Codes for "Part mutual information for quantifying direct associations in networks" https://doi.org/10.1073/pnas.1522586113
"PCA" is nothing to do with Principle Component Analysis. Here, it is PC algorithm (Peter Spirtes, Clark Glymour), a network structure inference algorithm. PMI is part mutual information, a new criteria to estimate condition independence.
All of the methods we implemented and the methods we want to compare with are in the folder /lib.
Please run make.m to add the path into Matlab Search path temporarily.
If you want to add the path permanently, please run make -p . You can remove the path from "Set Path" bottom.
cmi.m- Computing Conditional Mutual Information with bin method
pmi.m- Computing Part Mutual Information with bin method
pmiguass.m- Computing Part Mutual Information with Gaussian Distribution Approximation
dcorr.m- Computing Distance Correlation
pdcor- Computing Partial Distance Correlation
graphicalLasso.m- Network Structure Inference with graphicalLasso
pca_pmi.m- Network Structure Inference with PC algorithm combining with Part Mutual Information
kpca_pmi.m- Network Structure Inference with PC algorithm combining with Kernelized Part Mutual Information
pca_cmi.m- Network Structure Inference with PC algorithm combining with Conditional Mutual Information
pcapcc.m- Network Structure Inference with PC algorithm combining with Pearson Correlation
Please run configpath.m in the folder example first.
Please read example/README.md to get all the descriptions of codes in example .
- Windows, Unix/Linux, Mac OS
- Matlab (>2013b)
@article{zhao2016part,
title={Part mutual information for quantifying direct associations in networks},
author={Zhao, Juan and Zhou, Yiwei and Zhang, Xiujun and Chen, Luonan},
journal={Proceedings of the National Academy of Sciences},
volume={113},
number={18},
pages={5130--5135},
year={2016},
publisher={National Acad Sciences}
}
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