Dear Dr. Robert,

Thanks for the reply. When you say that you choose median for CV filtering,
do you mean the following?

g.mean <- apply(eset, 1, mean)
g.sd <- apply(eset, 1, sd)
g.cv <- g.sd / g.mean
ffun <- fiterfun( cv(0,median(g.cv) )
sel  <- genefilter(eset, ffun)
esub <- eset[sel, ]

So, the median CV cut-off is to avoid small mean values in the denominator.
Is that the right interpretation?

Thanks again for your suggestions in using CV filter.

Best regards,
Anbarasu

On 11/30/06, Robert Gentleman <rgentlem@fhcrc.org> wrote:
>
> Hi,
>
> Lourdusamy A Anbarasu wrote:
> > Dear Dr. Robert,
> >
> > You have mentioned that the filtering on the variability is preferred
> > than raw intensity value. I have also read your previous post on this
> > issue. For filters based on CV, are there any recommended cut-off
> values?
>
>   Not really. A widely held, but AFAIK undocumented, belief is that in
> any given tissue/cell about 40% of the genome is expressed at any time.
> So, I usually choose the median - that is somewhat conservative with
> respect to the above cited statistic - but this is a personal
> preference. I have not seen any research (and I think it would be hard).
>
>
>    best wishes
>     Robert
>
> >
> > Thanks in advance.
> >
> > Best regards,
> > Anbarasu
> >
> > On 11/30/06, *Robert Gentleman* <rgentlem@fhcrc.org
> > <mailto:rgentlem@fhcrc.org> > wrote:
> >
> >     Hi,
> >
> >     Amy Mikhail wrote:
> >      > Dear Bioconductors,
> >      >
> >      > I am annalysing 6 PlasmodiumAnopheles genechips, which have only
> >     Anopheles
> >      > mosquito samples hybridised to them (i.e. they are not infected
> >      > mosquitoes).  The 6 chips include 3 replicates, each consisting
> >     of two
> >      > time points.  The design matrix is as follows:
> >      >
> >      >> design
> >      >      M15d M43d
> >      > [1,]    1    0
> >      > [2,]    0    1
> >      > [3,]    1    0
> >      > [4,]    0    1
> >      > [5,]    1    0
> >      > [6,]    0    1
> >      >
> >      >
> >      > I have tried both gcRMA (in AffyLMGUI), and RMA, MBEI and MAS5
> >     (in affy).
> >      > Looking at the (BH) adjusted p values <0.05, this gave me 2, 12,
> >     0 and 0
> >      > DE genes, respectively... much less than I was expecting.
> >      >
> >      > As this affy chip contains probesets for both mosquito and
> malaria
> >      > parasite genes, I am wondering:
> >      >
> >      > (a) if it is better to remove all the parasite probesets before
> >     my analysis;
> >
> >       Yes, if you don't intend to use them, and they are not relevant to
> >     your analysis. There is no point in doing p-value corrections for
> tests
> >     you know are not interesting/relevant a priori.
> >
> >      >
> >      > (b) if so at what stage I should do this (before or after
> >     normalisation
> >      > and background correction, or does it matter?)
> >
> >       After both and prior to analysis - otherwise you are likely to
> >     need to
> >     do some serious tweaking of the normalization code.
> >
> >      >
> >      > (c) how would I filter out these probesets using genefilter (all
> the
> >      > parasite affy IDs begin with Pf. - could I use this prefix in the
> >     affy IDs
> >      > to filter out the probesets, and if so how?)
> >
> >        you don't need genefilter at all, this is a subseting problem.
> >       If you had an ExpressionSet you would do something like:
> >
> >        parasites = grep("^Pf", featureNames(myExpressionSet))
> >
> >        mySubset = myExpressionSet[!parasites,]
> >
> >      >
> >      > Secondly, I did not add any of the polyA controls to my
> >     samples.  I would
> >      > like to know:
> >      >
> >      > (d) Do any of the bg correct / normalisation methods I tried
> utilise
> >      > affymetrix control probesets, and if so, how?
> >
> >        I doubt it.
> >
> >      >
> >      > (e) Should I also filter out the control sets - again, if so at
> >     what stage
> >      > in the analysis and what would be an appropriate code to use?
> >      >
> >
> >        same place as you filter the parasite genes and pretty much in
> the
> >     same way. They are likely to start with AFFX.
> >
> >      > I did try the code for non-specific filtering (on my RMA dataset)
> >     from pg.
> >      > 232 of the bioconductor monograph, but the reduction in the
> number of
> >      > probesets was quite drastic;
> >      >
> >      >> f1 <- pOverA(0.25, log2(100))
> >      >> f2 <- function(x) (IQR(x) > 0.5)
> >
> >       that is a typo in the text - you probably want to filter out those
> >     with IQR below the median, not for some fixed value.
> >
> >      >> ff <- filterfun(f1, f2)
