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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
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FUNCTION-ON-SCALAR QUANTILE REGRESSION WITH APPLICATION TO MASS SPECTROMETRY PROTEOMICS DATA.

Yusha Liu1, Meng Li1, Jeffrey S Morris2

  • 1Rice University.

The Annals of Applied Statistics
|November 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian quantile regression framework for mass spectrometry proteomics data. The method enhances the detection of cancer biomarkers, particularly those present in a subset of patients, outperforming traditional mean regression approaches.

Keywords:
Bayesian hierarchical modelfunctional data analysisfunctional response regressionglobal-local shrinkageproteomic biomarkerquantile regression

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Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Statistical Bioinformatics

Background:

  • Mass spectrometry proteomics generates complex, heterogeneous data.
  • Cancer is characterized by inter-patient heterogeneity, complicating biomarker identification.
  • Traditional mean regression methods may miss biomarkers present in only a subset of cancer patients.

Purpose of the Study:

  • To develop a unified Bayesian framework for quantile regression on functional proteomics data.
  • To improve the detection of cancer biomarkers missed by conventional mean-based analyses.
  • To identify proteomic biomarkers for pancreatic cancer differentially expressed in a subset of patients.

Main Methods:

  • A Bayesian framework utilizing asymmetric Laplace working likelihood for quantile regression.
  • Basis representations for functional coefficients to enable borrowing of strength.
  • Global-local shrinkage priors on basis coefficients for adaptive regularization.
  • A scalable Gibbs sampler for posterior inference and multiple testing adjustment.
  • An adjustment procedure to enhance frequentist properties of posterior inference.

Main Results:

  • The proposed framework unifies quantile regression and coefficient regularization for improved performance.
  • Simulation studies demonstrate superior performance compared to competing methods.
  • The model successfully identified proteomic biomarkers in pancreatic cancer missed by mean regression.

Conclusions:

  • Bayesian quantile regression offers a powerful approach for analyzing heterogeneous mass spectrometry proteomics data.
  • This method enhances the discovery of subset-specific cancer biomarkers.
  • The framework provides a robust tool for biomarker identification in complex diseases like pancreatic cancer.