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Activity Number: 209 - Statistical methods for genomic and epigenetic data analysis
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #319115
Title: Multi-Omics Data Integration Using a Supervised Cox Multiblock PLS (Cox-SMBPLS) Model Incorporating Prior Biological Information
Author(s): Nasim Vahabi* and George Michailidis and Julio Duarte
Companies: University of Florida and U Florida and University of Florida
Keywords: Multi-Omics; Supervised Integration; cis-regulatory quantitative; Multi-block PLS; Survival Analysis
Abstract:

Background: The development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples. Results: We develop a supervised model for time to event data (e.g., death, biochemical recurrence) that simultaneously accounts for redundant information within Omics profiles and leverages prior biological associations between them through a multi-block PLS framework. The interactions between data from different molecular compartments (e.g., epigenome, transcriptome, methylome, etc.) were captured by using cis-regulatory quantitative effects in the proposed model. The model, coined Cox-sMBPLS, exhibits superior prediction performance and improved feature selection based on both simulation studies and analys


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