Computer Lab 4: scRNASeq โ€” Classic and MCP/Gemini

Module 4 Computer Lab: Single-Cell RNA-Seq of COVID vs Controls

๐ŸŒ Introduction

In this lab, you will explore single-cell RNA sequencing (scRNASeq) data from patients with COVID compared to healthy controls. The main goal is to identify gene expression differences at the single-cell level between these groups.

You will perform a standard scRNASeq analysis using Scanpy in a Jupyter notebook, followed by repackaging the workflow into MCP tools and orchestrating it with the Gemini CLI.

๐Ÿงช Data Description

  • Samples: 4 patients with COVID and 4 healthy controls
  • Cells: Each sample has been subsampled to ~1500 cells

This dataset is small enough to run quickly during class, but still captures the essential features of a real scRNASeq study.

Open the Module 4 lab notebook in Google Colab:

Notes:

  • Runtime can be CPU; GPU is not necessary.
  • You can run a cell with Shift+Enter.

Install Gemini CLI in Colab

Go to the terminal in Google Colab and run the following commands to install Gemini CLI:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash
source /root/.bashrc
nvm install 21
nvm use 21
npm install -g @google/gemini-cli

Alternative option (if VS Code in Colab does not work for you):

If the VS Code-in-Colab setup fails or you prefer a different setup, please try to setup a google colab local runtime by following the instructions here. You may need to download and setup Docker locally, then run a docker image.

Part I โ€” Classic scRNASeq Workflow in Jupyter

In this section, you will use the notebook like a bioinformatician at the bench, running cells step by step to build an analysis using Scanpy.

Work on your notebook

Youโ€™ll follow a standard Scanpy pipeline: load the .h5ad dataset with metadata, run QC (including doublet detection and optional cell-cycle regression), normalize and select HVGs, integrate batches, embed (PCA/UMAP), cluster (Leiden).

At the end of Part I, you will have processed the data as part of a basic scRNASeq pipeline using Scanpy inside Jupyter.

๐Ÿงฉ Part II โ€” Building an AI Agent for Differential Gene Expression with MCP Tools

Why Part II?

In Part I, you explored a typical single-cell RNA-seq workflow step by step. Now we move from manual analysis to system design: you will build a set of MCP tools, describe them in a GEMINI.md file, and let an AI agent (via the Gemini CLI) use these tools to perform a real biological task.

This teaches you how to design workflows that an AI can reason about and execute automatically.

Setting up

For this part, we will:

  • Start a VS Code Tunnel from the current notebook (environment already has all required packages installed).

  • Switch to the Module4 Folder Open a terminal in VS Code (View -> Terminal). Move working directory to folder on Google Drive and use this folder as your workspace.

cd /content/drive/MyDrive/DDLS-Course/Module4/
code .
  • Continue the work inside VS Code, where you will develop MCP tools and interact with the Gemini CLI agent.

What is MCP?

MCP (Model Context Protocol) lets you wrap analysis steps as tools that an AI agent can call.

  • Each tool has a clear name, input, and output.
  • The agent can decide how to combine tools to solve a task.
  • Instead of one long script, you provide modular building blocks that the AI can orchestrate.

Think of it as teaching the agent how to press the right buttons in your analysis pipeline.

Task: Differential Gene Expression (DGE)

Now that we have preprocessed our PBMC dataset (Covid vs Control) in Part I, we will analyze Differential Gene Expression.

DGE in scRNA-seq is used to:

  • Identify gene markers that distinguish cell populations.
  • Identify differentially regulated genes across conditions (Covid vs Control).

Your agent should be able to:

  1. Load the processed AnnData file.

  2. Select a cluster or cell type.

  3. Compare gene expression between Covid and Control cells within that cluster.

  4. Run DGE tests (parametric and non-parametric):

    • t-test
    • Wilcoxon rank-sum
    • (Optionally: logreg)

  5. Compare results between the tests.

  6. Visualize the results (volcano, dotplot, violin, UMAP overlays).

  7. Extend the comparison:

    • Between clusters (marker discovery)
    • Between conditions (Covid vs Control)
    • Between samples (consistency check)

  8. Summarize results in a short report.

๐Ÿ“Œ Hint: By default, Scanpy uses .raw in AnnData for DGE; this can be changed with use_raw=False. The dataset contains >19,000 genes after filtering.

