/DESeq2

Guide for the Differential Expression Analysis of RNAseq data using DESeq2

Primary LanguageROtherNOASSERTION

Guide for the Differential Expression Analysis of RNAseq data using DESeq2

Made by David Requena (drequena@rockefeller.edu) and James Saltsman (jsaltsman@rockefeller.edu).


This code includes some basic steps:

  1. SET UP:
  • Install and/or call the required libraries
  • Input sample metadata
  • Create the DESeq2 object
  1. Exploring the data:
  • Transformations of the Data
  • PCA plot
  • tSNE plot
  • HeatMap
  1. Data Analysis:
  • Model matrix
  • Comparison
  • Annotation and output tables
  1. Plots:
  • Histogram of p-values
  • Dispersion Estimates
  • MA Plot
  • Volcano Plot
  • HeatMap with genes
  • BoxPlot and ScatterPlot

To run this script, two 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

And two optional tables:

  • A table with genes to be filtered out (e.g. ribosomal genes)
  • A table with genes of interest, to prepare individual plots by gene