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Description:

ASCA 1.4

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.

Fork the Project
Create your Feature Branch (git checkout -b testbranch/prep)
Commit your Changes (git commit -m 'Improve testbranch/prep')
Push to the Branch (git push origin testbranch/prep)
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 sinyong.teng@ru.nl or tsyet12@gmail.com
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).