Question: [altirriba@hotmail.com: Design in factDesign] (fwd)
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gravatar for Denise Scholtens
15.7 years ago by
Denise Scholtens40 wrote:
Hello Jordi, My comments to your questions are below. I hope this helps. -Denise ______________________________________________________________________ ____ Denise Scholtens Department of Biostatistics Harvard School of Public Health dscholte@hsph.harvard.edu Hi all! I?ve been using RMA and LIMMA to analyse my data and I am currently trying to analyse it with the package factDesign. My design is a 2x2 factorial design with 4 groups: diabetic treated, diabetic untreated, health treated and health untreated with 3 biological replicates in each group. I want to know what genes are differentially expressed due to diabetes, to the treatment and to the combination of both (diabetes + treatment). My phenoData is: >pData(eset) DIABETES TREATMENT DNT1 TRUE FALSE DNT2 TRUE FALSE DNT3 TRUE FALSE DT1 TRUE TRUE DT2 TRUE TRUE DT3 TRUE TRUE SNT1 FALSE FALSE SNT2 FALSE FALSE SNT3 FALSE FALSE ST1 FALSE TRUE ST2 FALSE TRUE ST3 FALSE TRUE Are these commands correct to get the results listed below? Where are the errors? >lm.full<-function(y) lm(y ~ DIABETES + TREATMENT + DIABETES * TREATMENT) >lm.diabetes<-function(y) lm(y ~ DIABETES) >lm.treatment<-function(y) lm(y ~ TREATMENT) >lm.diabetestreatment<-function(y) lm(y ~ DIABETES + TREATMENT) >lm.f<-esApply(eset, 1, lm.full) >lm.d<-esApply(eset, 1, lm.diabetes) >lm.t<-esApply(eset, 1, lm.treatment) >lm.dt<-esApply(eset, 1, lm.diabetestreatment) ##### # Yes, these commands look correct for making the linear models and # running them for the exprSet called eset. ###### ## To get the genes characteristics of the treatment: >Fpvals<-rep(0, length(lm.f)) >for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.d[[i]], lm.f[[i]])$P[2]} >Fsub<-which(Fpvals<0.01) >eset.Fsub<-eset[Fsub] >lm.f.Fsub<-lm.f[Fsub] >betaNames<-names(lm.f[[1]] [["coefficients"]]) >lambda<-par2lambda(betaNames, c("TREATMENTTRUE"), c(1)) ## I get the same >genes if I write : > lambda2 <- par2lambda (betaNames, >list(c("TREATMENTTRUE" , "DIABETESTRUE:TREATMENTTRUE")),list( c(1,1))) >mainTR<-function(x) contrastTest(x,lambda,p=0.1) [[1]] >mainTRgenes<-sapply(lm.f.Fsub, FUN=mainES) ##### # I think the problem is the use of mainES rather than mainTR in the last # sapply. mainES is a function that is defined in the factDesign vignette # - your own function should be used here instead. If you define the # function differently for different contrasts, my guess is you will see # different gene lists for the lambda and lambda2 defined above. ##### ## To get the genes characteristics of the diabetes: >for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.t[[i]], lm.f[[i]])$P[2]} >Fsub<-which(Fpvals<0.01) >eset.Fsub<-eset[Fsub] >lm.f.Fsub<-lm.f[Fsub] >betaNames<-names(lm.f[[1]] [["coefficients"]]) >lambda<-par2lambda(betaNames, c("DIABETESTRUE"), c(1)) ## I get also the >same genes if I consider the intersection DIABETESTRUE:TREATMENTTRUE. >mainDI<-function(x) contrastTest(x,lambda,p=0.1) [[1]] >mainDIgenes<-sapply(lm.f.Fsub, FUN=mainES) ##### # See above comments. ##### ## To get the genes characteristics of the diabetes+treatment: >for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.dt[[i]], lm.f[[i]])$P[2]} >Fsub<-which(Fpvals<0.01) >eset.Fsub<-eset[Fsub] >lm.f.Fsub<-lm.f[Fsub] > betaNames<-names(lm.f[[1]] [["coefficients"]]) >lambda<-par2lambda(betaNames, c("DIABETESTRUE:TREATMENTTRUE"), c(1)) >mainDT<-function(x) contrastTest(x,lambda,p=0.1) [[1]] >mainDTgenes<-sapply(lm.f.Fsub, FUN=mainES) ## I don?t get any ?fail to >reject? gene. ##### # Again, I think