Part I: Basic Statistics1. Experimental Statistics for Biological SciencesHeejung Bang and Marie Davidian2. Nonparametric Methods in Molecular BiologyKnut M. Wittkowski and Tingting Song3. Basics of Bayesian MethodsSujit K. Ghosh4. The Bayesian t-Test and BeyondMithat GönenPart II: Designs and Methods for Molecular Biology5. Sample Size and Power Calculation for Molecular Biology StudiesSin-Ho Jung6. Designs for Linkage Analysis and Association Studies of Complex DiseasesYuehua Cui, Gengxin Li, Shaoyu Li, and Rongling Wu7. Introduction to Epigenomics and Epigenome-Wide AnalysisMelissa J. Fazzari and John M. Greally8. Exploration, Visualization, and Preprocessing of High Dimensional DataZhijin Wu and Zhiqiang WuPart III: Statistical Methods for Microarray Data9. Introduction to the Statistical Analysis of Two-Color Microarray DataMartina Bremer, Edward Himelblau, and Andreas Madlung10. Building Networks with Microarray DataBradley M. Broom, Waree Rinsurongkawong, Lajos Pusztai, and Kim-Anh DoPart IV: Advanced or Specialized Methods for Molecular Biology11. Support Vector Machines for Classification: A Statistical PortraitYoonkyung Lee12. An Overview of Clustering Applied to Molecular BiologyRebecca Nugent and Marina Meila13. Hidden Markov Model and Its Applications in Motif FindingsJing Wu and Jun Xie14. Dimension Reduction for High Dimensional DataLexin Li15. Introduction to the Development and Validation of Predictive Biomarker Models from High-Throughput DatasetsXutao Deng and Fabien Campagne16. Multi-GeneExpression-Based Statistical Approaches to Predicting Patients’ Clinical Outcomes and ResponsesFeng Cheng, Sang-Hoon Cho, and Jae K. Lee17. Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based DesignsAmy Murphy, Scott T. Weiss, and Christoph Lange18. Statistical Methods for ProteomicsKlaus JungPart V: Meta-Analysis for High-Dimensional Data19. Statistical Methods for Integrating Multiple Types of High-Throughput DataYang Xie and Chul Ahn20. A Bayesian Hierarchical Model for High-Dimensional Meta AnalysisFei Liu21. Methods for Combining Multiple Genome-Wide Linkage StudiesTrecia A. Kippola and Stephanie A. SantoricoPart VI: Other Practical Information22. Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial PoliciesMadhu Mazumdar, Samprit Banerjee, and Heather L. Van Epps23. Stata CompanionJennifer Sousa Brennan
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
