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single-cell

Execute single-cell RNA-seq and mass cytometry analysis pipelines with scanpy, scvi-tools, and CATALYST.

29|3|Updated Jun 11, 2026
Search Tags:#analysis#bioinformatics#single-cell#scanpy#scrna-seq#catalyst#cytof
One-click install
npx skills add https://github.com/inflexa-ai/inflexa --skill single-cell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell
Source: https://github.com/inflexa-ai/inflexa/tree/main/skills/single-cell
Command: npx skills add https://github.com/inflexa-ai/inflexa --skill single-cell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, scvi-tools, rpy2, celltypist, decoupler, liana, palantir, and includes references (resource) components.

What problem does it solve?

This Skill provides a robust, reproducible framework for analyzing complex single-cell (scRNA-seq, snRNA-seq) and mass cytometry (CyTOF) data, eliminating the ambiguity of manual pipeline construction.

Core Features & Use Cases

  • Multi-Omics Support: Handles both transcriptomic (scRNA-seq) and proteomic (CyTOF) data with specialized workflows.
  • Reproducible Pipelines: Implements standardized QC, integration, clustering, and differential analysis to ensure results are auditable and consistent.
  • Use Case: A researcher can use this Skill to process raw scRNA-seq counts through normalization, batch correction, and cell-type annotation, or to perform differential abundance testing on CyTOF data using the gold-standard R stack.

Quick Start

Use the single-cell skill to perform QC, normalization, and Leiden clustering on the provided AnnData object named 'experiment_data.h5ad'.

Frequently Asked Questions about single-cell

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 pipeline from raw counts to cell-type annotation?▼

Yes, you can perform differential abundance testing on CyTOF mass cytometry data by leveraging the CATALYST R ecosystem through rpy2 integration, which maintains statistical rigor across diverse biological datasets.

What is the best way to correct batch effects in single-cell transcriptomic data?▼

The best way to correct batch effects in single-cell data is by utilizing scvi-tools integration workflows, ensuring reproducible and statistically rigorous harmonization across diverse biological datasets.

Does this single-cell analysis pipeline support trajectory inference and differential state analysis?▼

Yes, the single-cell analysis pipeline supports trajectory inference and differential state analysis by executing comprehensive workflows using palantir and decoupler on normalized scRNA-seq and snRNA-seq datasets.

Can I use scanpy and scvi-tools together for reproducible single-cell clustering?▼

Yes, you can use scanpy and scvi-tools together to execute standardized quality control, batch correction, and Leiden clustering, ensuring your single-cell analysis results are auditable and consistent.

Do I need an AnnData object to start analyzing scRNA-seq data?▼

Yes, you need an AnnData object, such as an 'experiment_data.h5ad' file, to start the scRNA-seq analysis pipeline for quality control, normalization, and clustering.

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