🔬 Flow Cytometry Dimensionality Reduction (UMAP / t-SNE)

This project provides a Jupyter notebook for dimensionality reduction and visualization of flow cytometry .fcs files using UMAP and t-SNE. The notebook reduces high-dimensional single-cell data to 2D for visual exploration and identifies dominant marker expression per cell.

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🧰 Prerequisites

• Python 3.10+
• Visual Studio Code with the Jupyter extension
• Virtual environment (recommended for dependency isolation)

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🐍 Installing Python 3.10+

🔹 Windows

1. Go to the official download page: https://www.python.org/downloads/windows/
2. Download the latest Python 3.10 or above Windows installer.
3. Run the installer:
   • ✅ Check "Add Python to PATH"
   • ✅ Choose "Customize installation" if you want to enable pip or install for all users
4. Confirm installation:

   python --version

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🛠️ Setup Instructions

1. Clone the repository

   git clone https://github.com/AngxiaoLu123/dimension-reduction-plots.git
   cd dimension-reduction-plots

2. Create and activate a virtual environment

   Windows

   python -m venv venv
   venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

🧪 Running the Notebook in VS Code

Open the project folder in Visual Studio Code.

Install the Jupyter extension (if not already installed).

Open the .ipynb notebook file (e.g., dimension_reduction.ipynb) in the notebooks folder.

In the top-right corner of the notebook, select the Python virtual environment kernel.

Run the notebook cells step by step.