Tutorial for Differential Expression analysis

Hello everyone!

Welcome to this GitHub repository. This repository was designed to provide you a wide view for the Differential Expression (DE) analysis using the bioconductor package DESeq2. It is based on the pipeline published by Michael Love. Please, be aware that this tutorial is split in two pipelines:

Required packages

Before running the analysis, please install the following R packages from CRAN:

• tidyverse
• RColorBrewer
• pheatmap
• here

Also, make sure that you have installed the latest version of BioConductor. If not, run the next lines on R:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(version = "3.13")

and install:

• tximeta
• DESeq2
• PCAtools
• apeglm
• Glimma
• clusterProfiler
• enrichplot
• biomaRt
• fgsea

Folder content

This is the distribution of folders and files in this repository:

+-- Tutorial for Differential Expression analysis
|		+--bin/
|		+--data/
|		+--Output/
|		+--slides/
|		+--reference/

bin/

This folder stores the required scripts to run the DE analysis in R:

1.QC_analysis.sh. Script to perform quality control of raw reads using FastQC and MultiQC.

2.Read_cleaning.sh. Script for clean the reads and remove the Iluminna universal adapter sequence from raw reads using Cutadapt.

3.Pseudoalignment.sh. This script contains the code for performing pseudoalignment and abundance estimation using Salmon

4.Differential_expression_analysis.R. This script performs differential expression testing using the algorithm of DESeq2.

functions.R. Script containing useful functions.

data

Folder for storing the raw counts in fastq.gz format. After getting the raw counts, please copy them in this folder. Also, copy the SummarizedExperimentObj.rdata in this folder if you decide to follow the shortest pipeline.

Rmd and html

These folders contains the Rmd and html files for the slides and the cheatsheet. Follow this link to visualize the Slides. The cheatsheet is ubicated here.

If you decide to follow the shortest pipeline, this link redirects you to the slides or the cheatsheet

Results

This folder stores the results derived from:

• FastQC
• MultiQC
• Salmon_quants

reference

The reference transcriptome downloaded from the GENECODE site is stored here in fasta format.

I suggest you take a read to the user’s manual for each package in order to find useful information to perform DE analysis on your data.