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spatial-deconv

Estimate per-spot cell type proportions in spatial transcriptomics data using multiple deconvolution algorithms.

155|26|Updated Mar 14, 2026
Search Tags:#data analysis#bioinformatics#cell type#deconvolution#spatial transcriptomics#spatial biology
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-deconv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spatial-deconv
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-deconv
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-deconv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires flashdeconv, scanpy, anndata, numpy, pandas, scipy, matplotlib, rpy2, scvi, torch, tangram, cell2location, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of identifying and quantifying different cell types within spatial transcriptomics data, providing crucial insights into tissue architecture and cellular interactions.

Core Features & Use Cases

  • Multi-Method Support: Integrates 8 leading deconvolution algorithms (FlashDeconv, Cell2Location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, CARD).
  • Unified Interface: Provides a consistent command-line interface for all methods, simplifying workflow integration.
  • Use Case: Analyze a spatial transcriptomics dataset to determine the proportion of immune cells, stromal cells, and tumor cells in each spatial spot, enabling a deeper understanding of the tumor microenvironment.

Quick Start

Run cell type deconvolution on your spatial data using the default FlashDeconv method.

Frequently Asked Questions about spatial-deconv

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

FAQPage Schema
How do I perform cell type deconvolution on spatial transcriptomics data?▼

Cell type deconvolution for spatial transcriptomics data estimates per-spot cell type proportions using multiple integrated algorithms. This Skill supports various input formats and provides a unified CLI output for methods like FlashDeconv, Cell2Location, RCTD, DestVI, and Tangram.

What spatial transcriptomics deconvolution methods are supported by a unified interface?▼

A unified interface supports 8 leading deconvolution algorithms: FlashDeconv, Cell2Location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, and CARD, simplifying workflow integration for spatial data analysis.

Can I run spatial transcriptomics deconvolution using Scanpy and AnnData formats?▼

Yes, you can run spatial transcriptomics deconvolution using Scanpy and AnnData formats. The process requires scanpy and anndata as core dependencies to handle input data and structure the per-spot cell type estimations.

What is the best way to estimate per-spot cell type proportions in spatial data?▼

The best way to estimate per-spot cell type proportions in spatial data is using a multi-method deconvolution approach. Integrating algorithms like Tangram and SPOTlight allows you to uncover tissue architecture and cellular interactions robustly.

Do I need PyTorch and scvi-tools to run Cell2Location deconvolution for spatial biology?▼

Yes, you need PyTorch and scvi-tools to run Cell2Location deconvolution. The framework requires method-specific dependencies such as torch, scvi, and rpy2 alongside core packages like scanpy to execute advanced spatial biology algorithms.

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