Integrating Omics Data

George Tseng; Debashis Ghosh; Xianghong Jasmine Zhou

doi:10.1017/CBO9781107706484

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In most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. As the technologies have become mature and the price affordable, omics data are rapidly generated, and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field. This book provides comprehensive coverage of these topics and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.

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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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Contributors
pp vii-x
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Introduction
pp 1-6
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Part A - Horizontal Meta-Analysis
pp 7-8
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1 - Meta-Analysis of Genome-Wide Association Studies: A Practical Guide
pp 9-38
- By Wei Chen, University of Pittsburgh, Pittsburgh, PA
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2 - MetaOmics: Transcriptomic Meta-Analysis Methods for Biomarker Detection, Pathway Analysis and Other Exploratory Purposes
pp 39-67
- By Sunghwan Kim, University of Pittsburgh, Pittsburgh, PA, Zhiguang Huo, University of Pittsburgh, Pittsburgh, PA, Yongseok Park, University of Pittsburgh, Pittsburgh, PA, George C. Tseng, University of Pittsburgh, Pittsburgh, PA
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3 - Integrative Analysis of Many Biological Networks to Study Gene Regulation
pp 68-87
- By Wenyuan Li, University of Southern California, Los Angeles, CA, Chao Dai, University of Southern California, Los Angeles, CA, Xianghong Jasmine Zhou, University of Southern California, Los Angeles, CA
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4 - Network Integration of Genetically Regulated Gene Expression to Study Complex Diseases
pp 88-109
- By Zhidong Tu, Icahn School of Medicine at Mount Sinai, New York, NY, Bin Zhang, Icahn School of Medicine at Mount Sinai, New York, NY, Jun Zhu, Icahn School of Medicine at Mount Sinai, New York, NY
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5 - Integrative Analysis of Multiple ChIP-X Data Sets Using Correlation Motifs
pp 110-132
- By Hongkai Ji, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, Yingying Wei, The Chinese University of Hong Kong, Hong Kong
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Part B - Vertical Integrative Analysis (General Methods)
pp 133-134
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6 - Identify Multi-Dimensional Modules from Diverse Cancer Genomics Data
pp 135-154
- By Shihua Zhang, Chinese Academy of Sciences, Beijing, Wenyuan Li, University of Southern California, Los Angeles, CA, Xianghong Jasmine Zhou, University of Southern California, Los Angeles, CA
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7 - A Latent Variable Approach for Integrative Clustering of Multiple Genomic Data Types
pp 155-173
- By Ronglai Shen, Memorial Sloan Kettering Cancer Center, New York, NY
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8 - Penalized Integrative Analysis of High-Dimensional Omics Data
pp 174-204
- By Jin Liu, Duke-NUS Graduate Medical School, Xingjie Shi, Shanghai University of Finance and Economics, China, Jian Huang, University of Iowa, Shuangge Ma, Capital University of Economics and Business, China
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9 - A Bayesian Graphical Model for Integrative Analysis of TCGA Data: BayesGraph for TCGA Integration
pp 205-220
- By Yanxun Xu, Johns Hopkins University, Yitan Zhu, NorthShore University HealthSystem, Yuan Ji, University of Chicago
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10 - Bayesian Models for Flexible Integrative Analysis of Multi-Platform Genomics Data
pp 221-241
- By Elizabeth J. McGuffey, United States Naval Academy, Annapolis, MD, Jeffrey S. Morris, UT MD Anderson Cancer Center, Houston, TX, Ganiraju C. Manyam, UT MD Anderson Cancer Center, Houston, TX, Raymond J. Carroll, Texas A&M University, College Station, TX, Veerabhadran Baladandayuthapani, UT MD Anderson Cancer Center, Houston, TX
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11 - Exploratory Methods to Integrate Multisource Data
pp 242-268
- By Eric F. Lock, University of Minnesota, Minneapolis, MN, Andrew B. Nobel, University of North Carolina, Chapel Hill, NC
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Part C - Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
pp 269-270
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12 - eQTL and Directed Graphical Model
pp 271-290
- By Wei Sun, University of North Carolina, Chapel Hill, Chapel Hill, NC, Min Jin Ha, MD Anderson Cancer Center, Houston, TX
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13 - MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
pp 291-309
- By Panayiotis V. Benos, University of Pittsburgh, Pittsburgh, PA
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14 - Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
pp 310-336
- By Charles Vaske, NantOmics, Culver City, CA, Sam Ng, University of California at Santa Cruz, Santa Cruz, CA, Evan Paull, Joshua Stuart, University of California at Santa Cruz, Santa Cruz, CA
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15 - Analyzing Combinations of Somatic Mutations in Cancer Genomes
pp 337-361
- By Mark D. M. Leiserson, Brown University, Providence, RI, Benjamin J. Raphael, Brown University, Providence, RI
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16 - A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
pp 362-379
- By Guoshou Teo, National University of Singapore, Christine Vogel, New York University, Debashis Ghosh, University of Colorado Denver, Sinae Kim, Rutgers University, Hyungwon Choi, National University of Singapore
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17 - From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
pp 380-402
- By Chao Cheng, Geisel School of Medicine at Dartmouth, Hanover, NH
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18 - Data Integration on Noncoding RNA Studies
pp 403-424
- By Zhou Du, Harvard Medical School, Boston, MA, Teng Fei, Dana-Farber Cancer Institute, Boston, MA, Myles Brown, Dana-Farber Cancer Institute, Boston, MA, X. Shirley Liu, Dana-Farber Cancer Institute, Boston, MA, Yiwen Chen, UT MD Anderson Cancer Center, Houston, TX
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19 - Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
pp 425-444
- By Cong Li, Yale University, New Haven, CT, Can Yang, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China, Greg Hather, Takeda Pharmaceuticals International Co., Cambridge, MA, Ray Liu, Takeda Pharmaceuticals International Co., Cambridge, MA, Hongyu Zhao, Yale University, New Haven, CT
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Index
pp 445-462
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Color plates
pp 463-486
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Integrating Omics Data

