install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/spatial-transcriptomics-analysis" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-spatial-transcriptomics-analysis && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/spatial-transcriptomics-analysis" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-spatial-transcriptomics-analysis && rm -rf "$T"
manifest:
skills/spatial-transcriptomics-analysis/SKILL.mdsource content
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
name: spatial-transcriptomics-analysis description: Automated analysis pipeline for Spatial Transcriptomics (Visium, Xenium) integrating histology and gene expression. keywords:
- spatial-transcriptomics
- visium
- xenium
- scanpy
- squidpy measurable_outcome: Process a Visium dataset, identify spatially variable genes, and generate spatial feature plots within 30 minutes. license: MIT metadata: author: MD BABU MIA, PhD version: "1.0.0" compatibility:
- system: python 3.9+ allowed-tools:
- run_shell_command
- read_file
- write_file
Spatial Transcriptomics Skill
Version: 1.0.0 Author: MD BABU MIA, PhD Date: February 2026
Overview
This skill provides automated analysis capabilities for Spatial Transcriptomics data, specifically designed for 10x Visium and Xenium platforms. It enables the integration of histological data with gene expression profiles to uncover spatial organization of cell types.
Capabilities
- Data Loading: Supports Spaceranger output (h5, images).
- QC & Preprocessing: Spatial QC metrics, normalization.
- Spatial Variable Features: Identification of spatially variable genes (SVGs) using Moran's I and Geary's C.
- Deconvolution: Interface for cell type deconvolution (mapping scRNA-seq to spatial).
- Visualization: Interactive spatial plots overlaying gene expression on tissue images.
Usage
from Skills.Genomics.Spatial_Transcriptomics.spatial_analyzer import SpatialAnalyzer # Initialize sa = SpatialAnalyzer(data_path="./data/visium_sample1") # Run Pipeline sa.load_data() sa.preprocess() sa.find_spatial_features() sa.plot_spatial("INS", save_path="./output/insulin_spatial.png")
Requirements
- scanpy
- squidpy
- anndata
- matplotlib