ASCA: ANOVA-Simultaneous Component Analysis in Python

Table of Contents

• About the Project
• Getting Started
• Simple Examples
• Contributing
• License
• Contact
• References

About The Project

ASCA is a multivariate approach to the standard ANOVA, using simultaneous component analysis to interprete the underlying factors and interaction from a design of experiment dataset. This project implements ASCA in python to support open source development and a wider application of ASCA.

Getting Started

Install this library either from the official pypi or from this Github repository:

pip install ASCA

Install most updated version from Github

In a environment terminal or CMD:

pip install git+https://github.com/tsyet12/ASCA

Simple Example

    X = [[1.0000,0.6000], 
    [3.0000,0.4000],
    [2.0000,0.7000],
    [1.0000,0.8000],
    [2.0000,0.0100],
    [2.0000,0.8000],
    [4.0000,1.0000],
    [6.0000,2.0000],
    [5.0000,0.9000],
    [5.0000,1.0000],
    [6.0000,2.0000],
    [5.0000,0.7000]]
    X=np.asarray(X)

    F = [[1,     1],
     [1,     1],
     [1,     2],
     [1,     2],
     [1,     3],
     [1,     3],
     [2,     1],
     [2,     1],
     [2,     2],
     [2,     2],
     [2,     3],
     [2,     3]]
    F=np.asarray(F)
    interactions = [[0, 1]]

    ASCA=ASCA()
    ASCA.fit(X,F,interactions)
    ASCA.plot_factors()
    ASCA.plot_interactions()

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b testbranch/prep)
3. Commit your Changes (git commit -m 'Improve testbranch/prep')
4. Push to the Branch (git push origin testbranch/prep)
5. Open a Pull Request

License

Distributed under the Open Sourced BSD-2-Clause License. See LICENSE for more information.

Contact

Main Developer:

Sin Yong Teng [email protected] or [email protected] Radboud University Nijmegen

References

Smilde, Age K., et al. "ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data." Bioinformatics 21.13 (2005): 3043-3048.

Jansen, Jeroen J., et al. "ASCA: analysis of multivariate data obtained from an experimental design." Journal of Chemometrics: A Journal of the Chemometrics Society 19.9 (2005): 469-481.

Acknowledgements

The research contribution from S.Y. Teng is supported by the European Union's Horizon Europe Research and Innovation Program, under Marie Skłodowska-Curie Actions grant agreement no. 101064585 (MoCEGS).