Summary and Setup

Welcome

This hands-on workshop teaches you how to analyze single-cell RNA sequencing (scRNA-seq) data from start to finish. You will work with a real 10x Genomics PBMC dataset on Purdue’s HPC clusters, progressing from raw FASTQ files through alignment, quality control, clustering, cell type annotation, multi-sample integration, and differential expression analysis.

Learning Outcomes

After completing this workshop, you will be able to:

  • Process raw scRNA-seq data using Cell Ranger and STARsolo on an HPC cluster
  • Perform quality control, normalization, and dimensionality reduction with Seurat v5 in R
  • Identify cell types through marker gene analysis and automated annotation
  • Integrate multiple samples, run differential expression analysis, and interpret pathway enrichment results

Instructors

  1. Arun Seetharam: Arun is a Bioinformatics Scientist at Purdue’s Rosen Center for Advanced Computing (RCAC). He develops and delivers bioinformatics training workshops and provides research computing support for the Purdue community.

  2. Michael Carlson, Ph.D.: Michael is a Senior Computational Scientist at Purdue University’s Rosen Center for Advanced Computing (RCAC). Michael has a background in computational physics, specifically hypersonic materials. He also leads many introductory workshops in the High-Performance Computing domain.

Workshop Schedule (4/21/2026)

Time Topic
8:30 – 9:00 Arrival & Setup
9:00 – 9:20 Episode 1: Introduction to Single-Cell RNA-Seq
9:20 – 10:05 Episode 2: Raw Data Processing
10:05 – 10:20 Break
10:20 – 11:05 Episode 3: Quality Control
11:05 – 11:55 Episode 4: Normalization and Feature Selection
11:55 – 12:00 Morning Recap
12:00 – 1:00 Lunch
1:00 – 1:45 Episode 5: Dimensionality Reduction and Clustering
1:45 – 2:30 Episode 6: Cell Type Annotation
2:30 – 2:45 Break
2:45 – 3:25 Episode 7: Multi-Sample Integration
3:25 – 4:00 Episode 8: Differential Expression & Wrap-up
4:00 – 4:30 Q&A / Feedback

Prerequisites

This workshop assumes:

  • Basic Linux/command-line skills: navigating directories, running commands, editing files
  • Basic R skills: installing packages, reading/writing data, creating plots
  • A Purdue HPC account: access to the Negishi cluster or Scholar (provided)
  • An SSH client: terminal (macOS/Linux) or PuTTY/MobaXterm (Windows)

No prior experience with single-cell RNA-seq is required.

Scope of This Workshop

This workshop teaches a standard scRNA-seq analysis workflow using a specific set of tools and data:

  • Platform: 10x Genomics Chromium 3’ Gene Expression (v3 chemistry)
  • Organism: Human (GRCh38 reference genome)
  • Dataset: 10k PBMCs (peripheral blood mononuclear cells), a well-characterized immune cell mixture
  • Analysis toolkit: Seurat v5 in R, running on Purdue’s Negishi HPC cluster
  • Integration dataset: IFN-beta stimulated PBMCs from the SeuratData package

By the end of the workshop you will have a solid understanding of the core scRNA-seq analysis pipeline and be able to apply these same steps to your own datasets.

What is NOT covered in this workshop

  • Spatial transcriptomics (Visium, MERFISH, Slide-seq)
  • Multi-modal assays (CITE-seq, Multiome ATAC+GEX)
  • Trajectory and pseudotime analysis (Monocle3, RNA velocity, scVelo)
  • Python-based analysis (Scanpy / AnnData ecosystem)
  • Experimental design and wet-lab sample preparation
  • Custom reference genome building for non-model organisms

These are all important topics, but each warrants its own dedicated workshop. If you are interested in any of these areas, see the Additional Reading section in the Reference page for pointers to relevant resources.

SSH Setup

You need SSH access to the Negishi cluster for Episode 2 (raw data processing) and to copy the workshop data. Follow the instructions for your operating system below.

