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Activity Number: 231 - SBSS Student Paper Award Session II
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #301846 Presentation
Title: Function-On-Scalar Quantile Regression with Application to Mass Spectrometry Proteomics Data
Author(s): Yusha Liu* and Meng Li and Jeffrey S. Morris
Companies: and Rice University and M.D. Anderson Cancer Center
Keywords: Functional data analysis; Quantile regression; Bayesian hierarchical model; Global-local shrinkage; Proteomic biomarker

Mass spectrometry proteomics, characterized by spiky, spatially heterogeneous functional data, can be used to identify potential cancer biomarkers. Existing mass spectrometry analyses utilize mean regression to detect spectral regions that are differentially expressed across groups. However, given the inter-patient heterogeneity that is a key hallmark of cancer, many biomarkers are present at aberrant levels for a subset of, not all, cancer samples. Differences in these biomarkers can easily be missed by mean regression, but might be more easily detected by quantile-based approaches. Thus, we propose a unified Bayesian framework to perform quantile regression on functional responses. Our approach utilizes an asymmetric Laplace likelihood, represents the functional coefficients with basis representations which enable borrowing of strength from nearby locations, and places a global-local shrinkage prior on the basis coefficients to achieve adaptive regularization. Different types of basis transform and continuous shrinkage priors can be used in our framework. We apply this model to identify proteomic biomarkers of pancreatic cancer missed by previous mean-regression based approaches.

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

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