Gene Network Construction Tool Kit @ QBRC

Algorithms for gene network construction



Extended Sparse PArtial Correlation Estimation method

ESPACE is an R package to construct gene regulatory networks using hub gene information.

Donghyeon Yu, Johan Lim, Xinlei Wang, Faming Liang, and Guanghua Xiao. "Enhanced construction of gene regulatory networks using hub gene information." BMC bioinformatics 18.1 (2017): 186.
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Ensemble-Based Network Aggregation

ENA is an ensemble-based network aggregation method that can aggregate networks produced by various reconstruction methods into a single network that is more accurate than the network inferred by any individual method.

Rui Zhong, Jeffrey D. Allen, Guanghua Xiao, and Yang Xie. "Ensemble-based network aggregation improves the accuracy of gene network reconstruction." PloS one 9.11 (2014): e106319.
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GeneNet is an R package for learning high-dimensional dependency networks from genomic data (e.g. gene association networks). The current version of GeneNet also allows users to assign putative directions to edges in the network.

Schäfer, Juliane, and Korbinian Strimmer. "A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics." Statistical applications in genetics and molecular biology 4.1 (2005): 32.
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Coordinate Descent Algorithms for Lasso Penalized L1, L2, and Logistic Regression

Coordinate Descent Algorithms for Lasso Penalized Regression.

Wu, Tong Tong, and Kenneth Lange. "Coordinate descent algorithms for lasso penalized regression." The Annals of Applied Statistics (2008): 224-244.
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Graphical Lasso

Graphical lasso estimates a sparse inverse covariance matrix using a lasso (L1) penalty. It can be used for estimating a sparse undirected graph.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "Sparse inverse covariance estimation with the graphical lasso." Biostatistics 9.3 (2008): 432-441.
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Path Consistency Algorithm based on Conditional Mutual Information

PCA-CMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method based on a path consistency algorithm and conditional mutual information, which considers 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.

Zhang, Xiujun, Xing-Ming Zhao, Kun He, Le Lu, Yongwei Cao, Jingdong Liu, Jin-Kao Hao, Zhi-Ping Liu, and Luonan Chen. "Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information." Bioinformatics 28.1 (2011): 98-104.
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Conditional mutual inclusive information-based network inference

CMI2NI is a software for inferring gene regulatory networks from gene expression data. It is a novel method using a new proposed concept of Conditional Mutual Inclusive Information (CMI2) which can accurately measure direct dependences between genes. Given the small sample sizes of gene expression data, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the dependence or regulation strength between genes. CMI2NI provides a useful tool to infer gene regulatory networks.

Zhang, Xiujun, Juan Zhao, Jin-Kao Hao, Xing-Ming Zhao, and Luonan Chen. "Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks." Nucleic acids research 43.5 (2014): e31-e31.
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Space PArtial Correlation Estimation

Partial correlation estimation with joint sparse regression model.

Peng, Jie, Pei Wang, Nengfeng Zhou, and Ji Zhu. "Partial correlation estimation by joint sparse regression models." Journal of the American Statistical Association 104.486 (2009): 735-746.
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Bayesian Graphical Lasso

Implements a data-augmented block Gibbs sampler for simulating the posterior distribution of concentration matrices for specifying the topology and parameterization of a Gaussian Graphical Model (GGM). This sampler was originally proposed in Wang (2012).

Wang, Hao. "Bayesian graphical lasso models and efficient posterior computation." Bayesian Analysis 7.4 (2012): 867-886.