scholarly journals The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-10
Author(s):  
Martina Bremer ◽  
R. W. Doerge
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Statistical Power ◽  
Regulatory Networks ◽  
Real Data ◽  
Model Parameters ◽  
Expression Data ◽  
Data Set ◽  
Gene Expression Time Series ◽  
Expression Time

We present a statistical method to rank observed genes in gene expression time series experiments according to their degree of regulation in a biological process. The ranking may be used to focus on specific genes or to select meaningful subsets of genes from which gene regulatory networks can be built. Our approach is based on a state space model that incorporates hidden regulators of gene expression. Kalman (K) smoothing and maximum (M) likelihood estimation techniques are used to derive optimal estimates of the model parameters upon which a proposed regulation criterion is based. The statistical power of the proposed algorithm is investigated, and a real data set is analyzed for the purpose of identifying regulated genes in time dependent gene expression data. This statistical approach supports the concept that meaningful biological conclusions can be drawn from gene expression time series experiments by focusing on strong regulation rather than large expression values.

scholarly journals Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

2018 ◽  
Vol 19 (4) ◽  
pp. 444-465
Author(s):  
William Chad Young ◽  
Ka Yee Yeung ◽  
Adrian E Raftery
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Model ◽  
Model Parameters ◽  
Expression Data ◽  
Gene Regulatory

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.

MODELING AND ANALYSIS OF GENE EXPRESSION TIME-SERIES BASED ON CO-EXPRESSION

2005 ◽  
Vol 15 (04) ◽  
pp. 311-322 ◽  
Author(s):  
CARLA S. MÖLLER-LEVET ◽  
HUJUN YIN
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Clustering Algorithm ◽  
Real Data ◽  
Modeling And Analysis ◽  
Novel Approach ◽  
Gene Expression Time Series ◽  
Temporal Profiles ◽  
Expression Time

In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologicaly relevant clusters.

Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chi-Kan Chen
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
In Silico ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
E Coli ◽  
Reference Methods ◽  
Automatic Relevance Determination ◽  
Gene Expression Time Series ◽  
Expression Time

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.

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