Installation Guide

In mLLMCelltype: Cell Type Annotation Using Large Language Models

knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE,
  eval = FALSE
)

Installation Guide

This guide provides detailed instructions for installing and configuring mLLMCelltype for cell type annotation in single-cell RNA sequencing data.

System Requirements

Before installing mLLMCelltype, ensure your system meets the following requirements:

• R version: 4.0.0 or higher
• Memory: At least 8GB RAM recommended (more for large datasets)
• Operating System: Windows, macOS, or Linux
• Internet Connection: Required for API calls to LLM providers

Installing the R Package

Installation from CRAN (Recommended)

mLLMCelltype is now available on CRAN. You can install it directly using:

# Install from CRAN
install.packages("mLLMCelltype")

This will install the stable version of mLLMCelltype with all required dependencies.

Installation from GitHub (Development Version)

To install the latest development version from GitHub:

# Install devtools if not already installed
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

# Install mLLMCelltype development version
devtools::install_github("cafferychen777/mLLMCelltype", subdir = "R")

Installation from a Local Source

If you have downloaded the source code or need to install from a local copy:

# Assuming the package is in the current working directory
devtools::install_local("path/to/mLLMCelltype/R")

Dependencies

mLLMCelltype depends on several R packages that will be automatically installed during the installation process. The main dependencies include:

• dplyr: For data manipulation
• httr: For API requests
• jsonlite: For JSON parsing
• R6: For object-oriented programming
• digest: For caching mechanisms
• magrittr: For pipe operations

For visualization and integration with single-cell analysis workflows, the following packages are recommended but not required:

• Seurat: For integration with Seurat objects
• ggplot2: For visualization
• SCpubr: For publication-ready visualizations

API Keys Setup

mLLMCelltype requires API keys to access different LLM providers. You will need to obtain API keys for at least one of the supported providers:

Obtaining API Keys

1. OpenAI (GPT-4o/4.1)
2. Visit OpenAI Platform
3. Create an account or log in
4. Navigate to API keys section

5. Create a new API key

6. Anthropic (Claude-3.7/3.5)

7. Visit Anthropic Console
8. Create an account or log in

9. Generate an API key

10. Google (Gemini-2.0/2.5)

11. Visit Google AI Studio
12. Create a Google account or log in

13. Generate an API key

14. Other Providers

15. Similar processes apply for DeepSeek, Qwen, Zhipu, MiniMax, Stepfun, and Grok
16. Visit their respective websites to obtain API keys

Setting Up API Keys

There are three ways to set up your API keys:

1. Environment Variables

Create a .env file in your project directory with your API keys:

# API Keys for different LLM models
OPENAI_API_KEY=your-openai-key
ANTHROPIC_API_KEY=your-anthropic-key
GEMINI_API_KEY=your-gemini-key
DEEPSEEK_API_KEY=your-deepseek-key
QWEN_API_KEY=your-qwen-key
ZHIPU_API_KEY=your-zhipu-key
STEPFUN_API_KEY=your-stepfun-key
MINIMAX_API_KEY=your-minimax-key
GROK_API_KEY=your-grok-key
OPENROUTER_API_KEY=your-openrouter-key

Then load the environment variables in your R script:

library(dotenv)
dotenv::load_dot_env()

2. Direct Specification in Function Calls

You can directly provide API keys in function calls:

library(mLLMCelltype)

results <- annotate_cell_types(
  input = your_marker_data,
  tissue_name = "human PBMC",
  model = "claude-sonnet-4-6",
  api_key = "your-anthropic-key",
  top_gene_count = 10
)

3. R Environment Variables

Set API keys as R environment variables:

Sys.setenv(OPENAI_API_KEY = "your-openai-key")
Sys.setenv(ANTHROPIC_API_KEY = "your-anthropic-key")
# Set other API keys as needed

Verifying Installation

To verify that mLLMCelltype is installed correctly and API keys are set up properly:

library(mLLMCelltype)

# Check if the package is loaded correctly
packageVersion("mLLMCelltype")

# Verify API key setup for a specific provider
api_key <- get_api_key("anthropic")
if (!is.null(api_key) && api_key != "") {
  cat("Anthropic API key is set up correctly\n")
} else {
  cat("Anthropic API key is not set up\n")
}

Common Installation Issues

Package Installation Failures

If you encounter issues during installation:

1. Check R version: Ensure you're using R 4.0.0 or higher
2. Update devtools: Run install.packages("devtools") to ensure you have the latest version
3. Check dependencies: Some dependencies might require system libraries on Linux

API Connection Issues

If you encounter issues connecting to LLM APIs:

1. Verify API keys: Ensure your API keys are correct and have not expired
2. Check internet connection: Ensure you have a stable internet connection
3. Proxy settings: If you're behind a proxy, configure R to use your proxy settings

# Example of setting proxy for httr
httr::set_config(httr::use_proxy(url = "proxy_url", port = proxy_port))

Memory Limitations

For large datasets, you might encounter memory issues:

1. Increase R memory limit: Use memory.limit(size = 16000) on Windows to increase available memory
2. Process data in batches: Consider processing large datasets in smaller batches

Next Steps

Now that you have installed mLLMCelltype, you can proceed to:

• Getting Started: Learn the basics of using mLLMCelltype
• Usage Tutorial: Explore more advanced usage scenarios
• Visualization Guide: Learn how to visualize your results

If you encounter any issues not covered in this guide, please open an issue on our GitHub repository.

mLLMCelltype documentation built on May 11, 2026, 9:06 a.m.