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Activity Number: 33
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #318528 View Presentation
Title: Integrative Analysis of Transcriptomic and Metabolomic Data via Sparse Canonical Correlation Analysis with Incorporation of Biological Information
Author(s): Sandra Safo* and Qi Long
Companies: Emory University and Emory University
Keywords: Structural Information ; Canonical Correlation Analysis ; Metabolomics ; High dimensional data ; Sparsity

It is of increasing importance to integrate different types of omics data to examine biological mechanisms in disease etiology. Canonical Correlation Analysis (CCA) provides an attractive tool to investigate such mechanisms. Traditional CCA methods use all available variables and several sparse CCA methods have been proposed to constrain the size of the CCA vectors in order to yield interpretable results. It is well-known that variables in omics data are functionally structured in networks or pathways. We develop statistical methods for CCA that incorporate biological/structural information via undirected graphical networks. Our work is motivated by an in-vitro mouse toxicology study on the neurotoxicity of the combination of the herbicide paraquat and fungicide maneb in relation to Parkinson's disease (PD). We are interested in assessing association between transcriptomic and metabolomic data that may shed light on the etiology of PD. Our proposed methods use prior network structural information among genes and among metabolites to guide selection of relevant genes and metabolites in sparse CCA, providing insight on the molecular underpinning of PD.

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

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