expAscribe 1.2.7

ExpAscribe: a causal inference framework for quantitative experiment ascription and its derivative process. Documentation: https://seabirdshore.github.io/EAdocs/

Team ZJU-China 2024 Software Tool

ExpAscribe: a causal inference framework for quantitative experiment ascription and its derivative process, with python library and portable webapp provided by Team ZJU-China 2024.

Description

Features

• User-friendly UI and B/S Implemented WebApp
• API easy to distribute, with Python library provided. Pypi Docs
• Quantitative ascription-validation-intervention-optimization closed loop, equipped with state-of-the-art ML studies
• Validated by experimental work
• Compatible with existing database formats, i.e. GEO and ArrayExpress
• Detailed documentation, example notebooks and tutorial videos

For more information, please visit our Wiki.

Installation

Supported Platforms

Windows	Linux	macOS
✅(wsl2, amd64)	✅(amd64)	✅(amd64)

Install Python library

pip install expAscribe

If you encounter dependent version conflicts during the installation process. You can try to create a virtual environment and install it.

pip install virtualenv
virtualenv <your_env>
source <your_env>/bin/activate # Linux/macOS
<your_env>\Scripts\activate  # Windows
pip install expAscribe

Since the installation of this way requires conda, please install Anaconda first.

conda create -n <your_env> python=3.10
conda activate <your_env>
pip install expAscribe

Here is a way in Linux to install Anaconda.

wget https://repo.anaconda.com/archive/Anaconda3-2023.10-Linux-x86_64.sh
bash Anaconda3-2023.10-Linux-x86_64.sh
source ~/.bashrc
export PATH="$HOME/anaconda3/bin:$PATH"
source ~/.bashrc

Install Web App

This project requires docker for deployment, so please install docker first!

git clone https://gitlab.igem.org/2024/software-tools/zju-china
cd zju-china/web
docker build -t <your-docker-image-name> .
docker run -p 8501:8501 <your-docker-image-name>

After that app will be running at http://127.0.0.1:8501.

Or you can use the following command to run the app.

git clone https://gitlab.igem.org/2024/software-tools/zju-china
cd zju-china/web
pip install -r requirements.txt
streamlit run app.py

If you encounter dependent version conflicts during the installation process. You can try to create a virtual environment or use conda as above.

Usage

For more information on Usage, please visit our wiki which holds docs and tutorial videos. As for the usage examples, please visit notebooks folder in this repository.

Team-Related Specialty

Software development echoes team theme--"NeovioDye", and specially made an app prototype of user-defined picture drawing. With the support of hardware optical control projection, user-defined fashion printing and dyeing patterns were realized.

In our vision, user-defined patterns are not replicable. At present, we achieve this through public key cryptography and picture steganography, which is too classical and rigid. In our vision, a more innovative and interesting protocol can be achieved through blockchain NFT(Non-Fungible Token) technology

git clone https://gitlab.igem.org/2024/software-tools/zju-china
cd zju-china/UniDye
pip install -r requirements.txt
streamlit run app.py

Contributing

We greatly appreciate and encourage contributions to this project. To ensure a smooth process, please follow the steps below when submitting your pull requests.

Pull Requests

1. Fork the repository and create a new branch from the main branch.
2. If you’ve added new functionality, ensure you include appropriate tests.
3. For any changes to the API, be sure to update the documentation accordingly.
4. Ensure that your code passes all linting checks before submission.

Once you’ve completed these steps, feel free to submit your pull request. If you'd like to discuss any details or require assistance, you can reach me via 3220105728 at zju dot edu dot cn.

Issues

To report bugs or request new features, please use the GitLab issues tracker. Be sure to provide a clear description and detailed instructions for reproducing the issue to help us address it effectively.

Authors and Acknowledgments

Author

• Xiao Sibo, dev and algorithm design @College of Computer Science and Technology, Zhejiang University

Acknowledgments

I would like to express my deepest gratitude to the following individuals and organizations for their invaluable contributions...

• He Yichen, Liu Haoyang and Fu Jinyuan, bioinformatics design, experimental verification and data gathering
• Kun Kuang, Deputy head of the Department of Artificial Intelligence, Zhejiang University
• Microsoft, Azure sponsorship for Student, supporting webapp debugging and deployment