Guide for the Differential Expression Analysis of RNAseq data using limma-voom
Including also a commented section about the limma-trend approach
Made by David Requena (drequena@rockefeller.edu) and James Saltsman (jsaltsman@rockefeller.edu).
This code includes some basic steps:
- SET UP:
- Install and/or call the required libraries
- Input sample metadata
- Exploring the data:
- Steps available in our previous guide: https://github.com/SimonLab-RU/DEseq2
- Data Analysis:
- Prior filtering
- Model matrix
- Comparison
- Annotation and output tables
- Plots:
- Available in our previous guide: https://github.com/SimonLab-RU/DEseq2
To run this script, three tables are required:
- A table with the samples' data, containing features of interest (e.g. cases/controls, gender, etc...)
- A table with the gene counts by sample
- A table with genes to be filtered out (e.g. ribosomal genes)