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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry

Lin Lin¹, Cliburn Chan², Mike West³

¹Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA llin@fredhutch.org.

Biostatistics (Oxford, England)

|June 5, 2015

Summary

This summary is machine-generated.

This study introduces a new Bayesian classification analysis to identify the best variables for classifying data in biomolecular studies. The method effectively prioritizes subsets of variables for improved discrimination in mixture models.

Keywords:

Bayesian expectation–maximization Bayesian mixture models Classification error rates Concordance of densities Dirichlet process mixtures Discriminative information measure Discriminative threshold probabilities Flow cytometry data Non-Gaussian component mixtures Variable subset selection

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Area of Science:

Biostatistics
Computational Biology
Systems Biology

Background:

Variable selection is crucial in multivariate mixture modeling for classification, especially in biomolecular studies.
Large-scale single-cell data from flow cytometry presents challenges in design and variable selection.

Purpose of the Study:

To develop a novel Bayesian classification analysis for evaluating variable subsets in mixture modeling.
To define discriminative analysis and a computationally feasible method for assessing variable roles in discrimination.

Main Methods:

Defined discriminative analysis using concordance between mixture component densities.
Developed a method for assessing and prioritizing variable subsets based on their discriminatory roles.
Related new discriminative information measures to Bayesian classification probabilities and error rates.
Applied methods to Dirichlet process mixture models using Markov chain Monte Carlo and a novel Bayesian expectation-maximization algorithm.

Main Results:

Presented theoretical and simulated data examples to demonstrate the approach's utility.
Showcased application in automatic classification and discriminative variable selection for high-throughput systems biology.
Compared the novel approach with prior methods, highlighting its effectiveness.

Conclusions:

The developed Bayesian classification analysis provides an effective and feasible method for variable subset evaluation in mixture modeling.
The approach is particularly useful for large-scale biomolecular data, such as flow cytometry datasets.
This work advances discriminative variable selection in high-throughput biological studies.