[BioC] Statistical significance of clinical information

[Gordon K Smyth]

Dear Daniel,

Yes, limma handles continuous covariates, see

  https://stat.ethz.ch/pipermail/bioconductor/2007-January/015794.html

Best wishes
Gordon

> Date: Tue, 20 Mar 2007 16:12:35 +0000
> From: Daniel Brewer <daniel.brewer at icr.ac.uk>
> Subject: [BioC] Statistical significance of clinical information
> To: bioconductor at stat.math.ethz.ch
> Message-ID: <460007F3.8070209 at icr.ac.uk>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Hi,
>
> I have microarray data from a range of tumours.  With each of these
> tumours I have some associated clinical information, including test
> results/scores, patient characteristics (e.g. age) and other biochemical
> tests (FISH etc.).  I would like to do a number of things:
> 1) Determine whether any of these have a significant effect on the
> overall expression of the tumour.
> 2) Determine which genes expression correlate the best with a particular
> characteristics/test result
>
> This seems to the sort of thing you could do with linear models and
> limma, but it was unclear to me what is the best way forward.  In
> particular limma seems to be set up for the experimental design rather
> than other external factors.  Can limma take into account continuous
> variables rather than catergories?
>
> Any advice on this would be gratefully received as I find this whole
> area a bit confusing.
>
> Dan
>
> --
> **************************************************************
>
> Daniel Brewer, Ph.D.
>
> Institute of Cancer Research
> Email: daniel.brewer at icr.ac.uk
>
> **************************************************************
>
> The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by
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