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🔬 TRACK 21 • 10 MODULAR CHAPTERS 4-DAY HANDS-ON WORKSHOP • NOV 23–26, 2026 VISIUM HD • SEURAT V5 • SPACE RANGER
Tissue Architecture to Biological Insight • Clinical Oncology Case Study

Spatial Transcriptomics Data Analysis: From Tissue Architecture to Biological Insight

A Visium HD Clinical Case Study: Space Ranger Pipeline, Loupe Browser 8.0, Seurat v5 Sketch Workflows, BPCells Scaling, Cell Type Deconvolution (RCTD), Spatial Statistics (nnSVG / Moran's I) & Quarto Capstone.

Master the complete computational paradigm for high-resolution spatial biology. This intensive curriculum bridges pathology and bioinformatics, walking researchers through the end-to-end pipeline: from probe hybridization biophysics and CytAssist fiducial registration to memory-efficient sketching on massive 2 µm/8 µm bin arrays, automated SingleR cell typing, spatial niche decomposition, and publication-quality figure generation.

📦 Production Spatial Biology R Environment R 4.4+ • Bioconductor 3.20
# Install Core Spatial Ecosystem Packages
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("Seurat", "BPCells", "SingleR", "celldex", "spacexr",
                       "patchwork", "ggplot2", "viridis", "hdf5r", "arrow", "nnSVG"))
Official Workshop Flyer
Spatial Transcriptomics Data Analysis Course Flyer

Click flyer to view full high-resolution graphic

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Who Should Attend

🎓
Bioinformaticians & Computational Biologists Looking to master massive spatial arrays (>500k bins) and Seurat v5 sketch-based algorithms.
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Pathologists & Clinical Oncologists Integrating histological H&E images with whole-transcriptome tumor-stroma microenvironment profiling.
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Translational Cancer & Immunology Researchers Characterizing Tertiary Lymphoid Structures (TLS), immune exclusion zones, and drug response biomarkers.
💻
Single-Cell Practitioners Extending standard scRNA-seq clustering workflows into 2D histological coordinate space.
🎯

Core Competencies & Outcomes

  • Explain spatial transcriptomics principles, platform trade-offs, and dissociation artifact avoidance.
  • Deconstruct Space Ranger outputs and perform fiducial image overlays in Loupe Browser 8.0.
  • Formulate defensible spatial QC filtering thresholds based on UMI depth, gene counts, and mitochondrial ratio.
  • Execute Seurat v5 Sketch-Based Workflows and BPCells on-disk streaming to scale past RAM limits.
  • Perform automated cell type annotation with SingleR and cellular deconvolution with RCTD.
  • Model spatial autocorrelation with Moran's I and identify Spatially Variable Genes via nnSVG.
  • Synthesize an end-to-end, reproducible computational pipeline in Quarto / R Markdown.
📅 4-DAY INTENSIVE WORKSHOP BLUEPRINT

Daily Workshop Agenda & Milestones

Scheduled for November 23 – 26, 2026 • Complete hands-on scripts and real 10x Visium HD clinical cancer datasets.

November 23, 2026 • Day 1

Introduction to Spatial Transcriptomics

Beyond bulk and single-cell RNA-seq; dissociation artifacts; platform landscape (Visium HD, Slide-seq, Stereo-seq vs Xenium & MERFISH); 2 µm binning hierarchy; capture area and fiducial frames.

Takeaway: Explain what spatial transcriptomics measures and where computational analysis begins.
November 24, 2026 • Day 2

Space Ranger Outputs, Loupe Browser & QC

The Space Ranger pipeline; output directory anatomy; interpreting web summaries (saturation, reads in tissue); hands-on Loupe Browser ROI lassoing; computational QC in R (counts, features, percent.mt).

Takeaway: Assess run quality against published benchmarks and set defensible QC thresholds.
November 25, 2026 • Day 3

Data Import, Normalization, Sketching & Clustering

Loading Visium HD into Seurat v5; LogNormalize vs SCTransform; the sketch-based workflow for scaling past RAM; BPCells streaming; PCA, elbow plots, neighbor finding, and UMAP tissue alignment.

Takeaway: Build a clustered Seurat object using a sketch workflow that scales past system memory.
November 26, 2026 • Day 4

Annotation, Spatial Statistics & Capstone

SingleR automated annotation; FindAllMarkers differential expression; RCTD deconvolution; Spatially Variable Genes (Moran's I, nnSVG); microenvironment niches; 300 DPI figures and Quarto capstone.

