Dance

Abstract

1. Introduction

In release 1.0, the main usage of the DANCE is to provide readily available experiment reproduction (see detail information about the reproduced performance below). Users can easily reproduce selected experiments presented in the original papers for the computational single-cell methods implemented in DANCE, which can be found under examples/.

DANCE addresses these challenges by providing a unified Python package implementing many popular computational single-cell methods (see Implemented Algorithms), as well as easily reproducible experiments by providing unified tools for

In release 2.0, DANCE evolves from an experiment reproduction library into an automated and interpretable preprocessing platform. It provides powerful tools to optimize your single-cell analysis workflows: To discover the best preprocessing pipeline for a specific method, you can use our Method-Aware Preprocessing (MAP) module. For practical examples on how to run this locally, please see examples/tuning/custom-methods/. To get an instant, high-quality pipeline recommendation for a new dataset, you can use our Dataset-Aware Preprocessing (DAP) web service, available at http://omicsml.ai:81/dance/. Together, these features transform single-cell preprocessing from a manual, trial-and-error process into a systematic, data-driven, and reproducible workflow.

2. Objective

DANCE: a deep learning library and benchmark platform for single-cell analysis

This project demonstrates how Python, Deep Learning, Machine Learning can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/OmicsML/dance.git
cd dance

# Clone the repository via SSH
git clone git@github.com:OmicsML/dance.git && cd dance
# Alternatively, use HTTPS if you have not set up SSH
# git clone https://github.com/OmicsML/dance.git  && cd dance

# Run the auto installation script to install DANCE and its dependencies in a conda environment
source install.sh cu118 dance-env

conda create -n dance python=3.11 -y && conda activate dance

pip install torch==2.1.1 torchvision==0.16.1 --index-url https://download.pytorch.org/whl/cu118
pip install torch_geometric==2.4.0
pip install dgl==1.1.3 -f https://data/dgl.ai/wheels/cu118/repo.html

pip install torch==2.1.1 torchvision==0.16.1 --index-url https://download.pytorch.org/whl/cpu
pip install torch_geometric==2.4.0
pip install dgl==1.1.3 -f https://data/dgl.ai/wheels/repo.html

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by OmicsML and published on GitHub under the BSD 2-Clause "Simplified" License. Please follow the license terms and credit the original author when you use or modify this code.