Analyzing network data in biology

Analyzing Network Data in Biology and Medicine An Interdisciplinary Textbook for Biological, Medical, and Computational Scientists The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis The challenge is to detect patterns that provide a better understanding of the data However, this is not a straightforward task because of the size of the datasets and the computer power required for the analysis The solution is to devise methods for approximately answering the questions posed and these methods will vary depending on the datasets under scrutiny This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical datasets, using a wide range of real-life examples Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine Nataˇsa Prˇzulj is Professor of Biomedical Data Science at University College London (UCL) and an ICREA Research Professor at Barcelona Supercomputing Center She has been an elected academician of The Academy of Europe, Academia Europaea, since 2017 and is a Fellow of the British Computer Society (BCS) She is recognized for designing methods to mine large real-world molecular network datasets and for extending and using machine learning methods for integration of heterogeneous biomedical and molecular data, applied to advancing biological and medical knowledge She received two prestigious European Research Council (ERC) research grants, Starting (2012–2017) and Consolidator (2018–2023), and USA National Science Foundation (NSF) grants among others She is a recipient of the BCS Roger Needham Award for 2014 She was previously an Associate Professor (Reader, 2012–2016) and Assistant Professor (Lecturer, 2009–2012) in the Department of Computing at Imperial College London and an Assistant Professor in the Computer Science Department at University of California Irvine (2005–2009) She obtained a PhD in Computer Science from University of Toronto in 2005 Analyzing Network Data in Biology and Medicine An Interdisciplinary Textbook for Biological, Medical, and Computational Scientists Edited and authored by N ATA Sˇ A P R Zˇ U L J Professor of Biomedical Data Science, Computer Science Department, University College London ICREA Research Professor at Barcelona Supercomputing Center University Printing House, Cambridge CB2 8BS, United Kingdom One Liberty Plaza, 20th Floor, New York, NY 10006, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia 314-321, 3rd Floor, Plot 3, Splendor Forum, Jasola District Centre, New Delhi – 110025, India 79 Anson Road, #06–04/06, Singapore 079906 Cambridge University Press is part of the University of Cambridge It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning, and research at the highest international levels of excellence www.cambridge.org Information on this title: www.cambridge.org/bionetworks DOI: 10.1017/9781108377706 © Cambridge University Press 2019 This publication is in copyright Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published 2019 Printed and bound in Great Britain by Clays Ltd, Elcograf S.p.A A catalogue record for this publication is available from the British Library Library of Congress Cataloging-in-Publication Data Names: Prˇzulj, Nataˇsa, editor Title: Analyzing network data in biology and medicine : an interdisciplinary textbook for biological, medical and computational scientists / edited by Nataˇsa Prˇzulj, University College London Description: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2019 | Includes bibliographical references Identifiers: LCCN 2018034214 | ISBN 9781108432238 (hardback : alk paper) Subjects: LCSH: Medical informatics–Data processing | Bioinformatics Classification: LCC R858 A469 2019 | DDC 610.285–dc23 LC record available at https://lccn.loc.gov/2018034214 ISBN 978-1-108-43223-8 Paperback Additional resources for this publication at www.cambridge.org/bionetworks Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication and does not guarantee that any content on such websites is, or will remain, accurate or appropriate To my loving family: Cvita, Bogdan, Nina, Sofia, and Laurentino And to my best friend, Vesna Contents List of Contributors Preface xiii page ix From Genetic Data to Medicine: From DNA Samples to Disease Risk Prediction in Personalized Genetic Tests ˇ LUIS G LEAL , ROK KO Sˇ IR , AND NATA Sˇ A PR ZULJ Epigenetic Data and Disease 63 ´ , NICOL AS ´ RODRIGO GONZALEZ - BARRIOS , MARISOL SALGADO - ALBARR AN ALCARAZ , CRISTIAN ARRIAGA - CANON , LISSANIA GUERRA - CALDERAS , LAURA CONTRERAS - ESPINOSA , AND ERNESTO SOTO - REYES Introduction to Graph and Network Theory ˇ THOMAS GAUDELET AND NATA Sˇ A PR ZULJ Protein–Protein Interaction Data, their Quality, and Major Public Databases 151 ANNE - CHRISTIN HAUSCHILD , CHIARA PASTRELLO , MAX KOTLYAR , 111 AND IGOR JURISICA Graphlets in Network Science and Computational Biology ´ KHALIQUE NEWAZ AND TIJANA MILENKOVI C Unsupervised Learning: Cluster Analysis ă RICHARD R OTTGER Machine Learning for Data Integration in Cancer Precision Medicine: