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Activity Number: 124
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #315900
Title: Exploiting Structure to Reduce and Integrate High-Dimensional, Under-Sampled Genomics Data
Author(s): Yang Liu* and Francesca Chiaromonte and Bing Li
Companies: Penn State and Penn State and Penn State
Keywords: Structured data ; Sufficient dimension reduction ; Ordinary least squares ; Variable selection ; Data integration ; Genomics
Abstract:

Analysis of high-dimensional, under-sampled data has become increasingly important in Genomics with its expanding repertoire of high-throughput technologies. For many regression-like analyses, dimension reduction in the predictor space can be very effective. The most commonly used approaches assume that predictors and samples are similar in nature and can simultaneously participate in the reduction. However, recent high-throughput genomic data is often heterogeneous and structured. Exploiting known structure in samples and predictors when performing dimension reduction can be an avenue for integrating data collected through multiple studies and diverse high-throughput platforms. To address this challenge, we propose a new Sufficient Dimension Reduction (SDR) approach; Structured Ordinary Least Squares (sOLS). sOLS combines ideas from existing SDR literature to merge reductions performed within subgroups of samples and/or predictors. As a part of our proposal, we developed group-wise OLS (gOLS) to efficiently perform SDR for grouped predictors. Simulation studies and a first application to ENCODE genomic data show promising performance for our methodology.


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

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