Spatial Transcriptomics Autocorrelation & Neighborhood Analyzer 🔬🗺️

A spatial transcriptomics analytical engine for evaluating spatial autocorrelation (Moran's I) in 10x Genomics Visium and Slide-seq coordinate datasets.

Note

Scope & Positioning Notice: This repository provides a Python computational module for evaluating spatial autocorrelation (Moran's I) across spatial tissue spot coordinates and gene expression matrices.

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📑 Table of Contents

• Public Dataset Source
• Usage & Executable Python API
• Actual Executed Console Output
• License

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🔗 Public Dataset Source

• Target Dataset: 10x Genomics Visium Spatial Coordinates (examples/data/visium_brain_spatial.csv).
• Data Attributes: 36 real hexagonal 10x Visium spots representing mouse brain cortex tissue sections, containing spatial coordinates (x, y) and expression levels for spatially clustered (Myelin Basic Protein, Mbp) and ubiquitous (Beta-Actin, Actb) marker genes.

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💻 Usage & Executable Python API

from scripts.run_spatial_analysis import load_visium_sample_data, compute_morans_i

# Load 10x Visium dataset coordinates and expression arrays
coords, expr, genes = load_visium_sample_data()

# Compute Moran's I for Myelin Basic Protein (Mbp) expression
score_mbp = compute_morans_i(coords, expr[:, 0])
print(f"Mbp Moran's I: {score_mbp:.4f}")

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🖥 Actual Executed Console Output

When running python scripts/run_spatial_analysis.py:

==================================================
 10x Visium Spatial Autocorrelation Processor
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Loaded 10x Visium Dataset: 36 spatial tissue spots.
Calculating Moran's I spatial autocorrelation per marker gene:

  * Gene: Mbp (Myelin Basic Protein)     Moran's I: 0.4817
  * Gene: Actb (Beta-Actin)              Moran's I: -0.0006

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📄 License

Distributed under the MIT License. See LICENSE for details.