bio-spatial-transcriptomics-spatial-deconvolution
Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-deconvolution --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Spatial Deconvolution Estimate cell type composition in spatial spots using scRNA-seq references. ## Required Imports ```python import scanpy as sc import anndata as ad import numpy as np import pandas as pd import matplotlib.pyplot as plt ``` ## Overview Deconvolution estimates cell type proportions in each spatial spot using a reference single-cell dataset. Essential for Visium data where spots contain multiple cells. ## Using cell2location **Goal:** Estimate cell type abundances per spatial spot using a probabilistic model trained on scRNA-seq reference signatures. **Approach:** Train a regression model on reference scRNA-seq to extract cell type signatures, then decompose spatial spots using those signatures. **"Deconvolve my Visium spo
- Version Compatibility
- Required Imports
- Overview
- Using cell2location
- Train Reference Signature Model
- Run Spatial Deconvolution
- Access Deconvolution Results
- Using Tangram (Alternative)
- Using RCTD (via R)
- Visualize Cell Type Proportions
- Pie Chart Per Spot (Advanced)
- Evaluate Deconvolution Quality
- Compare Deconvolution Methods
- Export Results
What does the bio-spatial-transcriptomics-spatial-deconvolution skill do?
Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-deconvolution --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.