A workshop series on how to efficiently manage and analyze sequencing data. Bioinformatics Workshops

Introduction to R: Basics, Plots, and RNA-Seq Differential Expression Analysis

At a glance
Opportunity for
  • Participants to understand the basics of R and RStudio and their application to differential gene expression analysis on RNA-Seq count data
  • MD, DNP, PhD or equivalent, DDS/DMD
  • Receipt of endorsement from an applicant's supervisor stating the applicant will be able to attend all days of the workshop
Time commitment
  • Three days
Funding level
  • Tuition-free
Session dates
  • May 17-19, 2017
Application Due
  • 5:00pm on April 6, 2017
  • Endorsements due 5:00pm April 10, 2017
  • All applicants will be notified of their status via email no later than April 18, 2017.
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This three-day hands-on workshop will introduce participants to the basics of R and RStudio and their application to differential gene expression analysis on RNA-Seq count data. R is a simple programming environment that enables the effective handling of data, while providing excellent graphical support. RStudio is a tool that provides a user-friendly environment for working with R. Together, R and RStudio allow participants to manipulate data, plot, and use DESeq2 to obtain lists of differentially expressed genes from RNA-Seq count data.

This workshop is intended to provide both basic R programming knowledge AND its application. Participants should be interested in:

  • using R for increasing their efficiency for data analysis
  • plotting data visualizations using R, including ggplot2
  • using R to perform statistical analysis on RNA-Seq count data to obtain differentially expressed gene lists

Workshop segments will address the following:

  • R syntax: Understanding the different 'parts of speech' in R; introducing variables and functions, demonstrating how functions work, and modifying arguments for specific use cases.
  • Data structures in R: Getting a handle on the classes of data structures and the types of data used by R.
  • Inspecting and manipulating data: Reading in data from files. Using indexes and various functions to subset, merge, and create datasets.
  • Making plots to visualize data: Visualizing data using plotting functions in base R as well as from external packages such as ggplot2.
  • Exporting data and graphics: Generating new data tables and plots for use outside of the R environment.
  • Differential expression analysis for RNA-Seq data:
    • QC on count data
    • Using DESeq2 to obtain a list of significantly different genes
    • Visualizing expression patterns of differentially expressed genes
    • Performing functional analysis on gene lists with R-based tools

Harvard Catalyst Postgraduate Education Program's policy requires full attendance and the completion of all activity surveys to be eligible for CME credit; no partial credit is allowed.

The Harvard Catalyst Education Program is accredited by the Massachusetts Medical Society to provide continuing medical education for physicians.