scientific-single-cell-genomics

Automate scRNA-seq analysis with Scanpy/AnnData from QC to cell-type annotation.

3|1|Updated Feb 11, 2026
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-single-cell-genomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-single-cell-genomics
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-single-cell-genomics
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-single-cell-genomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a standardized, reproducible workflow for scRNA-seq analysis, covering quality control, normalization, dimensionality reduction, clustering, differential expression, and cell-type annotation, all built on the Scanpy/AnnData ecosystem.

Core Features & Use Cases

  • QC and preprocessing for scRNA-seq data, ensuring high-quality cells and reliable gene metrics.
  • Normalization, highly variable gene selection, PCA/UMAP, and Leiden clustering to reveal cellular heterogeneity.
  • Differential expression analysis and cell-type annotation to interpret clusters and cell states.
  • RNA velocity integration for lineage trajectory inference and dynamic cellular states.
  • Intercellular communication estimation with CellChat/CellPhoneDB to study cellular crosstalk.
  • Compatible with Scanpy/AnnData workflows for scalable, reproducible analyses.

Quick Start

Apply the standard scRNA-seq workflow to an AnnData object to generate QC metrics, normalization and HVG selection, PCA/UMAP, Leiden clustering, DEG identification, and cell-type annotation.

Frequently Asked Questions about scientific-single-cell-genomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an end-to-end scRNA-seq analysis workflow in Python?▼

To run an end-to-end scRNA-seq analysis workflow, use this Skill to automate QC, normalization, dimensionality reduction, Leiden clustering, differential expression, and cell-type annotation within the Scanpy and AnnData ecosystem.

What is the standard pipeline for single-cell RNA-seq clustering and cell-type annotation?▼

A standard single-cell RNA-seq pipeline applies QC, normalization, highly variable gene selection, PCA, UMAP, and Leiden clustering to reveal cellular heterogeneity, followed by differential expression analysis for cell-type annotation.

Can I infer lineage trajectories and cellular dynamics from snRNA-seq datasets?▼

Yes, you can infer lineage trajectories and dynamic cellular states from scRNA-seq and snRNA-seq datasets by integrating RNA velocity analysis using the scvelo package within this workflow.

Do I need preprocessed AnnData objects to estimate intercellular communication?▼

You need an AnnData object processed through QC, normalization, and clustering to estimate intercellular communication using CellChat or CellPhoneDB to study cellular crosstalk.

What standardized outputs does a Scanpy scRNA-seq pipeline generate?▼

A Scanpy scRNA-seq pipeline generates standardized outputs including QC metrics, highly variable gene lists, cluster assignments, differential expression tables, and cell-type annotations for reproducible analyses.

Does this single-cell analysis workflow handle missing data in exploratory studies?▼

Yes, this single-cell analysis workflow handles missing data while applying to scRNA-seq datasets across exploratory studies, cell type discovery, and lineage inference to ensure standardized outputs.