Faculty Opinions recommendation of Pathway network inference from gene expression data.

AbstractBackgroundGene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements.ResultsIn this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks.ConclusionNetwork reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.

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

Statistical Modelling ◽

10.1177/1471082x18776577 ◽

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.

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Network Inference from Gene Expression Data with Distance Correlation and Network Topology Centrality

Algorithms ◽

10.3390/a14020061 ◽

2021 ◽

Vol 14 (2) ◽

pp. 61

Author(s):

Kuan Liu ◽

Haiyuan Liu ◽

Dongyan Sun ◽

Lei Zhang

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Network Topology ◽

Regulatory Networks ◽

Network Inference ◽

Expression Data ◽

Distance Correlation ◽

Non Linear ◽

Gene Regulatory

The reconstruction of gene regulatory networks based on gene expression data can effectively uncover regulatory relationships between genes and provide a deeper understanding of biological control processes. Non-linear dependence is a common problem in the regulatory mechanisms of gene regulatory networks. Various methods based on information theory have been developed to infer networks. However, the methods have introduced many redundant regulatory relationships in the network inference process. A recent measurement method called distance correlation has, in many cases, shown strong and computationally efficient non-linear correlations. In this paper, we propose a novel regulatory network inference method called the distance-correlation and network topology centrality network (DCNTC) method. The method is based on and extends the Local Density Measurement of Network Node Centrality (LDCNET) algorithm, which has the same choice of network centrality ranking as the LDCNET algorithm, but uses a simpler and more efficient distance correlation measure of association between genes. In this work, we integrate distance correlation and network topological centrality into the reasoning about the structure of gene regulatory networks. We will select optimal thresholds based on the characteristics of the distribution of each gene pair in relation to distance correlation. Experiments were carried out on four network datasets and their performance was compared.

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