Discussion

Connecting to the Cluster

You will need SSH access to the Negishi cluster at Purdue. Choose the instructions for your operating system below.

  1. Download and install MobaXterm (recommended) or PuTTY
  2. Open MobaXterm and click Session > SSH
  3. Set Remote host to negishi.rcac.purdue.edu
  4. Check Specify username and enter your Purdue career account username
  5. Click OK and enter your password when prompted
  6. Complete Microsoft two-factor authentication
  1. Open Terminal (Applications > Utilities > Terminal)
  2. Connect to the cluster:

BASH 

ssh your_username@negishi.rcac.purdue.edu
  1. Enter your password (characters may not appear, but your password is being entered)
  2. Complete Microsoft two-factor authentication
  1. Open your terminal emulator
  2. Connect to the cluster:

BASH 

ssh your_username@negishi.rcac.purdue.edu
  1. Enter your password (characters may not appear, but your password is being entered)
  2. Complete Microsoft two-factor authentication

Data Setup

Copying Workshop Data

The workshop data is pre-staged on the cluster. Copy it to your scratch directory:

BASH 

mkdir -p ${RCAC_SCRATCH}/scrna_workshop
rsync -avP /depot/workshop/data/scrna_workshop/ ${RCAC_SCRATCH}/scrna_workshop/

This will copy:

  • FASTQ files: fastq/pbmc_10k_v3_S1_L00{1,2}_R{1,2}_001.fastq.gz
  • 10x Reference: reference/refdata-gex-GRCh38-2024-A/
  • Pre-computed count matrix: filtered_feature_bc_matrix/ (for episodes 3+)
  • Pre-computed R objects: rds/ (optional, for jumping into later episodes)

Verifying the Data

BASH 

ls ${RCAC_SCRATCH}/scrna_workshop/
ls ${RCAC_SCRATCH}/scrna_workshop/fastq/
ls ${RCAC_SCRATCH}/scrna_workshop/filtered_feature_bc_matrix/

Starting RStudio on Open OnDemand

For Episodes 3 to 8, we will use RStudio through Negishi’s Open OnDemand (OOD) web portal. Follow these steps to launch an RStudio session.

Step 1: Log in to Open OnDemand

Open your browser and navigate to gateway.negishi.rcac.purdue.edu. Complete the Purdue SSO login (BoilerKey/Duo with Microsoft authentication).

Step 2: Launch RStudio (Bioconductor)

From the top menu bar, click the Interactive Apps tab. Under the Bioinformatics Apps section, select RStudio (bioconductor).

Callout

Choose the correct app

Make sure you select RStudio (bioconductor) under Bioinformatics Apps — not the RStudio Server option listed under the GUIs section. The Bioconductor version includes pre-installed single-cell analysis packages that we need for this workshop.

Open OnDemand Interactive Apps menu showing RStudio (bioconductor) under the Bioinformatics Apps section

Step 3: Fill in the resource request

Enter the following settings:

Field Value
Partition cpu
Account (provided by instructor)
QoS normal
Wall Time (hours) 8
Cores 16
R version 4.4.0-bioconductor
Open OnDemand resource request form filled in with the workshop settings

Click Launch to submit the job.

Step 4: Connect to the session

Your job will be queued briefly. Once the status changes to Running, click the Connect to RStudio Server button to open RStudio in a new browser tab.

R Package Installation (skip this section if using OOD)

The R packages are pre-installed on the cluster. If you need to install them locally:

R 

# Install BiocManager if not already installed
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# Core packages
install.packages("Seurat")
install.packages("tidyverse")

# Bioconductor packages
BiocManager::install(c(
    "SingleR",
    "celldex",
    "clusterProfiler",
    "org.Hs.eg.db",
    "enrichplot"
))

# SeuratData for integration dataset
install.packages("remotes")
remotes::install_github("satijalab/seurat-data")