Intro to transcriptomics - from counts to functional analysis. Session 3: Differential gene expression of bulk RNA-seq data using “DESeq2” in RStudio.
Intro to transcriptomics - from counts to functional analysis. Session 3: Differential gene expression of bulk RNA-seq data using “DESeq2” in RStudio. In-Person
The Bioinformatics Support Hub has designed this teaching series for researchers interested in learning the basics steps for re-analysis of publicly available bulk-RNAseq data. We will introduce you to basic genomic data annotation concepts, including where to find data to re-analyze, how to perform differential gene analysis, how to add gene annotations, and how to visualize your results. Finally, we'll demonstrate different approaches to gene perform gene pathway analysis. Importantly, we have introduced interspersed coached-practice sessions to which attendees will be invited to participate. In these sessions, you will be able to try things for yourself and get immediate support if needed. (Please see below for overall schedule).
- Session 1 - Intro to annotation of gene expression data (Tue, August 2nd)
- Session 2 - Where in the World is the Data You Need? How to Find and Reuse Data (e.g. GREIN) – (Thurs, August 4th)
- Coached-practice 1 - Exercise sources and genomic information format (annotations and gene ID) of datasets (Tue, August 9th)
- Session 3 - Re-analyze public dataset with DESeq2 package (Thurs, August 11th)
- Session 4 - biomaRt using R package (Tue, August 16th)
- Coached-practice 2 - Differential Gene expression, annotation (Thurs, August 18th)
- Session 5 - Visualization with Qlucore (Tue, August 23rd)
- Coached-practice 3 - Visualization with Qlucore (Thurs, August 25th)
- Session 6 – Functional analysis of omics data: Overrepresentation Analysis ORA (Tue, August 30th)
- Session 7 – Gene Set Enrichment Analysis GSEA (Thurs, September 1st)
- Coached-practice 4 – Functional Pathway Analysis (Tue, August 6th)
Analysis of differential gene expression of bulk RNA-seq data using “DESeq2” in RStudio.
The detection of differentially expressed genes is one of the basic goals of RNAseq experiments. This can be done within a specific sample or between samples, each analysis demanding different mathematical approaches. “DESeq2” is an established computational tool developed to find changes in gene expression between conditions (e.g. wild type versus mutant). DESeq2 is a free tool or package developed using R command language and can be run in RStudio, a free and open-source software that provides a friendly environment to run R packages.
In this session you will learn how to use DESeq2 in RStudio to re-analyze publicly available bulk RNA-seq data deposited in the database GREIN - GEO RNA-seq Experiments Interactive Navigator.
This workshop is designed for: biomedical researchers with none to minimal knowledge of command language or R programming. Limited to 25 seats.
You will learn how to:
- Import data files into RStudio
- Load R packages
- Run “DESeq2”
- Save processed data files into your computer (e.g. normalized counts, DEX genes table)
- Perform basic data visualization (e.g. PCA plot, MA plot)
- Bring a laptop with R and RStudio installed and running.
- Prework documentation with instructions on how to install R and RStudio will be sent to you a week prior to the workshop to help you get ready.
- We will also provide you with the files of an RNA-seq dataset publicly available on NCBI’s GEO Database
- Thursday, August 11, 2022
- 10:00am - 12:00pm
- Time Zone:
- Eastern Time - US & Canada (change)
- SHM L 111, Cushing/Whitney Medical Library, 333 Cedar Street
- Medical School