Takeaway: Produce an annotated, spatially-resolved dataset and a reproducible Quarto report.
🖼️ WORKSHOP VISUAL SYLLABUS

Official 4-Day Training Flyer Infographics

Workshop Header & Tech Stack
Spatial Transcriptomics Header
Day 1 & Day 2 Curriculum
Spatial Transcriptomics Day 1 and 2
Day 3 & Day 4 Curriculum
Spatial Transcriptomics Day 3 and 4

Course Curriculum • 10 Modular Chapters

Structured step-by-step from biological fundamentals to advanced spatial statistics and publication figures.

Track 21 • 10 Detailed Guides
Day 1 • Foundations CHAPTER 01

Chapter 1: Spatial Transcriptomics Foundations: Beyond Bulk & Dissociation Artifacts

What spatial transcriptomics adds beyond bulk and scRNA-seq; what is lost during enzymatic tissue dissociation; biological applications in tumor microenvironments and developmental atlases; experimental design, sample quality criteria, and boundaries of spatial analysis.

Day 1 • Platform Landscape CHAPTER 02

Chapter 2: Platform Landscapes: Visium HD, Stereo-seq, Slide-seq vs Xenium & MERFISH

Sequencing-based vs imaging-based spatial technologies; resolution, capture area, and gene-panel trade-offs; Visium HD continuous oligo architecture, 2 µm binning hierarchy (2/8/16 µm), fiducial alignment, and segmented cell boundaries.

Day 2 • Space Ranger Pipeline CHAPTER 03

Chapter 3: The Space Ranger Pipeline: Probe Sets, Reference Genomes & Alignment

The 10x Genomics spaceranger count workflow: input FASTQs, reference transcriptome generation, whole-transcriptome probe sets, CytAssist fiducial registration, slide coordinates, and execution optimization.

Day 2 • Loupe Browser & QC CHAPTER 04

Chapter 4: Space Ranger Output Anatomy, Web Summary & Hands-On Loupe Browser

Deconstructing the Space Ranger directory: web_summary.html, cloupe files, filtered matrices, and spatial coordinate tables; hands-on Loupe Browser 8.0: fiducial alignment, H&E overlay, region selection, and custom binning.

Day 2 • Computational QC CHAPTER 05

Chapter 5: Computational Quality Control & Spatial Outlier Filtering in R

Ingesting spatial data into R; evaluating library size (nCount_Spatial), gene diversity (nFeature_Spatial), and mitochondrial percentage; spatial QC feature plots, identifying tissue tears and bubbles, and establishing defensible QC cutoffs.

Day 3 • Data Import & Normalization CHAPTER 06

Chapter 6: Data Import into Seurat v5 & Spatial Normalization (LogNormalize vs SCTransform)

Loading Visium HD into Seurat v5 via Load10X_Spatial; selecting bin resolutions; inspecting assays, layers, and coordinate images; spatial normalization strategies: log-normalization vs regularized negative binomial SCTransform v2.

Day 3 • Sketch Workflow & Clustering CHAPTER 07

Chapter 7: The Sketch-Based Workflow: BPCells, On-Disk Streaming & Dimension Reduction

Scaling past system RAM: Seurat v5 sketch-based workflow, subsampling diverse bin subsets, BPCells on-disk matrix streaming, PCA, elbow plots, neighbor graph generation, Louvain/Leiden clustering, and full dataset projection.

Day 3 • UMAP & Histology Alignment CHAPTER 08

Chapter 8: UMAP Embedding & Tissue Spatial Mapping: Validating Against Histology

Generating UMAP projections on the sketch and projecting to full tissue coordinates; side-by-side transcriptional vs spatial DimPlots; ground-truthing computational clusters against brightfield H&E and pathology annotations.

Day 4 • Annotation & Deconvolution CHAPTER 09

Chapter 9: Cell Type Annotation (SingleR/CellDex), Differential Expression & RCTD Deconvolution

Comparing marker-based, reference-based (SingleR), and label transfer annotations; assessing delta scores and pruned labels; differential expression with FindAllMarkers; cell type deconvolution in Visium HD using RCTD.

Day 4 • Spatial Stats & Capstone CHAPTER 10

Chapter 10: Spatial Statistics (Moran’s I, nnSVG), Niche Analysis & Quarto Reproducible Capstone

Identifying Spatially Variable Genes (SVGs) via Moran’s I and nnSVG; spatial domain discovery; cellular microenvironment niche analysis (tumor core, invasive border, TLS); publication figures (300 DPI) and Quarto reproducible capstone.

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