Single Cell RNA-Seq Analysis Using R

Data Science Training Program

Reuben Thomas

Michela Traglia

Ayushi Agrawal

This three-session workshop provides a conceptual and practical framework for analyzing single-cell RNA-seq data. We will utilize the Seurat R toolkit as our primary teaching vehicle to demonstrate the standard analytical workflow.

The goal of this workshop is to build conceptual fluency rather than to provide a rigid, automated pipeline. While we focus on Seurat/R, the underlying concepts (clustering logic, dimensionality reduction, batch effects) are universal and applicable to other frameworks.

Scope: We will walk through the critical decision points in a single-cell analysis. To ensure we cover the full workflow within the allotted time, computationally intensive steps (such as ambient RNA correction or doublet removal) will be discussed conceptually to provide context, but will not be executed live during the demo.

Attendance at all three sessions is required.

• April 13, 9:30am–12:00pm: Loading data, quality control, normalization, feature selection, dimensionality reduction
• April 13, 1:00pm–3:30pm: Dimensionality reduction (continued), clustering, finding marker genes
• April 14, 9:00am–12:00pm: Advanced discussion on normalization, differential analysis, and batch correction

Advanced: This is an advanced workshop in the RNA-Seq Analysis series. Prior experience with RNA-seq analysis is required. For introductory and intermediate workshops, check out:

• Introduction to RNA-Seq Analysis
• Intermediate RNA-Seq Analysis Using R

Visit the workshop site for more details and materials.