Applied biclustering methods for big and high dimensional data using R

398 Pages·2016·17.149 MB·English

by  Adetayo Kasim, Ziv Shkedy, Sebastian Kaiser, Sepp Hochreiter, Willem Talloen

Applied Biclustering
Methods for Big and
High-Dimensional Data
Using R
Edited by
Adetayo Kasim
Durham University
United Kingdom
Ziv Shkedy
Hasselt University
Diepenbeek, Belgium
Sebastian Kaiser
Ludwig Maximilian Universität
Munich, Germany
Sepp Hochreiter
Johannes Kepler University Linz
Austria
Willem Talloen
Janssen Pharmaceuticals
Beerse, Belgium
Editor-in-Chief
Shein-Chung Chow, Ph.D., Professor, Department of Biostatistics and Bioinformatics,
Duke University School of Medicine, Durham, North Carolina
Series Editors
Byron Jones, Biometrical Fellow, Statistical Methodology, Integrated Information Sciences,
Novartis Pharma AG, Basel, Switzerland
Jen-pei Liu, Professor, Division of Biometry, Department of Agronomy,
National Taiwan University, Taipei, Taiwan
Karl E. Peace, Georgia Cancer Coalition, Distinguished Cancer Scholar, Senior Research Scientist
and Professor of Biostatistics, Jiann-Ping Hsu College of Public Health,
Georgia Southern University, Statesboro, Georgia
Bruce W. Turnbull, Professor, School of Operations Research and Industrial Engineering,
Cornell University, Ithaca, New York
Published Titles
Adaptive Design Methods in Clinical Bayesian Analysis Made Simple:
Trials, Second Edition An Excel GUI for WinBUGS
Shein-Chung Chow and Mark Chang Phil Woodward
Adaptive Designs for Sequential Bayesian Methods for Measures
Treatment Allocation of Agreement
Alessandro Baldi Antognini Lyle D. Broemeling
and Alessandra Giovagnoli
Bayesian Methods for Repeated Measures
Adaptive Design Theory and Lyle D. Broemeling
Implementation Using SAS and R,
Bayesian Methods in Epidemiology
Second Edition
Lyle D. Broemeling
Mark Chang
Bayesian Methods in Health Economics
Advanced Bayesian Methods for
Gianluca Baio
Medical Test Accuracy
Bayesian Missing Data Problems: EM,
Lyle D. Broemeling
Data Augmentation and Noniterative
Applied Biclustering Methods for Big
Computation
and High-Dimensional Data Using R
Ming T. Tan, Guo-Liang Tian,
Adetayo Kasim, Ziv Shkedy,
and Kai Wang Ng
Sebastian Kaiser, Sepp Hochreiter,
Bayesian Modeling in Bioinformatics
and Willem Talloen
Dipak K. Dey, Samiran Ghosh,
Applied Meta-Analysis with R
and Bani K. Mallick
Ding-Geng (Din) Chen and Karl E. Peace
Benefit-Risk Assessment in
Basic Statistics and Pharmaceutical
Pharmaceutical Research and
Statistical Applications, Second Edition
Development
James E. De Muth
Andreas Sashegyi, James Felli,
Bayesian Adaptive Methods for and Rebecca Noel
Clinical Trials
Benefit-Risk Assessment Methods in
Scott M. Berry, Bradley P. Carlin,
Medical Product Development: Bridging
J. Jack Lee, and Peter Muller
Qualitative and Quantitative Assessments
Qi Jiang and Weili He
Published Titles
Biosimilars: Design and Analysis of Design and Analysis of Bridging Studies
Follow-on Biologics Jen-pei Liu, Shein-Chung Chow,
Shein-Chung Chow and Chin-Fu Hsiao
Biostatistics: A Computing Approach Design & Analysis of Clinical Trials for
Stewart J. Anderson Economic Evaluation & Reimbursement:
An Applied Approach Using SAS & STATA
Cancer Clinical Trials: Current and
Iftekhar Khan
Controversial Issues in Design and
Analysis Design and Analysis of Clinical Trials
Stephen L. George, Xiaofei Wang, for Predictive Medicine
and Herbert Pang Shigeyuki Matsui, Marc Buyse,
and Richard Simon
Causal Analysis in Biomedicine and
Epidemiology: Based on Minimal Design and Analysis of Clinical Trials with
Sufficient Causation Time-to-Event Endpoints
Mikel Aickin Karl E. Peace
Clinical and Statistical Considerations in Design and Analysis of Non-Inferiority Trials
Personalized Medicine Mark D. Rothmann, Brian L. Wiens,
