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}
}
You can contact either Juan Zhao (zhaojuan@sibs.ac.cn) or Yiwei Zhou (zhou_panda@foxmail.com) for any questions about the codes or the paper.
Apache License 2.0