Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL

Reconstruction of networks from high-throughput expression data is an important tool to identify new regulatory relations. Given that nonlinear and complex relations exist between biological units, methods that can utilize nonlinear dependencies may yield insights that are not provided by methods using linear associations alone. We have previously developed a distance to measure predictive nonlinear relations, the Distance based on Conditional Ordered List (DCOL), which is sensitive and computationally efficient on large matrices. In this study, we explore the utility of DCOL in the reconstruction of networks, by combining it with local false discovery rate (lfdr)–based inference. We demonstrate in simulations that the new method named nlnet is effective in recovering hidden nonlinear modules. We also demonstrate its utility using a single cell RNA seq dataset. The method is available as an R package at https://cran.r-project.org/web/packages/nlnet.

[1]  V.P. Roychowdhury,et al.  An Information Theoretic Exploratory Method for Learning Patterns of Conditional Gene Coexpression from Microarray Data , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  HighWire Press.  The journal of neuroscience : the official journal of the Society for Neuroscience. , 1981 .

[3]  Xiaohui Chen,et al.  BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network. , 2006, Bioinformatics.

[4]  S. Xuan,et al.  Insulin-Like Growth Factor Signaling Regulates the Timing of Sensory Cell Differentiation in the Mouse Cochlea , 2011, The Journal of Neuroscience.

[5]  Chun-li Zhang,et al.  Crosstalk between KLF4 and STAT3 regulates axon regeneration , 2013, Nature Communications.

[6]  S. Jackson,et al.  Gene Network Reconstruction by Integration of Prior Biological Knowledge , 2015, G3: Genes, Genomes, Genetics.

[7]  YuTianwei,et al.  Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences , 2013 .

[8]  Samik Ghosh,et al.  Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks , 2013, PLoS Comput. Biol..

[9]  Jean-Jacques Daudin,et al.  Correction: Determination of the differentially expressed genes in microarray experiments using local FDR , 2005, BMC Bioinformatics.

[10]  Min Chen,et al.  Comparing Statistical Methods for Constructing Large Scale Gene Networks , 2012, PloS one.

[11]  Robert Gentleman,et al.  Using GOstats to test gene lists for GO term association , 2007, Bioinform..

[12]  Xiaodong Wang,et al.  Gene Regulatory Network Reconstruction Using Conditional Mutual Information , 2008, EURASIP J. Bioinform. Syst. Biol..

[13]  Hongzhe Li,et al.  A penalized likelihood approach for bivariate conditional normal models for dynamic co-expression analysis. , 2011, Biometrics.

[14]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[15]  G. Siuzdak,et al.  Innovation: Metabolomics: the apogee of the omics trilogy , 2012, Nature Reviews Molecular Cell Biology.

[16]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[17]  Marc Garbey,et al.  Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information , 2013, Nucleic acids research.

[18]  Tianwei Yu,et al.  K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data , 2015, BioMed research international.

[19]  M. Cugmas,et al.  On comparing partitions , 2015 .

[20]  Y. León,et al.  AKT Signaling Mediates IGF-I Survival Actions on Otic Neural Progenitors , 2012, PloS one.

[21]  Tianwei Yu,et al.  Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[22]  Aviv Regev,et al.  Comparative analysis of gene regulatory networks: from network reconstruction to evolution. , 2015, Annual review of cell and developmental biology.

[23]  Michael C. Kelly,et al.  Single-cell RNA-Seq resolves cellular complexity in sensory organs from the neonatal inner ear , 2015, Nature Communications.

[24]  A. Rolfs,et al.  Expression patterns of the ADAMs in early developing chicken cochlea , 2013, Development, growth & differentiation.

[25]  A. Griffith,et al.  Col11a1 and col11a2 mRNA expression in the developing mouse Cochlea: Implications for the correlation of hearing loss phenotype with mutant type XI collagen genotype , 2004, Acta oto-laryngologica.

[26]  Ker-Chau Li,et al.  Genome-wide coexpression dynamics: Theory and application , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[27]  R. Tibshirani,et al.  Empirical bayes methods and false discovery rates for microarrays , 2002, Genetic epidemiology.

[28]  Chun-li Zhang,et al.  Role of Krüppel-Like Factor 4 in Neurogenesis and Radial Neuronal Migration in the Developing Cerebral Cortex , 2012, Molecular and Cellular Biology.

[29]  Thomas Schiex,et al.  Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis , 2011, PloS one.

[30]  A. Kiernan,et al.  The Notch Ligand JAG1 Is Required for Sensory Progenitor Development in the Mammalian Inner Ear , 2006, PLoS genetics.

[31]  Simon S. Gao,et al.  The Candidate Splicing Factor Sfswap Regulates Growth and Patterning of Inner Ear Sensory Organs , 2014, PLoS genetics.

[32]  J. Ross,et al.  MIDER: Network Inference with Mutual Information Distance and Entropy Reduction , 2014, PloS one.

[33]  Pei Wang,et al.  Partial Correlation Estimation by Joint Sparse Regression Models , 2008, Journal of the American Statistical Association.

[34]  Korbinian Strimmer,et al.  An empirical Bayes approach to inferring large-scale gene association networks , 2005, Bioinform..

[35]  Hong Wang,et al.  Regional expression of the ADAMs in developing chicken cochlea , 2010, Developmental dynamics : an official publication of the American Association of Anatomists.

[36]  G. Wagner,et al.  The road to modularity , 2007, Nature Reviews Genetics.

[37]  T. Ohyama,et al.  Fgf10 is required for specification of non-sensory regions of the cochlear epithelium. , 2015, Developmental biology.

[38]  Korbinian Strimmer,et al.  fdrtool: a versatile R package for estimating local and tail area-based false discovery rates , 2008, Bioinform..

[39]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[40]  Luonan Chen,et al.  Part mutual information for quantifying direct associations in networks , 2016, Proceedings of the National Academy of Sciences.

[41]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  A. Brazma,et al.  Reuse of public genome-wide gene expression data , 2012, Nature Reviews Genetics.

[43]  M. Kelley,et al.  Rbpj regulates development of prosensory cells in the mammalian inner ear. , 2011, Developmental biology.

[44]  Michelangelo Ceci,et al.  Semi-Supervised Multi-View Learning for Gene Network Reconstruction , 2015, SEBD.

[45]  Robert Tibshirani,et al.  Boolean implication networks derived from large scale, whole genome microarray datasets , 2008, Genome Biology.

[46]  Yun Bai,et al.  Network-based modular latent structure analysis , 2014, BMC Bioinformatics.

[47]  K. Beisel,et al.  Expression and function of FGF10 in mammalian inner ear development , 2003, Developmental dynamics : an official publication of the American Association of Anatomists.

[48]  Rudiyanto Gunawan,et al.  Assessment of Network Inference Methods: How to Cope with an Underdetermined Problem , 2014, PloS one.

[49]  Teresa M. Przytycka,et al.  Chapter 5: Network Biology Approach to Complex Diseases , 2012, PLoS Comput. Biol..

[50]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.