spatial-transcriptomics-analysis

name	spatial-transcriptomics-analysis
display_name	Spatial Transcriptomics: Automated Pipeline
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	{"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 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

1. Data Loading: Supports Spaceranger output (h5, images).
2. QC & Preprocessing: Spatial QC metrics, normalization.
3. Spatial Variable Features: Identification of spatially variable genes (SVGs) using Moran's I and Geary's C.
4. Deconvolution: Interface for cell type deconvolution (mapping scRNA-seq to spatial).
5. 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