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Activity Number: 43 - Discovering Homology in Multi-View Data: New Statistical Methods for Data Integration
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #326499 Presentation
Title: Clustering Multiple-View Data: Are Two Clusterings Independent?
Author(s): Lucy Gao* and Jacob Bien and Daniela Witten
Companies: University of Washington and University of Southern California and University of Washington
Keywords: clustering; multi-view; multiple-view

In recent years, it has become increasingly commonplace for biologists to perform more than one type of measurement on a single set of observations. For instance, a researcher might profile gene expression, methylation, and DNA sequence on a single set of tissue samples. On the basis of such multiple-view data, many authors have considered the task of determining whether there are subgroups, or clusters, among the observations. In this paper, we instead consider a more nuanced question: are the sets of clusters from each data view related or independent? In order to answer this question, we propose a mixture model for multiple data views. We use this model to develop a pseudo likelihood ratio test for whether the clusterings of the observations in two data views are independent. We explore the performance of the proposed approach in a simulation study, and in applications to multiple-view gene expression and DNA copy number data sets.

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

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