this is perhaps obvious to some, but I've seen colleagues surprised by it
nonetheless: if each sample has been sequenced to a depth of ~20 million
reads, then with cpm >= 1, you're effectively/approximately requiring raw
counts >= 20; if your depth is 100 million reads, then you're requiring
counts > 100 (and presumably the whole reason you paid for 100 million
reads was to get larger dynamic range at the low end, which you've just
thrown away).  That "1 cpm rule of thumb" seems to be pervasive, and often
used without thought to library size and dynamic range.  We did want to try
to be better than microarrays, right?

So, is there a disadvantage for filtering based on "raw count >= X (where X
is 5, 10, etc.) in at least Y samples" rather than CPM?  Or would you
suggest in such cases still normalizing by read depth but lowering the
threshold (e.g. cpm >= 1/(mean lib. size in millions)).  I'm assuming
non-pathological cases of fairly homogenous library size per sample.

-Aaron


On Wed, Apr 30, 2014 at 3:34 PM, Mark Robinson <mark.robinson@imls.uzh.ch>wrote:

>
> In my lab, we typically follow a "CPM of at least X in at least Y samples"
> rule, where X=1 (arbitrary but reasonable, can be changed) and Y=size of
> smallest replicate group, according to one of the case studies in the
> user's guide, for example:
>
> ------
> 4.3.6 Filtering
> We fi lter out very lowly expressed tags, keeping genes that are expressed
> at a reasonable level in at least one treatment condition. Since the
> smallest group size is three, we keep genes that achieve at least one count
> per million (cpm) in at least three samples:
>
> > keep <- rowSums(cpm(y)>1) >= 3
> > y <- y[keep,]
> ------
>
> (
> http://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
> )
>
> Cheers, Mark
>
>
> ----------
> Prof. Dr. Mark Robinson
> Statistical Bioinformatics, Institute of Molecular Life Sciences
> University of Zurich
> http://ow.ly/riRea
>
>
>
>
>
>
>
> On 30.04.2014, at 21:23, "Ryan C. Thompson" <rct@thompsonclan.org> wrote:
>
> > Dear Mahnaz,
> >
> > Total count filtering and mean count filtering are equivalent, since the
> only difference is a constant factor (dividing by number of samples), so
> the mean count filter demonstrated in the genefilter vignette corresponds
> to your question.
> >
> > If you are expecting the vignette to simply give you a specific number
> to use a as a cutoff, that's not possible, because the threshold depends on
> the data. I suggest that you adapt the R code in this vignette to your data
> in order to choose an appropriate cutoff.
> >
> > -Ryan
> >
> > On Wed 30 Apr 2014 12:04:33 PM PDT, Mahnaz Kiani wrote:
> >> Thanks for quick response, I did check that but didn't find any
> information
> >> about total count filter cutoff, would you please help me with that.
> >>
> >> Thanks,
> >> Mahnaz
> >>
> >>
> >> On Wed, Apr 30, 2014 at 1:47 PM, Wolfgang Huber <whuber@embl.de> wrote:
> >>
> >>> Dear Mahnaz
> >>> http://bioconductor.org/packages/release/bioc/html/genefilter.html ->
> >>> Diagnostics for independent filtering -> Section 4 provides some
> options.
> >>>         Wolfgang
> >>>
> >>> Il giorno 30 Apr 2014, alle ore 20:29, mahnaz Kiani [guest] <
> >>> guest@bioconductor.org> ha scritto:
> >>>
> >>>>
> >>>> I'm using edgeR for analysis of may data and I'm not sure what total
> >>> count filter value cutoff value I should use, My reads are paired 50bP
> >>> reads and total reads per sample is about 80,000,000. I tried cutoff
> values
> >>> of 5,10,15,30,50 and 100 and I only saw differences between 50 and 100
> but
> >>> still looking for logical reason to chose the cutoff value.
> >>>>
> >>>> Appreciate your help,
> >>>> Mahnaz
> >>>>
> >>>> -- output of sessionInfo():
> >>>>
> >>>> R 3.0.2
> >>>>
> >>>> --
> >>>> Sent via the guest posting facility at bioconductor.org.
> >>>>
> >>>> _______________________________________________
> >>>> Bioconductor mailing list
> >>>> Bioconductor@r-project.org
> >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>>> Search the archives:
> >>> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>>
> >>>
> >>
> >>      [[alternative HTML version deleted]]
> >>
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