Past Mini-Courses

Advanced Graphics in R

Laurel Stell, February 7, 2018

Featuring the lattice, ggplot2 and ComplexHeatmap packages, with appearances by reshape2 and plyr packages for data manipulation. Create high quality versions of standard plot types such as scatter, box, and histograms. Add embellishments and use multiple panels to increase information content. Invent entirely new types of graphics. Examples will demonstrate how to add, customize, and combine graphical elements in order to accomplish all of these goals. 

SUPPORTING MATERIALS

Slides and R Markdown file

Analysis of Gene Expression Data From RNA-seq

Laurel Stell, May 2, 2018

Although I will briefly give a few tips on the RNA-seq process and obtaining expression level data from FASTQ files, the focus of this presentation will be how to use R packages from the Bioconductor project to analyze the data output by that pipeline. Topics will include normalizing across libraries, transformations for exploratory analysis, adjusting for hidden artifacts such as batch and RIN, differential expression testing, and gaining insights from your results. I will give examples from real world data.

SUPPORTING MATERIALS

Supporting materials are not available at this time because the author has changed her mind about some of the information in them.  New materials will be posted when available.

Accounting for Unobserved Factors When Testing RNA Seq Data for Differential Expression

Laurel Stell, March 6, 2019

Unobserved technical and biological factors can affect RNA-seq data, resulting in both false positives and loss of power when testing for differential expression between groups.  This problem is widespread and commonly handled with surrogate variable analysis (SVA), using the sva or SmartSVA package in R to estimate the number of latent factors as well as the factors themselves.  Unfortunately, these algorithms may fail under some conditions such as strong correlations between the factors and the variable being tested for differential expression.  Newer packages such as cate address these issues.  I will discuss both theory and practical implications and give examples from real world data. I will make the slides and sample code publicly available, although the data cannot yet be released.

SUPPORTING MATERIALS

Slides. Example R code coming soon. 

An Introduction to ggplot2 for Advanced Graphics in R

Laurel Stell, May 1, 2019

Still using base graphics in R? Struggling to visualize your data in a way that reveals its secrets? Would you like your plots to look more professional? Tired of using clunky tools (such as par() and for() loops) to arrange multiple plots in one _gure? This presentation will help you get started using ggplot2 and on the path to more beautiful and informative visualizations with less code and frustration. I will provide many examples, tips for learning more, and the R Markdown _le used to create the slides.

 

SUPPORTING MATERIALS

Slides and RMD file