Spatial transcriptomics analysis

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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

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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

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

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