Exploratory Analysis of Biological Data using R
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 Exploratory Data Analysis (EDA). This workshop introduces the essential tools and strategies that are available for EDA through the free statistical workbench R.
Working from hands-on scripts that cover key aspects of EDA, participants learn to use R and its analysis tools, read and modify code, and explore protocols that can be adapted for their own research tasks. Steps covered in this workshop are broadly relevant for many areas of modern, quantitative biology such as flow cytometry, expression profile analysis, function prediction and more. 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.
Module 1 - Exploratory Data Analysis (EDA)
Module 2 - Regression
Module 3 - Dimension Reduction
Module 4 - Clustering
Module 5 - Hypothesis Testing for EDA