Matrix Factorization Approaches 286 ă L MALOD - DOGNIN , SAM F L WINDELS , AND NO E ˇ NATA Sˇ A PR ZULJ Machine Learning for Biomarker Discovery: Significant Pattern Mining ´ FELIPE LLINARES - L OPEZ AND KARSTEN BORGWARDT Network Alignment 369 ă L MALOD - DOGNIN AND NATA Sˇ A PR ZULJ ˇ NO E 10 Network Medicine 193 241 313 414 ¨ PISANU BUPHAMALAI , MICHAEL CALDERA , FELIX M ULLER , ă AND J ORG MENCHE 11 Elucidating Genotype-to-Phenotype Relationships via Analyses of Human Tissue Interactomes 459 IDAN HEKSELMAN , MORAN SHARON , OMER BASHA , AND ESTI YEGER - LOTEM vii viii CONTENTS 12 Network Neuroscience 490 ALBERTO CACCIOLA , ALESSANDRO MUSCOLONI , AND CARLO VITTORIO CANNISTRACI 13 Cytoscape: A Tool for Analyzing and Visualizing Network Data JOHN H MORRIS 533 14 Analysis of the Signatures of Cancer Stem Cells in Malignant Tumors Using Protein Interactomes and the STRING Database 593 ´ , MARKO KLOBU CAR ˇ , DOLORES KUZELJ , NATA Sˇ A PR ZULJ ˇ KRE Sˇ IMIR PAVELI C , ´ PAVELI C´ SANDRA KRALJEVI C Index 621 618 ´ , KLOBUC ˇ AR, KUZELJ, ET AL PAV E L I C [77] Franceschini A, Szklarczyk D, Frankild S, et al STRING v9.1: Protein–protein interaction networks, with increased coverage and integration Nucleic Acids 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Touhami-Carrier M, Thomaidis A, Legrand C Thrombospondin-1 (TSP-1) and TSP-1-derived heparin-binding peptides induce promyelocytic leukemia cell differentiation and apoptosis Anticancer Res, 2005;25(2A):757–764 [119] Seidler DG, Goldoni S, Agnew C, et al Decorin protein core inhibits in vivo cancer growth and metabolism by hindering epidermal growth factor receptor function and triggering apoptosis via caspase-3 activation Journal of Biological Chemistry, 2006;281(36):26408–26418 [120] Suhovskih AV, Mostovich LA, Kunin IS, et al Proteoglycan expression in normal human prostate tissue and prostate cancer ISRN Oncology, 2013;2013:680136 [121] Harisi R, Dudas J, Nagy-Olah J, et al Extracellular matrix induces doxorubicin-resistance in human osteosarcoma cells by suppression of p53 function Cancer Biology and Therapy, 2007;6(8):1240–1246 Index Locators in bold refer to tables; those in italic to figures adjacency lists, 130, 130 matrices, 129, 129–130, 291, 294, 295, 491–492, 499 matrix visualizations, 541, 544 Affymetrix SNP microarrays, 10, 12–14, 15 aging, 220–223 PPI network analysis, 194, 199, 212, 220, 221–223 algorithms; see also network alignment alignment, 23, 24 clustering, 242, 256–270, 549, 585 force-directed, 549–550, 574, 575 FUSE, 394–397, 395, 397 gene prioritization, 437 genotypes, 10, 16, 14–18, 21–27 graph search, 130–131 hierarchical clustering, 257, 264 Hungarian, 128, 375 Isorank, 200, 217, 382–384, 383 layouts, 549 machine learning, 33–39, 601 mapping, 372 optimization, 374 orbit counting, 227, 228 pattern mining, 329, 330, 340, 361 permutation testing, 347 AlignMCL, pairwise network alignment, 382 alignment algorithms, 23, 24 graphs, 374 network see network alignment strategy, 216–217 alignment scoring schemes, 375 agreements and trade-offs, 378, 378–379 F-score, 376 global network alignments, 376–378 local network alignments, 375–376 multiple network alignments, 392 symmetric sub-structure score, 377 AlignNemo, pairwise network alignment, 381–382 Alzheimer’s disease, 517–518 animations, node-link diagrams, 551 annotations databases, 166, 165–167, 170 PPI networks, 171, 370, 371 apriori property, pattern mining, 331 articulation points, 168, 177 assortative networks, 135 asthma, 441, 445, 447, 474 asymmetric interactions, 113, 118 automorphism orbits, 203, 384, 384, 385 edge, 204 node, 205 average closeness centrality, 511 average clustering coefficient, 135, 511 average edge betweenness centrality, 512 average efficiency, 511 average node betweenness centrality, 511–512 average node degree, 510 average radiality, 512 Bayesian networks, 39, 41–42, 292, 291–293 betweenness centrality, 136, 168, 178 biases microarrays, 75, 97 PPI datasets, 158–159, 438 BioFabric, 542, 545 bioinformatics Hi-C analysis, 82 lncRNAs analysis, 88–89 protein-DNA interactions, 600 biological heterogeneity, 159–160, 288–290 biological interpretation, disease modules, 445–448 biological networks (BN), 111, 167, 193–194, 256, 271–272; see also molecular networks; visualizing networks data integration, 229 metabolic networks, 113, 418 biomarkers cancer precision medicine, 287 cancer stem cells, 605, 606, 603–612 covariate factors, 349–359 discovery, 313–314, 359–361 exercises, 362–364 ovarian cancer, 608, 609, 608–610 pattern mining, 315–328 621 622 INDEX biomarkers (cont.) prostate cancer, 606–608 statistical redundancy, 341–349 Tarone’s method for discovery, 329–341 bipartite graphs, 113, 123, 123, 375, 374–375 bisulfite based arrays, DNA methylation, 72–73 bisulfite conversion, 72–73 BLUEPRINT epigenome, 79, 96 Bonferroni correction, 326–327 Boolean variables, 254–255 brain anatomy, 515, 516 connectomes, 490–491, 499, 500, 514 geometry, 492 MRI scanning, 492–493 topology, 492 