Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-Scale RNAi Research

by Xiaohua Douglas Zhang

• ISBN13: 9780521734448
• ISBN10: 0521734444
• eBook ISBN(s): 9781139062817
• Edition: 1st
• Format: Paperback
• Copyright: 2011-02-21
• Publisher: Cambridge University Press
• More Book Details

Summary

This concise, self-contained, and cohesive book focuses on commonly used and recently developed methods for designing and analyzing high-throughput screening (HTS) experiments from a statistically sound basis. Combining ideas from biology, computing, and statistics, the author explains experimental designs and analytic methods that are amenable to rigorous analysis and interpretation of RNAi HTS experiments. The opening chapters are carefully presented to be accessible both to biologists with training only in basic statistics and to computational scientists and statisticians with basic biological knowledge. Biologists will see how new experiment designs and rudimentary data-handling strategies for RNAi HTS experiments can improve their results, whereas analysts will learn how to apply recently developed statistical methods to interpret HTS experiments.

Author Biography

DR. Xiaohua Douglaszhang is an associate director at Merck Research Laboratories. He has worked on data analysis for genome-wide RNAi research and microarrays in drug discovery and development for various diseases for many years. He has continuously developed novel analytic methods and experimental designs for quality control and hit selection in genome-scale RNAi research. Dr. Zhang and his colleagues have published many papers in various peer-reviewed journals, including Cell Host Microbe, Nucleic Acids Research, Bioinformatics, Genetic Epidemiology, Journal of Biological Chemistry, Pharmacogenomics, Genomics, and Journal of Biomolecular Screening, among many others.

Table of Contents

Preface	p. ix
Acknowledgments	p. xv
Acronyms and Abbreviations	p. xvii
RNAi HTS and Data Analysis
Introduction to Genome-Scale RNAi Research	p. 3
RNAi: An Effective Tool for Elucidating Gene Functions and a New Class of Drugs	p. 3
High-Throughput Screening: A Vital Technology in Drug Discovery	p. 5
Genome-Scale RNAi Screens	p. 7
An Example of Genome-Scale RNAi Research	p. 9
Challenges in Genome-Scale RNAi Research	p. 10
Experimental Designs	p. 13
siRNA Designs	p. 13
Control Designs	p. 15
Plate Designs	p. 16
Designs of siRNA Delivery and Optimization of Transfection	p. 24
Design of Sample Size	p. 24
Conclusions	p. 25
Data Display and Normalization	p. 27
Data Display Using Graphics	p. 27
Transformation of Measured Raw Values	p. 32
Identification and Adjustment of Systematic Spatial Effects	p. 33
Strategy for Data Display and Normalization	p. 41
Quality Control in Genome-Scale RNAi Screens	p. 42
Introduction	p. 42
Quality Assessment Metrics	p. 42
Quality Control Criteria	p. 47
Adoption of Effective Plate Designs	p. 53
Integration of Experimental and Analytic Approaches to Improve Data Quality	p. 56
Application	p. 57
Discussion and Conclusions	p. 60
Hit Selection in Genome-Scale RNAi Screens without Replicates	p. 62
Introduction	p. 62
Methods for Hit Selection in Primary Screens without Replicates	p. 63
Decision Rules for Hit Selection in RNAi Screens	p. 69
Sample Size Determination	p. 74
Applications	p. 77
Conclusions	p. 82
Hit Selection in Genome-Scale RNAi Screens with Replicates	p. 83
Metrics for Hit Selection in Screens with Replicates	p. 83
Dual-Flashlight Plot	p. 86
Decision Rules for Hit Selection in Screens with Replicates	p. 88
False Discovery Rate, False Non-Discovery Rate, q-Value, and q* -Value	p. 90
Sample Size Determination	p. 92
Analytic Methods Adjusting for Off-Target Effects	p. 93
Applications	p. 98
Discussion and Conclusions	p. 106
Methodological Development for Analyzing RNAi HTS Screens
Statistical Methods for Group Comparison	p. 111
Illustration of Issues in Traditional Contrast Analysis	p. 112
Contrast Variable, SMCV, and c+-Probability	p. 114
A Classifying Rule for Interpreting Strength of Group Comparisons	p. 116
A Theorem to Facilitate the Estimation and Inference of SMCV	p. 119
Estimation of SMCV and c+-Probability	p. 127
Contrasts in Multifactor ANOVA	p. 133
Case Studies and Simulation	p. 142
Discussion and Conclusions	p. 152
Statistical Methods for Assessing the Size of siRNA Effects	p. 154
SSMD and d+-Probability	p. 154
Estimation of SSMD	p. 156
Comparing SSMD with Standardized Mean Difference and Classical t-Statistic	p. 159
SSMD-Based Ranking Methods for Hit Selection in Genome-Scale RNAi Screens	p. 165
SSMD-Based FPR, FNR, and Power	p. 166
FDR and FNDR in RNAi Screens	p. 176
Analytic Methods Adjusting for Off-Target Effects	p. 179
Discussion and Conclusions	p. 186
References	p. 189
Index	p. 201
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