Thank you very much Mark and Rory for your kind help.
I'm not a statistician, so by just looking at the design matrix I can't be
sure its what I meant, so I hope you understood what I'm looking for, which
is differential binding between the two IP conditions  after taking into
account differences between the two Input conditions.
Before consulting you, I just thought of finding differential sites
between  IP samples of the two conditions, and separately to find
differential sites between the Input of the two conditions, and then
looking for those sites found to be DE between IP but not between Input. I
suppose that fitting this into a single design matrix is much better.

Rory - I have a questions regarding the subtraction in DiffBind, does the
software account for differences in library size before subtraction? If I
understood what you said, normalization is done after subtraction and only
for the IP samples. I think subtraction make sense only if the IP and its
corresponding Input have similar number of sequenced reads. Do I miss
something?
Thanks again
Mali


On Thu, Jan 5, 2012 at 12:11 AM, Mark Robinson <mark.robinson@imls.uzh.ch>wrote:

> Hi all,
>
> Mali: Very interesting dataset.
>
> Rory: As I mentioned offline, I don't think the offset style normalization
> will work here, since the "inputs" are observations, and have associated
> variability.  Offsets are meant to be fixed quantities, even though in
> practice they are estimated, but from a lot of data (e.g. TMM).
>
> I'm not keen on the idea of direct subtraction -- anyone for a Skellam
> distribution?
>
> What about just fitting a GLM ?  If I understand it correctly, this seems
> appropriate:
>
> ------
> > g <- rep( 0:1, each=4 )
> > p <- rep( c("input","IP"), 4 )
> > d <- model.matrix( ~ g*p )
> > rownames(d) <- paste(g,p,sep=".")
> > d
>        (Intercept) g pIP g:pIP
> 0.input           1 0   0     0
> 0.IP              1 0   1     0
> 0.input           1 0   0     0
> 0.IP              1 0   1     0
> 1.input           1 1   0     0
> 1.IP              1 1   1     1
> 1.input           1 1   0     0
> 1.IP              1 1   1     1
> ------
>
> First three columns account for expression levels and differences between
> IP/input.  Column 4 specifies the difference b/w conditions and should be
> what you are interested in.
> Once you have a table of counts, edgeR and DESeq can do this.
>
> Of course, the challenge is how you do the counting.  I'm not sure what
> sort of features are expected/possible with this assay, maybe a peak/region
> finder will do the trick.  And, there might be some normalization
> challenges too.
>
> Hope that helps,
> Mark
>
>
> On 04.01.2012, at 17:24, Rory Stark wrote:
>
> > That's the big question Mali! The more I think about it, the less
> confident I am that it will work.
> >
> > As I understand it you want to control for transcripts whose expression
> may change but whose affinity (rate at which the protein binds) stays the
> same. Without the control, higher expression = more transcripts = more RNA
> pulled down by the IP even at the same affinity. I'm not sure that
> subtracting the transcripts independent of binding (the RNA-Seq) will work.
> Besides the normalization issue relating to the RNA-Seq counts, the problem
>  is that there should always be more transcripts in the control (as they
> include both bound and unbound transcripts) than in the IP (only bound
> transcripts). So even if the normalization was perfect, the subtraction
> would always result in a negative number of counts (set to a minimum of 1
> count per peak in DiffBind).
> >
> > In this case, subtracting the control is probably too crude. Mark
> Robinson, an author of edgeR, is doing some interesting work on
> incorporating copy number information into differential ChIP-Seq analysis,
> and I am adding it to DiffBind. He is able to cast the problem as a
> normalization issue. I'm thinking that would be a better approach to your
> problem: the RNA-Seq gives "copy number" information (overall transcript
> abundance), and this is incorporated as a normalization term, leaving  the
> differential analysis to identify changes in affinity.  I'm working on this
> right now, so if you are interested you might be a beta tester — let me
> know.
> >
> > I still think it is worth running your data in DiffBind to see how it
> looks as a start.
> >
> > Cheers-
> > Rory
> >
> > From: mali salmon <shalmom1@gmail.com<mailto:shalmom1@gmail.com>>
> > Date: Wed, 4 Jan 2012 15:09:11 +0000
> > To: Cancer Research UK <rory.stark@cancer.org.uk<mailto:
> rory.stark@cancer.org.uk>>
> > Cc: "bioconductor@r-project.org<mailto:bioconductor@r-project.org>" <
> bioconductor@r-project.org<mailto:bioconductor@r-project.org>>
> > Subject: Re: [BioC] Fwd: differential binding question
> >
> > Thanks Rory and Heidi for replying.
> > Would read subtraction is enough in order to account for the difference
> in gene expression?
> > Mali
> >
> > On Wed, Jan 4, 2012 at 2:11 PM, Rory Stark <Rory.Stark@cancer.org.uk
> <mailto:Rory.Stark@cancer.org.uk>> wrote:
> > Hi Mali-
> >
> > You can try this pretty easily using DiffBind. I suggest calling peaks
> on each IP separately (each IP and its matching RNA-Seq control) and read
> these four peaksets into DiffBind (you could also use two peak callers and
> read in all eight peaksets to identify more potential sites). DiffBind lets
> you derive an overall set of peaks (either a superset of all the peaks, or
> any that overlap in at least two [or more] peaksets), does the read
> counting (by default subtracting reads in the matching RNA-seq controls),
> runs edgeR and/or DESeq to identify differentially bound regions, and
> offers several plots and reports to characterize the results.
> >
> > A couple of caveats: With only two replicates of each condition, your
> power to reliably identify significant differences is limited. Also, while
> the IP reads will be normalized, the control reads will not be (unless you
> do some normalization separately prior to loading it into DiffBind).
> However this does seem to be a a good place to start!
> >
> > Cheers-
> > Rory
> >
> >
> ----------------------------------------------------------------------------
> > Dr. Rory Stark
> >
> > Principal Bioinformatics  Analyst
> >
> > Cambridge Research Institute - Cancer Research UK
> > Robinson Way
> > Cambridge CB2 0RE
> > United Kingdom
> > +44 1223 404 311<tel:%2B44%201223%20404%20311>
> >
> > rory.stark@cancer.org.uk<mailto:rory.stark@cancer.org.uk>
> >
> ----------------------------------------------------------------------------
> >
> > On 04/01/2012 13:37, "mali salmon" <shalmom1@gmail.com<mailto:
> shalmom1@gmail.com><mailto:shalmom1@gmail.com<mailto:shalmom1@gmail.com>>>
> wrote:
> >
> > Dear Users
> > We have RNA-IP-seq for two conditions with two biological replicates
> each.
> > So in total we have 8 samples:
> > 2 for condition1 IP
> > 2 for condition1 Input
> > 2 for condition2 IP
> > 2 for condition2 Input
> > We would like to find differential binding between the two conditions
> which
> > are not influenced from differences in gene expression (Input samples are
> > actually regular RNA-seq).
> > I thought of first finding peak regions (maybe by pooling all IP and all
> > Input samples) and run ChIP-seq peak caller, count how many reads fall
> > within these regions in each of the samples, and then run DESeq and edgeR
> > in order to find differential binding.
> > Is this can be done with edgeR and DESeq (again the Input is different
> for
> > the two conditions, and we would like to cancel out differential gene
> > expression)?
> > Thanks
> > Mali
> >
> > [[alternative HTML version deleted]]
> >
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