brain networks disorders, 517–519 functional, 499–503 structural, 492–499 tools for analysis, 505–506 BRCA1 gene, 476, 480 breadth first search (BFS), 130, 131 breast cancer, 79, 96, 473–474, 560, 572 cancer; see also tumors BRCA1 gene, 476, 480 breast, 79, 96, 473–474, 560, 572 gene mutations, 175–179, 177, 300, 462, 548–549 genome atlas, 22, 95, 462, 479–480, 577 precision medicine, 287 prostate, 606–608 proteins, 223–224 stem cells, 595–598, 603–612, 605, 606 CART (classification and regression tree classifiers), 37 categorical values, proximity calculation, 256 causal variants, 466–470, 473 cell signaling networks, 112, 113 centrality average closeness, 511 average edge betweenness, 512 average node betweenness, 511–512 betweenness, 136, 168, 178 closeness, 135, 136, 139 eccentricity, 135, 137 eigenvector, 135, 137 GDV, 207, 221, 225 subgraphs, 137 characteristic path length, 511 chemokines, 597, 609 ChIA-PET technology, chromatin conformation, 81, 83 ChIP see chromatin immunoprecipitation chromatin, 77 conformation, 67, 81–83 higher order organization, 80–87 modifications, 70, 315 topological associated domains, 86, 86–87 chromatin immunoprecipitation (ChIP), 69, 77–78, 418–419 data analysis, 79, 78–79 differential binding, 80 chromosome territories, 70 cis-acting, 67 classification and regression tree classifiers (CARTs), 37 cliques (complete subgraphs), 120, 170, 210, 265, 269 clonal theory, 598 closeness centrality, 135, 136, 139 cluster analysis, 241–243, 277 definitions, 243–245 exercises, 277–280 preprocessing, 246–251 proximity calculation, 252–256 workflow, 245, 245–246 cluster evaluation external, 271–272 internal, 272–274 optimization strategies, 275–277 validity indices, 270, 271 clustering algorithms, 242, 256–270, 549, 585 coefficients, 170 data formats, 244–245 networks, 209, 210, 209–210 partitional, 243 types, 243 clusters, number of, 275, 276 Cochran-Mantel-Haenszel (CMH) test, 351–354 minimum attainable P-value, 354–355, 356, 358 pruning condition, 355–359 co-expression networks, 419–420 colored graphlets, 195, 200–202 columns, cortex, 491 comparative genomics, 156 complex diseases, network approaches, 473–474 computational biology workflows, 175–179 computational complexity, 117–118, 128, 197, 200, 226, 297, 338 computational methods, PPI, 222–223, 601 INDEX conditional probabilities, 41–42, 291–293, 320 confounding effect, pattern mining, 350, 351 connectedness graph theory, 122, 122 subgraphs, 119 connectomes, brain, 490–491, 499, 500, 514 contagious diseases spread, 423–424, 426 transportation networks, 424–425 context-sensitive interactomes, 479–480 contingency table analysis, 31–32 continues variables, proximity calculation, 252–254 correlation networks, 539 correlation, continues variables, 253, 254 covariate factors, pattern mining, 349–359 CpG islands, 67, 70 curated databases, 161, 162, 161–162, 179 cystic fibrosis (CF), 473, 480 Cytoscape, 177, 179, 533–536, 553 apps, 574–577 command language, 587, 586–589 control panel, 555, 556 example workflow, 577–585 exercises, 589 hierarchical clustering, 541, 579, 580 importing data files, 561, 563 importing from public databases, 560 importing networks, 560 integration of data, 559–562 k-means clustering, 580, 580 network analysis, 574 network panel, 557, 558 results panel, 557 scripting, 586 STRING network, 540, 560, 572, 576, 579, 581 table panel, 557 user interface, 554, 555–559 view menu, 559 visualizing data, 565, 562–573, 574, 575, 586 visualizing networks, 534, 540–552 DAG1 gene, 476 data integration, 159–160, 288–290 Bayesian approaches, 292, 291–293 biological networks, 229 early, 289 heterogeneous, 289, 300–306 homogeneous, 289, 294–300 intermediate, 289 kernel-based methods, 293, 293–294 late, 289 network-based, 291, 290–291 623 precision medicine, 287–288, 290–294 protein-protein interactions, 159–160 training methods, 290, 289–290 databases annotation, 166, 165–167, 170 curated, 161, 162, 161–162, 179 epigenetic, 75, 93–96, 97 integrated, 160, 164, 165, 163–179 interaction, 160–167, 372, 463, 602 interactome, 439 lncRNA, 91 molecular interaction, 463 prediction, 163, 164, 162–165, 179 protein-protein interactions, 112, 160–167, 602, 603 public, 2, 22, 72, 74, 427, 559–562 Davies-Bouldin index, 273 DBSCAN, 267, 266–268 degree distribution, 133, 133, 178, 428 degree of vertices, 119, 119, 168 de-noising networks, 212, 213 density, 133, 134 density based clustering algorithms, 257, 266–268 depth first search (DFS), 130–131 deterministic measures, network topology, 507, 510–513 differential binding, 79, 80 differential methylation CpGs (DMC), 76, 76–77 differential methylation regions (DMR), 76, 76–77 differential network analysis, 481 diffusion tensor imaging (DTI), 495, 498, 500 diffusion-based methods, disease identification, 173 diffusion-weighted MRI scanning, 495, 496 direct to consumer services, 3, 9, 13, 45 predictive genetic risk models, 39–44 directed acyclic graphs (DAG), 39 directed graphlets, 197–198 directed graphs, 113, 118, 118 