bio-single-cell-preprocessing
Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-single-cell-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: ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Single-Cell Preprocessing **"Preprocess my scRNA-seq data"** → Filter low-quality cells/genes, normalize counts, identify highly variable genes, and prepare data for dimensionality reduction and clustering. - Python: `scanpy.pp.filter_cells()` → `normalize_total()` → `log1p()` → `highly_variable_genes()` - R: `Seurat::NormalizeData()` → `FindVariableFeatures()` → `ScaleData()` Quality control, filtering, normalization, and feature selection for scRNA-seq data. ## Scanpy (Python) **Goal:** Preprocess scRNA-seq data through QC filtering, normalization, and feature selection using Scanpy. **Approach:** Calculate per-cell quality metrics, filter low-quality cells/genes, normalize libra
- Version Compatibility
- Scanpy (Python)
- Required Imports
- Calculate QC Metrics
- Visualize QC Metrics
- Filter Cells and Genes
- Store Raw Counts
- Normalization
- Highly Variable Genes
- Subset to HVGs (Optional)
- Scaling (Z-score)
- Regress Out Confounders
- Complete Preprocessing Pipeline
- Seurat (R)
What does the bio-single-cell-preprocessing skill do?
Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-single-cell-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.