CASSIA (Collaborative Agent System for Single-cell Interpretable Annotation) is a tool that enhances cell type annotation using multi-agent Large Language Models (LLMs).
🌐 CASSIA Web UI - A web interface supporting most of CASSIA’s functionality
📚 Complete R Documentation/Vignette
📝 Example Python workflow/Vignette
2025-05-05
📊 CASSIA annotation benchmark is now online!
The latest update introduces a new benchmarking platform that evaluates how different LLMs perform on single-cell annotation tasks, including accuracy and cost.
LLaMA4 Maverick, Gemini 2.5 Flash, and DeepSeekV3 are the top three most balanced options—nearly free!
🔧 A new auto-merge function unifies CASSIA output across different levels, making subclustering much easier. 🐛 Fixed a bug in the annotation boost agent to improve downstream refinement.
2025-04-19
🔄 CASSIA adds a retry mechanism and optimized report storage!
The latest update introduces an automatic retry mechanism for failed tasks and optimizes how reports are stored for easier access and management.
🎨 The CASSIA logo has been drawn and added to the project!
2025-04-17
🚀 CASSIA now supports automatic single-cell annotation benchmarking!
The latest update introduces a new function that enables fully automated benchmarking of single-cell annotation. Results are evaluated automatically using LLMs, achieving performance on par with human experts.
A dedicated benchmark website is coming soon—stay tuned!
🏗️ Installation (R, for Python tutorial please visit here)
# Install dependencies
install.packages("devtools")
install.packages("reticulate")
# Install CASSIA
devtools::install_github("ElliotXie/CASSIA/CASSIA_R")Note: If the environment is not set up correctly the first time, please restart R and run the code below
library(CASSIA)
setup_cassia_env()It should take about 3 minutes to get your API key.
We recommend starting with OpenRouter since it provides access to most models through a single API key.
# For OpenRouter
setLLMApiKey("your_openrouter_api_key", provider = "openrouter", persist = TRUE)
# For OpenAI
setLLMApiKey("your_openai_api_key", provider = "openai", persist = TRUE)
# For Anthropic
setLLMApiKey("your_anthropic_api_key", provider = "anthropic", persist = TRUE)
- API Provider Guides:
CASSIA includes example marker data in two formats:
# Load example data
markers_unprocessed <- loadExampleMarkers(processed = FALSE) # Direct Seurat output
markers_processed <- loadExampleMarkers(processed = TRUE) # Processed format# The default provider is set to OpenRouter.
runCASSIA_pipeline(
output_file_name = "cassia_test", # Base name for output files
tissue = "Large Intestine", # Tissue type (e.g., "brain")
species = "Human", # Species (e.g., "human")
marker = markers_unprocessed, # Marker data from findallmarker
max_workers = 4 # Number of parallel workers
)You can choose any model for annotation and scoring. Some classic models are listed below. OpenRouter supports most of the current popular models, although some have not been extensively benchmarked in the CASSIA paper — feel free to experiment with them.
google/gemini-2.5-flash-preview: One of the best-performed low-cost models, comparable with models like gpt-4o (Most recommended)deepseek/deepseek-chat-v3-0324: One of the best-performed open-source models, turns to gives very detailed annotations (Recommended)deepseek/deepseek-chat-v3-0324:free: Free but slower
gpt-4o: Used in the benchmark
claude-3-7-sonnet-latest: The latest High-performance model
The pipeline generates four key files:
- Complete Annotation Results CSV File
- Annotation Summary HTML Report
- Annotation Boost Agent Report for Low Quality Annotation
# Check if API key is set correctly
key <- Sys.getenv("ANTHROPIC_API_KEY")
print(key) # Should not be empty
# Reset API key if needed
setLLMApiKey("your_api_key", provider = "anthropic", persist = TRUE)- Use absolute paths when necessary
- Check file permissions
- Ensure files aren't open in other programs
- Keep API keys secure
- Maintain sufficient API credits
Note: This README covers only basic CASSIA functionality. For a complete tutorial including advanced features and detailed examples, please visit: CASSIA Complete Tutorial.
📖 Read our preprint (v2, latest)
📖 Original preprint (v1, historical)
CASSIA: a multi-agent large language model for reference-free, interpretable, and automated cell annotation of single-cell RNA-sequencing data
Elliot Xie, Lingxin Cheng, Jack Shireman, Yujia Cai, Jihua Liu, Chitrasen Mohanty, Mahua Dey, Christina Kendziorski
bioRxiv 2024.12.04.626476; doi: https://doi.org/10.1101/2024.12.04.626476
If you have any questions or need help, feel free to email us. We are always happy to help: [email protected] If you find this project helpful, please share it with your friend, and give this repo a star ⭐. Many thanks!