Claudio Carini, Sandeep Menon, and Mark Chang and Ivan S. F. Chan
Clinical Trial Data Analysis using R Difference Equations with Public Health
Ding-Geng (Din) Chen and Karl E. Peace Applications
Lemuel A. Moyé and Asha Seth Kapadia
Clinical Trial Methodology
Karl E. Peace and Ding-Geng (Din) Chen DNA Methylation Microarrays:
Experimental Design and Statistical
Computational Methods in Biomedical
Analysis
Research
Sun-Chong Wang and Arturas Petronis
Ravindra Khattree and Dayanand N. Naik
DNA Microarrays and Related Genomics
Computational Pharmacokinetics
Techniques: Design, Analysis, and
Anders Källén
Interpretation of Experiments
Confidence Intervals for Proportions
David B. Allison, Grier P. Page,
and Related Measures of Effect Size
T. Mark Beasley, and Jode W. Edwards
Robert G. Newcombe
Dose Finding by the Continual
Controversial Statistical Issues in
Reassessment Method
Clinical Trials Ying Kuen Cheung
Shein-Chung Chow
Dynamical Biostatistical Models
Data Analysis with Competing Risks
Daniel Commenges and
and Intermediate States
Hélène Jacqmin-Gadda
Ronald B. Geskus
Elementary Bayesian Biostatistics
Data and Safety Monitoring Committees
Lemuel A. Moyé
in Clinical Trials
Empirical Likelihood Method in
Jay Herson
Survival Analysis
Design and Analysis of Animal Studies
Mai Zhou
in Pharmaceutical Development
Exposure–Response Modeling: Methods
Shein-Chung Chow and Jen-pei Liu
and Practical Implementation
Design and Analysis of Bioavailability
Jixian Wang
and Bioequivalence Studies, Third Edition
Frailty Models in Survival Analysis
Shein-Chung Chow and Jen-pei Liu
Andreas Wienke
Published Titles
Fundamental Concepts for New Clinical Multiregional Clinical Trials for
Trialists Simultaneous Global New Drug
Scott Evans and Naitee Ting Development
Joshua Chen and Hui Quan
Generalized Linear Models: A Bayesian
Perspective Multiple Testing Problems in
Dipak K. Dey, Sujit K. Ghosh, and Pharmaceutical Statistics
Bani K. Mallick Alex Dmitrienko, Ajit C. Tamhane,
and Frank Bretz
Handbook of Regression and Modeling:
Applications for the Clinical and Noninferiority Testing in Clinical Trials:
Pharmaceutical Industries Issues and Challenges
Daryl S. Paulson Tie-Hua Ng
Inference Principles for Biostatisticians Optimal Design for Nonlinear Response
Ian C. Marschner Models
Valerii V. Fedorov and Sergei L. Leonov
Interval-Censored Time-to-Event Data:
Methods and Applications Patient-Reported Outcomes:
Ding-Geng (Din) Chen, Jianguo Sun, Measurement, Implementation and
and Karl E. Peace Interpretation
Joseph C. Cappelleri, Kelly H. Zou,
Introductory Adaptive Trial Designs:
Andrew G. Bushmakin, Jose Ma. J. Alvir,
A Practical Guide with R
Demissie Alemayehu, and Tara Symonds
Mark Chang
Quantitative Evaluation of Safety in Drug
Joint Models for Longitudinal and Time-
Development: Design, Analysis and
to-Event Data: With Applications in R
Reporting
Dimitris Rizopoulos
Qi Jiang and H. Amy Xia
Measures of Interobserver Agreement
Quantitative Methods for Traditional
and Reliability, Second Edition
Chinese Medicine Development
Mohamed M. Shoukri
Shein-Chung Chow
Medical Biostatistics, Third Edition
Randomized Clinical Trials of
A. Indrayan
Nonpharmacological Treatments
Meta-Analysis in Medicine and
Isabelle Boutron, Philippe Ravaud,
Health Policy
and David Moher
Dalene Stangl and Donald A. Berry
Randomized Phase II Cancer
Mixed Effects Models for the Population
Clinical Trials
Approach: Models, Tasks, Methods
Sin-Ho Jung
and Tools
Sample Size Calculations for Clustered
Marc Lavielle
and Longitudinal Outcomes in Clinical
Modeling to Inform Infectious Disease
Research
Control
Chul Ahn, Moonseong Heo,
Niels G. Becker
and Song Zhang
Modern Adaptive Randomized Clinical
Sample Size Calculations in Clinical
