SMOPCA

SMOPCA performs joint dimension reduction on spatial multi-omics multimodal data while preserving spatial dependencies to improve spatial domain detection.

Key Features:

• Joint dimension reduction: SMOPCA integrates multiple omics layers simultaneously rather than analyzing single modalities separately.
• Spatial dependency preservation: The method maintains spatial information during dimension reduction, accounting for the spatial structure of tissue data.
• Multi-omics compatibility: SMOPCA works across diverse spatial multi-omics datasets derived from different technologies and tissue structures.
• Performance across modalities: The method outperforms existing single-modal and multimodal dimension reduction and clustering methods.

Scientific Applications:

• Spatial domain detection: SMOPCA improves the identification of distinct spatial regions in tissue samples.
• Spatial multi-omics analysis: The method enables integrated analysis of multiple molecular layers with spatial context.
• Biomedical research: SMOPCA advances research fields that rely on understanding tissue organization and cellular heterogeneity.

Methodology:

SMOPCA implements a Spatial Multi-Omics Principal Component Analysis approach for joint dimension reduction.