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Single2Spatial spatial mapping

Convert scRNA-seq references into spatially resolved profiles using Single2Spatial spatial mapping.

1.2k|145|Updated Mar 22, 2021
Search Tags:#single-cell RNA-seq#bioinformatics#visualization#cell mapping#omicverse#data integration#spatial transcriptomics
One-click install
npx skills add https://github.com/Starlitnightly/omicverse --skill single2spatial-spatial-mapping
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Single2Spatial spatial mapping
Source: https://github.com/Starlitnightly/omicverse/tree/main/.claude/skills/single-to-spatial-mapping
Command: npx skills add https://github.com/Starlitnightly/omicverse --skill single2spatial-spatial-mapping

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires omicverse, scanpy, anndata, pandas, numpy, matplotlib, and includes references (resource) components.

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.

Frequently Asked Questions about Single2Spatial spatial mapping

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I map single-cell RNA-seq data onto spatial transcriptomics slides?▼

Single2Spatial maps scRNA-seq references to spatial context by training deep-forest models on normalized input matrices with spot coordinates, reconstructing cell-type proportions at each spatial spot and enabling visualization of marker expression across tissue regions.

What input data and preprocessing do I need before using spatial mapping?▼

Prepare normalized or log-scaled scRNA-seq reference matrices, spatial transcriptomics spot data with correct coordinate keys, and define model parameters including spot_num, cell_num, k, num_epochs, and batch_size for training deep-forest mappers.

Can I use Single2Spatial to link single-cell atlases with spatial transcriptomics for tissue reconstruction?▼

Yes, Single2Spatial integrates single-cell atlases with spatial slides to reconstruct spot-level cell-type proportions, identify spatial distribution patterns, and visualize marker genes in tissue context, supporting cross-modal analysis and reproducible reporting via AnnData export.

Does Single2Spatial support GPU acceleration and pretrained model weights?▼

Single2Spatial supports both GPU and CPU execution for model training and includes functionality to load pretrained weights, assess spot quality, and export results as AnnData objects for downstream analysis and visualization.

What's the workflow for reconstructing and visualizing cell-type maps from spatial data?▼

Train the deep-forest model on your reference and spatial data, reconstruct spot-level cell-type proportions, then generate and plot cell-type maps for direct comparison with histology, enabling identification of spatial patterns and marker distribution.

What are the limitations when applying Single2Spatial to spatial transcriptomics?▼

Success depends on input data normalization quality, accurate spot coordinate assignment, appropriate parameter tuning for your dataset scale, and sufficient computational resources; model performance varies with reference atlas resolution and tissue complexity.

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