bio-spatial-transcriptomics-spatial-preprocessing
Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-preprocessing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: matplotlib 3.8+, numpy 1.26+, scanpy 1.10+, squidpy 1.3+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Spatial Preprocessing **"Preprocess my spatial transcriptomics data"** → Calculate spatial QC metrics (genes/spot, mitochondrial fraction), filter spots by expression and tissue coverage, normalize, and select variable genes. - Python: `scanpy.pp.calculate_qc_metrics()` → `filter_cells()` → `normalize_total()` on spatial AnnData QC, filtering, normalization, and feature selection for spatial data. ## Required Imports ```python import squidpy as sq import scanpy as sc import numpy as np import matplotlib.pyplot as plt ``` ## Calculate QC Metrics **Goal:** Compute per-spot and per-gene quality control statistics. **Approach:** Use Scanpy's `calculate_qc_metrics` to generate total counts, gene counts, and other summary statistics. ```python # Calculate stan
- Version Compatibility
- Required Imports
- Calculate QC Metrics
- Calculate Mitochondrial Content
- Visualize QC Metrics on Tissue
- QC Metric Distributions
- Filter Spots
- Filter Genes
- Normalization
- SCTransform-like Normalization
- Highly Variable Genes
- Spatially Variable Genes
- Combine HVG and SVG
- Scale Data
What does the bio-spatial-transcriptomics-spatial-preprocessing skill do?
Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-spatial-transcriptomics-spatial-preprocessing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.