Detection of statistically significant network changes in complex biological networks

BackgroundBiological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks.MethodsIn this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation.ResultsIn the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.ConclusionsWe show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.

[1]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[2]  R. Hamming The Unreasonable Effectiveness of Mathematics. , 1980 .

[3]  L. Hubert Assignment methods in combinatorial data analysis , 1986 .

[4]  Edward R. Scheinerman,et al.  Fractional isomorphism of graphs , 1994, Discret. Math..

[5]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[6]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[7]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[8]  Richard Mankiewicz The Story of Mathematics , 2001 .

[9]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Benno Schwikowski,et al.  Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.

[11]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[13]  Thomas Lengauer,et al.  ROCR: visualizing classifier performance in R , 2005, Bioinform..

[14]  Don R. Hush,et al.  A Classification Framework for Anomaly Detection , 2005, J. Mach. Learn. Res..

[15]  Panos M. Pardalos,et al.  Statistical analysis of financial networks , 2005, Comput. Stat. Data Anal..

[16]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[18]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[19]  George Kesidis An Introduction to Communication Network Analysis , 2007 .

[20]  Kay W. Axhausen,et al.  Graph-Theoretical Analysis of the Swiss Road and Railway Networks Over Time , 2009 .

[21]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[22]  S. Horvath,et al.  Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.

[23]  George Kesidis An Introduction to Communication Network Analysis: Kesidis/An Introduction , 2007 .

[24]  Serban Nacu,et al.  Gene expression network analysis and applications to immunology , 2007, Bioinform..

[25]  Sing-Hoi Sze,et al.  Path Matching and Graph Matching in Biological Networks , 2007, J. Comput. Biol..

[26]  Tobias Müller,et al.  Identifying functional modules in protein–protein interaction networks: an integrated exact approach , 2008, ISMB.

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

[28]  M. Dehmer,et al.  Analysis of Microarray Data: A Network-Based Approach , 2008 .

[29]  M. Dehmer,et al.  Comprar Analysis of Microarray Data: A Network-Based Approach | Matthias Dehmer | 9783527318223 | Wiley , 2008 .

[30]  Günce Keziban Orman,et al.  A Comparison of Community Detection Algorithms on Artificial Networks , 2009, Discovery Science.

[31]  Christina Backes,et al.  A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis , 2009, Bioinform..

[32]  Susmita Datta,et al.  A statistical framework for differential network analysis from microarray data , 2010, BMC Bioinformatics.

[33]  R. Wilson,et al.  Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. , 2010, Cancer cell.

[34]  J. Uhm,et al.  The transcriptional network for mesenchymal transformation of brain tumours , 2010 .

[35]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations , 2010 .

[36]  Martin Rosvall,et al.  Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems , 2010, PloS one.

[37]  Gabriele Sales,et al.  parmigene - a parallel R package for mutual information estimation and gene network reconstruction , 2011, Bioinform..

[38]  Kurt Mehlhorn,et al.  Weisfeiler-Lehman Graph Kernels , 2011, J. Mach. Learn. Res..

[39]  Erwin G. Van Meir,et al.  Overexpression of MBD2 in glioblastoma maintains epigenetic silencing and inhibits the antiangiogenic function of the tumor suppressor gene BAI1. , 2011, Cancer research.

[40]  S. Ambs,et al.  Interactions among genes, tumor biology and the environment in cancer health disparities: examining the evidence on a national and global scale. , 2011, Carcinogenesis.

[41]  Koray Kavukcuoglu,et al.  A Binary Classification Framework for Two-Stage Multiple Kernel Learning , 2012, ICML.

[42]  A. Viale,et al.  IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype , 2012, Nature.

[43]  Gang Wu,et al.  MIMO: an efficient tool for molecular interaction maps overlap , 2013, BMC Bioinformatics.

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

[45]  Johan A. K. Suykens,et al.  Self-tuned kernel spectral clustering for large scale networks , 2013, 2013 IEEE International Conference on Big Data.

[46]  Chad L. Myers,et al.  Comparison of Profile Similarity Measures for Genetic Interaction Networks , 2013, PloS one.

[47]  Athanasios V. Vasilakos,et al.  Understanding user behavior in online social networks: a survey , 2013, IEEE Communications Magazine.

[48]  Johan A. K. Suykens,et al.  Kernel Spectral Clustering for Big Data Networks , 2013, Entropy.

[49]  Andrew E. Teschendorff,et al.  An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways , 2013, Scientific Reports.

[50]  Andrew E. Sloan,et al.  Molecular Subtypes of Glioblastoma Are Relevant to Lower Grade Glioma , 2014, PloS one.

[51]  R. Abounader,et al.  Regulatory factor X1 is a new tumor suppressive transcription factor that acts via direct downregulation of CD44 in glioblastoma. , 2014, Neuro-oncology.

[52]  Johan A. K. Suykens,et al.  Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks , 2014, PloS one.

[53]  Antonella Santone,et al.  De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods. , 2014, Methods.

[54]  Andrew E. Teschendorff,et al.  A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control , 2014, Bioinform..

[55]  Alexander R. Pico,et al.  Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors. , 2015, The New England journal of medicine.

[56]  F. Ducray,et al.  CIC inactivating mutations identify aggressive subset of 1p19q codeleted gliomas , 2015, Annals of neurology.

[57]  Giovanni Montana,et al.  Differential analysis of biological networks , 2015, BMC Bioinformatics.

[58]  Kim-Anh Do,et al.  DINGO: differential network analysis in genomics , 2015, Bioinform..

[59]  Steven J. M. Jones,et al.  Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. , 2015, The New England journal of medicine.

[60]  A. Vortmeyer,et al.  Integrated genomic characterization of IDH1-mutant glioma malignant progression , 2015, Nature Genetics.

[61]  Steven J. M. Jones,et al.  Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma , 2016, Cell.

[62]  T. Ahern,et al.  Colorectal cancer, comorbidity, and risk of venous thromboembolism: assessment of biological interactions in a Danish nationwide cohort , 2015, British Journal of Cancer.