CORAL

Multi-scale, Multi-modal Integration of Spatial Omics via Deep Generative Model

CORAL is a probabilistic, graph-based deep generative model for integrating diverse spatial omics datasets that differ in resolution and detected features. Given two unmatched spatial omics modalities, CORAL:

• Generates joint single-cell embeddings informed by both data modalities

• Deconvolves the lower-resolution modality to infer molecular profiles at single-cell resolution

• Predicts cell-cell interactions between neighboring cells

• Identifies spatial niches and predicts spatial variables

Supported Data Modalities

Modality	Examples
Spatial transcriptomics	MERFISH, seqFISH, Visium, SLIDE-seq
Spatial proteomics	CODEX, MIBI, IMC
Spatial metabolomics	MALDI, DESI
Spatial epigenomics	spatial ATAC-seq

Contents

• Installation
  • Install from GitHub
  • Conda Environment
  • Key Dependencies
• Tutorials
  • CORAL Tutorial — Multi-scale Multi-modal Integration of Spatial Omics
  • Align Images from Different Slices Using SIFT
  • CORAL Tutorial
  • Slide Alignment Tutorial
• API Reference
  • Model
  • Preprocessing
  • Visualization
  • Inference
  • Trainer

Citation

If you use CORAL in your research, please cite:

He, S. et al. CORAL: Multi-scale Multi-modal integration of Spatial Omics
via Deep Generative Model. bioRxiv (2025).
https://doi.org/10.1101/2025.02.01.636038