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Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data.

Lai Jiang^1,2, Celia M T Greenwood^3,4,5, Weixin Yao⁶

¹Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada. lai.jiang@mail.mcgill.ca.

Scientific Reports

|June 18, 2020

Summary

BayesHL, a new Bayesian method, effectively selects important genes from high-dimensional genomic data by considering gene expression patterns. It outperforms other methods in predicting cancer survival and uncovering biological pathways.

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Area of Science:

Genomics
Biostatistics
Bioinformatics

Background:

High-dimensional genomic data analysis is crucial for identifying disease-related gene signatures.
Gene expression data often exhibits inherent grouping structures based on biological functions.
Existing feature selection methods may not fully leverage these grouping structures.

Purpose of the Study:

To propose a novel Bayesian Robit regression method with Hyper-LASSO priors (BayesHL) for feature selection in high-dimensional genomic data.
To develop a method that automatically handles grouping structures without pre-specification.
To identify gene signatures associated with endometrial cancer survival.

Main Methods:

Bayesian Robit regression with Hyper-LASSO priors (BayesHL).
Application to high-dimensional gene expression data with inherent grouping.
Comparison with established methods like LASSO, group LASSO, and machine learning algorithms.

Main Results:

BayesHL demonstrates superior predictive power and sparsity compared to alternative methods.
The method effectively identifies gene subsets contributing to endometrial cancer survival.
BayesHL successfully uncovers grouping structures and provides insights into genetic pathways.

Conclusions:

BayesHL is a powerful tool for feature selection in high-dimensional genomic data, especially when grouping structures are present.
The method offers advantages in predictive accuracy, feature sparsity, and biological interpretability.
Findings contribute to understanding the genetic mechanisms underlying endometrial cancer survival.