Differential drug effect on clinical groups

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Dave Canvhet ▴ 40

@dave-canvhet-5253

Last seen 10.2 years ago

Dear all, I have 32 transcriptomics profile of A. thaliana (single color), among which15 received a drug treatment and 15 are the control group. For all these samples, 2 biological observations were also obtained : - life time of the plant (short or long) - expression of an integrine (with or without) I would like to get the following contrast : (short life time without integrine) versus (long life time with integrine) into treated samples versus (short life time without integrine) versus (long life time with integrine) into control samples I've set up my design matrix (target is below): drug = as.factor(targetATH$drug) integr = as.factor(targetATH$integrin) lifetime = as.factor(targetATH$lifetime) design = model.matrix(~drug+integr+lifetime) I can't figure out how to set up the correct contrast matrix to get the coefficient I want. I would be very grateful if you could give any pieces of advices for that. I hope I have enough sample to get enough power to detect some genes. many thanks by advance, best regards, -- Dave target : > targetATH FileName drug lifetime integrin 1 sample1.cel Y S + 2 sample2.cel Y S + 3 sample3.cel Y S + 4 sample4.cel Y S + 5 sample5.cel Y L + 6 sample6.cel Y L + 7 sample7.cel Y L + 8 sample8.cel Y L + 9 sample9.cel Y S - 10 sample10.cel Y S - 11 sample11.cel Y S - 12 sample12.cel Y S - 13 sample13.cel Y L - 14 sample14.cel Y L - 15 sample15.cel Y L - 16 sample16.cel Y L - 17 sample17.cel N S + 18 sample18.cel N S + 19 sample19.cel N S + 20 sample20.cel N S + 21 sample21.cel N L + 22 sample22.cel N L + 23 sample23.cel N L + 24 sample24.cel N L + 25 sample25.cel N S - 26 sample26.cel N S - 27 sample27.cel N S - 28 sample28.cel N S - 29 sample29.cel N L - 30 sample30.cel N L - 31 sample31.cel N L - 32 sample32.cel N L - [[alternative HTML version deleted]]

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ADD COMMENT • link updated 12.4 years ago by James W. MacDonald 67k • written 12.4 years ago by Dave Canvhet ▴ 40

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James W. MacDonald 67k

@james-w-macdonald-5106

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Hi Dave, On 6/19/2012 5:10 PM, Dave Canvhet wrote: > Dear all, > > > I have 32 transcriptomics profile of A. thaliana (single color), among > which15 received a drug treatment and 15 are the control group. For all > these samples, 2 biological observations were also obtained : > - life time of the plant (short or long) > - expression of an integrine (with or without) > > I would like to get the following contrast : > (short life time without integrine) versus (long life time with integrine) > into treated samples > versus > (short life time without integrine) versus (long life time with integrine) > into control samples This isn't really clear, and I might be way off base with this answer, but it looks to me like you are after an interaction term. If I were to restate, I would say that you are looking for genes that react differently to treatment between the long lived integrine positive samples and the short lived integrine negative samples. If true, this isn't difficult to set up, although I wouldn't do it the way you are. Personally, I would combine the samples into four types, based on life and integrine (where for brevity, life is long/short and integrine is +/-): long+ short+ long- short- Now your interaction as I understand it will only utilize the long+ and short- samples, so you would restrict your samples to just those samples that fulfill those criteria. Then you could make a lifeinteg factor that is long+ and short- and create a design matrix design <- model.matrix(~drug*lifeinteg) and the lifeinteg2 coefficient is the interaction, and gives you the genes that react differently to the drug based on being long+ or short-. Best, Jim > > > I've set up my design matrix (target is below): > > drug = as.factor(targetATH$drug) > > integr = as.factor(targetATH$integrin) > > lifetime = as.factor(targetATH$lifetime) > > design = model.matrix(~drug+integr+lifetime) > > I can't figure out how to set up the correct contrast matrix to get the > coefficient I want. > I would be very grateful if you could give any pieces of advices for that. > I hope I have enough sample to get enough power to detect some genes. > > > many thanks by advance, best regards, > -- > Dave > > > target : >> targetATH > FileName drug lifetime integrin > 1 sample1.cel Y S + > 2 sample2.cel Y S + > 3 sample3.cel Y S + > 4 sample4.cel Y S + > 5 sample5.cel Y L + > 6 sample6.cel Y L + > 7 sample7.cel Y L + > 8 sample8.cel Y L + > 9 sample9.cel Y S - > 10 sample10.cel Y S - > 11 sample11.cel Y S - > 12 sample12.cel Y S - > 13 sample13.cel Y L - > 14 sample14.cel Y L - > 15 sample15.cel Y L - > 16 sample16.cel Y L - > 17 sample17.cel N S + > 18 sample18.cel N S + > 19 sample19.cel N S + > 20 sample20.cel N S + > 21 sample21.cel N L + > 22 sample22.cel N L + > 23 sample23.cel N L + > 24 sample24.cel N L + > 25 sample25.cel N S - > 26 sample26.cel N S - > 27 sample27.cel N S - > 28 sample28.cel N S - > 29 sample29.cel N L - > 30 sample30.cel N L - > 31 sample31.cel N L - > 32 sample32.cel N L - > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099

