By Xiaohua Douglas Zhang
This concise, self-contained, and cohesive booklet specializes in common and lately constructed tools for designing and reading high-throughput screening (HTS) experiments from a statistically sound foundation. Combining principles from biology, computing, and information, the writer explains experimental designs and analytic tools which are amenable to rigorous research and interpretation of RNAi HTS experiments. the hole chapters are conscientiously offered to be obtainable either to biologists with education in simple terms in uncomplicated information and to computational scientists and statisticians with simple organic wisdom. Biologists will see how new scan designs and rudimentary data-handling innovations for RNAi HTS experiments can increase their effects, while analysts will how one can follow lately constructed statistical easy methods to interpret HTS experiments.
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Additional info for Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-Scale RNAi Research
Example text
When controls are arranged in a plate, we may use different colors or point types to display the values of various control wells in a plate. The plate-well series plot can also be used to display the results of hit selection in a screen. For example, the sample wells for selected hits are labeled with one color, and the sample wells for non-hits are labeled with another color. 645 (see Chapters 5 and 8 for more details about SSMD). The advantage of a plate-well series plot is that it can effectively display plate-toplate variability, show selected hits for all plates, and present common data features of multiple plates in a single plot.
Data display allows the identification of potential problems such as row and column effect, pin issues, and so forth, as they occur [20;58;99;166;173]. If the identified spatial effects are caused by systematic experimental error, we need to adjust for them [20;83;166;173]. Otherwise, they will produce misleading results in both quality control and hit selection. In this chapter, I present graphics for displaying data and explore analytic methods for identifying and/or adjusting for spatial effects that are caused by systematic experimental error.
One common plate image is the so-called heat map, in which green represents low values and red represents high values. However, the regular heat map is dominated by extreme data values. 2A). It is well-known that strong true hits behave like outliers and outliers commonly exist in genome-scale RNAi screens. Therefore, the standard heat map or plate image does not work effectively in genome-scale RNAi screens. 4. In the legends, Background, Positive Control, and Misc Control denote empty wells, stronger inhibition control wells, and weaker inhibition control wells, respectively.