Registration required Class 1 (you must attend Class 1 in order to take Class 2)* and In-Person

Summary: This two-part workshop is designed to equip you with the essential skills for performing differential gene expression analysis on bulk RNA-seq data using the powerful DESeq2 package within the RStudio environment.

Class 1 (September 23): Instructor-Led Deep Dive (2 hours) - In this hands-on session, we will guide you through the fundamental steps of DESeq2 analysis using a provided demo dataset. You will learn the theoretical underpinnings of differential expression and gain practical experience in setting up your analysis.

Class 2 (September 24): Practice and Application (2 hours) - This dedicated practice session allows you to apply the knowledge and skills gained in Class 1 to your own RNA-seq datasets. Instructors will be available to provide support, answer questions, and assist with troubleshooting as you navigate the analysis of your unique data.

You will learn how to:

• Prepare and import count data and metadata into Positron for DESeq2 analysis.
• Assemble a DESeqDataSet object and design appropriate experimental models.
• Perform differential gene expression analysis using the DESeq2 package.
• Extract, interpret, and export key results, including normalized counts and differentially expressed gene lists.
• Generate and interpret essential visualizations for DESeq2 output (e.g., PCA plots, MA plots, Volcano plots).
• Troubleshoot common issues encountered during DESeq2 analysis (during the practice session).
• Apply best practices for analyzing your own bulk RNA-seq datasets.

Requirements: Bring a laptop with a working setup. This includes:

• Basic knowledge of R programming is required.
• An IDE such as Posit Workbench installed and running.
• The latest version of R installed, compatible with Posit Workbench.
• The DESeq2 package (version 1.52) installed in your R environment.