SISBID

Notes for workshop

Module 2: Visualization of Biomedical Big Data

Instructors: Hadley Wickham and Dianne Cook

Module description: In this module, we will present general-purpose techniques for visualizing any sort of large data sets, as well as specific techniques for visualizing common types of biological data sets. Often the challenge of visualizing Big Data is to aggregate it down to a suitable level. Understanding Big Data involves an iterative cycle of visualization and modeling. We will illustrate this with several case studies during the workshop. The first segment of this module will focus on structured development of graphics using static graphics. This will use the ggplot2 package in R. It enables building plots using grammatically defined elements, and producing templates for use with multiple data sets. We will show how to extend these principles for genomic data using the ggplot2-based ggbio package. The second segment will focus on interactive graphics for rapid exploration of Big Data. We will also demonstrate interactive techniques for high-performance local display using cranvas, and for easily creating interactive web graphics with ggvis. In addition we will explain how to create simple web GUIs for managing complex summaries of biological data using the shiny package. We will use a hands-on teaching methodology that combines short lectures with longer practice sessions. As students learn about new techniques, they will also be able to put them into practice and receive feedback from experts. We will teach using R and Rstudio. We will assume some familiarity with R.

Recommended Reading: Cookbook for R, by Winston Chang, available at www.cookbook-r.com.

Day 1:

• Session 1: Grammar of graphics for making plots
• Session 2: Rearranging data
• Session 3: Multivariate plots used in pre-processing biological data
• Session 4: Advanced grammar for making sophisticated plots

Day 2:

• Session 1: Genomic plots using ggbio
• Session 2: Interactive graphics using ggvis
• Session 3: Plots for assessing differential expression
• Session 4: Clustering expression data or inference or genealogy??

Day 3:

• Session 1: Shiny apps
• Session 2: Wrap-up

Software list:

• ggplot2
• dplyr
• GGally
• ggbio
• epivizr
• edgeR
• EDAseq
• Biobase
• rtracklayer
• Rsamtools
• GenomicAlignments
• nullabor
• shiny
• stringr
• ggenealogy