Spatial Transcriptomics & Single-Cell Multi-Omics Data Analysis in R

The aim of this workshop is to equip researchers and professionals with practical skills to analyze, integrate, and interpret single-cell and spatial omics datasets using modern computational biology workflows.

• To introduce participants to the principles of single-cell multi-omics and spatial transcriptomics analysis.
• To demonstrate advanced quality control, filtering, clustering, and dimensionality reduction workflows for single-cell data.
• To teach participants how to work with spatial transcriptomics datasets and map gene expression within tissue sections.
• To enable participants to identify spatially variable genes and tissue-region-specific expression patterns.
• To train participants in cell-type deconvolution using single-cell data as a reference.
• To help participants analyze ligand-receptor interactions and cell-to-cell communication networks.
• To provide hands-on exposure to open-source tools such as Seurat v5, Signac, SpatialExperiment, Voyager, CellChat, MISTy, Bioconductor, and Google Colab.

📅 Day 1: Single-Cell Multi-Omics – From Data to Cell States

Core Objective: Unlock deep cellular insights by integrating single-cell RNA sequencing with protein and epigenetic data.

• Introduction to single-cell multi-omics and integrated cellular profiling
• Advanced quality control and filtering strategies
• Ambient RNA removal and technical noise reduction
• Doublet detection to ensure clean single-cell profiles
• Integration of transcriptomic, protein, and epigenetic datasets
• Dimensionality reduction using PCA and UMAP algorithms
• Cell clustering, annotation, and identification of distinct cell states

🛠️ Hands-on Lab: Run a complete multi-omics quality-control, dimensionality-reduction, and clustering pipeline using a real-world PBMC dataset in Google Colab.

🧰 Tools Covered: Seurat v5, Signac, Google Colab

📅 Day 2: Spatial Transcriptomics – Mapping Tissue Architecture

Core Objective: Move beyond isolated cells and map gene expression directly within its native physical tissue environment.

• Introduction to spatial transcriptomics and tissue-level gene-expression mapping
• Fundamentals of working with 10x Genomics Visium datasets
• Importing and organizing spatial gene-expression data
• Spatial data quality assessment and normalization techniques
• Identifying Spatially Variable Genes (SVGs)
• Visualizing gene-expression patterns across tissue sections
• Mapping distinct morphological and molecular tissue regions

🛠️ Hands-on Lab: Overlay gene-expression matrices directly onto physical tissue histology images and identify structural and molecular boundaries using Google Colab.

🧰 Tools Covered: SpatialExperiment, Voyager, Google Colab

📅 Day 3: Spatial Deconvolution and Cellular Communication

Core Objective: Identify the exact cell types populating tissue samples and map how they interact with one another.

• Introduction to spatial deconvolution and tissue-composition analysis
• Using single-cell data as a reference for cell-type identification
• Estimating cell-type proportions within spatial locations
• Spatial ligand–receptor interaction analysis
• Quantifying cell-to-cell proximity and signaling dynamics
• Identifying communication patterns within specialized tissue microenvironments
• Translating complex spatial data into clean and interpretable interaction networks

🛠️ Hands-on Lab: Build and visualize a cellular interaction network to examine cross-talk and signaling relationships within specialized tissue microenvironments using Google Colab.

🧰 Tools Covered: CellChat, MISTy, Google Colab

• Ph.D. scholars and research scholars in life sciences, biotechnology, bioinformatics, genomics, and computational biology.
• Faculty members and academicians working in molecular biology, cancer biology, immunology, neuroscience, or systems biology.
• Bioinformatics researchers interested in single-cell and spatial omics data analysis.
• Industry professionals working in genomics, drug discovery, diagnostics, precision medicine, or biomedical data analytics.
• Students with basic knowledge of biology, genomics, or R programming who want to build practical skills in advanced omics analysis.

• Perform quality control and preprocessing of single-cell multi-omics datasets.
• Apply clustering and dimensionality reduction methods to identify cellular states.
• Analyze 10x Genomics Visium spatial transcriptomics datasets.
• Visualize gene expression patterns directly on tissue histology images.
• Identify spatially variable genes and interpret tissue-region-specific expression.
• Use single-cell data as a reference for spatial cell-type deconvolution.
• Build and interpret cellular communication networks using ligand-receptor analysis.
• Understand how tools like Seurat v5, Signac, SpatialExperiment, Voyager, CellChat, and MISTy are used in real research workflows.