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

Contributors
pp vii-x

Introduction
pp 1-6

Part A - Horizontal Meta-Analysis
pp 7-8

1 - Meta-Analysis of Genome-Wide Association Studies: A Practical Guide
pp 9-38

2 - MetaOmics: Transcriptomic Meta-Analysis Methods for Biomarker Detection, Pathway Analysis and Other Exploratory Purposes
pp 39-67

3 - Integrative Analysis of Many Biological Networks to Study Gene Regulation
pp 68-87

4 - Network Integration of Genetically Regulated Gene Expression to Study Complex Diseases
pp 88-109

5 - Integrative Analysis of Multiple ChIP-X Data Sets Using Correlation Motifs
pp 110-132

Part B - Vertical Integrative Analysis (General Methods)
pp 133-134

6 - Identify Multi-Dimensional Modules from Diverse Cancer Genomics Data
pp 135-154

7 - A Latent Variable Approach for Integrative Clustering of Multiple Genomic Data Types
pp 155-173

8 - Penalized Integrative Analysis of High-Dimensional Omics Data
pp 174-204

9 - A Bayesian Graphical Model for Integrative Analysis of TCGA Data: BayesGraph for TCGA Integration
pp 205-220

10 - Bayesian Models for Flexible Integrative Analysis of Multi-Platform Genomics Data
pp 221-241

11 - Exploratory Methods to Integrate Multisource Data
pp 242-268

Part C - Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
pp 269-270

12 - eQTL and Directed Graphical Model
pp 271-290

13 - MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
pp 291-309

14 - Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
pp 310-336

15 - Analyzing Combinations of Somatic Mutations in Cancer Genomes
pp 337-361

16 - A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
pp 362-379

17 - From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
pp 380-402

18 - Data Integration on Noncoding RNA Studies
pp 403-424

19 - Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
pp 425-444

Index
pp 445-462

Color plates
pp 463-486

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Integrating Omics Data

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