Step 1: Build MCP Tools

Now to allow the AI agent to perform the task, you need to build a set of MCP tools. A minimal MCP server template is provided in the starter notebook.

Minimal MCP server pattern (example):

pip install mcp

"""
FastMCP quickstart example.
"""

from mcp.server.fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Demo")


# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b


if __name__ == "__main__":
   # Run as an MCP stdio server (no prints to stdout!)
   mcp.run(transport="stdio")

You can refer to full documentation to MCP SDK This skeleton demonstrates the pattern only. In your real server, use an MCP library that implements the protocol and wire real functions that call Scanpy.

Suggested tools for this task are (you may adjust, add, or remove):

  • load_anndata(path) โ€“ load dataset
  • subset_cells(query) โ€“ select cluster or type
  • check_group_sizes(groupby="condition") โ€“ confirm enough cells per group
  • run_dge(groupby, case, ref, method) โ€“ perform test
  • plot_volcano(dge_results) โ€“ visualize top DEGs
  • plot_umap(genes) โ€“ show expression on UMAP
  • compare_tests(results1, results2) โ€“ overlap between methods
  • save_table(results) โ€“ export DEG table
  • generate_report(dge_results, plots) โ€“ assemble Markdown report

๐Ÿ“Œ Write clear documentation for each tool (inputs, outputs, usage). This is what enables the AI agent to reason correctly.

Hint: The input arguments and returned types for the MCP tool functions should be primitive Python types (str, int, float, bool, list, dict). Complex objects like AnnData should be handled inside the tool and saved as a file and returned as a file path (str).

Save this MCP server script as sc_mcp_server.py in your Google Drive folder /content/drive/MyDrive/DDLS-Course/Module4/.

Before you try your MCP tools with Gemini, you should test each function directly. You can do this by importing the functions in a separate Python script or Jupyter notebook cell. For example, create a new cell in your notebook and run:

from sc_mcp_server import add

result = add(2, 3)  # should return 5
print(result)

If this doesnโ€™t through any errors, your MCP server is ready to be wired into Gemini CLI.

Step 2: Wire the server into Gemini CLI

Create a new file named settings.json and add an mcpServers entry like this:

{
   "mcpServers": {
      "sc-mcp": {
         "type": "stdio",
         "command": "/usr/local/bin/python",
         "args": ["/content/drive/MyDrive/DDLS-Course/Module4/sc_mcp_server.py"]
      }
   }
}

Then move this file to the Gemini config directory. WARNING: This will overwrite any existing settings.json file, so back it up if needed.

mv /content/drive/MyDrive/DDLS-Course/Module4/settings.json /root/.gemini/settings.json

Tips: Gemini CLI discovers MCP servers from this file; use /mcp list in the chat to list available tools.

Step 3: Write the GEMINI.md

Create a new file named GEMINI.md in your Google Drive folder /content/drive/MyDrive/DDLS-Course/Module4/.

This file should describe the overall plan for the agent, including:

  • Context: Covid PBMC dataset, processed and clustered in Part I.
  • Goal: Perform DGE analysis to identify marker genes and condition-specific genes.
  • Instructions:
    • Outline how the agent should perform the task step by step
    • Specify what output the agent is expected to produce
    • Define the data path (/content/drive/MyDrive/DDLS-Course/Module4/data/covid/)
    • How the agent should use the MCP tools

Step 4: Run the DGE analysis with your Gemini CLI Agent

Important: In order for Gemini CLI to discover your GEMINI.md file and MCP server, you need to start a new Gemini CLI session.

Note: If you are already in Gemini CLI, save your chat history /chat save computer-lab-4 and then exit Gemini CLI /exit.

Ensure you are in the correct folder:

cd /content/drive/MyDrive/DDLS-Course/Module4/

Then start a new Gemini CLI session:

gemini

If you saved a previous chat session, you can continue that session by loading the saved history /chat load computer-lab-4.

Make sure the GEMINI.md file and the MCP server are discovered by Gemini CLI. You should see this on top of the input box in Gemini CLI: Using: 1 GEMINI.md file | 1 MCP server (ctrl+t to view)

To check if all your MCP tools are available, enter the command /mcp list in the chat interface. You should see a list of all your configured MCP tools.

Now that everything is set up, you can instruct the agent to perform the DGE analysis as described in the section โ€œTask: Differential Gene Expression (DGE)โ€. The agent will call your MCP tools when needed to perform the task.

