[BioC] microarray analysis of a dose response * strain experiment

Sun Nov 5 04:25:11 CET 2006

I am not aware of the approach in maSigPro.  However, the two-way 
ANOVA approach as provided by limma would be the usual analysis for 
this type of experiment.  The relationship among the doses can be 
expressed by a polynomial if the response is not linear.

--Naomi

At 04:12 PM 11/4/2006, Kimpel, Mark William wrote:
>Thanks Ana, I will take a look :)  Mark
>
>Mark W. Kimpel MD
>
>
>
>(317) 490-5129 Work, & Mobile
>
>
>
>(317) 663-0513 Home (no voice mail please)
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>1-(317)-536-2730 FAX
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>
>-----Original Message-----
>From: Ana Conesa [mailto:aconesa at ivia.es]
>Sent: Saturday, November 04, 2006 4:04 PM
>To: Kimpel, Mark William; sdavis2 at mail.nih.gov;
>bioconductor at stat.math.ethz.ch
>Cc: McBride, William J.
>Subject: Re: [BioC] microarray analysis of a dose response * strain
>experiment
>
>Hi Mark
>
>I recommend you to have a look to the maSigPro package and the
>corresponding
>publication (Bioinformatics 2006 22(9):1096-1102). The methodology has
>been
>designed for decting genes that change between experimental conditions
>on a
>data series, normally this would be different treatments along the time
>component, but could also be different strains along a increasing dose
>value. You can model different type of responses (linear, quadratic or
>more
>sophisticated) although the method finnaly finds the model most suited
>for
>each gene. The method does not focus on pair-wise comparisons, but
>rather
>detects differences in expression patterns between conditons (thus,
>strain-
>dose interactions) or genes that significantly change "somewhere".
>
>I hope this package suits your analysis needs
>
>Best regards
>
>Ana
>
>On Sat, 4 Nov 2006 14:45:37 -0500, Kimpel, Mark William wrote
> > Sean,
> >
> > Perhaps it will have to be, I can think of two ways to do that and
> > neither seems entirely satisfactory. Firstly, one could assume that
>the
> > response (for responding genes) would be linearly related to the
> > dose or the log of the dose, but this might not be the case. So
> > regressing by dose in a linear model might not be correct for all or
> > even most of the affected genes. Secondly, one could simply assume
> > that the dose is a factor with 4 non-ordered levels and look at
> > contrasts and interactions for each level. This would be the
> > approach I am most familiar with using Limma. This would, however,
> > seem to be throwing information away regarding the relationship of
> > the doses to one another.
> >
> > Mark
> >
> > Mark W. Kimpel MD
> >
> > (317) 490-5129 Work, & Mobile
> >
> > (317) 663-0513 Home (no voice mail please)
> >
> > 1-(317)-536-2730 FAX
> >
> > -----Original Message-----
> > From: Sean Davis [mailto:sdavis2 at mail.nih.gov]
> > Sent: Saturday, November 04, 2006 1:39 PM
> > To: bioconductor at stat.math.ethz.ch
> > Cc: Kimpel, Mark William; McBride, William J.
> > Subject: Re: [BioC] microarray analysis of a dose response * strain
> > experiment
> >
> > On Saturday 04 November 2006 13:20, Kimpel, Mark William wrote:
> > > My group is writing a grant with a proposed dose response experiment
> > on two
> > > different rat strains that I have been tasked to provide an analysis
> > method
> > > for. Briefly, we have two rat strains that have different
>preferences
> > for
> > > alcohol (one drinks, the other doesn't). We are going to give each
> > line
> > > injections for alcohol to see if gene expression in the brain is
> > > differentially affected between the 3 strains. We don't, however,
>know
> > > which of several possible doses of alcohol will provide the greatest
> > effect
> > > on each of the thousands of genes on our Affy chipset. So, we are
> > proposing
> > > to give each line one of 4 doses (zero, 0.5, 1.0, and 2.0 mg/kg).
>For
> > any
> > > gene, we have no way of knowing a priori what shape the dose
>response
> > curve
> > > will take. We are, for screening purposes, not really interested in
> > the
> > > shape of the curve, only that it is not a line with a slope of zero
> > (i.e.
> > > no response). We are also, for screening purposed, only interested
>to
> > know,
> > > for each gene, if the response of strain A is different from strain
>B.
> > In
> > > other words, what we want to know is the interaction between strain
> > and
> > > dose response.
> > >
> > > I have searched the literature and the Bioconductor mailing list and
> > cannot
> > > find a reference to an experiment of this sort. Can anyone provide
> > some
> > > advice?
> >
> > This can't be handled with a linear model?
> >
> > Sean
> >
> > _______________________________________________
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111