While there is a wide selection of 'by experts, for experts' books in statistics and molecular biology, there is a distinct need for a book that presents the basic principles of proper statistical analyses and progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology. Statistical Methods in Molecular Biology strives to fill that gap by covering basic and intermediate statistics that are useful for classical molecular biology settings and advanced statistical techniques that can be used to help solve problems commonly encountered in modern molecular biology studies, such as supervised and unsupervised learning, hidden Markov models, methods for manipulation and analysis of high-throughput microarray and proteomic data, and methods for the synthesis of the available evidences. This detailed volume offers molecular biologists a book in a progressive style where basic statistical methods are introduced and gradually elevated to an intermediate level, while providing statisticians knowledge of various biological data generated from the field of molecular biology, the types of questions of interest to molecular biologists, and the state-of-the-art statistical approaches to analyzing the data. As a volume in the highly successful Methods in Molecular Biology series, this work provides the kind of meticulous descriptions and implementation advice for diverse topics that are crucial for getting optimal results. Comprehensive but convenient, Statistical Methods in Molecular Biology will aid students, scientists, and researchers along the pathway from beginning strategies to a deeper understanding of these vital systems of data analysis and interpretation within one concise volume. "Here is a comprehensive book that systematically covers both basic and advanced statistical topics in molecular biology, including parametric and nonparametric, and frequentist and Bayesian methods. I am highly impressed by the breadth and depth of the applications. I strongly recommend this book for both statisticians and biologists who need to communicate with each other in this exciting field of research." Robert C. Elston, PhD., Director, Division of Genetic and Molecular Epidemiology, Case Western Reserve University "An extraordinary exposition of the central topics of modern molecular biology, presented by practicing experts who weave together rigorous theory with practical techniques and illustrative examples." George C. Newman, MD, PhD, Chairman, Neurosensory Sciences, Albert Einstein Medical Center "I cannot think of anything we need now in translation research field more than more efficient cross talk between molecular biology and statistics. This book is just on target. It fills the gap." Iman Osman, MB, BCh, MD, Director, Interdisciplinary Melanoma Cooperative Program, New York University Langone Medical Center

Part I: Basic Statistics 1. Experimental Statistics for Biological Sciences Heejung Bang and Marie Davidian 2. Nonparametric Methods in Molecular Biology Knut M. Wittkowski and Tingting Song 3. Basics of Bayesian Methods Sujit K. Ghosh 4. The Bayesian t-Test and Beyond Mithat Gönen Part II: Designs and Methods for Molecular Biology 5. Sample Size and Power Calculation for Molecular Biology Studies Sin-Ho Jung 6. Designs for Linkage Analysis and Association Studies of Complex Diseases Yuehua Cui, Gengxin Li, Shaoyu Li, and Rongling Wu 7. Introduction to Epigenomics and Epigenome-Wide Analysis Melissa J. Fazzari and John M. Greally 8. Exploration, Visualization, and Preprocessing of High Dimensional Data Zhijin Wu and Zhiqiang Wu Part III: Statistical Methods for Microarray Data 9. Introduction to the Statistical Analysis of Two-Color Microarray Data Martina Bremer, Edward Himelblau, and Andreas Madlung 10. Building Networks with Microarray Data Bradley M. Broom, Waree Rinsurongkawong, Lajos Pusztai, and Kim-Anh Do Part IV: Advanced or Specialized Methods for Molecular Biology 11. Support Vector Machines for Classification: A Statistical Portrait Yoonkyung Lee 12. An Overview of Clustering Applied to Molecular Biology Rebecca Nugent and Marina Meila 13. Hidden Markov Model and Its Applications in Motif Findings Jing Wu and Jun Xie 14. Dimension Reduction for High Dimensional Data Lexin Li 15. Introduction to the Development and Validation of Predictive Biomarker Models from High-Throughput Datasets Xutao Deng and Fabien Campagne 16. Multi-Gene Expression-Based Statistical Approaches to Predicting Patients¿ Clinical Outcomes and Responses Feng Cheng, Sang-Hoon Cho, and Jae K. Lee 17. Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs Amy Murphy, Scott T. Weiss, and Christoph Lange 18. Statistical Methods for Proteomics Klaus Jung Part V: Meta-Analysis for High-Dimensional Data 19. Statistical Methods for Integrating Multiple Types of High-Throughput Data Yang Xie and Chul Ahn 20. A Bayesian Hierarchical Model for High-Dimensional Meta Analysis Fei Liu 21. Methods for Combining Multiple Genome-Wide Linkage Studies Trecia A. Kippola and Stephanie A. Santorico Part VI: Other Practical Information 22. Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial Policies Madhu Mazumdar, Samprit Banerjee, and Heather L. Van Epps 23. Stata Companion Jennifer Sousa Brennan