How to know if the agent is calling your MCP tools?

When Gemini first uses your MCP tool, it will ask you whether to allow the tool call. You should see a prompt like this:

 โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
 โ”‚ ?  add (sc-mcp MCP Server) {"a":5,"b":3} โ†                                          โ”‚
 โ”‚                                                                                     โ”‚
 โ”‚   MCP Server: sc-mcp                                                                โ”‚
 โ”‚   Tool: add                                                                         โ”‚
 โ”‚                                                                                     โ”‚
 โ”‚ Allow execution of MCP tool "add" from server "sc-mcp"?                             โ”‚
 โ”‚                                                                                     โ”‚
 โ”‚ โ— 1. Yes, allow once                                                                โ”‚
 โ”‚   2. Yes, always allow tool "add" from server "sc-mcp"                              โ”‚
 โ”‚   3. Yes, always allow all tools from server "sc-mcp"                               โ”‚
 โ”‚   4. No, suggest changes (esc)                                                      โ”‚
 โ”‚                                                                                     โ”‚
 โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ

If you allow the tool call only once, you will also see this message the next time the agent calls the same tool.

After your MCP tool has been executed, you should see which tool was called with what arguments, and the returned result, like this:

โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
โ”‚  > Add the number 5 to the number 3  โ”‚
โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ

 โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ
 โ”‚ โœ“  add (sc-mcp MCP Server) {"a":5,"b":3}                                      โ”‚
 โ”‚                                                                               โ”‚
 โ”‚    8                                                                          โ”‚
 โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ
โœฆ 8

You can also find the same information in your Gemini chat history:

{
   "role": "model",
   "parts": [
   {
      "functionCall": {
         "name": "add",
         "args": {
         "a": 5,
         "b": 3
         }
      }
   }
   ]
}

Step 5: Generate the Report

Finally, use the agent to produce a Markdown report REPORT.md file that includes:

  • Which clusters you analyzed
  • A table of top DEGs with statistics
  • At least one visualization

Add a short interpretation of the results in your own words to the report.

โœ… Deliverables:

Your final submission in Google Drive folder should include:

  1. Notebook for Part I.

    • The completed and executed Jupyter notebook from Part I, saved as .ipynb.

  2. MCP Tools + Orchestration (Part II)

    • MCP tool scripts or server (source code).
    • A Gemini CLI chat history showing your tool calls and results summaries.

  3. GEMINI.md โ€” Plan

    • Short plan of your MCP tool surface, parameters, and I/O contracts.

  4. README.md โ€” Submission Guide

    • Folder structure, how to reproduce, dependencies, known limitations.

  5. A Markdown report REPORT.md with DEGs, figures, and your own interpretation.

Submission Tips:

Save Your Gemini Chat History

Regularly save your Gemini chat history to avoid losing records of your work. In the Gemini CLI, run:

/chat save computer-lab-4

Copy the Checkpoint File

After you closed the Gemini CLI, copy the checkpoint file from the temporary Gemini directory to your Google Drive folder.

cd /content/drive/MyDrive/DDLS-Course/Module4/
cp /root/.gemini/tmp/*/checkpoint-computer-lab-4.json .

You can also instruct Gemini to copy the checkpoint file to your Google Drive folder while you are still in the Gemini CLI:

Please run this command: `cp /root/.gemini/tmp/*/checkpoint-computer-lab-4.json .

This will prevent the loss of your chat history if runtime in Google Colab disconnects and gets reset.

Add a README.md

Explain the folder structure and how to run your tools or notebook.

Verify and Share Your Folder

Ensure your Google Drive DDLS-Course/Module4 folder contains all deliverables. Then:

  1. Right-click the Module4 folder and select Share.
  2. Set permissions to โ€œAnyone with the link can view and commentโ€.
  3. Copy the sharing link.

Submit the Form

Submission Deadline: 24h after the computer lab ends

Grading

Pass/Fail based on:

  • Completion: Core workflow executed and deliverables submitted.
  • Engagement: Notebooks or tools fully executed; evidence of iteration and troubleshooting.
  • Clarity: Organized Module4 folder; clear README.md.
  • Reproducibility: Tool I/O is explicit; intermediate files saved; report generated.
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Last updated on Sep 19, 2025

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