Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections.
Stat Appl Genet Mol Biol
; 20(4-6): 121-143, 2021 12 28.
Article
em En
| MEDLINE
| ID: mdl-34963205
ABSTRACT
The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Escherichia coli
/
Redes Reguladoras de Genes
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Stat Appl Genet Mol Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2021
Tipo de documento:
Article