LtransHeteroGGM: local transfer learning for Gaussian graphical model-based heterogeneity analysis.

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Biological systems exhibit heterogeneity at both macro (complex diseases) and micro (single-cell) levels.
• Gaussian Graphical Models (GGM) are valuable for analyzing biological regulatory networks but struggle with scarce data in rare subgroups.
• Existing transfer learning methods for GGM heterogeneity analysis assume unrealistic global similarity and fixed subgroup structures.

Purpose of the Study:

• To develop a novel local transfer learning framework, LtransHeteroGGM, for robust GGM-based heterogeneity analysis.
• To enable effective subgroup-level knowledge transfer from informative auxiliary domains, even with unknown subgroup structures.
• To address the limitations of existing methods in handling local similarities and mitigating interference from non-informative domains.

Main Methods:

• Proposed LtransHeteroGGM, a local transfer learning framework for GGM heterogeneity analysis.
• Implemented a method for powerful subgroup-level local knowledge transfer.
• Designed to handle unknown subgroup structures and numbers, and mitigate negative interference from non-informative domains.

Main Results:

• Demonstrated the effectiveness and robustness of LtransHeteroGGM through comprehensive numerical simulations.
• Validated the approach on real-world T cell heterogeneity data.
• Achieved powerful subgroup-level local knowledge transfer, outperforming existing methods.

Conclusions:

• LtransHeteroGGM provides a powerful and robust framework for GGM-based heterogeneity analysis.
• The method successfully enables local knowledge transfer, improving analysis of complex biological systems.
• The R implementation is available for broader application in biological research.