Differential expression analysis with DESeq2
Differential gene expression analysis is a technique used to identify genes that are differentially expressed, or turned on or off, between two or more biological conditions or samples. This can be used to identify genes that are specifically involved in a particular process or disease, or to understand the underlying molecular mechanisms of a biological system.
Once the differentially expressed genes have been identified, researchers can use various techniques, such as gene ontology analysis and pathway analysis, to further understand the biological functions and pathways in which these genes are involved. This information can be used to gain insights into the underlying mechanisms of a biological process or disease, and may ultimately lead to the development of new treatments or therapies.
There are several tools used to identify genes differentially expressed between different conditions or groups of samples. Here, we will explore DESeq2 (Differential Expression analysis for Sequencing). It uses statistical methods to analyze RNA-seq data and identify genes that are differentially expressed between two or more conditions or samples. It takes into account various sources of variability in the data, such as batch effects and technical noise, to accurately identify differentially expressed genes.
We are offering you two tutorials to run DESeq2 on two different samples.
DESeq2 tutorial #1 by Griffith lab
Use your local R Studio and replicate the pipeline described in this tutorial.
DESeq2 tutorial #2 by Altuna Akalin
You can also try out this tutorial, where you will find additional explanation for the pipeline. Note that in order to be able to access the dataset for this tutorial you'll need to have run the previous R practicals of this textbook subchapter.
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