THIS REPO IS ARCHIVED, PLEASE GO TO https://hbctraining.github.io/main FOR CURRENT LESSONS.
Introduction to R and differential gene expression (DGE) analysis
| Audience | Computational Skills | Prerequisites | Duration |
|---|---|---|---|
| Biologists | Beginner/Intermediate | None | 3-day workshop (~19.5 hours of trainer-led time) |
Description
This repository has teaching materials for a 3-day, hands-on Introduction to R and differential gene expression (DGE) analysis workshop. The 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 wrangle 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
- visualizing data using R (ggplot2)
- using R to perform statistical analysis on RNA-seq count data to obtain differentially expressed gene lists
Learning Objectives
- R syntax: Understand the different ‘parts of speech’.
- Data types structures in R: Describe the various data types and data structures.
- Data inspection and wrangling: Demonstrate the utilization of functions and indices to inspect and subset data from various data structures.
- Visualizing data: Demonstrate the use of the ggplot2 package to create plots for easy data visualization.
- Differential expression analysis for RNA-seq data:
- Perform QC on count data
- Use DESeq2 to obtain a list of significantly differentially expressed genes
- Visualize expression patterns of differentially expressed genes
- Perform functional analysis on gene lists with R-based tools
These materials are developed for a trainer-led workshop, but also amenable to self-guided learning.
Lessons
- Scheule with links to lessons on DGE on pseudocounts generated by Salmon will be posted soon!
- Click here for the schedule with links to lessons on DGE on counts from STAR & FeatureCounts
Installation Requirements
- Download the most recent versions of R and RStudio for your laptop:
- Packages to be installed:
- Click here for required packages for lessons on DGE with pseudocounts generated by Salmon
- Click here for required packages for lessons on DGE on counts from STAR-Featurecounts
Dataset
All the files used for the above lessons are linked within as needed.
These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Some materials used in these lessons were derived from work that is Copyright © Data Carpentry (http://datacarpentry.org/). All Data Carpentry instructional material is made available under the Creative Commons Attribution license (CC BY 4.0).