One of the major advantages of HTS technologies is their ability to simultaneously interrogate thousands of genes/compounds and generate large amounts of data per experiment. To glean biological information from large volumes of data, the first step in data analysis is to use specific graphics to visualize the data and display important features of data. Data display allows the identification of potential problems such as row and column effect, pin issues, and so forth, as they occur. If the identified spatial effects are caused by systematic experimental error, we need to adjust for them. 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. The commonly used analytic methods such as z-score and t-test are based on normal distributions or at least symmetric distributions with constant variance. However, the raw values from RNAi screens are usually skewed with unequal variance. Data transformation is one of the most effective techniques for handling this issue. I also explore data transformation in this chapter.

Data Display Using Graphics

Plate-Well Series Plot

In a typical RNAi HTS experiment, there are tens to hundreds of plates, each with 384 or 96 wells in which siRNAs are transfected.