Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

• Published in: Journal of applied statistics Vol. 43; no. 5; pp. 893 - 913
• Main Authors: Aghdam, Rosa, Ganjali, Mojtaba, Niloofar, Parisa, Eslahchi, Changiz
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.04.2016; Taylor & Francis Ltd
• Subjects: Algorithms; Bayesian analysis; Bayesian networks; Bioinformatics; Consistency; Genes; Government regulations; inferring the structure of gene regulatory networks; Mathematical analysis; Mean square errors; Methods; missing value imputation; MIT score; mutual information test; Networks; PC algorithm; Tasks
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2015.1079307

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA-CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA-CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA-CMI-OI, for inferring GRNs. After imputation of missing data, the performances of PCA-CMI and PCA-CMI-OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA-CMI-OI algorithm outperform other imputation methods and PCA-CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.