Trials: Statistical and Practical Aspects
Research, Second Edition
Oleksandr Sverdlov
Shein-Chung Chow, Jun Shao,
Monte Carlo Simulation for the and Hansheng Wang
Pharmaceutical Industry: Concepts,
Statistical Analysis of Human Growth
Algorithms, and Case Studies
and Development
Mark Chang
Yin Bun Cheung
Published Titles
Statistical Design and Analysis of Clinical Statistical Testing Strategies in the
Trials: Principles and Methods Health Sciences
Weichung Joe Shih and Joseph Aisner Albert Vexler, Alan D. Hutson,
and Xiwei Chen
Statistical Design and Analysis of
Stability Studies Statistics in Drug Research:
Shein-Chung Chow Methodologies and Recent
Developments
Statistical Evaluation of Diagnostic
Shein-Chung Chow and Jun Shao
Performance: Topics in ROC Analysis
Kelly H. Zou, Aiyi Liu, Andriy Bandos, Statistics in the Pharmaceutical Industry,
Lucila Ohno-Machado, and Howard Rockette Third Edition
Ralph Buncher and Jia-Yeong Tsay
Statistical Methods for Clinical Trials
Mark X. Norleans Survival Analysis in Medicine and
Genetics
Statistical Methods for Drug Safety
Jialiang Li and Shuangge Ma
Robert D. Gibbons and Anup K. Amatya
Theory of Drug Development
Statistical Methods for Immunogenicity
Eric B. Holmgren
Assessment
Harry Yang, Jianchun Zhang, Binbing Yu, Translational Medicine: Strategies and
and Wei Zhao Statistical Methods
Dennis Cosmatos and Shein-Chung Chow
Statistical Methods in Drug Combination
Studies
Wei Zhao and Harry Yang
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Library of Congress Cataloging‑in‑Publication Data
Names: Kasim, Adeyto, editor.
Title: Applied biclustering methods for big and high dimensional data using R
/ editors, Adeyto Kasim, Ziv Shkedy, Sebastian Kaiser, Sepp Hochreiter and
Willem Talloen.
Description: Boca Raton : Taylor & Francis, CRC Press, 2016. | Includes
bibliographical references and index.
Identifiers: LCCN 2016003221 | ISBN 9781482208238 (alk. paper)
Subjects: LCSH: Big data. | Cluster set theory. | R (Computer program
language)
Classification: LCC QA76.9.B45 A67 2016 | DDC 005.7--dc23
LC record available at https://lccn.loc.gov/2016003221
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
http://www.crcpress.com
Contents
Preface xvii
Contributors xix
R Packages and Products xxiii
1 Introduction 1
Ziv Shkedy, Adetayo Kasim, Sepp Hochreiter, Sebastian Kaiser
and Willem Talloen
1.1 From Clustering to Biclustering . . . . . . . . . . . . . . . . 1
1.2 We R a Community . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Biclustering for Cloud Computing . . . . . . . . . . . . . . . 3
1.4 Book Structure . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.5.1 Dutch Breast Cancer Data . . . . . . . . . . . . . . . 5
1.5.2 Diffuse Large B-Cell Lymphoma (DLBCL) . . . . . . 5
1.5.3 Multiple Tissue Types Data . . . . . . . . . . . . . . . 6
1.5.4 CMap Dataset . . . . . . . . . . . . . . . . . . . . . . 6
1.5.5 NCI60 Panel . . . . . . . . . . . . . . . . . . . . . . . 6
1.5.6 1000 Genomes Project . . . . . . . . . . . . . . . . . . 7
1.5.7 Tourism Survey Data . . . . . . . . . . . . . . . . . . 7
1.5.8 Toxicogenomics Project . . . . . . . . . . . . . . . . . 7
1.5.9 Yeast Data . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5.10 mglu2 Project. . . . . . . . . . . . . . . . . . . . . . . 8
1.5.11 TCGA Data . . . . . . . . . . . . . . . . . . . . . . . 9
1.5.12 NBA Data . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5.13 Colon Cancer Data . . . . . . . . . . . . . . . . . . . . 9
2 From Cluster Analysis to Biclustering 11
Dhammika Amaratunga, Javier Cabrera, Nolen Joy Perualila,
Adetayo Kasim and Ziv Shkedy
2.1 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 An Introduction . . . . . . . . . . . . . . . . . . . . . 11
