"3D" hierarchical clustering question
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@phguardiolaolcom-152
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Aedin Culhane ▴ 510
@aedin-culhane-1526
Last seen 4.5 years ago
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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
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