directed networks, 402, 402 disassortative networks, 135 discriminative pattern mining see pattern mining disease gene prioritization, 440, 442 connectivity-based methods, 440–443 diffusion-based methods, 443–444 path-based methods, 443 diseases, 437, 474 analysis, 438 biological interpretation, 445–448 contagious diseases spread, 423–426 624 INDEX diseases (cont.) enrichment, 445, 444–445 epigenetic mechanisms, 65–66 gene prioritization, 440–444 identification, PPI, 152, 173–174 interactome analysis, 430 interactome construction, 427, 438 molecular basis, 474, 470–475 network approaches, 422–423, 437, 438, 440, 442, 473–474 resources, 439 seed clusters, 438–440 treatment, PPI, 166 validation, 444 DNA methylation, 66–68, 479; see also epigenetics bisulfite based arrays, 72–73 experimental strategies, 69–71 microarrays, 73–77 role in genomic profiles, 69 DNA modifications, 68, 70 DNA sequencing, next generation, 18–27 domains, proteins, 156 dominant models, 32 dominating set (DS), 211, 210–211 drug repurposing, 174, 290, 294, 423 precision medicine, 287 drug targeting, 174 DTC services see direct-to-consumer services Dunn index, 273 dynamic graphlets, 199, 198–200 dynamic networks, 199, 198–200, 221, 225 eccentricity centrality, 135, 137 edge conservation, 217, 375 edge correctness, alignment scoring, 377 edge-colored graphlets, 200, 201 edges, 463, 464 edgetic perturbations, 466, 470 edgotype prediction tools, 468 edgotype scenarios, 467, 467 effect sizes, SNPs, eigenvector centrality, 135, 137 electroencephalography (EEG), 501–503, 504 Encyclopedia of DNA elements (ENCODE), 93 enrichment, disease modules, 445, 444–445 epigenetic databases, 75, 93–96, 97 BLUEPRINT Epigenome, 79, 96 Encyclopedia of DNA elements, 93 Functional Annotation of the Mammalian Genome, 95 International Human Epigenome Consortium, 96 Roadmap Epigenomics Project, 95 epigenetics, 65–66, 66, 98; see also DNA methylation changes in tumors, 595 disease mechanisms, 65–66 exercises, 98–100 higher order chromatin organization, 80–87 histone modifications, 77–79 long non-coding RNAs, 87–93 mapping mechanisms, 72, 74 epigenomics, 67 Erdos–Renyi (ER) random graphs, 138–139 error rates, PPI datasets, 156–158 euchromatin, 67 Euclidean distances, 252 Eulerian circuits, 126–127 events, dynamic networks, 198 exact cluster ratio, 392 expectation–maximization (EM) algorithm, 16 false discovery rate (FDR) control, 361 family-wise error rate (FWER), 326, 361 Bonferroni correction, 326–327 empirical approximations, 344–346 Tarone’s improved Bonferroni correction, 327–328 FANTOM (Functional Annotation of the Mammalian Genome), 95, 477 feature selection, cluster analysis, 247–248 Fischer’s exact test, 323–325, 336, 337 5C technology, chromatin conformation, 81, 83 force-directed algorithms, 549–550, 574, 575 4C technology, chromatin conformation, 81, 83 frequent pattern mining see apriori property, pattern mining Fruchterman-Reingold algorithm, 549 F-score, alignment scoring, 376 Functional Annotation of the Mammalian Genome (FANTOM), 95, 477 functional annotations, PPI networks, 174–175 functional brain networks, 499–503 functional consistency, alignment scoring, 376 functional MRI (fMRI), 499, 502, 518 FUSE, multiple network alignment method, 395, 394–397 fuzzy clustering, 243–244 fuzzy C-means (FCM) clustering, 262 FWER see family-wise error rate gain-of-interactions, 466 gap statistic, 276–277 GATK (Genome Analysis Toolkit), 22, 27 GCD (graphlet correlation distance), 215 INDEX GCM (graphlet correlation matrix), 207, 208–209 GDD (graphlet degree distributions), 207, 208 GDDA (graphlet degree distribution agreement), 214 GDV see graphlet degree vectors GDV-centrality, 207, 221, 225 GDV-matrices, 207, 208–209 GDV-similarity, 205–207, 210, 218, 219 gene co-expression networks, 113 gene duplication and divergence (SF-GD), 139 gene expression analysis, 241 gene mutations, 152, 459, 461 BRCA1 gene, 476, 480 cancer, 177, 175–179, 300, 462, 548–549 cystic fibrosis, 480 DAG1 gene, 476 edgetic perturbations, 466 KRAS gene, 610 loss-of-function, 465, 466 monogenic diseases, 470–473 oncogenes, 595 Parkinson’s disease, 473, 480 personalized genetic tests, 3, 7, 13, 45 RAS genes, 462 sickle cell disease, 461–462, 479–480 tumor suppressor genes, 595 gene ontology (GO) annotation set, 370, 371 gene prioritization algorithms, 437 disease analysis, 440–444 gene regulatory networks, 418–419 gene signature improvements, PPI networks, 174 generalized random graph models (ER-DD), 138 genetic data, risk prediction, 1–6, 45–47 exercises, 47–50 glossary, 4–6 SNP-disease association, 31–44 SNPs identification, 9, 30 tests in healthcare, 6–9 genetic interactions, 113–114, 420–422, 463, 481, 536 genetic tests, healthcare, 6–9 Genome Analysis Toolkit (GATK), 22, 27 genome atlas, cancer, 22, 95, 462, 479–480, 577 genome-wide association studies (GWAS), 2, 473, 479 genotype-phenotype relationships, 460–461, 482 definitions, 460–461 exercises, 482–483 molecular networks, 459, 461–464 network approaches, diseases, 