Entering edit mode
Denise Scholtens
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@denise-scholtens-718
Last seen 10.0 years ago
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/
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