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Related Experiment Video

Updated: Jun 3, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Integrative analysis and variable selection with multiple high-dimensional data sets.

Shuangge Ma¹, Jian Huang, Xiao Song

¹School of Public Health, Yale University, 60 College Street, New Haven, CT 06520, USA. shuangge.ma@yale.edu.

Biostatistics (Oxford, England)

|March 19, 2011

Summary

This summary is machine-generated.

Reproducibility in high-throughput omics studies is improved by pooling data. We propose a novel penalized regression method for integrative analysis of heterogeneous omics data, enhancing marker selection across studies.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Last Updated: Jun 3, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

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Published on: September 20, 2024

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

Area of Science:

Bioinformatics
Genomics
Statistical Genetics

Background:

High-throughput omics studies often yield non-reproducible markers due to limited sample sizes.
Pooling data from multiple studies offers a cost-effective solution for robust marker identification.
Integrative analysis of diverse omics datasets presents challenges in high dimensionality and study heterogeneity.

Purpose of the Study:

To develop a method for effective marker selection in integrative analysis of multiple heterogeneous omics studies.
To identify markers with consistent effects across studies while accounting for inter-study variability.
To enhance the reproducibility and reliability of findings from pooled omics data.

Main Methods:

Proposed a 2-norm group bridge penalization approach for marker selection.
Developed an efficient computational algorithm to implement the proposed method.
Established the asymptotic consistency property of the statistical approach.

Main Results:

The 2-norm group bridge penalization effectively identifies consistent markers across heterogeneous studies.
The proposed method accommodates and addresses heterogeneity among multiple omics datasets.
Simulations and cancer profiling applications demonstrated satisfactory performance.

Conclusions:

The proposed penalized regression approach offers a robust solution for marker selection in integrative omics research.
This method improves the identification of reproducible biomarkers from pooled, heterogeneous data.
The approach has practical implications for cancer profiling and other high-throughput omics applications.