Spatial Multi-Omics Using R: Single-Cell RNA-Seq, Tissue Mapping & Biomarker Discovery

Aim

Workshop Objectives

Workshop Structure

Day 1: Single-Cell RNA-Seq Analysis, Cell Clustering & Gene Expression Profiling Using R

• Introduction to single-cell RNA-seq, spatial transcriptomics and multi-omics technologies.
• Overview of 10x Genomics, Visium HD, Xenium and spatial proteomics platforms.
• Fundamentals of R programming and the Seurat single-cell analysis workflow.
• Understanding gene expression count matrices, cell metadata and quality-control metrics.
• Single-cell preprocessing, normalization and highly variable gene identification.
• Dimensionality reduction using PCA and UMAP.
• Cell clustering, cell-type annotation and marker gene identification.
• Biological interpretation of cell populations and disease-associated gene expression signatures.

Hands-On 1: Single-Cell RNA-Seq Analysis & Cell-Type Identification Using R

Analyze a prepared scRNA-seq dataset using Seurat to perform cell clustering, UMAP visualization and marker gene identification.

Tools: R, Seurat v5, ggplot2, dplyr, RStudio.

Output: UMAP clustering plot and cell-type marker visualizations.

Day 2: Spatial Transcriptomics, Tissue Mapping & Single-Cell Integration Using R

• Principles of spatial transcriptomics and tissue-resolved gene expression analysis.
• Understanding spatial gene-expression matrices, tissue images and spatial coordinates.
• Introduction to spatial data analysis using Seurat and SpatialExperiment.
• Spatial data quality assessment, normalization and tissue-domain identification.
• Visualization of gene expression across tissue sections using spatial feature plots.
• Mapping cell-type marker genes from single-cell RNA-seq onto spatial tissue data.
• Introduction to spatially aware clustering with BANKSY.
• Overview of reference-based cell-type mapping and spatial deconvolution.
• Interpretation of tissue heterogeneity and cellular microenvironments.

Hands-On 2: Spatial Tissue Mapping & Single-Cell Integration Using R

Visualize spatial gene expression and map cell-type signatures onto tissue regions using Seurat and prepared integration results.

Tools: R, Seurat v5, SpatialExperiment, ggplot2.

Output: Spatial tissue maps and cell-type distribution visualizations.

Day 3: Spatial Multi-Omics Integration, Disease Biomarkers & Molecular Pathway Analysis

• Introduction to spatial multi-omics integration: transcriptomics and proteomics.
• Spatially variable gene analysis and interpretation using nnSVG.
• Differential gene expression and tissue-region-specific molecular signatures.
• Integration of spatial RNA and protein marker measurements.
• Candidate disease biomarker prioritization based on expression and spatial localization.
• Introduction to cell–cell communication and ligand–receptor analysis.
• Functional pathway enrichment using clusterProfiler.
• Interpreting tissue microenvironment signatures and candidate biomarkers.
• Applications in cancer research, molecular diagnostics and precision medicine.

Hands-On 3: Spatial Multi-Omics Integration & Biomarker Discovery Using R

Analyze matched spatial RNA–protein data, visualize molecular signatures and perform pathway enrichment to identify candidate disease biomarkers.

Tools: R, Seurat v5, ggplot2, clusterProfiler, prepared nnSVG results.

Output: RNA–protein expression plots, candidate biomarker shortlist and pathway enrichment results.

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure