A Dynamic Graphics Approach to Exploring High-Content Screening Data

Abstract

The basic idea of high-content screening (or HCS for short) is to use automatic microscopes to perform image acquisition and analysis on individual cells. It is great for characterizing cellular responses to drug treatments. While the potential of HCS in providing content-rich information is well accepted, its data analysis aspect, i.e., how to easily extract as much information out of the giant HCS data sets, remains the least addressed and yet probably the most important issue. Here I demonstrate a dynamic graphics approach to integrate many data visualization techniques long treasured by statisticians to explore HCS data sets. Two HCS data sets are used in this talk. One is from an experiment of cellular responses to a drug affecting cell cycle progression; the other is from 2 RNAi experiments, where the ability to examine subpopulations and correlating them to other parameters are crucial. This dynamic graphics approach empowers HCS users to examine easily any part of their data according to the biological context they know best.