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🚀 Quickstart Guide

This guide will walk you through setting up and launching CLAIREscope on your local machine or high-performance compute server.

💻 Hardware & System Requirements

CLAIREscope is designed to be lightweight and resource-efficient. Memory footprint scales with the number of cells in your active AnnData (.h5ad) object:

Cohort Scale Number of Cells Minimum RAM Recommended RAM Recommended CPU
Small / Pilot < 10,000 cells 4 – 8 GB 8 GB 4 Cores
Standard Atlas 10,000 – 50,000 cells 8 – 16 GB 16 GB 8 Cores
Large-Scale Atlas 50,000 – 150,000+ cells 16 – 32 GB 32 – 64 GB 8–16 Cores
Million-Cell Atlas > 200,000 cells 64 GB 128 GB+ 16–32 Cores
  • Storage: 2 GB free disk space for dependencies; NVMe SSD recommended for rapid .h5ad file I/O.
  • Operating Systems: Linux (Ubuntu 20.04/22.04/24.04), macOS (Intel / Apple Silicon M1/M2/M3), Windows 10/11 (WSL2 or Native).
  • Python: Version >= 3.10 (up to 3.13).

uv is an extremely fast Python package and environment manager.

# 1. Clone repository
git clone https://github.com/ccneko/CLAIREscope.git
cd CLAIREscope

# 2. Create isolated virtual environment
uv venv --python 3.12
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 3. Install core single-cell dependencies
uv pip install scanpy anndata streamlit pandas numpy scipy matplotlib plotly pyyaml openpyxl

🔬 Launching the Platform

Run the unified launcher: 

streamlit run app.py

CLAIREscope will initialize and open your default browser at http://localhost:8501.

🧪 Exploring with Demo Data

  1. Navigate to the sidebar 🧭 Navigation → Single-Cell Preprocessing & Scanpy Pipeline.
  2. Click 💾 Download Demo AnnData (.h5ad) or click 🚀 Load Synthetic Template into Pipeline.
  3. Explore all 11 analysis studios immediately!