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Activity Number: 441 - The Key to Integrative Analysis for Precision Medicine: Statistics!
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #321908
Title: Clustering Patients Across Multiple Data Views
Author(s): Lucy Gao and Jacob Bien* and Daniela Witten
Companies: University of Washington and Cornell University and University of Washington
Keywords: clustering ; multi-view
Abstract:

In the context of precision medicine, we often wish to identify patient subgroups, in the hope that a given patient's subgroup membership will be predictive of his or her prognosis, response to therapy, or other quantities of interest. Typically we identify such subgroups by performing clustering. It is becoming increasingly common for "multi-view data" to be available: that is, data in which multiple data types (e.g. gene expression, DNA sequence, clinical measurements) have been measured on a single set of observations (e.g. patients).

In this talk, we will consider the following question: given a set of n observations with measurements on L data types, can a single clustering of the n observations be defined on all L data types, or does each data type have its own clustering of the observations? To answer this question, we will introduce a general framework for modeling multi-view data, as well as hypothesis tests that can be used in order to characterize the extent to which the clusterings on each of the L data types are the same or different.


Authors who are presenting talks have a * after their name.

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