Name: Spatial Transcriptomics & Single-Cell Multi-Omics Data Analysis in R
Start: 2026-09-03
End: 2026-09-03

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

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

🛠️ 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

Important Dates

Registration Ends

4:30 PM

Workshop Dates

2026-09-03

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.

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