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spatial-preprocess

Load, quality control, normalize, and preprocess spatial transcriptomics data into a standardized AnnData object.

155|26|Updated Mar 14, 2026
Search Tags:#quality control#normalization#spatial transcriptomics#data preprocessing#clustering#scanpy
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-preprocess
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spatial-preprocess
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-preprocess
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-preprocess

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, squidpy, anndata, numpy, pandas, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the complex and repetitive process of loading, quality controlling, normalizing, and preparing spatial transcriptomics data for downstream analysis, ensuring consistency and reproducibility.

Core Features & Use Cases

  • Multi-platform Data Loading: Supports various spatial transcriptomics formats (Visium, Xenium, MERFISH, Slide-seq, generic h5ad).
  • Automated QC and Filtering: Applies standard metrics to filter low-quality cells and genes.
  • Normalization and Feature Selection: Performs library-size normalization, log transformation, and highly variable gene selection.
  • Dimensionality Reduction and Clustering: Computes PCA, UMAP, and Leiden clustering for exploratory analysis.
  • Use Case: You have raw Visium data from a new experiment. Use this Skill to load it, perform QC, normalize counts, select important genes, and generate UMAP and Leiden clusters, all ready for further biological interpretation.

Quick Start

Run the spatial-preprocess skill on the provided Visium dataset located at '/path/to/visium_data' to generate a processed AnnData object and analysis report.

Frequently Asked Questions about spatial-preprocess

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I preprocess and normalize spatial transcriptomics data for downstream analysis?▼

To preprocess spatial transcriptomics data, you load multi-platform formats like Visium or Xenium, apply quality control to filter low-quality cells, perform library-size normalization, and select highly variable genes to generate a standardized AnnData object.

What is the best way to perform quality control on Visium data using scanpy and squidpy?▼

The best way to perform quality control on Visium data is applying automated standard metrics to filter low-quality cells and genes, then using scanpy and squidpy for data manipulation and visualization to ensure dataset consistency.

Can I use this preprocessing workflow to generate Leiden clusters for Xenium or MERFISH data?▼

Yes, you can use this workflow to load Xenium or MERFISH data, compute PCA and UMAP for dimensionality reduction, and generate Leiden clusters to facilitate exploratory biological interpretation.

Does this spatial omics preprocessing approach support generic h5ad files or only specific platforms?▼

This spatial omics preprocessing approach supports multiple data formats including generic h5ad files, alongside specific spatial transcriptomics platforms like Visium, Xenium, MERFISH, and Slide-seq.

Why do I need to standardize spatial transcriptomics data before clustering and dimensionality reduction?▼

You need to standardize spatial transcriptomics data to ensure consistency and reproducibility, automating complex repetitive processes like quality control, normalization, and feature selection before computing PCA, UMAP, and Leiden clustering.

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