CASSIA (Collaborative Agent System for Single cell Interpretable Annotation) is a tool that enhances cell type annotation using multi-agent Large Language Models (LLMs).
π Read our paper for detailed methodology and benchmarking results.
π Example R workflow with TS large intestine data
π Try CASSIA Web UI - A web interface for basic CASSIA functionality
Option 1: Install from GitHub
# Install dependencies
install.packages("devtools")
install.packages("reticulate")
# Install CASSIA
devtools::install_github("ElliotXie/CASSIA/CASSIA_R")
Option 2: Install from source
install.packages("reticulate")
install.packages("remotes")
remotes::install_url("https://github.com/ElliotXie/CASSIA/raw/main/CASSIA_source_R/CASSIA_0.1.0.tar.gz")
- API Provider Guides:
We recommend starting with OpenRouter as it provides access to most models with a single API key. While slightly more expensive, it offers greater convenience. Direct access via OpenAI or Anthropic provides more stability for production use.
# For OpenAI
setLLMApiKey("your_openai_api_key", provider = "openai", persist = TRUE)
# For Anthropic
setLLMApiKey("your_anthropic_api_key", provider = "anthropic", persist = TRUE)
# For OpenRouter
setLLMApiKey("your_openrouter_api_key", provider = "openrouter", persist = TRUE)
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
runCASSIA_pipeline(
output_file_name, # Base name for output files
tissue, # Tissue type (e.g., "brain")
species, # Species (e.g., "human")
marker, # Marker data from findallmarker
max_workers = 4, # Number of parallel workers
annotation_model = "gpt-4o", # Model for annotation
annotation_provider = "openai", # Provider for annotation
score_model = "anthropic/claude-3.5-sonnet", # Model for scoring
score_provider = "openrouter", # Provider for scoring
annotationboost_model="anthropic/claude-3.5-sonnet", #model for annotation boost
annotationboost_provider="openrouter", #provider for annotation boost
score_threshold = 75, # Minimum acceptable score
additional_info = NULL # Optional context information
)
gpt-4o
(recommended): Balanced performance and costgpt-4o-mini
: Faster, more economical optiono1-mini
: Advanced reasoning capabilities (higher cost)
claude-3-5-sonnet-20241022
: High-performance model
anthropic/claude-3.5-sonnet
: High rate limit access to Claudeopenai/gpt-4o-2024-11-20
: Alternative access to GPT-4ometa-llama/llama-3.2-90b-vision-instruct
: Cost-effective open-source option
The pipeline generates four key files:
- Initial annotation results
- Quality scores with reasoning
- Summary report
- Annotation boost report
# 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
- Verify sufficient disk space
- Keep API keys secure
- Maintain sufficient API credits
- Backup data before overwriting files
- Double-check file paths and permissions
Note: This README covers basic CASSIA functionality. For a complete tutorial including advanced features and detailed examples, please visit: CASSIA Complete Tutorial.