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bio-single-cell-preprocessing

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-single-cell-preprocessing🧬 bioinformatics (gptomics bio-* suite)bioinformatics — single-cell & spatial omicsquality

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-single-cell-preprocessing

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Python: scanpy.pp.filter_cells() → normalize_total() → log1p() → highly_variable_genes().
  • R: Seurat::NormalizeData() → FindVariableFeatures() → ScaleData().
  • 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.
  • adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True).

Source Doc

Excerpt From SKILL.md

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 library sizes, identify highly variable genes, and scale for downstream analysis.

Filter cells by QC metrics

sc.pp.filter_cells(adata, min_genes=200) sc.pp.filter_cells(adata, max_genes=5000)

Filter by mitochondrial percentage

adata = adata[adata.obs['pct_counts_mt'] < 20, :].copy()

Use cases

  • 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.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • data-io - Load data before preprocessing
  • clustering - PCA and clustering after preprocessing
  • markers-annotation - Find markers after clustering

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