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Optimal high-throughput screening : practical experimental design and data analysis for genome-scale RNAi research / Xiaohua Douglas Zhang. — Cambridge : Cambridge University Press, 2011. – (63.32/Z63) |
Contents
Contents
Preface
Acknowledgments
Acronyms and Abbreviations
Part I RNAi HTS and Data Analysis
1 Introduction to Genome-Scale RNAi Research
1.1 RNAi: An Effective Tool for Elucidating Gene Functions and a New Class of Drugs
1.2 High-Throughput Screening: A Vital Technology in Drug Discovery
1.3 Genome-Scale RNAi Screens
1.4 An Example of Genome-Scale RNAi Research
1.5 Challenges in Genome-Scale RNAi Research
2 Experimental Designs
2.1 siRNA Designs
2.2 Control Designs
2.3 Plate Designs
2.4 Designs of siRNA Delivery and Optimization of Transfection
2.5 Design of Sample Size
2.6 Conclusions
3 Data Display and Normalization
3.1 Data Display Using Graphics
3.2 Transformation of Measured Raw Values
3.3 Identification and Adjustment of Systematic Spatial Effects
3.4 Strategy for Data Display and Normalization
4 Quality Control in Genome-Scale RNAi Screens
4.1 Introduction
4.2 Quality Assessment Metrics
4.3 Quality Control Criteria
4.4 Adoption of Effective Plate Designs
4.5 Integration of Experimental and Analytic Approaches to Improve Data Quality
4.6 Application
4.7 Discussion and Conclusions
5 Selection in Genome-Scale RNAi Screens without Replicates
5.1 Introduction
5.2 Methods for Hit Selection in Primary Screens without Replicates
5.3 Decision Rules for Hit Selection in RNAi Screens
5.4 Sample Size Determination
5.5 Applications
5.6 Conclusions
6 Selection in Genome-Scale RNAi Screens with Replicates
6.1 Metrics for Hit Selection in Screens with Replicates
6.2 Dual-Flashlight Plot
6.3 Decision Rules for Hit Selection in Screens with Replicates
6.4 False Discovery Rate， False Non-Discovery Rate， q-Value， and q*-Value
6.5 Sample Size Determination
6.6 Analytic Methods Adjusting for Off-Target Effects
6.7 Applications
6.8 Discussion and Conclusions
Part II Methodological Development for Analyzing RNAi HTS Screens
7 Statistical Methods for Group Comparison
7.1 Illustration of Issues in Traditional Contrast Analysis
7.2 Contrast Variable， SMCV， and c+-Probability
7.3 A Classifying Rule for Interpreting Strength of Group Comparisons
7.4 A Theorem to Facilitate the Estimation and Inference of SMCV
7.5 Estimation of SMCV and c+-probability
7.6 Contrasts in Multifactor ANOVA
7.7 Case Studies and Simulation
7.8 Discussion and Conclusions
8 Statistical Methods for Assessing the Size of siRNA Effects
8.1 SSMD and d+-Probability
8.2 Estimation of SSMD
8.3 Comparing SSMD with Standardized Mean Difference and Classical t-Statistic
8.4 SSMD-Based Ranking Methods for Hit Selection in Genome-Scale RNAi Screens
8.5 SSMD-Based FPR， FNR， and Power
8.6 FDR and FNDR in RNAi Screens
8.7 Analytic Methods Adjusting for Off-Target Effects
8.8 Discussion and Conclusions
References
Index
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