ADD COMMENT • link 12.4 years ago James W. MacDonald 67k

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Hi James, > This isn't really clear, and I might be way off base with this answer, but > it looks to me like you are after an interaction term. If I were to > restate, I would say that you are looking for genes that react differently > to treatment between the long lived integrine positive samples and the > short lived integrine negative samples. > This is exactly what I want, so thanks to your clear restate. > If true, this isn't difficult to set up, although I wouldn't do it the way > you are. Personally, I would combine the samples into four types, based on > life and integrine (where for brevity, life is long/short and integrine is > +/-): > > long+ > short+ > long- > short- > > Now your interaction as I understand it will only utilize the long+ and > short- samples, so you would restrict your samples to just those samples > that fulfill those criteria. Then you could make a lifeinteg factor that is > long+ and short- and create a design matrix > design <- model.matrix(~drug*lifeinteg) > OK I still to progress on the differences between interaction model and additive model (with which I'm more familiar) Do you think it will be useful to set up an Intercept ? design <- model.matrix(~0+drug*lifeinteg) Again many for your time and your help. Bests -- Dave > and the lifeinteg2 coefficient is the interaction, and gives you the genes > that react differently to the drug based on being long+ or short-. > > Best, > > Jim > > > > >> >> I've set up my design matrix (target is below): >> >> drug = as.factor(targetATH$drug) >> >> integr = as.factor(targetATH$integrin) >> >> lifetime = as.factor(targetATH$lifetime) >> >> design = model.matrix(~drug+integr+**lifetime) >> >> I can't figure out how to set up the correct contrast matrix to get the >> coefficient I want. >> I would be very grateful if you could give any pieces of advices for that. >> I hope I have enough sample to get enough power to detect some genes. >> >> >> many thanks by advance, best regards, >> -- >> Dave >> >> >> target : >> >>> targetATH >>> >> FileName drug lifetime integrin >> 1 sample1.cel Y S + >> 2 sample2.cel Y S + >> 3 sample3.cel Y S + >> 4 sample4.cel Y S + >> 5 sample5.cel Y L + >> 6 sample6.cel Y L + >> 7 sample7.cel Y L + >> 8 sample8.cel Y L + >> 9 sample9.cel Y S - >> 10 sample10.cel Y S - >> 11 sample11.cel Y S - >> 12 sample12.cel Y S - >> 13 sample13.cel Y L - >> 14 sample14.cel Y L - >> 15 sample15.cel Y L - >> 16 sample16.cel Y L - >> 17 sample17.cel N S + >> 18 sample18.cel N S + >> 19 sample19.cel N S + >> 20 sample20.cel N S + >> 21 sample21.cel N L + >> 22 sample22.cel N L + >> 23 sample23.cel N L + >> 24 sample24.cel N L + >> 25 sample25.cel N S - >> 26 sample26.cel N S - >> 27 sample27.cel N S - >> 28 sample28.cel N S - >> 29 sample29.cel N L - >> 30 sample30.cel N L - >> 31 sample31.cel N L - >> 32 sample32.cel N L - >> >> [[alternative HTML version deleted]] >> >> ______________________________**_________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.e="" thz.ch="" mailman="" listinfo="" bioconductor=""> >> Search the archives: http://news.gmane.org/gmane.** >> science.biology.informatics.**conductor<http: news.gmane.org="" gmane="" .science.biology.informatics.conductor=""> >> > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > [[alternative HTML version deleted]]

