Upcoming Online Courses on Spatial Omics and Spatial Transcriptomics (2026)

We are excited to announce three online courses designed to introduce and advance your skills in spatial transcriptomics and spatial omics data analysis using R/Bioconductor and modern computational workflows (scverse ). These courses are ideal for researchers, bioinformaticians, and graduate students aiming to explore spatially resolved molecular biology.

1) SPATIAL TRANSCRIPTOMICS WITH R/Bioconductor

Dates: 9–13 March 2026 (Online)

Course website: https://www.physalia-courses.org/courses-workshops/spatial-transcriptomics/

Course Overview: Spatial transcriptomics allows studying gene expression in its native tissue context. This 5-day course provides a complete introduction to theory and hands-on computational labs using R/Bioconductor. Participants will learn to process, integrate, and explore spatial and single-cell omics data, culminating in a project-design workshop and interactive hackathon.

Target Audience: Advanced students and researchers with basic proficiency in R and Bioconductor.

Learning Outcomes:

• Understand key concepts and technologies of spatial transcriptomics
• Perform data processing, cell segmentation, spatial mapping, and integration
• Conduct downstream analyses like niche characterization and cell–cell communication
• Design and execute spatial transcriptomics projects

Schedule Highlights (Berlin Time, 2–6 PM):

• Day 1: Intro & computational setup
• Day 2–4: Cell segmentation, spatial mapping, niche analysis
• Day 5: 4D spatial multiomics, project workshop, hackathon

2) SPATIAL OMICS IN R/Bioconductor

Dates: 18–20 May 2026 (Online)

Course website: https://www.physalia-courses.org/courses-workshops/spatial-omics-1

Course Overview: This course covers spatial omics technologies, experimental design, and data analysis using R/Bioconductor. It is suitable for biologists and researchers with basic omics and data analysis experience. Emphasis is placed on tidy data principles for spatial omics datasets.

Learning Outcomes:

• Differentiate imaging- vs sequencing-based spatial omics
• Apply analytical frameworks and tools for spatial omics
• Perform spatial data analyses and interpret insights

Schedule Highlights (Berlin Time, 9:30 AM–1:30 PM):

• Session 1: Introduction to spatial omics and analysis frameworks
• Session 2: Sequencing-based spatial analyses in Bioconductor & Seurat
• Session 3: Tidyomics approaches
• Session 4: Imaging-based spatial analyses
• Session 5: Advanced spatial analyses, including differential expression, cell-neighbour analysis, and deconvolution

3) SPATIAL OMICS DATA ANALYSIS: FROM RAW DATA TO AI INSIGHTS

Dates: 14–16 September 2026 (Online)

Course website: https://www.physalia-courses.org/courses-workshops/scverse/

Course Overview: Learn to integrate and analyze spatial omics data from raw preprocessing to AI-driven insights. This course focuses on reproducible, SpatialData-centric workflows, covering spatial statistics, cell–cell interactions, gene-level patterns, and deep learning applications.

Target Audience: Higher-degree students and early-career researchers with some familiarity with Python and scRNA-seq. No prior experience with spatial omics or AI is required.

Learning Outcomes:

• Perform preprocessing and quality control of spatial omics data
• Analyze spatial structure, cell–cell interactions, and spatial gene patterns
• Apply AI-based methods where appropriate
• Execute reproducible spatial omics workflows

Schedule Highlights (Berlin Time, 2–6 PM):

• Day 1: Data foundations, SpatialData model, QC, segmentation tools
• Day 2: Core spatial analyses, niche characterization, cell–cell communication
• Day 3: AI-driven methods, deep learning, multimodal integration, discussion of future directions