Optimal High-Throughput Screening : Practical Experimental Design and Data Analysis for Genome-Scale RNAi Research
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.
- Electronic book text
- CAMBRIDGE UNIVERSITY PRESS
- Cambridge University Press (Virtual Publishing)
- Cambridge, United Kingdom
- 47 b/w illus. 23 tables
Table of contents
Part I. RNAi HTS and Data Analysis: 1. Introduction to genome-scale RNAi research; 2. Experimental designs; 3. Data display and normalization; 4. Quality control in genome-scale RNAi screens; 5. Hit selection in genome-scale RNAi screens without replicates; 6. Hit selection in genome-scale RNAi screens with replicates; Part II. Methodological Development for Analyzing RNAi HTS Screens: 7. Statistical methods for group comparison; 8. Statistical methods for assessing the size of siRNA effects.