On Wed, Nov 3, 2010 at 2:07 PM, Saurin D. Jani <saurin_jani@yahoo.com>wrote:

> Hi Steve,
>
>
> May be something like this:
>
>  d  d  u   | u   u  d n geneY
>  u  u  d   | u   u  d n geneX
> ----------------------
> c1 c5 c10  | e1 e4 e8 e2
>
>
> where: d = down, u = up, n = normal , c= control , e = exprimental
>
> original expression set has this order:
> c1,c2,c3...c10 e1,e2,e3..e10
>
> Thank you so much,
> Saurin
>
>
>
Hi, Saurin.

You're going to need to write some code to do this, I think.  I would
suggest clustering your two sample groups, c and e, using hclust.  Grab the
order from that for each of the two groups, combine them in whatever way you
like, figuring out the appropriate indexes for ALL the samples based on the
separate hclust results, and then feeding that custom ordering to the
heatmap function.  I don't think this is as common a use case as you might
think, so I doubt there is a simple canned solution.

Sean



> --- On Wed, 11/3/10, Steve Lianoglou <mailinglist.honeypot@gmail.com>
> wrote:
>
> > From: Steve Lianoglou <mailinglist.honeypot@gmail.com>
> > Subject: Re: [BioC] heatmap Clustering help, two class (Control vs Exp)
> Experiments,
> > To: saurin_jani@yahoo.com
> > Cc: "Bioconductor Bioconductor" <bioconductor@stat.math.ethz.ch>
> > Date: Wednesday, November 3, 2010, 12:56 PM
> > Hi Saurin,
> >
> > On Wed, Nov 3, 2010 at 12:43 PM, Saurin D. Jani <saurin_jani@yahoo.com>
> > wrote:
> > > Hi,
> > >
> > > You are right but When I do this:
> > >
> > >
> >
> heatmap.2(FeatureX,col=gmpalette,Colv=as.dendrogram(hclust(col.dist,method="average")),
> >
> Rowv=as.dendrogram(hclust(row.dist,method="average")),scale="row",key=TRUE,keysize=0.60,symkey=FALSE,
> density.info
> ="none",trace="none",margins=c(5,MapMargin),cexRow=1,cexCol=1,cex.sub=1);
> > >
> > > my control and exp. samples get mixed up..!! is there
> > anyway I can pass a parameter ..not to do that just cluster
> > samples on control and then exp. so, sorted view will be
> > there.
> >
> > But why would you cluster the samples to begin with, if you
> > just want
> > to reorder them in some (your) arbitrary way?
> >
> > Assuming your data is properly nomralised, etc. and
> > clustering your
> > samples "mixes them up," then the heatmap is showing you
> > visually that
> > your treatment examples aren't "strikingly different" than
> > your
> > controls. Your data is trying to tell you that (apparently)
> > all of
> > these experiments kind of look (expression wise) like each
> > other.
> >
> > Maybe that's telling you something about the quality of
> > your data, or
> > its annotation?
> >
> > Maybe you can try the plotPCA function in the affycoretools
> > package as
> > another way to see how your experiments "cluster
> > together".
> >
> > I'm not sure that it would change things, but what happens
> > if you
> > remove all probes w/ low variance across your entire
> > dataset and
> > re-cluster them?
> >
> > > May be something like this: cluster control samples
> > then exp. samples and then cluster based on Signal
> > Intensity. so, I keep the order ctrl1,ctrl5,ctrl6,ctrl2,...
> > and then Exp1,Exp5,Ex2,Exp10 ....so on...
> >
> > But then this is kind of misrepresenting what one would
> > expect to see
> > in such a plot .. you could, of course, plot and save
> > heatmaps over
> > just your control data, then again with just your
> > experiment, then
> > photoshop them together, but ... what's the point?
> >
> > I guess the question is: what are you trying to show in the
> > heatmap
> > you are trying to  produce?
> >
> > Given that, people might be able to then suggest things you
> > could try.
> >
> > --
> > Steve Lianoglou
> > Graduate Student: Computational Systems Biology
> >  | Memorial Sloan-Kettering Cancer Center
> >  | Weill Medical College of Cornell University
> > Contact Info: http://cbio.mskcc.org/~lianos/contact
> >
>
>
>
>
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