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
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