[BioC] heatmap Clustering help, two class (Control vs Exp) Experiments,

[Saurin D. Jani]

Hi Steve,

you are right..ordering in my way...i thought clustering is same is ordering..! sorry, may be my description of problem was not clear.

Here is why i want to do such a thing: for Example:  

I like to know if treatment T is making effect in listening music. 

I don't know which control samples were regularly listening to music at home. so, if geneX is over expressed. in Ctrl1 and Ctrl5 so they are grouped together and geneX is expressed normal levels in Ctrl20 then in heatmap those two ctrl samples are together. 

in Exp. sample..situation..same thing...if treatment T is given how music loving gene's intensity is behaving in different in exp. samples.

I mean this is not new at all ..I am sure...somebody already has done this but I can't find something like that in order to display using heatmap !! 


Saurin

--- On Wed, 11/3/10, Steve Lianoglou <mailinglist.honeypot at gmail.com> wrote:

> From: Steve Lianoglou <mailinglist.honeypot at gmail.com>
> Subject: Re: [BioC] heatmap Clustering help, two class (Control vs Exp) Experiments,
> To: saurin_jani at yahoo.com
> Cc: "Bioconductor Bioconductor" <bioconductor at 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 at 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
>