Data & ReproScientific VisualizationFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bioinformatics-singlecell

Maintainer FreedomIntelligence · Last updated April 1, 2026

General single-cell bioinformatics: clustering, trajectory, cell communication.

OpenClawNanoClawAnalysisWritingbioinformatics-singlecell🧠 bioos extended suitesingle-cell & spatial agentsgeneral

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bioinformatics-singlecell

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

import scanpy as sc import anndata as ad import scvi import muon as mu import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns.
sc.settings.verbosity = 3 sc.settings.set_figure_params(dpi=100, frameon=False, figsize=(6, 6)).

Source Doc

Excerpt From SKILL.md

1. Load and QC

adata = sc.read_10x_mtx('path/to/filtered_feature_bc_matrix/') sc.pp.filter_cells(adata, min_genes=200) sc.pp.filter_genes(adata, min_cells=3) adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True) adata = adata[adata.obs.pct_counts_mt < 20, :]

2. Normalization & HVG

sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key='batch')

3. Dimensionality reduction

sc.pp.scale(adata, max_value=10) sc.tl.pca(adata, svd_solver='arpack') sc.pp.neighbors(adata, n_neighbors=15, n_pcs=40) sc.tl.umap(adata) sc.tl.leiden(adata, resolution=0.5)