Inferring GRNs from Gene Expression Data by PC-Algorithm Based on Conditional Mutual Information

MATLAB source code, datasets and validation files

This page provides the free MATLAB source codes, datasets and the validation files.

Introduction:

PCA-CMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method based on path consistency algorithm and conditional mutual information, which consider the non-linear dependence and topological structure of GRNs. In this algorithm, the (conditional) dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles.

Author:

By Xiujun Zhang , Xingming Zhao, Kun He, Le Lv, Yongwei Cao, Jingdong Liu, Jin-Kao Hao , Zhi-Ping Liu and Luonan Chen.

Download:

Source Code: pca_cmi.m ;

Datasets: DATASETS;

Validation Files: VALIDATION_FILES.

Contact:

The MATLAB codes and datasets are free to use. If you encounter any problem, please do not hesitate to contact us at zhang-xiujun@163.com or zhaoxingming@gmail.com or zpliu@sibs.ac.cn or lnchen@sibs.ac.cn. Thanks!