464–475 network-based tools, 471, 472 625 tissue interactomes, 480–482 tissue-sensitive molecular interaction networks, 475–480 genotypes algorithms, 14–18 calling algorithms, 10, 16, 21–27 definition, 460, 461 geometric graph with gene duplications and mutations (GEO-GD), 136, 139 geometric networks, 139–140 Gini importance, 40 global pairwise network alignment methods GRAAL, 384–387 IsoRank, 382–384 other, 387–390 GRAAL, global network aligner, 384–387 GRAFENE, 215–216, 220 graph(s), 167 alignment, 374 bipartite, 113, 123, 375, 374–375 density, 169, 169 kernels, 216 regularization, 300, 299–300 types, 122–126 weighted, 113, 123, 123 graph based clustering algorithms, 257, 268–270 graph search algorithms, 130–131 graph theory, 111–114, 140 classic problems, 126–128 computational complexity, 117–118 data structures, 128–130 definitions, 118–119 degree and neighborhood, 119–120 exercises, 140–142 mathematical basis, 114–116 network measures, 132–140 search algorithms, 130–131 spectral graph theory, 131–132 subgraphs and connectedness, 120–122 trees, 124, 124 GraphCrunch, 226, 227, 227 graphlet correlation distance (GCD), 215 graphlet correlation matrix (GCM), 207, 208–209 graphlet counting, 196, 197, 227, 226–229 orbit-aware, 206, 226 orbit-unaware, 199, 226 graphlet degree distribution agreement (GDDA), 214 graphlet degree distributions (GDD), 207, 208 graphlet degree vectors, 203–205, 384, 385, 384–387; see also GDV-centrality; GDV-matrices; GDV-similarity 626 INDEX graphlet degree vectors (cont.) edge, 204 node-pair, 205 non-edge, 205 graphlet frequency vector (GFV), 208 graphlet kernel, 293 graphlet-based alignment-free network approach (GRAFENE), 215–216, 220 graphlets, 195, 193–196, 205 biological applications, 218–226 colored, 195, 200–202 computational approaches, 209–218 directed, 197–198 dynamic, 199, 198–200 edge-colored, 200, 201 exercises, 230–234 heterogeneous, 195, 200–202 homogeneous, 195, 196, 201–202 network topology, 196–209 node-colored, 200–201 orbits, 203, 203, 204, 205 ordered, 202, 202 software tools, 230, 226–230 static, 195, 196, 202 undirected, 196, 202 unordered, 196, 202 graph-structured samples, 318 GWAS see genome-wide association studies Hall’s theorem, 128 Hamiltonian paths, 127–128 hedgehog signaling pathway, 182, 597 heterogeneity biological, 288–290 cancers, 287 condition specific, 160 data integration, 289, 300–306 experimental, 159 graphlets, 195, 200–202 molecular, 159 nomenclature, 160 Hi-C analysis, bioinformatics, 82 Hi-C technology, chromatin conformation, 67, 77, 82 mapping and filtering, 82 normalization, 82, 84 statistical analysis, 84 tools, 85 topological associated domains, 86, 86–87 visualization, 84, 86 hierarchical clustering, 244, 262, 263, 265–266; see also linkage functions algorithms, 257, 264 Cytoscape, 541, 579, 580 high angular resolution diffusion imaging (HARDI), 497, 498 high-throughput methods (HT), 154 histone modifications, 77–79 homogeneity data integration, 289, 294–300 graphlets, 195, 196, 201–202 Hotelling’s T2 statistic, 32–33 human tissues, mapping, 463, 477 Hungarian algorithm, 128, 375 Huntington’s disease, 470–473 hypergraphs, 125, 124–125, 543, 546 hyper-networks, 403, 403 IHEC (International Human Epigenome Consortium), 96 Illumina NGS Platform, 19–21 Illumina SNP BeadChips, 14, 72, 74 inborn error of metabolism (IEM), 465, 466 induced cancer stem cells, 596 induced conserved sub-structure score (ICS), alignment scoring, 377 integrated databases, 160, 164, 165, 163–179 interaction databases, 160–167, 372, 463, 602 interaction networks, 536–537, 540 interactome analysis, 151, 163, 162–165, 427–437, 464 basic properties, 428, 427–429 biological function, 429–430 construction, 427, 438 context-sensitive, 479–480 databases, 439 diseases, 430 network localization, 430–432 randomization, 431–437 interactomics, 601 intermediate data integration, 289 International Human Epigenome Consortium (IHEC), 96 inter-organismal networks, 474–475 isomorphic graphs, 120, 121, 373 isomorphism, 194, 199, 203, 212, 220 IsoRank, global network aligner, 200, 217, 383, 382–384 Jacard index, 256, 271–272 joint probabilities, 320 k-correctness, alignment scoring, 375 k-coverage, 392 INDEX k-means clustering, 258–262, 259, 260, 267, 580, 580 k-partite matching, 397 Kamada-Kawai algorithm, 549 kernel functions, 38 kernel-based methods, data integration, 293, 293–294 KRAS gene, 606 Lance-Williams recurrence formula, 265, 266 large scale clustering algorithms, 258 largest connected component, alignment scoring, 378 late data integration, 289 layouts, 550, 551, 570–573 algorithms, 549 force directed, 549–550 node-link diagrams, 549, 550, 550–551 leukemia, cancer stem cells, 598 linear algebra, 114 link prediction, 213, 212–213, 511–513 linkage disequilibrium, 5, 31, 34 linkage functions, 257, 263, 264–265, 267 average linkage, 265 complete linkage, 265 Lance-Williams recurrence formula, 265, 266 single linkage, 265 linkage methods, disease identification, 173 lncRNA see long