Welcome to the main course website for Bioinformatics and Computational Biology. Please use our sakai site to access the syllabus, assigned readings, and submit assignments.
Shiny and Geo-spatial visualization. In this tutorial we'll make a simple geo-spatial visualization use Shiny and Leaflet. We'll cover the basics of working with geo-spatial data on a map of the US and see examples of how to use Shiny layout features along the way. (slides)
Tidy Data (part 1). A simple case study in working with data in-the-wild. We'll start with a raw data set donated by the Johnson lab that is in Excel format and walk through how to load it into R. In the process, we will explore R factors and some basic visualization and summary statistics. (slides)
Tidy Data (part 2). Today we'll continue to explore the Johnson lab Mechanical Turk data set. Following on the reading for today, we'll talk about how to best coerce our data models to fit with our analysis goals using the `tidyr` package. (slides)
Genome annotation (part 1). In this exercise you will write a set of functions that find and annotation genomic features in raw DNA sequence. Along the way we'll learn some best-practices for organizing our code and data structures. We'll also explore regular expressions, a powerful declarative language for finding pasterns in text. (slides)
Analysis of gene expression data. In this exercise we'll perform a *de novo* analysis of an existing microarray dataset. The tools we'll explore will be useful for any data analysis project focused on a gene expression dataset. (slides)
Machine learning and exploratory analysis (Part 1). In this lab we'll explore several machine learning algorithms commonly used to find patterns in biological data sets, including clustering and building network graphs.. (slides)
Quick-R. A great collection of quick-and-dirty How-to’s for common data analysis tasks in R. I’d recommend starting with a search here if you’re stuck trying to figure out how to do something that should be simple.
R-Bloggers. This site is a collection of R-related blogs from around the web. It’s a great site to browse if you want to be inspired by cool things other people are doing in R.
R Documentation. Documentation for R and all the current CRAN packages are available on this website with an advanced search interface and community submitted comments.
Advanced R Programming. A new WIP book on advanced R programming by Hadley Wickham. The current draft is incomplete in parts but also freely available!
R Inferno. A quirky but useful exploration of common R pitfalls and many of the less-than-intuitive aspects of R language semantics (told in the model of Dante’s work by the same name).
Evaluating the Design of the R Language. For programming language geeks this 2012 paper takes a pass at describing a formal semantics for the core langauge and provides a deep analysis of the run-time behavior of the reference R interpreter.
Parellel Computation on the GPU. This is a nice guide to a couple of R pacakges that let you use CUDA GPUs to perform computationally intensive operations. All of the PCs in the IQ Center 2D lab have dual high-end nVidia GPUs.