A Scalable Algorithm for Structure Identification of Complex Gene Regulatory Network from ...

A Scalable Algorithm for Structure Identification of Complex Gene Regulatory Network from ...

14 Pages · 2016 · 1.04 MB ·

erties (i.e., sparsity and hub gene structure) of complex networks in the roles in numerous biological processes by modulating the expression levels 

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A Scalable Algorithm for Structure Identi\fcation of Complex Gene Regulatory Network from Temporal Expression Data Shupeng Gui 1 , Rui Chen 2 , Liang Wu 3 , Ji Liu 1,4* , and Hongyu Miao 3* 1 Department of Computer Science, University of Rochester, Rochester, 14620, USA 2 Molecular and Human Genetics, Baylor Col lege of Medicine, Houston, 77030, USA 3 Department of Biostatistics, School of Public Health, UTHealth, Houston, 77030, USA 4 Goergen Institute for Data Science, University of Rochester, Rochester, 14620, USA September 2, 2016 Abstract Motivation: Gene regulatory interactions are of fundamental importance to various biological functions and processes However, only a few previous computational studies have claimed success in revealing genomewide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human Moreover, recent work suggests that these methods still su er from the curse of dimension ality if network size increases to 100 or higher Result: We present a novel scalable algorithm for identifying genomewide regulatory network struc tures The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 10 4 , and is thus readily applicable to largescale complex networks Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological prop erties (ie, sparsity and hub gene structure) of complex networks in the regularized formulation We also illustrate the application of our algorithm in practice using the timecourse expression data from an in uenza infection study in respiratory epithelial cells Availability and Implementation: The algorithm described in this article is implemented in MATLAB r The source code is freely available from https://githubcom/Hongyu Miao/DMIgit Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available online 1 Introduction Gene regulatory network (GRN), consisting of multiple regulators and their target molecules, plays critical roles in numerous biological processes by modulating the expression levels of RNAs and proteins [26] While remarkable successes in dissecting single genes that are responsible for certain biological functions, behavior or diseases have been achieved over the past few decades, it has been increasingly recognized that elucidating gene functions and interactions in the context of networks becomes more and more important to gain novel insight into mechanisms, e ects and interventions of molecular, cellular or organlevel biological processes [4, 5, 27] Clearly, one of the prerequisites for investigators to harvest the bene\fts of such systematic network approaches is whether the structures of gene regulatory networks can be accurately revealed from experimental data Modern highthroughput experimental technologies such as next generation sequencing [40]can gener ate timecourse data at a much more a ordable cost [10], thus provide unprecedented opportunities for 1 CCBYNCND 40 International license It is made available under a was not peerreviewed) is the author/funder, who has granted bioRxiv a \ license to display the preprint in perpetuity The copyright holder for this preprint (which http://dxdoiorg/101101/073296 doi: bioRxiv preprint first posted online Sep 4, 2016; researchers to systematically investigate the temporal patterns of gene expressions and infer gene regula tory relationships However, two wellknown ma jor obstacles have signi\fcantly hampered our ability to interrogate such data for novel scienti\fc \fndings First, limited by resources or technical and ethic issues, the sampling frequency of timecourse gene expression pro\fling data is low (eg, most of the timecourse GEO datasets[14]have less than 6 time points), which renders the sample size far less than the number of unknown parameters in the context of GRN structure identi\fcation Targeting at such scenarios, it is of signi\fcant importance to borrow information from additional sources (eg, previous biological knowledge) Second, considering the fact that for complex eukaryotes like human, the number of proteincoding genes is approximately 19,000[15]so the problem dimension is ultrahigh(ie, tens of thousands or even millions unknown parameters are involved) The development of novel and more ecient algorithms that can scale to such highdimensional networks is still necessary A number of modeling and computational approaches have been developed for gene network structure identi\fcation[35], including information theory method[eg

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