Single2Spatial spatial mapping

What problem does it solve?

Bridging the gap between high-resolution single-cell RNA-seq and spatially resolved transcriptomics is challenging. This Skill automates the complex process of mapping single-cell atlases onto spatial slides, providing crucial contextual insights.

Core Features & Use Cases

• Deep-Forest Model Training: Train robust deep-forest models to accurately map single-cell data onto spatial spots.
• Spot-Level Reconstruction: Reconstruct and assess spot-level cell-type proportions and visualize marker expression.
• Cell-Type Map Visualization: Generate and plot reconstructed cell-type maps, allowing for direct comparison with histology.
• Use Case: Integrate a single-cell atlas of a tumor with a spatial transcriptomics slide to identify the precise spatial distribution of different tumor cell clones and immune infiltrates within the tissue.

Quick Start

Train Single2Spatial on my PDAC scRNA-seq and Visium data, then visualize the spatial expression of REG1A and CLDN1.