ASCA: ANOVA-simultaneous component analysis
Project description
ASCA: ANOVA-Simultaneous Component Analysis in Python
Table of Contents
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).
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