Hi Sean,

Yes, thank you.

Yet my problem of the data did not get sorted out. I have tried different
filtering methods including gapfilter and a combination of IQR with pOverA
or cv etc. But my adj p values are above the FDR limit of 0.05 after the
limma analysis. Also B values are generally -3.  As Gorden has mentioned in
one of the previous mails, this is a indication of little evidance for
differential expression.

What could be the reason for this. Is this really an indicative of absence
of differential expression?

Thank you in advance
Abhilash

On Fri, Jul 11, 2008 at 4:17 PM, Sean Davis <sdavis2@mail.nih.gov> wrote:

> On Fri, Jul 11, 2008 at 5:32 AM, Abhilash Venu <abhivenu@gmail.com> wrote:
> > Dear Dr. Huber,
> >
> > Thank you for the advice. I have tried the script that you have advised
> to
> > use. As you mentioned I have used the script after the normalization, but
> > that has shown the following error, which I do not understand, whether I
> am
> > using in the right way.
> >
> > MA<-normalizeBetweenArrays(log2(Rgene$G), method="quantile")#
> normalization
> >  rs = rowSds(MA)
> >  fx = fx[ rs > quantile(rs, 0.05), ]
> > Error: object "fx" not found
>
> Hi, Abhilash.  I think that line should read:
>
> fx = x[rs > quantile(rs,0.05),]
>
> Wolfgang was simply suggesting subsetting x by the results of sd filtering.
>
> Sean
>
> > Can you advise me on the same.
> > Thanks in advance.
> >
> > Abhilash
> >
> > On Fri, Jul 11, 2008 at 4:06 AM, Wolfgang Huber <huber@ebi.ac.uk> wrote:
> >
> >> Hi Abhilash
> >>
> >>
> >>  I am working with single color data from Agilent platform. After the
> limma
> >>> analysis the adjusted p values were higher than 5% of FDR. At this
> >>> instance
> >>> I am thinking of filtering the genes using genefilter. As my data set
> >>> contains only raw intensities of normal and test before the
> normalization,
> >>> where I am uisng 'normalizeBetweenArrays' command after log
> transforming
> >>> the
> >>> data.
> >>> In this scenario I am quite confused whether I should use the filter
> >>> functions prior to normalization of after the normalization but efore
> >>> fitting the linear model?
> >>> As my data is not an expressionSet I cannot use the nonfilter commands,
> in
> >>> this case any suggestions of using other filtering methods?
> >>>
> >>> Appreciate the suggestions
> >>>
> >>>
> >> Such filtering is performed after normalisation, but it is essential
> that
> >> the filter criterion does *not use the sample annotations*. E.g. you can
> use
> >> for each gene the overall variance or IQR across the experiment.
> >>
> >> If x is a matrix with rows=genes and columns=samples, then this can be
> as
> >> simple as:
> >>
> >>  rs = rowSds(x)
> >>  fx = fx[ rs > quantile(rs, lambda), ]
> >>
> >> where rowSds is in the genefilter package, and lambda is a parameter
> >> between 0 and 1 that contains your belief in what fraction of probes on
> the
> >> array correspond to target molecules that are never expressed in the
> >> conditions you study.
> >>
> >> Also note that after such filtering, strictly speaking, the nominal
> >> p-values from the subsequent testing could be too small - but one can
> show
> >> that in typical microarray applications the bias is negligible (compared
> to
> >> the impact of other effects), and in any case the p-values can be used
> for
> >> ranking.
> >>
> >>  Best wishes
> >>        Wolfgang
> >>
> >>
> >> --
> >> ----------------------------------------------------
> >> Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber
> >>
> >
> >
> >
> > --
> >
> > Regards,
> > Abhilash
> >
> >        [[alternative HTML version deleted]]
> >
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> >
>



-- 

Regards,
Abhilash

	[[alternative HTML version deleted]]