non-coding RNAs local community paradigm (LCP) theory, 512–513, 517 local network alignments, 375–376 localization network, 430–432, 435, 440, 505 subcellular, 154, 166, 600 logistic regression models, multi-SNP, 3, 34, 36–37 long non-coding RNAs (lncRNA), 87–88 algorithms, 92, 94 bioinformatic tools, 88–89 databases, 91 epigenetics, 87–93 precision medicine, 88 previously annotated, 89–93 unannotated, 93 long-read sequencing, 71 low-throughput methods (LT), 154 machine learning algorithms, 33–39, 601 data integration, 290–294 non-negative matrix factorization, 294–295 627 pattern mining, 313–314, 359–361 precision medicine, 287, 306 macroscale, neuronal connectivity, 491 magnetic resonance imaging see MRI mapping algorithms, 372 mapping mechanisms, epigenetics, 72, 74 marginal probabilities, 320 matching bipartite, 375, 374–375 graph theory, 128 index, 137 k-partite, 397 matrices adjacency, 129, 129–130, 291, 294, 295, 491–492, 499 GDV, 207, 208–209 operations, 115 special, 115–116 spectral decomposition, 116 MaWish, pairwise network alignment, 381 mean normalized entropy (MNE), 392 medicine, 414, 448 disease module analysis, 437–448 disease networks, 422–423 exercises, 449 interactome analysis, 427–437 molecular networks, 415–422 social networks, 423–426 types of network, 415 mesoscale, neuronal connectivity, 491 metabolic networks, 113, 418, 419 metastases, 595–598 microarrays, 1, 11, 9–18, 70 biases, 75, 97 DNA methylation, 73–77 genotyping algorithms, 15, 16 limitations, 71 vs next generation sequencing, 26–28 normalization, 75–76 microscale, neuronal connectivity, 491 minicolumns, cortex, 491 minimum attainable P-value, 328, 332–337, 336, 337, 346, 354–355, 356, 358 Minkowski distances, 252–253 mixed graphs, 122–123 mixture models, 16 model based clustering algorithms, 257–258 modularity, 509 module-based methods, disease identification, 173 molecular networks, 415, 464 causal variants, 466–470 628 INDEX molecular networks (cont.) co-expression networks, 419–420 databases, 463 diseases, 470–475 genetic interactions, 420–422 genotype-phenotype relationships, 459, 461–464 metabolic networks, 113, 418, 419 protein-protein interactions, 415–417 regulatory networks, 418–419 tissue-sensitive, 475–480 monogenic diseases, 470–473 motifs, network, 138, 197 MRI (magnetic resonance imaging) brain structure, 492–493 diffusion tensor imaging, 495, 498, 500 diffusion-weighted, 495, 496 high angular resolution diffusion imaging, 497, 498 T1-weighted, 68, 494, 496 multi-graphs, 122, 123, 543, 546 multilayer networks, 125, 125–126, 401, 402, 401–402 multiple network alignment, 391 definitions, 390–391 FUSE example method, 394–397 other methods, 397–399 scoring alignments, 392 SMETANA example method, 392–394 multiple testing correction, pattern mining, 325–326 multi-SNP association studies, 35, 33–39 multi-threshold permutation correction (MTPC), 506 mutations see gene mutations myoglobin, 370 neighborhood of vertices, 119, 119 NetAligner, pairwise network alignment, 381 netdis, 215 network(s) alignment-based comparison, 216–218 alignment-free comparison, 214–216 brains see brain networks clustering, 209, 210, 209–210 comparison, 213–214 construction, 167–168 definition, 167 de-noising, 212, 213 edges, 463, 464 genetic interactions, 113–114, 463, 481, 536 genotype-phenotype relationships, 464–475 geometry, 492, 515, 516, 513–516 inter-organismal, 474–475 localization, 430–432, 435, 440, 505 measures, 132–140 molecular see molecular networks molecular basis of diseases, 474, 470–475 motifs, 138, 197 neuroscience, 491–492 nodes, 194, 199, 212, 220, 435–436, 463, 464 pathways, 537–538, 539, 541 properties, 135, 132–138 protein-protein interactions, 111, 112, 153, 167–170 similarity, 538–539, 542, 543 taxonomy, 536–539 theory see graph theory topology, 196–209, 492, 506–508 network alignment (NA), 223, 369–373, 374, 403 alternative formalisms, 399 directed networks, 402 exercises, 404–407 hyper-networks, 403, 403, 403 methods, 400 multilayer networks, 125–126, 401, 402, 401–402 multiple see multiple network alignment pairwise see pairwise network alignment probabilistic networks, 400, 399–401 protein-protein interactions, 372, 371–373 search-based method, 217–218 two-stage method, 216–218 network analysis Cytoscape, 574 differential, 481 protein-protein interactions, 173–175, 176, 194, 199, 212, 220, 221–223 network-based data integration, 291, 290–291 network-based disease modules, 437–438, 440, 442 network-based statistics (NBS), 506 network-based tools, 471, 472 network models, 138, 462, 463 Erdos–Renyi Random Graphs, 138–139 geometric networks, 139 scale-free networks, 139 NetworkBLAST, pairwise network alignment, 381 neuroscience, 490–492 brain network analysis tools, 505–506 brain network disorders, 517–519 exercises, 519–520 functional brain networks, 499–503 INDEX network geometry, 513–516 network topology, 506–513 nodes, 503–505 structural brain networks, 492–499 next generation sequencing, 2, 18–27, 70, 477 vs microarrays, 26–28 