changing mainES to mainDT will do the trick. ##### When I get the ?rejected? and the ?failed to reject? genes, can I classify them by their Fvalues? How? ##### # Currently, the contrastTest function only returns the contrast estimate # (cEst), the pvalue from the F-test (pvalue), and a statement of either # "REJECT" or "FAIL TO REJECT" based on the p-value cutoff you specify. # This can be changed to return the F-value as well, and I'm happy to put # this change into the package. Then you can use the Fvalues for whatever # you would like. # # One thing to consider if you are going to use p-values from the F tests # to select genes - you will want to corrent for multiple testing. The # multtest package is very useful for this. ###### Thank you very much for your comments and help. Yours sincerely, Jordi Altirriba IDIBAPS-Hospital Clinic (Barcelona, Spain) _________________________________________________________________ D?janos tu CV y recibe ofertas de trabajo en tu buz?n. Multiplica tus oportunidades con MSN Empleo. http://www.msn.es/Empleo/ _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
multtest limma factdesign • 748 views
ADD COMMENTlink modified 15.7 years ago by Jordi Altirriba Gutiérrez350 • written 15.7 years ago by Denise Scholtens40
Answer: [altirriba@hotmail.com: Design in factDesign] (fwd)
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gravatar for Jordi Altirriba Gutiérrez
15.7 years ago by
Thank you very much for your answers! And thanks for updating the factDesign so fast! Yours sincerely, Jordi Altirriba IDIBAPS - Hospital Cl?nic - Barcelona >From: Denise Scholtens <dscholte@hsph.harvard.edu> >To: Kasper Daniel Hansen <k.hansen@biostat.ku.dk> >CC: Jordi Altirriba Guti?rrez ><altirriba@hotmail.com>,bioconductor@stat.math.ethz.ch >Subject: Re: [altirriba@hotmail.com: [BioC] Design in factDesign] (fwd) >Date: Mon, 12 Apr 2004 11:30:20 -0400 (EDT) > >I'm sorry I missed the difference between "*" and ":" previously - I >always forget which means which. > >Kaspar is right - in statistics, it's not a good idea to fit an >interaction between two terms in a linear model without including the main >effects in the model as well. In the situation you describe below, you >could compare the "D+T+D:T" model to the "T" to find genes that are >differentially expressed for diabetic patients compared to nondiabetic >patients in either the presence or absence of treatment. This will take >account of the observations under all four treatment conditions. You >could then further analyze specific tests of contrasts for the parameters >in the model to get at more directed questions about the behavior of the >genes under the experimental conditions - the factDesign vignette contains >examples of this. > >BTW - factDesign is now updated so that the contrastTest function returns >the F-value referred to previously. > >Denise > >On Fri, 9 Apr 2004, Kasper Daniel Hansen wrote: > > > On Thu, Apr 08, 2004 at 08:35:08PM +0200, Jordi Altirriba Guti?rrez >wrote: > > > But there is something that I don't understand (sorry if it is a very >basic > > > question), because when I want to get the genes that are >differentially > > > expressed due to diabetes I "fit" my data to the functions: lm(y ~ >DIABETES > > > + TREATMENT + DIABETES * TREATMENT) and lm(y ~ TREATMENT). Therefore >the > > > genes that "fit" better to the first function are differentially >expressed > > > due to diabetes, but why don't I fit my data to the functions: lm(y ~ > > > DIABETES + TREATMENT + DIABETES * TREATMENT) and lm(y ~ TREATMENT + > > > DIABETES * TREATMENT)? I know that the parameter DIABETES * TREATMENT >is > > > the intersection of the other two parameters, but it