Intermediate RNA Seq Analysis Using R

Description

The learning objectives for this workshop include: 1) How to go from a matrix of raw gene expression counts to differentially expressed genes. 2) How to analyze experimental designs that go beyond 2-group comparisons using edgeR’s generalized linear modeling capabilities. 3) Ways to test specific hypotheses using a joint model fit. Prerequisites: A minimum of 4 to 6 months experience programming in R and have taken the RNAseq Analysis in R workshop or something equivalent.

Learning Path

   This is an intermediate workshop in the RNA-Seq Analysis series. Prior experience with R and pre-processing of RNA-Seq data is required. See introductory workshops:

• Introduction to RNA-Seq Analysis
• Introduction to R

Materials

Click here to download the materials and the slides.

Pre-workshop Instructions

Before the workshop, please make sure that you have R and RStudio installed on your laptops. Please also install the statmod, edgeR, tidyverse, magrittr, org.Mm.eg.db and ggplot2 packages in R. Follow this instructions:

1. Open Rstudio
2. Enter the commands below to install the required packages.

install.packages("magrittr")

install.packages("statmod")

install.packages("BiocManager")

BiocManager::install("edgeR")

BiocManager::install("org.Mm.eg.db")

install.packages("tidyverse")

install.packages("ggplot2")

3. Verify the correct installation uploading the library with library(package_name)

library(magrittr)

library(statmod)

library(edgeR)

library(org.Mm.eg.db)

library(tidyverse)

library(ggplot2)

Online Learning

1. Refer to this tutorial article by Chen et al. for a step-by-guide.

2. Check out the RNA-Seq Analysis with R Bioconductor module in the UCSF library's Collaborative Learning Environment.