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Activity Number: 406 - Advances of Statistical Methodologies in Proteogenomics Research
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #313290
Title: Sparse Multiple Co-Inertia Analysis with Application to Integrative Analysis of Multi -Omics Data
Author(s): Eun Jeong Min* and Qi Long
Companies: Univerisity of Pennsylvania and University of Pennsylvania
Keywords: integrative analysis; multiple co-intertia analysis; omic data; high dimensional data; gene network information; l0 penalty

Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and the structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease.

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

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