should be >independent > > > of these parameters. > > > > (I have not read the rest of the discussion) > > > > In R, T * D in a model formula is short hand for T + D + T:D. Using this >we get > > T + D + T*D "=" T + D + T + D + T:D "=" T + D + T:D > > (since you only need one occurence of a term), and > > T + T*D "=" T + T + D + T:D "=" T + D + T:D > > so the two formulas are equal. > > > > However, if I understand the intention behind the question, you want to >exclude a main effect in the presence of an interaction (or > > to be presice, you want to test T + T:D vs T*D). This is something which >makes no sense at all. I suggest you pick up a basic book on > > statistics and read on main effects and interactions. > > > > But ok, a very quick explanation: an interaction between diabetes and >treatment means that the effect of diabetes (on gene > > expression) is different for the different treatment groups (eg. the >effect of diabetes may disappear amongst the treated patients). > > Hence you have some effect of treatment as well as diabetes. > > > > /Kasper > > > > > Jordi Altirriba > > > IDIBAPS - Hospital Clinic (Barcelona, Spain) > > > > > > > >Hello Jordi, > > > > > > > >My comments to your questions are below. I hope this helps. -Denise > > > > > > > > >___________________________________________________________________ _______ > > > >Denise Scholtens > > > >Department of Biostatistics > > > >Harvard School of Public Health > > > >dscholte@hsph.harvard.edu > > > > > > > >Hi all! > > > >I?ve been using RMA and LIMMA to analyse my data and I am currently >trying > > > >to analyse it with the package factDesign. My design is a 2x2 >factorial > > > >design with 4 groups: diabetic treated, diabetic untreated, health >treated > > > >and health untreated with 3 biological replicates in each group. I >want to > > > >know what genes are differentially expressed due to diabetes, to the > > > >treatment and to the combination of both (diabetes + treatment). > > > >My phenoData is: > > > >>pData(eset) > > > > DIABETES TREATMENT > > > >DNT1 TRUE FALSE > > > >DNT2 TRUE FALSE > > > >DNT3 TRUE FALSE > > > >DT1 TRUE TRUE > > > >DT2 TRUE TRUE > > > >DT3 TRUE TRUE > > > >SNT1 FALSE FALSE > > > >SNT2 FALSE FALSE > > > >SNT3 FALSE FALSE > > > >ST1 FALSE TRUE > > > >ST2 FALSE TRUE > > > >ST3 FALSE TRUE > > > > > > > >Are these commands correct to get the results listed below? Where are >the > > > >errors? > > > >>lm.full<-function(y) lm(y ~ DIABETES + TREATMENT + DIABETES * >TREATMENT) > > > >>lm.diabetes<-function(y) lm(y ~ DIABETES) > > > >>lm.treatment<-function(y) lm(y ~ TREATMENT) > > > >>lm.diabetestreatment<-function(y) lm(y ~ DIABETES + TREATMENT) > > > >>lm.f<-esApply(eset, 1, lm.full) > > > >>lm.d<-esApply(eset, 1, lm.diabetes) > > > >>lm.t<-esApply(eset, 1, lm.treatment) > > > >>lm.dt<-esApply(eset, 1, lm.diabetestreatment) > > > > > > > >##### > > > ># Yes, these commands look correct for making the linear models and > > > ># running them for the exprSet called eset. > > > >###### > > > > > > > >## To get the genes characteristics of the treatment: > > > >>Fpvals<-rep(0, length(lm.f)) > > > >>for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.d[[i]], >lm.f[[i]])$P[2]} > > > >>Fsub<-which(Fpvals<0.01) > > > >>eset.Fsub<-eset[Fsub] > > > >>lm.f.Fsub<-lm.f[Fsub] > > > >>betaNames<-names(lm.f[[1]] [["coefficients"]]) > > > >>lambda<-par2lambda(betaNames, c("TREATMENTTRUE"), c(1)) ## I get the >same > > > >>genes if I write : > lambda2 <- par2lambda (betaNames, > > > >>list(c("TREATMENTTRUE" , "DIABETESTRUE:TREATMENTTRUE")),list( >c(1,1))) > > > >>mainTR<-function(x) contrastTest(x,lambda,p=0.1) [[1]] > > > >>mainTRgenes<-sapply(lm.f.Fsub, FUN=mainES) > > > > > > > >##### > > > ># I