A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints

Barbosa, Sara Pinto; Niebel, Bastian; Wolf, Sebastian I.; Mauch, Klaus

doi:10.1016/j.biosystems.2018.10.008

Cited by 30 publications

(19 citation statements)

References 57 publications

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“…can roughly be divided in correlation-based and information-theoretic models. These and more methods have been reviewed in detail 8,10,28,29 and hence we only provide a brief overview. Information-theoretic approaches started out with relevance networks 12 , in which the pairwise mutual information (MI) is computed between all pairs of genes.…”

Section: Classic Methods That Infer Network From Multiple Samples Ofmentioning

confidence: 99%

“…The classical approaches come from information theory and employ some kind of mutual information, or correlation and regression-based approaches (classification and theoretical background have been reviewed before 8 ). These tools have been continuously developed, but more recently the focus has also shifted more to machine learning methods such as random forest and neural networks (recent overview of methods reviewed in 10 ).…”

Section: Introductionmentioning

confidence: 99%

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Gene networks in cancer are biased by aneuploidies and sample impurities

Schubert

Colomé-Tatché

Foijer

2019

Preprint

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Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, among other data sources, gene expression measurements.Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. They are often applied to gene expression of human cancers.However, in contrast to the evaluations, these data sets often contain fewer samples, more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtaining reliable networks, and (2) even when taking these into account, we should expect between 20 and 80% of edges to be caused by copy number changes and cell mixtures rather than transcription factor regulation.

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Section: Classic Methods That Infer Network From Multiple Samples Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gene networks in cancer are biased by aneuploidies and sample impurities

Schubert

Colomé-Tatché

Foijer

2019

Preprint

View full text Add to dashboard Cite

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“…Pairs of nodes are connected by edges where one of the nodes in the pair influences (via inhibition or activation) the activity of the other [38]. The construction of GRNs is usually performed by applying statistical approaches based on inference algorithms (including Bayesian, artificial neuronal, and Boolean networks, regression-based model, ordinal differential equation and information theory) [39,40]. These methods are all aimed at extracting the probability of the reciprocal regulation for all pairs of nodes within large datasets used as input (e.g.…”

Section: Complex Neurodegeneration and Network Analysismentioning

confidence: 99%

Network Analysis for Complex Neurodegenerative Diseases

2020

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Purpose of Review Biomedicine is witnessing a paradigm shift in the way complex disorders are investigated. In particular, the need for big data interpretation has led to the development of pipelines that require the cooperation of different fields of expertise, including medicine, functional biology, informatics, mathematics and systems biology. This review sits at the crossroad of different disciplines and surveys the recent developments in the use of graph theory (in the form of network analysis) to interpret large and different datasets in the context of complex neurodegenerative diseases. It aims at a professional audience with different backgrounds. Recent Findings Biomedicine has entered the era of big data, and this is actively changing the way we approach and perform research. The increase in size and power of biomedical studies has led to the establishment of multi-centre, international working groups coordinating open access platforms for data generation, storage and analysis. Particularly, pipelines for data interpretation are under development, and network analysis is gaining momentum since it represents a versatile approach to study complex systems made of interconnected multiple players. Summary We will describe the era of big data in biomedicine and survey the major freely accessible multi-omics datasets. We will then introduce the principles of graph theory and provide examples of network analysis applied to the interpretation of complex neurodegenerative disorders. Keywords Complex neurodegeneration. Network analysis. Protein-protein interactions. GWAS loci. Omics data. Data integration. Gene co-expression This article is part of the Topical Collection on Bioinformatics

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“…The problem may be approached using much formalism, including coexpression and information-theoretical methods [13], literature mining strategies [14], and even reconstruction using the Boolean formalism itself [15]. Such approaches may become quite computationally expensive, and the reconstructed network itself must be validated against known bibliographic information.…”

Section: Construction Of the Estrogen Receptor Regulatory Networkmentioning

confidence: 99%

A Boolean Network Approach to Estrogen Transcriptional Regulation

et al. 2019

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Gene expression governs important biological processes such as the cell’s growth cycle and its response to environmental signals. Alterations of this complex network of transcriptional interactions often lead to unstable expression states and disease. Estrogen is a sex hormone known for its roles in cell proliferation. Its expression has been involved in several physiological functions such as regulating the menstrual and reproduction cycles in women. Altered expression states where estrogen levels are atypically high have been associated with an increased incidence of breast, ovarian, and cervix cancer. To better understand the implications of deregulation of the estrogen and estrogen receptor regulatory networks, in this work we generated a dynamical model of gene regulation of the estrogen receptor transcription network based on known regulatory interactions. By using an adaptation to classical Boolean Networks dynamics we identified proliferative and antiproliferative gene expression states of the network and also to identify key players that promote these altered states when perturbed. We also modeled how pairwise gene alterations may contribute to shifts between these two proliferative states and found that the coordinated subexpression of E2F1 and SMAD4 is the most important combination in terms of promoting proliferative states in the network.

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A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints

Cited by 30 publications

References 57 publications

Gene networks in cancer are biased by aneuploidies and sample impurities

Gene networks in cancer are biased by aneuploidies and sample impurities

Network Analysis for Complex Neurodegenerative Diseases

A Boolean Network Approach to Estrogen Transcriptional Regulation

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