Gene-level RNA-Seq Data Analysis (lagacy)
  • Introduction
  • RNA-Seq Analysis Workflow
    • Login to server
    • Obtain data and software
    • Create mapping indices
    • Mapping with STAR
    • Quantification using RSEM
  • De novo assembly using Trinity
    • De novo assembly of RNA-Seq reads
    • Compare de novo reconstructed transcripts to reference annotations
    • Quantification using RSEM
  • Differential expression analysis using R
    • Install R libraries
    • Perform DE analysis
    • Perform DE analysis (Trinity)
    • Extracting DE transcripts and generating heatmaps (Trinity)
  • Visualization
Powered by GitBook
On this page

Was this helpful?

Differential expression analysis using R

PreviousQuantification using RSEMNextInstall R libraries

Last updated 5 years ago

Was this helpful?

In the this chapter, I will demonstrate how to use R and the Bioconductor package edgeR to analyze the expected counts produced by RSEM.

For reader who do not have an account on the ALPS1 server, please check if you have access to the R environment for statistical analysis in your computer or server. If not, please download and compile the software.

Below is an example. Always check and download the latest version. At the time of writing, the latest R version v3.3.2 was released on 2016-10-31.

wget http://cran.csie.ntu.edu.tw/src/base/R-3/R-3.3.2.tar.gz
tar zxfv R-3.3.2.tar.gz
cd R-3.3.2
./configure
make

R is also available on many operating system, including Windows. Please visit its website at .

http://www.r-project.org/