CASSIA 1.3.1

CASSIA (Cell type Annotation using Specialized System with Integrated AI) is a Python package for automated cell type annotation in single-cell RNA sequencing data using large language models.

CASSIA

CASSIA (Collaborative Agent System for Single-cell Interpretable Annotation) is a Python and R package designed for automated, accurate, and interpretable single-cell RNA-seq cell type annotation using a modular multi-agent LLM framework.

📖 Read our paper in Nature Communications

Highlights

• 🔬 Reference-free and interpretable LLM-based cell type annotation
• 🧠 Multi-agent architecture with dedicated agents for annotation, validation, formatting, quality scoring, and reporting
• 📈 Quality scores (0–100) and optional consensus scoring to quantify annotation reliability
• 📊 Detailed HTML reports with reasoning and marker validation
• 💬 Supports OpenAI, Anthropic, OpenRouter, DeepSeek, and any OpenAI-compatible API (including local LLMs)
• 🧬 Compatible with markers from Seurat (FindAllMarkers) and Scanpy (tl.rank_genes_groups)
• 🚀 Optional agents: Annotation Boost, Subclustering, RAG (retrieval-augmented generation), Uncertainty Quantification
• 🌎 Cross-species annotation capabilities, validated across human, mouse, and non-model organisms
• 🧪 Web UI also available: cassia.bio

Installation

pip install CASSIA

To enable optional RAG functionality:

pip install CASSIA_rag

Note: For R users, see the R package on GitHub.

Set Up API Key

You only need one API key to use CASSIA. We recommend OpenRouter since it provides access to most models (OpenAI, Anthropic, Google, etc.) through a single API key.

import CASSIA

# For OpenRouter (recommended — access all models with one key)
CASSIA.set_api_key("your_openrouter_api_key", provider="openrouter")

# For OpenAI
CASSIA.set_api_key("your_openai_api_key", provider="openai")

# For Anthropic
CASSIA.set_api_key("your_anthropic_api_key", provider="anthropic")

# For custom OpenAI-compatible APIs (e.g., DeepSeek)
CASSIA.set_api_key("your_deepseek_api_key", provider="https://api.deepseek.com")

Quick Start

import CASSIA

# Load example marker data
unprocessed_markers = CASSIA.load_example_markers(processed=False)

# Run the full CASSIA pipeline (annotation + scoring + boost + report)
CASSIA.runCASSIA_pipeline(
    output_file_name="MyAnalysis",
    tissue="large intestine",
    species="human",
    marker=unprocessed_markers,
    max_workers=4,
    overall_provider="openrouter",
    annotation_model="anthropic/claude-sonnet-4.6",
    score_model="anthropic/claude-sonnet-4.6",
    score_threshold=75
)

Quick annotation only? Use CASSIA.runCASSIA_batch() for fast batch annotation without scoring or boosting.

Supported Models

You can choose any model for annotation and scoring. CASSIA also supports custom providers and local open-source models.

Provider	Model	Notes
OpenRouter	anthropic/claude-sonnet-4.6	Best-performing (Recommended)
OpenRouter	openai/gpt-5.4	Best-performing
OpenRouter	google/gemini-3-flash-preview	Best low-cost option
OpenRouter	x-ai/grok-4.20-beta	Best low-cost option
OpenAI	gpt-5.4	Balanced option
Anthropic	claude-sonnet-4-6	Latest best-performing
DeepSeek	deepseek-chat	Very affordable
Local	Any Ollama model	Zero cost, full privacy

Documentation

📚 Complete Documentation & Vignettes

🤖 LLMs Annotation Benchmark

Citation

Xie, E., Cheng, L., Shireman, J. et al. CASSIA: a multi-agent large language model for automated and interpretable cell annotation. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67084-x

Contributing

We welcome contributions! Please submit pull requests or open issues via GitHub.

License

MIT License © 2025 Elliot Xie and contributors.

Support

Open an issue on GitHub or email [e-mail] for help.