OBJECTIVES

Before we can begin to apply rigorous statistical tools to research data, we often need to approach our data intuitively, and look for meaningful associations, surprising patterns, or irregularities, to formulate hypotheses. This is commonly referred to as Exploratory Data Analysis--EDA. This workshop introduces the essential tools and strategies that are available through the free statistical workbench R. Participants should be able to modify the scripts and protocols we discuss for their research tasks, identify potential problems with their own data, and define their statistics needs for cases in which expert advice is required. Case studies with common research scenarios such as microarray data, and flow cytometry will emphasize practical skills. Writing your own R functions and analysis scripts will be introduced at the beginning of the workshop and skills will be gradually built on over the course of the lectures. Plotting and visualization is a key element of EDA and we will gradually build skills--from the elementary built-in routines via their (sometimes bewildering) array of parameters to sophisticated, publication-ready presentations.