Dear Philippe
There are bi-clustering approaches available, however I don't know of
a
"3D" HC approach that is available in BioC. Maybe someone else does?
However if you wish to use a principal components analysis approach,
you
can use coinertia analysis (CIA) available in the Bioc package made4
(or
multiple coinertia analysis available in the R package ade4). There is
an example of how to use CIA to link genes across different studies in
RNews in Dec 2006. In that example we link across different platforms,
in your case as all of the studies are on the same platform it will be
easier.
Coinertia analysis constrained the axes of the principal component
analysis so that they are maximally covariant. Therefore the axes
capture the variance (principal gene expression trends) from each
dataset, and will highlight those that are covariant across datasets.
So you visualize correlated gene expression patterns across datasets.
We also have described how to link gene expression data from different
studies using samples (Culhane et al., 2003, BMC Bioinformatics
//*4(1):*59). If you wish to use a supervised approach to find which
genes are most associated with a classifier across datasets ,we have
described a supervised extension to CIA (Jeffery et al., 2007
/Bioinformatics/ 23(3) 298-305) and in a paper in press we describe
linking protein and gene expression datasets (Fagan et al., Proteomics
In Press), So this method can be applied to different types of data,
not
only gene expression profiles.
As you are specifically interested in one gene and genes "associated"
with it, the "iterative gene signature algorithm" approach described
by Bergmann et al., (Phys Rev E Stat Nonlin Soft Matter Phys. 2003
Mar;67(3 Pt 1):031902.). I don't know if its available in BioC,
however
Jan Ihmels implemented it in Expression Profiler available from the
EBI
website.
Please contact me if you need further information about CIA, or how to
use the package made4.
Regards
Aedin
Hi,
I would like to know if a package already available on BioC can do
this:
I have data from multiple series of micrarrays coming from different
experiments dealing with different tissus (different questions and
projects but all on Affy U133A) etc... I would like to know more about
one gene and the genes that are "linked" to this one.
What I do is 2D hierarchical clustering (samples/genes)? for each
experiment/project, and look at which genes are close to mine? and
look
with Venn D through all projects what are the common genes? close to
the
one I m interested in.
Is there a way/package that could "run" some kind of hierarchical
clustering adding a third dimension (regarding the Projects) so that
we
obtain some kind of a 3D hierarchica lclustering...integrating the
variability accross the different projects (different tissus etc...).
I would greatly appreciate any comment or help on this?
Regards
Philippe Guardiola, MD
