hi there,
I would like to estimate the effect on gene expression levels of two
factors Age
and activity (each with 4 levels) using Limma.
for my design matrix i have
dataB<-data.frame(samples = arrays,Age=factor(FFAge),activity =
factor(FFfibrosis))
design<-model.matrix(~activity*Age, data=dataB)
(samples contain the names of my arrays)
fit <- lm.series(MANormBetween$M,design)
toptable(coef=2,num=10,genelist=gal,fit=fit,adjust="fdr",sort.by="P")
I would like to know to which effect the quoted P.values in toptable()
correspond to ?
Also i would like to know how to extract the P.value for the
activity:Age effect.
all the best.
Ivan,
>
toptable(coef=2,num=10,genelist=gal,fit=fit,adjust="fdr",sort.by="P")
>
> I would like to know to which effect the quoted P.values in
toptable()
> correspond to ?
coef=2 means the effect described by the second column of your
design matrix.
Hope this helps,
James
At 01:28 AM 3/04/2004, ivan.borozan@utoronto.ca wrote:
>hi there,
>
>I would like to estimate the effect on gene expression levels of two
>factors Age
>and activity (each with 4 levels) using Limma.
>
>for my design matrix i have
>
>dataB<-data.frame(samples = arrays,Age=factor(FFAge),activity =
>factor(FFfibrosis))
>
>design<-model.matrix(~activity*Age, data=dataB)
>
>(samples contain the names of my arrays)
>
>fit <- lm.series(MANormBetween$M,design)
>
>toptable(coef=2,num=10,genelist=gal,fit=fit,adjust="fdr",sort.by="P")
>
>I would like to know to which effect the quoted P.values in
toptable()
>correspond to ?
Look at colnames(design). You've asked for the 2nd coefficient.
>Also i would like to know how to extract the P.value for the
activity:Age
>effect.
The activity:effect interaction is on 9 degrees of freedom and I
assume
that you understand that you need an F-statistic rather than a t-test
statistic to test this composite hypothesis. Your fitted factorial
model
has 16 parameters of which the last 9 are interaction terms.
Using limma 1.5.2 or later, you can use
fit <- lmFit(MANormBetween@M, design)
cont.matrix <- rbind( matrix(0,7,9), diag(9) ) # pick out last 9
coefficients
fit <- contrasts.fit(fit, cont.matrix)
fit <- eBayes(fit)
F.stat <- FStat(fit)
P.value <- pf(F.stat, df1=attr(F.stat,"df1"), df2=attr(F.stat,"df2"),
lower.tail=FALSE)
Gordon
>all the best.
