PQG Short Course

July 16-18, 2018: Introduction to R & Differential Gene Expression Analysis

Description: This 3-day hands-on workshop will introduce participants to the basics of R (using RStudio) and its application to differential gene expression analysis on RNA-seq count data. This workshop requires no prior programming knowledge. Participants should be interested in:

  • using R for increasing their efficiency for data analysis
  • visualizing data using R (ggplot2)
  • using R to perform statistical analysis on RNA-seq count data to obtain differentially expressed gene lists (DESeq2)

*Please note that this workshop does NOT cover single-cell RNA-seq analysis.

Instructors: Bioinformatics Training Team of the Harvard Chan Bioinformatics Core (HBC)

Venue and logistics: 9 AM – 5 PM. There will be two short coffee breaks during the day and a longer lunch break (lunch will not be provided).

Harvard Medical School
Armenise 108
200 Longwood Ave
Boston, MA 02115

Tuition: $1100 per person, to be paid at the time of registration. We will be accepting 25 participants on a first-come, first-served basis: 


Logistical inquiries can be directed to Amanda King
Inquiries regarding course content can be directed to HBC’s Training Team

Workshop segments will address the following:

  • R syntax: Understanding 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: Understanding classes of data structures and the data types used by R.
  • Data inspection and wrangling: Reading in data from files. Using indices and functions from base R and the tidyverse suite of packages to subset, merge, and create datasets.
  • Visualizing data: Visualizing data using plotting functions in base R as well as external packages such as ggplot2.
  • Exporting data and graphics: Generating data tables and plots for use outside of R.
  • Differential expression analysis for RNA-seq data:
    • QC on count data
    • Using DESeq2 to obtain a list of significant differentially expressed genes
    • Visualizing expression patterns of differentially expressed genes
    • Performing functional analysis on gene lists with R-based tools
We will also cover good data management practices, installing & utilizing data packages from various sources, and different ways to get help when coding in R.