About Workshop
This hands-on workshop introduces participants to single-cell multi-omics and spatial transcriptomics analysis using open-source R-based tools for exploring cell states, tissue architecture, and cell-to-cell communication.
Aim
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.
What Participants Will Learn
- 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.
Structure
📅 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
📅 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
📅 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
Important Dates
Registration Ends
4:30 PM
Workshop Dates
2026-09-03
5:30 PM
5:30 PM
What You Will Gain
Outcomes
- 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.
Who Should Attend
- 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.