Chapter 42: Link Claude, Chronulus and R together

Understanding ClaudeR and Chronulus MCP

Installation Guide

Configuration

After installing both packages, you need to configure the Claude desktop client to use them. Edit your claude_desktop_config.json file and add the following configurations:

Combined Configuration (ClaudeR + Chronulus)

{
  "mcpServers": {
    "r-studio": {
      "command": "python",
      "args": ["PATH_TO_REPOSITORY/ClaudeR/scripts/persistent_r_mcp.py"],
      "env": {
        "PYTHONPATH": "PATH_TO_PYTHON_SITE_PACKAGES",
        "PYTHONUNBUFFERED": "1"
      }
    },
    "chronulus-agents": {
      "command": "python",
      "args": ["-m", "chronulus_mcp"],
      "env": {
        "CHRONULUS_API_KEY": "YOUR_CHRONULUS_API_KEY"
      }
    }
  }
}

"chronulus-agents": {
  "command": "docker",
  "args": ["run", "-i", "--rm", "-e", "CHRONULUS_API_KEY", "chronulus-mcp"],
  "env": {
    "CHRONULUS_API_KEY": "YOUR_CHRONULUS_API_KEY"
  }
}

Starting and Using ClaudeR

1. Load the ClaudeR Package

   Run the following in your RStudio console:

   library(ClaudeR)
   claudeAddin()

2. Start the Server

   In the addin window that appears, click "Start Server" to launch the connection. Configure logging settings if desired.

3. Keep the Addin Active

   Keep the addin window active while using Claude. You can switch to other views (Files, Plots, Viewers, etc.) but don't close the addin window.

4. Open Claude and Start Coding

   Open Claude Desktop and ask it to execute R code in your session. For example:

   • "Load the cytometry data from 'my_fcs_data.fcs' and perform basic exploratory analysis"
   • "Create a heatmap visualization of marker expression across identified cell populations"
   • "Perform clustering analysis using FlowSOM on my cytometry data"

Using Chronulus for Forecasting

Once you have configured Chronulus MCP, you can use Claude to create sophisticated forecasting models. Here are some example prompts for cytometry-related forecasting:

Chronulus works by creating sessions for specific forecasting use cases, then using specialized forecasting agents to generate predictions. Claude will handle all the technical details, allowing you to focus on interpreting the results.

Security Considerations

Troubleshooting

"Listening on http://127.0.0.1:3071
createTcpServer: address already in use
Error starting HTTP server: Failed to create server"

Practical Example: Cytometry Analysis Workflow

Here's a complete example of how to use ClaudeR and Chronulus together for a typical cytometry analysis workflow:

1. Set Up Your Environment

   # Start ClaudeR
   library(ClaudeR)
   claudeAddin()  # Click "Start Server" in the addin window

2. Import and Preprocess Data

   Ask Claude:

   "I have FCS files in the 'cytometry_data' folder. Can you help me import them using flowCore, perform compensation if needed, and transform the data appropriately for further analysis?"

3. Perform Clustering and Population Identification

   Ask Claude:

   "Using the preprocessed data, can you perform clustering using FlowSOM and identify major cell populations based on marker expression patterns?"

4. Visualize Results

   Ask Claude:

   "Create UMAP visualizations of the identified cell populations and generate heatmaps showing marker expression across clusters."

5. Forecast Future Trends

   Ask Claude to use Chronulus:

   "I have longitudinal data for these cell populations in 'patient_followup.csv'. Can you use Chronulus to forecast how these populations might change over the next 6 months?"

Conclusion

By integrating ClaudeR and Chronulus with your R environment, you're unlocking a powerful new approach to cytometry data analysis. These tools allow you to leverage AI capabilities to accelerate your workflow, uncover hidden patterns in your data, and develop sophisticated forecasting models—all while maintaining full control over your analysis environment.

As you become more familiar with these tools, you'll discover numerous ways to enhance your cytometry analysis workflow, from automated preprocessing and population identification to advanced statistical analysis and predictive modeling.