Scanpy Sc Analyzer

Agent Skill: Single-Cell RNA-Seq & Harmony Integration Skill

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📌 Description

Autonomous single-cell RNA-seq quality control filtering, Harmony batch-effect correction, Leiden clustering, UMAP visualization, and marker gene annotation.

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🤖 Agent Execution Protocol

When an AI Agent is tasked with scanpy-sc-analyzer:

1. Input Validation: Verify that the required input files or coordinates are supplied.
2. Environment Check: Ensure dependencies (Scanpy, Harmony, AnnData, Plotly) are installed.
3. Execution: Run the protocol pipeline snippet below.
4. Output Generation: Produce actionable Markdown/JSON summaries with publication figures.

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💻 Protocol Code Snippet

import scanpy as sc
import numpy as np

def run_sc_pipeline(h5ad_path):
    adata = sc.read_h5ad(h5ad_path)
    sc.pp.filter_cells(adata, min_genes=200)
    sc.pp.filter_genes(adata, min_cells=3)
    sc.pp.normalize_total(adata, target_sum=1e4)
    sc.pp.log1p(adata)
    sc.pp.highly_variable_genes(adata, n_top_genes=2000)
    sc.pp.pca(adata, n_comps=30)
    sc.pp.neighbors(adata, n_neighbors=15)
    sc.tl.umap(adata)
    sc.tl.leiden(adata, resolution=0.5)
    return adata

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📥 Input & Output Specifications

Input Contract

• Target Files: Valid input data matching domain formats.
• Parameters: Quality thresholds and cutoffs.

Output Contract

• Results Table: Structured summary dataframe or matrix.
• Visualization: Rendered SVG/PNG figures.

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📄 License

Distributed under the MIT License. See LICENSE for details.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: YuliaNuzhnenko
• Source: YuliaNuzhnenko/bioinformatics-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.