Single-Cell RNA-Seq Workshop

We are facing a Medical Mystery in this course. two patients get the same virus. David recovers in 5 days. Emily ends up in the ICU. Their standard blood tests look nearly identical. What's different? Spoiler: Bulk RNA-seq can't tell us, but single-cell can.

What You'll Learn

We'll walk through the complete single-cell analysis pipeline:

• Quality Control: Filter dead cells, empty droplets, and doublets
• Normalization: Use SCTransform to remove technical noise
• Dimensionality Reduction: PCA and UMAP to visualize thousands of genes in 2D
• Clustering: Group similar cells together
• Cell Type Annotation: Figure out what each cluster actually is (T cells? Monocytes?)
• Differential Expression: Compare David vs Emily the right way (pseudo-bulk, not cell-level!)

Prerequisites

Basic R knowledge. Install these packages:

install.packages(c("Seurat", "tidyverse", "patchwork"))
BiocManager::install(c("scDblFinder", "SingleR", "celldex"))

Questions or concerns?

Open an issue and we'll help you out.