node(s) brain networks, 503–505 conservation, 216–217, 389 correctness/coverage, alignment scoring, 377 network, 194, 199, 212, 220, 435–436, 463, 464 removals, 466 node-colored graphlets, 200–201 node-link diagrams, 542–547 animations, 551 combining layouts, 550, 551 force directed layouts, 549–550 layering visualizations, 552, 552 layouts, 549, 550, 550–551 network embedding, 550 simple layout algorithms, 549 visual mappings, 548–549 visualizing networks, 547, 547–548 non-negative matrix factorization (NMF), 295, 294–295 homogeneous data integration, 298–300 precision medicine, 294–300 solutions, 295–298 non-negative matrix tri-factorization (NMTF), 301, 300–301 FUSE, 394–397 heterogeneous data integration, 305 precision medicine, 300–306 solutions, 301–305 normalization, cluster analysis, 246–247 Notch signaling pathway, 597 odds ratio, 5, 43 oncogenes, 595 one-mode data format, 244 optimization algorithms, 374 orbit aware graphlet counting, 206, 226 orbit aware quad census (Oaqc), 227, 228 orbit counting algorithm (Orca), 227, 228 orbit unaware graphlet counting, 199, 226 orbit weights, 386 orbits, graphlets, 203, 203, 204, 205 ordered graphlets, 202, 202 ovarian cancer, 305, 476 biomarkers, 608, 609, 608–610 cancer stem cells, 598 overlapping clustering, 243 629 pairwise network alignment, 391 definitions, 373–375 example method, PathBlast, 379–381 global methods, 382–390 other methods, 381–382 scoring alignments, 375–379 parallel parameterized graphlet decomposition (PGD) library, 227, 229 Parkinson’s disease, 34, 473–475, 480, 518–519 partitional clustering, 243 partitioning around medoids (PAM), 261–262 path lengths, 169–170 PathBlast, pairwise network alignment, 380, 379–381 pathogenicity, graphlet-based approach, 224 pathway enrichment analysis, 179, 180 pathways, networks, 537–538, 539, 541 patient subtyping, cancer precision medicine, 287 pattern enumeration tree, 330, 331 pattern mining, 315–328 algorithms, 329, 330, 340, 361 confounding effect, 350, 351 covariate factors, 349–359 machine learning, 313–314, 359–361 permutation testing, 346–349 software tools, 360 statistical redundancy, 341–349 Tarone’s method, 329–341 pattern occurrence indicator, 315 peak calling, 79, 79 Pearson’s chi-squared test, 323–325, 336 permutation importance, 40 permutation testing algorithms, 347 pattern mining, 346–349 personalized genetic tests (PGT), 3, 7, 13, 45 personalized medicine see precision medicine personalized oncology, 287–288, 594 phenotypes, definition, 460, 461; see also genotype-phenotype relationships power-lawness, 509 PPI see protein-protein interactions precision medicine, 286, 288, 306, 596 cancer, 287 data integration, 287–288 data integration methods, 290–294 data integration types, 288–290 drug repurposing, 287 exercises, 306–308 long non-coding RNAs, 88 machine learning, 287, 306 630 INDEX precision medicine (cont.) non-negative matrix factorization, 294–300 non-negative matrix tri-factorization, 300–306 prediction databases, 163, 164, 162–165, 179 predictive genetic risk models, 39–44 preferential attachment, 194, 199, 212, 213, 220 preprocessing, cluster analysis, 242, 246–251 principal component analysis (PCA), 206, 215–216, 226, 251, 248–251 probabilistic networks, 400, 399–401 prostate cancer, biomarkers, 606–608 protein complex detection, 241 protein function prediction, 218–220 protein homology detection, 241–242 protein structure network (PSN), 193 protein-DNA interactions, 600 protein-protein interactions (PPI), 151–154, 179–181, 193, 536, 537 annotations, 171, 370, 371 biases, 158–159, 438 computational biology workflows, 175–179 computational method types, 156 computational prediction, 156–159 data integration, 159–160 databases, 112, 160–167, 602, 603 dominating set, 211 exercises, 181 experimental detection, 155, 154–156 high-throughput methods (HT), 154 human aging, 220–223 interaction types, 155 limitations, detection methods, 154, 155 low-throughput methods (LT), 154 molecular networks, 415–417 network alignment, 372, 371–373 network analysis, 173–175, 176, 194, 199, 212, 220, 221–223 network visualizations, 170–172 networks, 111, 112, 153, 167–170 stem cell therapy, 598–603, 603 tissue interactomes, 479 proteins cancer, 223–224, 598–603 domains, 156 functions, 370, 369–371 sequencing, 156 proteomics, 599–601 prototype based clustering algorithms, 257 proximity calculation, cluster analysis, 252–256 pruning condition, 338, 339 Cochran-Mantel-Haenszel test, 355–359 public databases, 2, 22, 72, 74, 427, 559–562 qualitative annotations, 172 quantitative annotations, 172 Rand index, 271 random forest methods, 37–40 random walks, 132 randomization, network properties, 431–433, 435, 436–437 nodes, 435–436 topology, 433–435 rapid graphlet enumerator (RAGE), 227, 228 RAS genes, mutations, 462 recessive models, 32 redundancy, pattern mining, 342, 341–349 regulatory networks, 418–419 relative graphlet frequency distance (RGFD), 214 relative risk, 43 repression, transcriptional, 491 resting state networks (RSN), 499 rich-clubness, 509–510, 517 risk indicators, 43 Roadmap Epigenomics Project, 95 SAND/SAND-3D subgraph tools, 227, 229 scale-free networks, 139 schema, 160 scoring schemes see alignment scoring schemes search algorithms, graph theory, 130–131 Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), 606–608 search-based network alignment, 217–218 seed clusters, disease module analysis, 438–440 semantic similarity, alignment scoring, 376 sequencing alignment, 369 by synthesis, 71 proteins, 156 SF-GD (scale-free gene duplication and divergence), 139 short-read sequencing, 71 sickle cell disease, 461–462, 479–480 significant itemset mining, 317, 315–317 significant pattern mining see pattern mining significant subgraph mining, 317–318, 319 silhouette values, 273, 274 similarity networks, 538–539, 542, 543 simultaneous decomposition, 298, 299 single nucleotide polymorphisms (SNP), calling and genotyping, 21–27 definition, disease association, 10, 41, 31–47 effect sizes, INDEX identification, 9, 30 significant itemset mining, 315 single-SNP association studies, 31–33 small-worldness, 508 SMETANA, multiple network alignment method, 392–394 social networks, 225, 224–226, 423–426, 538 contagious diseases spread, 423–424 social contagion, 425 transportation, 424–425 software tools, pattern mining, 360 spectral clustering, 132 spectral graph theory, 131–132 spinocerebellar ataxia type (SCA1), 473 spreadsheets, visualizing networks, 541, 544 standardization, cluster analysis, 246–247 static graphlets, 195, 196, 202 statistical association testing, pattern mining, 318 statistical redundancy, pattern mining, 341–349 stem cell therapy, 594–595 cancer stem cells, 595–598, 605, 606, 603–612 exercises, 612 protein interactions, 598–603 tumor stemness biomarkers, 596–597, 605, 606, 610, 603–612 stochastic measures, network topology, 507–510 STRING (Search Tool for the Retrieval of Interacting Genes/Proteins), 606–608 structural brain networks, 492–499 structural consistency, 509 subcellular localization, 154, 166, 600 subgraphs, 120, 120 centrality, 137 connectedness, 119 isomorphism problem, 373 subset/superset relationships, pattern mining, 341, 343 support vector machines, 34–38 suppressor genes, tumors, 595 symmetric sub-structure scores, alignment scoring, 377 T1-weighted MRI scanning, 68, 494, 496 targeted therapy, 594 targeting, drugs, 174 Tarone’s improved Bonferroni correction, 327–328 Tarone’s method, pattern mining, 329–341 ten-eleven-translocation (TET) proteins, 69 631 tertiary structure, 156–158 testability, pattern mining, 329, 337, 359 1000 genomes project, 1, 18, 28–29 3C technology, chromatin conformation, 81, 83 time-respecting path, 199, 226 tissue annotation, 166 tissue interactomes, 478–482 differential network analysis, 481 genome-wide association studies, 479 meta-analysis, 481–482 PPI networks, 479 tools, 478 tissue profiles, 477 tissue-sensitive molecular networks, 475–480 tissue-specific interactions, 476 topological associated domains (TAD), chromatin conformation, 86, 86–87 topology, networks, 196–209, 492, 506–508 training methods, data integration, 290, 289–290 transcriptional regulation networks, 113 transcriptional, repression, 491 transivity clustering, 268–270 transportation networks, contagious diseases, 424–425 trees, graph theory, 124, 124 tumors; see also cancer metastases, 595–598 stemness biomarkers, 596–597, 603–612, 605, 606, 610 suppressor genes, 595 Turing machines, 117 two-mode data format, 244 two-stage network alignment, 216–218 undirected graphlets, 196, 202 undirected graphs, 118 unordered graphlets, 196, 202 validity indices, cluster evaluation, 270–271 variable importance measures (VIMs), 37 variant calling algorithms, 25–26 variety, databases, 161 variety/velocity/veracity, databases, 160 vector spaces, 116 velocity, databases, 161 veracity, databases, 161 VIM (variable importance measures), 37 visual mappings continuous, 548 discrete, 548 632 INDEX visual mappings (cont.) node-link diagrams, 548–549 passthrough, 548 visualizing networks, 170–172, 533, 534, 535, 540–552 adjacency matrices, 541, 544 BioFabric, 542, 545 Cytoscape, 534 node-link diagrams, 547, 542–548 spreadsheets, 541, 544 walks, graph theory, 121, 121 weighted graphs, 113, 123, 123 weighted transitive graph projection problem (WTGPP), 269, 268–269 Wnt signaling pathway, 597 writer enzymes, 68 yeast two-hybrid (Y2H) assays, 155, 417, 427 ... Analyzing Network Data in Biology and Medicine An Interdisciplinary Textbook for Biological, Medical, and Computational Scientists The increased and widespread availability of large network data. .. A Tool for Analyzing and Visualizing Network Data JOHN H MORRIS 533 14 Analysis of the Signatures of Cancer Stem Cells in Malignant Tumors Using Protein Interactomes and the STRING Database 593... practice by using a major software package for analyzing network data, Cytoscape, and a major protein interaction database, STRING, are presented in the last two chapters I hope you will find this