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Jordi Altirriba GutiƩrrez
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350
@jordi-altirriba-gutierrez-682
Last seen 5.6 years ago
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)
## 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]])$P[2]}
>Fsub<-which(Fpvals<0.01)
>eset.Fsub<-eset[Fsub]
>lm.f.Fsub<-lm.f[Fsub]
>betaNames<-names(lm.f[[1]] [["coefficients"]])
>lambda<-par2lambda(betaNames, c("TREATMENTTRUE"), c(1)) ## I get the
same
>genes if I write : > lambda2 <- par2lambda (betaNames,
>list(c("TREATMENTTRUE" , "DIABETESTRUE:TREATMENTTRUE")),list(
c(1,1)))
>mainTR<-function(x) contrastTest(x,lambda,p=0.1) [[1]]
>mainTRgenes<-sapply(lm.f.Fsub, FUN=mainES)
## To get the genes characteristics of the diabetes:
>for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.t[[i]],
lm.f[[i]])$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)
## 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.
When I get the ?rejected? and the ?failed to reject? genes, can I
classify
them by their Fvalues? How?
Thank you very much for your comments and help.
Yours sincerely,
Jordi Altirriba
IDIBAPS-Hospital Clinic (Barcelona, Spain)
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