Question: microarray analysis of a dose response * strain experiment
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gravatar for Naomi Altman
13.1 years ago by
Naomi Altman6.0k
Naomi Altman6.0k wrote:
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) > >1-(317)-536-2730 FAX > > >-----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 > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor at stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor > > >-- >IVIA (http://www.ivia.es) >Open WebMail Project (http://openwebmail.org) >Debian Project (http://www.debian.org) > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor 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
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