> >      >> selected <- genefilter(Baseage.transformed , ff)
> >      >> sum(selected)
> >      > [1] 404   ###(The origninal no. of probesets is 22,726)###
> >      >> Baseage.sub <- Baseage.transformed[selected, ]
> >      >
> >      > Also, I understood from the monograph that "100" was to filter
> out
> >      > fluorescence intensities less than this, but I am not clear if
> >     this is
> >      > from raw intensities or log2 values?
> >
> >       raw - 100 on the log2 scale is larger than can be represented in
> the
> >     image file formats used. And don't do that - it is not a good idea -
> >     filter on variability.
> >
> >
> >      >
> >      > All the parasite probesets have raw intensities <35 .... so could
> >     I apply
> >      > this as a simple filter, and would this have to be on raw (rather
> >     than
> >      > normalised data)?
> >
> >
> >       Best wishes
> >         Robert
> >
> >      >
> >      > Appologies for the long posting...
> >      >
> >      > Looking forward to any replies,
> >      > Regards,
> >      > Amy
> >      >
> >      >> sessionInfo()
> >      > R version 2.4.0 (2006-10-03)
> >      > i386-pc-mingw32
> >      >
> >      > locale:
> >      > LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
> >      > States.1252;LC_MONETARY=English_United
> >      > States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252
> >      >
> >      > attached base packages:
> >      >  [1] "tcltk"     "splines"   "tools"     "methods"   "stats"
> >      > "graphics"  "grDevices" "utils"     "datasets"  "base"
> >      >
> >      > other attached packages:
> >      > plasmodiumanophelescdf              tkWidgets
> DynDoc
> >      >      widgetTools            agahomology
> >      >               "1.14.0"               " 1.12.0"               "
> 1.12.0"
> >      >         "1.10.0"               "1.14.2"
>
> >      >                affyPLM                  gcrma            matchprobes
> >      >         affydata                annaffy
> >      >               "1.10.0"                "2.6.0"                "
> 1.6.0"
> >      >         "1.10.0"                "1.6.0"
> >      >                   KEGG
> GO                  limma
> >      >      geneplotter               annotate
> >      >               "1.14.0"               "1.14.0"                "
> 2.9.1"
> >      >         "1.12.0"               "1.12.0"
> >      >                   affy                 affyio
> genefilter
> >      >         survival                Biobase
> >      >               "1.12.0"                "1.2.0"               "
> 1.12.0 "
> >      >           "2.29"               "1.12.0"
> >      >
> >      >
> >      > -------------------------------------------
> >      > Amy Mikhail
> >      > Research student
> >      > University of Aberdeen
> >      > Zoology Building
> >      > Tillydrone Avenue
> >      > Aberdeen AB24 2TZ
> >      > Scotland
> >      > Email: a.mikhail@abdn.ac.uk <mailto:a.mikhail@abdn.ac.uk>
> >      > Phone: 00-44-1224-272880 (lab)
> >      >        00-44-1224-273256 (office)
> >      >
> >      > _______________________________________________
> >      > Bioconductor mailing list
> >      > Bioconductor@stat.math.ethz.ch
> >     <mailto:Bioconductor@stat.math.ethz.ch>
> >      > https://stat.ethz.ch/mailman/listinfo/bioconductor
> >      > Search the archives:
> >     http://news.gmane.org/gmane.science.biology.informatics.conductor
> >     <http://news.gmane.org/gmane.science.biology.informatics.conductor>
> >      >
> >
> >     --
> >     Robert Gentleman, PhD
> >     Program in Computational Biology
> >     Division of Public Health Sciences
> >     Fred Hutchinson Cancer Research Center
> >     1100 Fairview Ave. N, M2-B876
> >     PO Box 19024
> >     Seattle, Washington 98109-1024
> >     206-667-7700
> >     rgentlem@fhcrc.org <mailto:rgentlem@fhcrc.org>
> >
> >     _______________________________________________
> >     Bioconductor mailing list
> >     Bioconductor@stat.math.ethz.ch <mailto:
> Bioconductor@stat.math.ethz.ch>
> >     https://stat.ethz.ch/mailman/listinfo/bioconductor
> >     Search the archives:
> >     http://news.gmane.org/gmane.science.biology.informatics.conductor
> >
> >
> >
> >
> > --
> > Lourdusamy A Anbarasu
> > Dipartimento Medicina Sperimentale e Sanita Pubblica
> > Via Scalzino 3
> > 62032 Camerino (MC)
>
> --
> Robert Gentleman, PhD
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M2-B876
> PO Box 19024
> Seattle, Washington 98109-1024
> 206-667-7700
> rgentlem@fhcrc.org
>



-- 
Lourdusamy A Anbarasu
Dipartimento Medicina Sperimentale e Sanita Pubblica
Via Scalzino 3
62032 Camerino (MC)

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