think the problem is the use of mainES rather than mainTR in the >last > > > ># sapply. mainES is a function that is defined in the factDesign >vignette > > > ># - your own function should be used here instead. If you define the > > > ># function differently for different contrasts, my guess is you will >see > > > ># different gene lists for the lambda and lambda2 defined above. > > > >##### > > > > > > > > > > > >## To get the genes characteristics of the diabetes: > > > >>for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.t[[i]], >lm.f[[i]])$P[2]} > > > >>Fsub<-which(Fpvals<0.01) > > > >>eset.Fsub<-eset[Fsub] > > > >>lm.f.Fsub<-lm.f[Fsub] > > > >>betaNames<-names(lm.f[[1]] [["coefficients"]]) > > > >>lambda<-par2lambda(betaNames, c("DIABETESTRUE"), c(1)) ## I get also >the > > > >>same genes if I consider the intersection >DIABETESTRUE:TREATMENTTRUE. > > > >>mainDI<-function(x) contrastTest(x,lambda,p=0.1) [[1]] > > > >>mainDIgenes<-sapply(lm.f.Fsub, FUN=mainES) > > > > > > > >##### > > > ># See above comments. > > > >##### > > > > > > > >## To get the genes characteristics of the diabetes+treatment: > > > >>for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.dt[[i]], >lm.f[[i]])$P[2]} > > > >>Fsub<-which(Fpvals<0.01) > > > >>eset.Fsub<-eset[Fsub] > > > >>lm.f.Fsub<-lm.f[Fsub] > > > >> betaNames<-names(lm.f[[1]] [["coefficients"]]) > > > >>lambda<-par2lambda(betaNames, c("DIABETESTRUE:TREATMENTTRUE"), c(1)) > > > >>mainDT<-function(x) contrastTest(x,lambda,p=0.1) [[1]] > > > >>mainDTgenes<-sapply(lm.f.Fsub, FUN=mainES) ## I don?t get any ?fail >to > > > >>reject? gene. > > > > > > > >##### > > > ># Again, I think changing mainES to mainDT will do the trick. > > > >##### > > > > > > > > > > > >When I get the ?rejected? and the ?failed to reject? genes, can I >classify > > > >them by their Fvalues? How? > > > > > > > >##### > > > ># Currently, the contrastTest function only returns the contrast >estimate > > > ># (cEst), the pvalue from the F-test (pvalue), and a statement of >either > > > ># "REJECT" or "FAIL TO REJECT" based on the p-value cutoff you >specify. > > > ># This can be changed to return the F-value as well, and I'm happy to >put > > > ># this change into the package. Then you can use the Fvalues for >whatever > > > ># you would like. > > > ># > > > ># One thing to consider if you are going to use p-values from the F >tests > > > ># to select genes - you will want to corrent for multiple testing. >The > > > ># multtest package is very useful for this. > > > >###### > > > > > > > > > > > >Thank you very much for your comments and help. > > > >Yours sincerely, > > > > > > > >Jordi Altirriba > > > >IDIBAPS-Hospital Clinic (Barcelona, Spain) > > > > > > _________________________________________________________________ > > > D?janos tu CV y recibe ofertas de trabajo en tu buz?n. Multiplica tus > > > oportunidades con MSN Empleo. http://www.msn.es/Empleo/ > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor@stat.math.ethz.ch > > > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > > > > -- > > Kasper Daniel Hansen, Research Assistant > > Department of Biostatistics, University of Copenhagen > > > > > > >_____________________________________________________________________ _____ >Denise Scholtens >Department of Biostatistics >Harvard School of Public Health >Office: M1B11, DFCI >Phone: 617.632.4494 >dscholte@hsph.harvard.edu _________________________________________________________________ Hor?scopo, tarot, numerolog?a... Escucha lo que te dicen los astros.
ADD COMMENTlink written 15.7 years ago by Jordi Altirriba Gutiérrez350
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