Welcome to SpaMV’s documentation!

Interpretable spatial multi-omics data integration and dimension reduction with SpaMV

Contents:

• Installation
  • Tutorial Instruction
• Tutorial for simulated data
  • Domain Clustering on Simulated data
  • Interpretable Dimension Reduction on Simulated Data
• Tutorial for Real-World Data
  • Domain Clustering on Real-World Data
  • Interpretable Dimension Reduction on Real-World Data

Overview

Spatial multi-omics technologies have revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Traditional integration methods often assume that data from different modalities share a common underlying distribution, aiming to project them into a single unified latent space. This assumption, however, can obscure the unique insights offered by each modality, thereby limiting the full potential of multi-omics analyses. To address this limitation, we present the Spatial Multi-View (SpaMV) representation learning algorithm. SpaMV is specifically designed to capture both the shared information across modalities and the distinct, modality-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior integration performance and provides users with more interpretable insights for downstream analysis