General Information



Stephen Turner 
Stephen Turner, Ph.D. is faculty in the Department of Public Health Sciences, and director of the Bioinformatics Core at the UVA School of Medicine.

VP Nagraj  Pete Nagraj teaches, consults and contributes to scientific programming and data analysis projects in UVA SOMRC. With expertise in R and Python, he’s been active in package development and a variety of open-source collaborations.

Where: BIMS Education Center (McKim Hall)

Spring 2017 Module S1
Feb 12 - Mar 26, 2017 (No exam – final class period will be held March 26.)
2:00pm - 5:00pm

About this course

This course introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, the course introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analyses of real experimental NGS data using R and Bioconductor packages.

This is not a “Tool X” or “Software Y” class. I want you to take away from this series the ability to use an extremely powerful scientific computing environment (R) to do many of the things that you’ll do across study designs and disciplines – managing, manipulating, visualizing, and analyzing large, sometimes high-dimensional data. Whether that data is gene expression data from yeast, microbial genomics data from B. pertussis, public health data from Gapminder, RNA-seq data from humans, influenza outbreak data, movie preference trends from Netflix, or truck routing data from FedEx, you’ll need the same computational know-how and data literacy to do the same kinds of basic tasks in each. I might show you how to use specific tools here and there (DESeq2 for RNA-seq analysis, ggtree for drawing phylogenetic trees, etc.), but these are not important – you probably won’t be using the same specific software or methods 10 years from now, but you’ll still use the same underlying data and computational foundation. That is the point of this series – to arm you with a basic foundation, and more importantly, to enable you to figure out how to use this tool or that tool on your own, when you need to.

This is not a statistics class. There is a short lesson on essential statistics using R but this 3-hour lesson offers neither a comprehensive background on underlying theory nor in-depth coverage of implementation strategies using R. Some general knowledge of statistics and study design is helpful, but isn’t required for this course.


Click the Setup link on the navbar at the top and review all the information and follow the instructions prior to the workshop.

You should set aside a couple hours to download, install, and test all the software needed for the course. All the software we’re using in class is open-source and freely available online. This setup must be completed prior to class, as we will not have much time for troubleshooting software installation issues during class. Email us if you’re having difficulty.

After installing and testing the software we’ll be using, please download the data as instructed, and review the required reading prior to class.

Course Schedule

(Subject to change)

Week 1: Intro to R

This novice-level introduction is directed toward life scientists with little to no experience with statistical computing or bioinformatics. This interactive introduction will introduce the R statistical computing environment. The first part of this workshop will demonstrate very basic functionality in R, including functions, functions, vectors, creating variables, getting help, filtering, data frames, plotting, and reading/writing files.

Week 2: Advanced Data Manipulation with R

Data analysis involves a large amount of janitor work – munging and cleaning data to facilitate downstream data analysis. This session assumes a basic familiarity with R and covers tools and techniques for advanced data manipulation. It will cover data cleaning and “tidy data,” and will introduce R packages that enable data manipulation, analysis, and visualization using split-apply-combine strategies. Upon completing this lesson, students will be able to use the dplyr package in R to effectively manipulate and conditionally compute summary statistics over subsets of a “big” dataset containing many observations.

Week 3: Advanced Data Visualization with R and ggplot2

This session will cover fundamental concepts for creating effective data visualization and will introduce tools and techniques for visualizing large, high-dimensional data using R. We will review fundamental concepts for visually displaying quantitative information, such as using series of small multiples, avoiding “chart-junk,” and maximizing the data-ink ratio. After briefly covering data visualization using base R graphics, we will introduce the ggplot2 package for advanced high-dimensional visualization. We will cover the grammar of graphics (geoms, aesthetics, stats, and faceting), and using ggplot2 to create plots layer-by-layer. Upon completing this lesson, students will be able to use R to explore a high-dimensional dataset by faceting and scaling arbitrarily complex plots in small multiples.

On your own: Reproducible Research & Dynamic Documents

Further instructions for learning RMarkdown on your own will be forthcoming. Contemporary life sciences research is plagued by reproducibility issues. This session covers some of the barriers to reproducible research and how to start to address some of those problems during the data management and analysis phases of the research life cycle. In this session we will cover using R and dynamic document generation with RMarkdown and RStudio to weave together reporting text with executable R code to automatically generate reports in the form of PDF, Word, or HTML documents.

Week 4: Essential Statistics

This session will provide hands-on instruction and exercises covering basic statistical analysis in R. This will cover descriptive statistics, t-tests, linear models, chi-square, clustering, dimensionality reduction, and resampling strategies. We will also cover methods for “tidying” model results for downstream visualization and summarization.

Week 5: Survival Analysis

This session will provide hands-on instruction and exercises covering survival analysis using R. The data for parts of this session will come from The Cancer Genome Atlas (TCGA), where we will also cover programmatic access to TCGA through Bioconductor.

Week 6: Introduction to RNA-seq Data Analysis

This session focuses on analyzing real data from a biological application - analyzing RNA-seq data for differentially expressed genes. This session provides an introduction to RNA-seq data analysis, involving reading in count data from an RNA-seq experiment, exploring the data using base R functions and then analysis with the DESeq2 Bioconductor package. The session will conclude with downstream pathway analysis and exploring the biological and functional context of the results.

Week 7: Predictive Modeling & Forecasting

This session will provide hands-on instruction for using machine learning algorithms to predict a disease outcome. We will cover data cleaning, feature extraction, imputation, and using a variety of models to try to predict disease outcome. We will use resampling strategies to assess the performance of predictive modeling procedures such as Random Forest, stochastic gradient boosting, elastic net regularized regression (LASSO), and k-nearest neighbors. We will also demonstrate demonstrate how to forecast future trends given historical infectious disease surveillance data using methodology that accounts for seasonality and nonlinearity.


What are the pre-requisites?

There are none! (But there is some required reading and software setup required before the course). This course doesn’t assume any knowledge of programming or using a command-line interface, but if you’ve ever had any experience here, the content won’t come as so much of a shock. But don’t panic. Command-line interfaces and programming languages like R are incredibly powerful and will be utterly transformative on your research. There’s a learning curve, and it’s near-vertical in the beginning, but it’s surmountable and the payoff is worth it!

Do I need a laptop?

YES. You must have access to a computer on which you can install software. The class will be a mix of lecture, discussion, but primarily live coding. You must bring your laptop to the course every day. Bring your charging cable also. Please follow the setup instructions prior to the workshop.

Where can I get more help?

Glad you asked! See here.