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Related Experiment Video

Updated: May 13, 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

Cross-modality representation and multi-sample integration of spatially resolved omics data.

Zhen Li¹, Xuejian Cui², Xiaoyang Chen¹

¹Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Shuangqing Road, Haidian District, Beijing 100084, China.

Briefings in Bioinformatics

|May 11, 2026

Summary

This summary is machine-generated.

PRESENT integrates spatial multi-omics data using contrastive learning. This computational framework identifies spatial domains and regulatory mechanisms, even across multiple samples and technologies.

Keywords:

cross-modality representation multi-sample integration spatial domain identification spatial multi-omics data

Related Experiment Videos

Last Updated: May 13, 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

Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Spatially resolved sequencing technologies capture cellular states and spatial organization.
Analyzing heterogeneous spatial multi-omics data requires advanced computational methods.

Purpose of the Study:

To develop PRESENT, a contrastive learning framework for integrating spatial multi-omics data.
To enable accurate identification of spatial domains and regulatory mechanisms.
To extend the framework for multi-sample integration and batch effect removal.

Main Methods:

PRESENT utilizes omics-specific encoders (graph attention networks, Bayesian neural networks) and distribution-aware decoders.
An inter-omics alignment module integrates multi-modal data, incorporating spatial dependencies.
A two-stage training workflow with batch-alignment strategies addresses multi-sample integration.

Main Results:

PRESENT accurately identifies spatial domains and elucidates regulatory mechanisms across diverse species and technologies.
The framework effectively integrates spatial and multi-omics information.
PRESENT successfully eliminates batch effects in multi-sample integration while preserving biological signals.

Conclusions:

PRESENT provides a robust computational framework for integrative analysis of spatial multi-omics data.
The method facilitates characterization of hierarchical tissue structures from a spatiotemporal perspective.
PRESENT advances the understanding of biological regulatory processes within tissue microenvironments.