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Activity Number: 167
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #311704
Title: Group Structured Integrative Clustering for Feature Discovery and Coherent Samples Identification in Inter-Related Multiple Genomic Data Sets
Author(s): SungHwan Kim*+ and Yong Seok Park and George Tseng
Companies: and University of Pittsburgh and University of Pittsburgh
Keywords: clustering ; integrative ; lasso ; group lasso ; EM algorithm ; microarray
Abstract:

As recent technologies for genomic profiling have increasingly advanced, the task of clustering under multiple data sources comes into the spotlight in various applications. Above all, there are overarching needs to carry out a systematic integrative clustering with a feature selection underpinned by multiple inter-related omics datasets, and to identify coherent clusters among a vast collection of samples. We propose the group-structured integrative clustering model that accommodates two challenging aspects: (1) performing an integrative clustering with features aligned in correlated genomic profiling data sets (2) discovering stable and coherent samples in cluster. The proposed clustering algorithm tightly adheres to each inferred cluster, facilitating to perform a subtype discovery of complex cancer diseases, and to reveal underlying molecular mechanisms attributed to the genes lying on a coherent cis-regulatory. We describe a computationally straightforward framework employing a group-lasso penalty to attain superiority to the pre-existing approach in the light of a grouped feature discovery. This research provides an application to breast cancer dataset to reveal the subtype i


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