, then you

> can spot 10 of each at random positions on the chip. This not only 
> affects the normalisation (highly correlated spots) but also the 
> analysis aspect (is there a better approach than averaging?).
>
> d) classification : How does one assign the probability that a patient

> has a disease given the expression profile of thousands of genes. I 
> think we may require pattern recognition techniques or machine 
> learning approaches and a large enough learning set.
>
> e) better classification : Is the diagnostic chip better than existing

> tests (if any) and is it cost efficient.
>
> Sorry for pointing out more questions than answers but I feel that 
> more people should be be asking these before buying/designing a 
> designer boutique arrays.
>
> I think what people are currently doing is using microarrays as 
> filtering tool along with other knowledge to obtain a marker 
> gene/protien that they can easily test for. The relevant key word is 
> metabolonomics.
>
> HTH, Adai.
>
>
> On Thu, 2004-07-08 at 09:12, michael watson (IAH-C) wrote:
>> Hi
>> 
>> Obviously the greatest use for Microarrays is for gene expression
>> studies, but increasingly scientists wish to use Microarrays for a 
>> variety of diagnostic studies, which centre more around "Is it there 
>> or not?" type questions rather than "How much of it is there?".  Does

>> anyone know of any statistical tools or software that can be used 
>> specifically for diagnostic microarrays?
>> 
>> Thanks
>> 
>> Mick
>> 
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at stat.math.ethz.ch
>> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>>
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch 
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>

-- 
Anthony Rossini			    Research Associate Professor
rossini at u.washington.edu            http://www.analytics.washington.edu/

Biomedical and Health Informatics   University of Washington
Biostatistics, SCHARP/HVTN          Fred Hutchinson Cancer Research
Center
UW (Tu/Th/F): 206-616-7630 FAX=206-543-3461 | Voicemail is unreliable
FHCRC  (M/W): 206-667-7025 FAX=206-667-4812 | use Email

CONFIDENTIALITY NOTICE: This e-mail message and any attachme...{{dropped}}