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Statistical methods in molecular biology / edited by Heejung Bang ... [et al.]. — New York ; London : Humana, c2010. – (58.17/M592/v.620) |
Contents
Contents
PART I BASIC STATISTICS
1. Experimental Statistics for Biological Sciences
2. Nonparametric Methods for Molecular Biology
3. Basics of Bayesian Methods
4. The bayesian t-Test and Beyond
Part II DESIGNS AND METHODS FOR MOLECULAR BIOLOGY
5. Sample Size and Power calculation for Molecular biology Studies
6. Designs for linkage Analysis and Association Studies of Complex Diseases
7. Introduction to Epigenomics and Epigenome-Wide Analysis
8. Exploration， Visualization， and Preprocessing of High-Dimensional data 267
PART III STAIISTICAL METHODS FOR MICROARRAY DATA
9. Introduction to the Statistical Analysis of Two Color Microarray Data
10. Building Networks with Microarray Data
PART IV ADVANCED OR SPECIALIZED METHODS FOR MOLECULAR BIOLOGY
11. Support Vector Machines for Classification: A Statistical Portrait
12. An Overview of Clustering Applied to Molecular Biology
13. Hidden Markov Model and Its Applications in Motif Findings 405
14. Dimension Reduction for High-Dimensional Data 417
15. Introduction to the Development and Validation of Predictive Biomarker Models from High-Throughput Data Sets 435
16. Multi-gene Expression-based Statistical Approaches to Predicting Patients' Clinical Outcomes and Responses 471
17. Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs 485
18. Statistical Methods for Proteomics 497
PART V META-ANALYSIS FOR HIGH-DIMENSIONAL DATA 509
19. Statistical Methods for Integrating Multiple Types of High-Throughput Data
20. A Bayesian Hierarchical Model for High-Dimensional Meta-analysis 531
21. Methods for Combining Multiple Genome-Wide Linkage Studies 541
PART VI OTHER PILACTICAL INFORMATION 561
22. Improved Reporting of Statistical Design and Analysis: Guidelines， Education， and Editorial Policies 563
23. Stata Companion 599
Subject Index
