Single-Cell and Spatial Multi-Omics in R: From Cell Annotation to Tissue-Level Discovery

To equip participants with the practical and analytical skills required to process, integrate and interpret single-cell and spatial multi-omics data in R for research and translational applications.

The workshop is designed to help participants:

• Understand single-cell and spatial multi-omics technologies
• Perform quality control and preprocessing of scRNA-seq data
• Identify cell clusters and cluster-specific marker genes
• Apply manual and reference-based cell annotation
• Detect doublets and assess annotation reliability
• Integrate RNA, chromatin-accessibility and protein modalities
• Analyze joint cellular and regulatory states
• Process and visualize spatial transcriptomics data
• Map single-cell identities onto tissue sections
• Identify spatial domains, tissue niches and molecular patterns
• Generate reproducible and publication-ready visualizations

📅 Day 1: Single-Cell RNA-Seq Quality Control & Cell Annotation

• Focus: Understanding single-cell RNA sequencing workflows, quality control, clustering, and accurate cell-type annotation.
• Introduction to single-cell and spatial multi-omics landscape.
• Understanding count matrices, cell metadata, and single-cell data structure.
• Seurat object structure and complete scRNA-seq analysis workflow.
• Cell- and gene-level quality-control metrics for identifying reliable datasets.
• Detection of low-quality cells, doublets, and technical artifacts.
• Normalization, feature selection, scaling, PCA, neighbourhood graph construction, UMAP, and clustering.
• Marker-gene identification and biological interpretation of clusters.
• Manual versus reference-based cell annotation using marker genes and computational approaches.
• Understanding annotation confidence and common cell misclassification errors.

🛠️ Hands-on: Annotate Cell Types from scRNA-Seq Data

• Process a PBMC scRNA-seq dataset using Seurat.
• Perform quality control, clustering, and marker-gene identification.
• Annotate cell types using marker genes and SingleR reference mapping.

🧰 Tools Covered: R, Seurat, SingleR, scDblFinder, Azimuth, ggplot2, ComplexHeatmap

📌 Output: Annotated single-cell atlas with UMAP and marker-gene profiles.

📅 Day 2: Single-Cell Multi-Omics Integration & Regulatory Analysis

• Focus: Integrating multimodal single-cell datasets to understand cellular states, regulatory programs, and gene-expression control mechanisms.
• Principles of multimodal single-cell analysis and emerging multi-omics approaches.
• Understanding scRNA-seq, scATAC-seq, and CITE-seq modalities.
• Distinguishing batch effects from biological variation in single-cell datasets.
• Dataset integration, batch correction, and reproducible multi-omics workflows.
• RNA–ATAC integration and gene-activity score calculation.
• Weighted Nearest Neighbor (WNN) analysis for multimodal clustering.
• Joint multimodal visualization, clustering, and cellular-state interpretation.
• Linking chromatin accessibility with gene expression patterns.
• Differential expression, accessibility analysis, and regulatory-program interpretation.
• Reproducibility, validation, and quality assessment of integrated results.

🛠️ Hands-on: Integrate RNA and ATAC Modalities

• Explore paired RNA–ATAC single-cell datasets.
• Construct multimodal neighbour graphs and generate integrated UMAP visualizations.
• Compare RNA expression patterns with chromatin accessibility profiles.

🧰 Tools Covered: R, Seurat, Signac, Harmony, ggplot2, ComplexHeatmap

📌 Output: Integrated single-cell multi-omic map of cell types and regulatory states.

📅 Day 3: Spatial Multi-Omics & Tissue-Level Discovery

• Focus: Exploring spatial multi-omics workflows to map cellular organization, tissue niches, and disease-associated molecular patterns.
• Introduction to spatial transcriptomics technologies and spatial data structures.
• Understanding spot-based and single-cell-resolution spatial platforms.
• Spatial quality control, normalization, and tissue-image integration.
• Spatial dimensionality reduction, clustering, and identification of tissue domains.
• Detection of spatially variable genes and molecular regions.
• Mapping annotated cell types onto tissue sections.
• Cell-type deconvolution of mixed spatial spots.
• Cell–cell communication analysis in spatial context.
• Differential spatial-expression analysis and biological hypothesis generation.
• Limitations, validation strategies, and research-reporting guidelines.

🛠️ Hands-on: Map Cell Types & Discover Spatial Tissue Domains

• Analyze a spatial transcriptomics dataset using Seurat.
• Visualize gene expression patterns across tissue sections.
• Identify spatial clusters, marker genes, and tissue-specific molecular regions.
• Transfer single-cell annotations to spatial locations.

🧰 Tools Covered: R, Seurat, SpatialExperiment, BayesSpace, ggplot2, ComplexHeatmap

📌 Output: Tissue-level spatial map showing cell-type distribution, molecular regions, and candidate tissue niches.

This workshop is suitable for:

• Researchers and research scientists
• Faculty members and academicians
• PhD scholars and postgraduate students
• Bioinformaticians and computational biologists
• Molecular and cellular biologists
• Genomics and transcriptomics researchers
• Cancer and tumour-microenvironment researchers
• Immunology and infectious-disease researchers
• Neuroscience and developmental-biology researchers
• Pathologists and precision-medicine professionals
• Researchers working with scRNA-seq, scATAC-seq, CITE-seq or spatial transcriptomics data
• Professionals interested in biomarker discovery and tissue-level molecular analysis

After completing the workshop, participants will be able to:

• Create and manage single-cell data objects in R
• Evaluate cell quality and remove technical artefacts
• Normalize, reduce and visualize high-dimensional datasets
• Perform clustering and marker-gene analysis
• Annotate cell types using known markers and reference datasets
• Integrate scRNA-seq and scATAC-seq data
• Interpret relationships between gene expression and chromatin accessibility
• Visualize molecular expression across tissue sections
• Identify spatially variable genes and tissue regions
• Transfer single-cell annotations to spatial locations
• Interpret cell-type distributions and tissue microenvironments
• Develop biological hypotheses from integrated multi-omics results