[BioC] Statistics for Diagnostic Microarrays

[Adaikalavan Ramasamy]

Surely, you mean "more fundamental" and not "simpler". 

I agree with you that there are very good works in classical pattern
recognition. Building a classifier that works well on one dataset is not
too difficult. What is more difficult is its ability to be generalised
to whole population or sub-populations. Rigorous testing for large
enough sample size is important. This is important if microarrays are to
be part of the standard diagnostic kit.

If this was a drug, there are various organisations policing its testing
and sales.

On Thu, 2004-07-08 at 13:51, michael watson (IAH-C) wrote:
> 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
> >> 
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