On Wed, Mar 5, 2008 at 2:05 PM, ian white <i.m.s.white@ed.ac.uk> wrote:

> Don't you need to make some allowance for multiple testing? E.g. to get
> a experiment-wise significance level of 0.01 you need
>
> which(p.values < very small number)
>
> where the very small number is approximately 0.01/(total number of
> genes).
>
> On Wed, 2008-03-05 at 14:38 +0200, Eleni Christodoulou wrote:
> > I am sorry, the test is unpaired...But my question remains
> >
> > Thanks,
> > Eleni
> >
> > On Wed, Mar 5, 2008 at 2:33 PM, Eleni Christodoulou <elenichri@gmail.com
> >
> > wrote:
> >
> > > Hello list,
> > >
> > > I am trying to apply the paired t.test between diseased and not
> diseased
> > > patients to identify genes that are more expressed in the one
> situation
> > > under the other. In order to retrieve the genes that are more
> expressed in
> > > the positive disease state I do:
> > > p.values<-c()
> > > for(i in 1:length(Significant[,1])){
> > > p.values[i]<-try(t.test(positive[i,],negative[i,],alternative
> > > ="greater")$p.value)
> > > }
> > >
> > > which(p.values<0.01)
> > >
> > >
> > > where Significant is my matrix of  genes  and their expression in
> tumors
> > > and positive, negative are subsets of thes matrix.
> > > Whn p<0.01, I reject the null hypothesis and I accept the alternative
> one,
> > > that I have greater gene expression in positive than in negative.
> > > I assume I must be doing sth wrong because the heatmap that I get with
> the
> > > genes that pass the filter of p-value is wrong.
> > >
> > > Could anyone help me with this?
> > >
> > > thanks a lot,
> > > Eleni
> > >
> > >
> >
> >       [[alternative HTML version deleted]]
> >
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> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
>

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