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NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks

Yijie Wang, Dong-Yeon Cho, Hangnoh Lee, Brian Oliver, Teresa M Przytycka
doi: https://doi.org/10.1101/126664
Yijie Wang
1National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda MD 20894, USA
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  • For correspondence: przytyck@ncbi.nlm.nih.gov
Dong-Yeon Cho
1National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda MD 20894, USA
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  • For correspondence: przytyck@ncbi.nlm.nih.gov
Hangnoh Lee
2Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, 50 South Drive, Bethesda, MD 20892, USA
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Brian Oliver
2Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, 50 South Drive, Bethesda, MD 20892, USA
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Teresa M Przytycka
1National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda MD 20894, USA
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  • For correspondence: przytyck@ncbi.nlm.nih.gov
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Abstract

Understanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is the ability to infer the transcription factor (TF)-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Due to lack of the biological context specificity, these context-agnostic networks may not provide insight for revealing the precise actions of genes for a specific biological system under concern. Collecting multitude of features required for a reliable construction of GRNs such as physical features (TF binding, chromatin accessibility) and functional features (correlation of expression or chromatin patterns) for every context of interest is costly. Therefore we need methods that is able to utilize the knowledge about a context-agnostic network (or a network constructed in a related context) for construction of a context specific regulatory network.

To address this challenge we developed a computational approach that utilizes expression data obtained in a specific biological context such as a particular development stage, sex, tissue type and a GRN constructed in a different but related context (alternatively an incomplete or a noisy network for the same context) to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis (NCA) that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, which is non-convex and non-smooth, we provide general mathematical framework allowing use of the recently proposed Proximal Alternative Linearized Maximization technique and prove that our formulation has the properties required for convergence.

We tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from 99 hemizygotic lines of the Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method.

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Posted April 18, 2017.
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NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks
Yijie Wang, Dong-Yeon Cho, Hangnoh Lee, Brian Oliver, Teresa M Przytycka
bioRxiv 126664; doi: https://doi.org/10.1101/126664
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NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks
Yijie Wang, Dong-Yeon Cho, Hangnoh Lee, Brian Oliver, Teresa M Przytycka
bioRxiv 126664; doi: https://doi.org/10.1101/126664

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