Inference of gene regulatory networks from genetic perturbations with linear regression model

It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by S...

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Bibliographic Details
Published in:PloS one 2013-12, Vol.8 (12), p.e83263-e83263
Main Authors: Dong, Zijian, Song, Tiecheng, Yuan, Chuang
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Bayes Theorem
Bayesian analysis
Bioinformatics
Biology
Comparative analysis
Computer Science
Computer Simulation
Gene expression
Gene Expression Regulation, Fungal
Gene Regulatory Networks
Genes
Genetics
Genomics
Information science
Iterative methods
Linear Models
Mathematics
Methods
Models, Genetic
Networks
Parameter estimation
Regression analysis
Regression models
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae Proteins - genetics
Statistical inference
Yeast
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Summary:It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by Structural Equation Modeling (SEM). In this paper, a linear regression (LR) model is formulated based on the SEM, and a novel iterative scheme using Bayesian inference is proposed to estimate the parameters of the LR model (LRBI). Comparative evaluations of LRBI with other two algorithms, the Adaptive Lasso (AL-Based) and the Sparsity-aware Maximum Likelihood (SML), are also presented. Simulations show that LRBI has significantly better performance than AL-Based, and overperforms SML in terms of power of detection. Applying the LRBI algorithm to experimental data, we inferred the interactions in a network of 35 yeast genes. An open-source program of the LRBI algorithm is freely available upon request.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0083263