ADD REPLY • link 12.4 years ago Dave Canvhet ▴ 40

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Hi Dave, On 6/20/2012 12:07 PM, Dave Canvhet wrote: > Hi James, > > This isn't really clear, and I might be way off base with this > answer, but it looks to me like you are after an interaction term. > If I were to restate, I would say that you are looking for genes > that react differently to treatment between the long lived > integrine positive samples and the short lived integrine negative > samples. > > > This is exactly what I want, so thanks to your clear restate. > > If true, this isn't difficult to set up, although I wouldn't do it > the way you are. Personally, I would combine the samples into four > types, based on life and integrine (where for brevity, life is > long/short and integrine is +/-): > > long+ > short+ > long- > short- > > Now your interaction as I understand it will only utilize the > long+ and short- samples, so you would restrict your samples to > just those samples that fulfill those criteria. Then you could > make a lifeinteg factor that is long+ and short- and create a > design matrix > > > design <- model.matrix(~drug*lifeinteg) > > > > OK I still to progress on the differences between interaction model > and additive model (with which I'm more familiar) > Do you think it will be useful to set up an Intercept ? > design <- model.matrix(~0+drug*lifeinteg) There won't be a difference. As an example: > drug <- factor(rep(1:2, 4)) > lifeinteg <- factor(rep(1:2, each = 4)) > model.matrix(~drug*lifeinteg) (Intercept) drug2 lifeinteg2 drug2:lifeinteg2 1 1 0 0 0 2 1 1 0 0 3 1 0 0 0 4 1 1 0 0 5 1 0 1 0 6 1 1 1 1 7 1 0 1 0 8 1 1 1 1 attr(,"assign") [1] 0 1 2 3 attr(,"contrasts") attr(,"contrasts")$drug [1] "contr.treatment" attr(,"contrasts")$lifeinteg [1] "contr.treatment" > model.matrix(~0+drug*lifeinteg) drug1 drug2 lifeinteg2 drug2:lifeinteg2 1 1 0 0 0 2 0 1 0 0 3 1 0 0 0 4 0 1 0 0 5 1 0 1 0 6 0 1 1 1 7 1 0 1 0 8 0 1 1 1 attr(,"assign") [1] 1 1 2 3 attr(,"contrasts") attr(,"contrasts")$drug [1] "contr.treatment" attr(,"contrasts")$lifeinteg [1] "contr.treatment" So the interaction term will be drug2:lifeinteg2 regardless of how you specify the model. Best, Jim > > Again many for your time and your help. > > Bests > -- > Dave > > > > > and the lifeinteg2 coefficient is the interaction, and gives you > the genes that react differently to the drug based on being long+ > or short-. > > Best, > > Jim > > > > > > I've set up my design matrix (target is below): > > drug = as.factor(targetATH$drug) > > integr = as.factor(targetATH$integrin) > > lifetime = as.factor(targetATH$lifetime) > > design = model.matrix(~drug+integr+lifetime) > > I can't figure out how to set up the correct contrast matrix > to get the > coefficient I want. > I would be very grateful if you could give any pieces of > advices for that. > I hope I have enough sample to get enough power to detect some > genes. > > > many thanks by advance, best regards, > -- > Dave > > > target : > > targetATH > > FileName drug lifetime integrin > 1 sample1.cel Y S + > 2 sample2.cel Y S + > 3 sample3.cel Y S + > 4 sample4.cel Y S + > 5 sample5.cel Y L + > 6 sample6.cel Y L + > 7 sample7.cel Y L + > 8 sample8.cel Y L + > 9 sample9.cel Y S - > 10 sample10.cel Y S - > 11 sample11.cel Y S - > 12 sample12.cel Y S - > 13 sample13.cel Y L - > 14 sample14.cel Y L - > 15 sample15.cel Y L - > 16 sample16.cel Y L - > 17 sample17.cel N S + > 18 sample18.cel N S + > 19 sample19.cel N S + > 20 sample20.cel N S + > 21 sample21.cel N L + > 22 sample22.cel N L + > 23 sample23.cel N L + > 24 sample24.cel N L + > 25 sample25.cel N S - > 26 sample26.cel N S - > 27 sample27.cel N S - > 28 sample28.cel N S - > 29 sample29.cel N L - > 30 sample30.cel N L - > 31 sample31.cel N L - > 32 sample32.cel N L - > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org <mailto:bioconductor at="" r-project.org=""> > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > -- > James W. MacDonald, M.S. > Biostatistician > University of Washington > Environmental and Occupational Health Sciences > 4225 Roosevelt Way NE, # 100 > Seattle WA 98105-6099 > > -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099

ADD REPLY • link 12.4 years ago James W. MacDonald 67k

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