2.1.2 Dissimilarity Measures and Similarity Measures . . . . 13
2.1.2.1 Example 1: Clustering Compounds in the
CMAP Data Based on Chemical Similarity . 16
vii
viii Contents
2.1.2.2 Example 2 . . . . . . . . . . . . . . . . . . . 16
2.1.3 Hierarchical Clustering. . . . . . . . . . . . . . . . . . 19
2.1.3.1 Example 1 . . . . . . . . . . . . . . . . . . . 21
2.1.3.2 Example 2 . . . . . . . . . . . . . . . . . . . 21
2.1.4 ABC Dissimilarity for High-Dimensional Data. . . . . 23
2.2 Biclustering: A Graphical Tour . . . . . . . . . . . . . . . . . 27
2.2.1 Global versus Local Patterns . . . . . . . . . . . . . . 27
2.2.2 Bicluster’s Type . . . . . . . . . . . . . . . . . . . . . 27
2.2.3 Bicluster’s Configuration . . . . . . . . . . . . . . . . 32
I Biclustering Methods 35
3 δ-Biclustering and FLOC Algorithm 37
Adetayo Kasim, Sepp Hochreiter and Ziv Shkedy
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 δ-Biclustering . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2.1 Single-Node Deletion Algorithm . . . . . . . . . . . . 39
3.2.2 Multiple-Node Deletion Algorithm . . . . . . . . . . . 39
3.2.3 Node Addition Algorithm . . . . . . . . . . . . . . . . 40
3.2.4 Application to Yeast Data . . . . . . . . . . . . . . . . 41
3.3 FLOC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3.1 FLOC Phase I . . . . . . . . . . . . . . . . . . . . . . 45
3.3.2 FLOC Phase II . . . . . . . . . . . . . . . . . . . . . . 45
3.3.3 FLOC Application to Yeast Data . . . . . . . . . . . . 45
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4 The xMotif algorithm 49
Ewoud De Troyer, Dan Lin, Ziv Shkedy and Sebastian Kaiser
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 xMotif Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.1 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.2 Search Algorithm . . . . . . . . . . . . . . . . . . . . . 50
4.3 Biclustering with xMotif . . . . . . . . . . . . . . . . . . . . 53
4.3.1 Test Data . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.3.2 Discretisation and Parameter Settings . . . . . . . . . 55
4.3.2.1 Discretisation. . . . . . . . . . . . . . . . . . 55
4.3.2.2 ParametersSetting . . . . . . . . . . . . . . 55
4.3.3 Using the biclust Package . . . . . . . . . . . . . . . 57
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5 Bimax Algorithm 61
Ewoud De Troyer, Suzy Van Sanden, Ziv Shkedy and Sebastian Kaiser
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Bimax Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 62
5.2.1 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Contents ix
5.2.2 Search Algorithm . . . . . . . . . . . . . . . . . . . . . 62
5.3 Biclustering with Bimax . . . . . . . . . . . . . . . . . . . . 65
5.3.1 Test Data . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.2 Biclustering Using the Bimax Method . . . . . . . . . 66
5.3.3 Influence of the ParametersSetting . . . . . . . . . . . 67
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6 The Plaid Model 73
Ziv Shkedy, Ewoud De Troyer, Adetayo Kasim, Sepp Hochreiter
and Heather Turner
6.1 Plaid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.1.1 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.1.2 Overlapping Biclusters . . . . . . . . . . . . . . . . . . 74
6.1.3 Estimation . . . . . . . . . . . . . . . . . . . . . . . . 75
6.1.4 Search Algorithm . . . . . . . . . . . . . . . . . . . . . 78
6.2 Implementation in R . . . . . . . . . . . . . . . . . . . . . . 79
6.2.1 Constant Biclusters . . . . . . . . . . . . . . . . . . . 79
6.2.2 Misclassification of the Mean Structure . . . . . . . . 81
6.3 Plaid Model in BiclustGUI . . . . . . . . . . . . . . . . . . 82
6.4 Mean Structure of a Bicluster . . . . . . . . . . . . . . . . . 83
6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7 Spectral Biclustering 89
