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Spatial Preprocessing - Usage Guide

Overview

This skill covers quality control, filtering, normalization, and feature selection for spatial transcriptomics data using Squidpy and Scanpy.

Prerequisites

pip install squidpy scanpy matplotlib

Quick Start

Tell your AI agent what you want to do:

  • "Run QC on my spatial data"
  • "Filter and normalize my Visium data"

Example Prompts

QC

"Calculate QC metrics for my spatial data"

"Show QC metrics on the tissue"

Filtering

"Filter spots with less than 500 counts"

"Remove spots with high mitochondrial content"

Normalization

"Normalize my spatial data"

"Find highly variable genes"

What the Agent Will Do

  1. Calculate QC metrics (counts, genes, MT%)
  2. Visualize QC metrics on tissue
  3. Filter low-quality spots
  4. Normalize expression data
  5. Identify highly variable genes
  6. Optionally find spatially variable genes

Tips

  • Spatial QC - Always visualize QC metrics on the tissue to identify spatial artifacts
  • Mitochondrial threshold - Often higher for spatial data (~20-25%)
  • SVG vs HVG - Spatially variable genes may differ from highly variable genes
  • Keep raw counts - Store in adata.layers['counts'] before normalization