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Finding interesting clusters using Bayesian data fusion

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If you have a question about this talk, please contact Florian Markowetz .

We are increasingly able to make multiple types of measurement of interesting biological systems. To benefit fully from these advances, we need to develop statistical methods that can combine multiple data sets in sensible ways.

I’ll present some of our recent work on data fusion. Our work is a development of the hierarchical Dirichlet Process mixture model and can be regarded as data fusion clustering, with the added benefit that we can identify subsets of items that are most strongly clustered across the data sets. This turns out to give us greater insight into the underlying biology, which I’ll illustrate with some of our work on gene clustering. I’ll also talk briefly about where we’re starting to take this work in relation to clustering samples from cancer studies.

REF : http://bioinformatics.oxfordjournals.org/content/26/12/i158.full

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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