Adetayo Kasim, Setia Pramana and Ziv Shkedy
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7.2 Normalisation . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.2.1 Independent Rescaling of Rows and Columns (IRRC) 91
7.2.2 Bistochastisation . . . . . . . . . . . . . . . . . . . . . 91
7.2.3 Log Interactions . . . . . . . . . . . . . . . . . . . . . 91
7.3 Spectral Biclustering . . . . . . . . . . . . . . . . . . . . . . 92
7.4 Spectral Biclustering Using the biclust Package . . . . . . . 93
7.4.1 Application to DLBCL Dataset . . . . . . . . . . . . . 94
7.4.2 Analysis of a Test Data . . . . . . . . . . . . . . . . . 95
7.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
8 FABIA 99
Sepp Hochreiter
8.1 FABIA Model . . . . . . . . . . . . . . . . . . . . . . . . . . 100
8.1.1 The Idea . . . . . . . . . . . . . . . . . . . . . . . . . 100
8.1.2 Model Formulation . . . . . . . . . . . . . . . . . . . . 101
8.1.3 Parameter Estimation . . . . . . . . . . . . . . . . . . 103
8.1.4 Bicluster Extraction . . . . . . . . . . . . . . . . . . . 106
8.2 Implementation in R . . . . . . . . . . . . . . . . . . . . . . 107
8.3 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
8.3.1 Breast Cancer Data . . . . . . . . . . . . . . . . . . . 109
x Contents
8.3.2 Multiple Tissues Data . . . . . . . . . . . . . . . . . . 112
8.3.3 Diffuse Large B-Cell Lymphoma (DLBCL) Data . . . 113
8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
9 Iterative Signature Algorithm 119
Adetayo Kasim and Ziv Shkedy
9.1 Introduction: Bicluster Definition . . . . . . . . . . . . . . . 119
9.2 Iterative Signature Algorithm . . . . . . . . . . . . . . . . . 122
9.3 Biclustering Using ISA . . . . . . . . . . . . . . . . . . . . . 123
9.3.1 isa2 Package . . . . . . . . . . . . . . . . . . . . . . . 123
9.3.2 Application to Breast Data . . . . . . . . . . . . . . . 124
9.3.3 Application to the DLBCL Data . . . . . . . . . . . . 126
9.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
10 Ensemble Methods and Robust Solutions 131
Tatsiana Khamiakova, Sebastian Kasier and Ziv Shkedy
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
10.2 Motivating Example (I) . . . . . . . . . . . . . . . . . . . . 132
10.3 Ensemble Method . . . . . . . . . . . . . . . . . . . . . . . . 134
10.3.1 Initialization Step . . . . . . . . . . . . . . . . . . . . 134
10.3.2 Combination Step . . . . . . . . . . . . . . . . . . . . 134
10.3.2.1 Similarity Indices . . . . . . . . . . . . . . . 135
10.3.2.2 CorrelationApproach . . . . . . . . . . . . . 135
10.3.2.3 Hierarchical Clustering . . . . . . . . . . . . 136
10.3.2.4 Quality Clustering . . . . . . . . . . . . . . . 136
10.3.3 Merging Step . . . . . . . . . . . . . . . . . . . . . . . 137
10.4 Application of Ensemble Biclustering for the Breast Cancer
Data Using superbiclustPackage . . . . . . . . . . . . . . 138
10.4.1 Robust Analysis for the Plaid Model . . . . . . . . . . 138
10.4.2 Robust Analysis of ISA . . . . . . . . . . . . . . . . . 141
10.4.3 FABIA: Overlap between Biclusters . . . . . . . . . . 142
10.4.4 Biclustering Analysis Combining Several Methods . . 143
10.5 ApplicationofEnsembleBiclusteringtotheTCGADataUsing
biclust Implementation . . . . . . . . . . . . . . . . . . . . 146
10.5.1 Motivating Example (II) . . . . . . . . . . . . . . . . 146
10.5.2 Correlation Approach . . . . . . . . . . . . . . . . . . 146
10.5.3 Jaccard Index Approach . . . . . . . . . . . . . . . . . 149
10.5.4 ComparisonbetweenJaccardIndexandtheCorrelation
Approach . . . . . . . . . . . . . . . . . . . . . . . . . 149
10.5.5 Implementation in R . . . . . . . . . . . . . . . . . . . 151
10.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
II Case Studies and Applications 157