spatial Omics in R/Bioconductor


20-22 May 2024


To foster international participation, this course will be held online



Course overview

This course aims to provide participants with a comprehensive understanding of spatial omics, emphasising the various technologies involved and the differences between imaging and sequencing methodologies in experimental and analytical contexts. Designed for biologists and researchers with a basic background in omics and data analysis, the workshop does not require prior extensive knowledge of spatial omics. Key topics include exploring the different spatial omics technologies, practical considerations in experimental design, and challenges in data analysis. The workshop also introduces various analytical frameworks and tools used in spatial omics, focusing on understanding and applying 'tidy' data principles specific to this field.


Learning outcomes

  •     Understand spatial omics concepts, differentiating between imaging and sequencing methodologies.
  •     Explore various spatial omics technologies and gain practical skills in experimental design.
  •     Address challenges in spatial omics data analysis, utilizing analytical frameworks and tools.
  •     Apply 'tidy' data principles to efficiently handle and analyze spatial omics datasets.
  •     Interpret and derive meaningful insights from spatial omics data




Monday– Classes from 9.30 AM - 1.30 PM Berlin time

  ** Session 1 – Introduction

  • Introduction to spatial omic technologies
  • Overview of Analysis Frameworks

  ** Session 2 – Spatial analyses of sequencing data

  • Introduction to Bioconductor
  • Working with sequencing-based data in Bioconductor with SpatialExperiment
  •     Working with sequencing-based data in R with Seurat

Tuesday– Classes from 9.30 AM - 1.30 PM Berlin time


** Session 3 – Introduction to tidyomics

  • The use of tidy R with spatial data analysis with tidySpatialExperiment
  • The use of tidy R with spatial data analysis with tidySeurat

** Session 4 – Spatial analyses of imaging data

  •  Working with imaging-based data in Bioconductor with MoleculeExperiment
  •     Working with imaging-based data in R with Seurat


Wednesday– Classes from 9.30 AM - 1.30 PM Berlin time


** Session 5 – Advanced analyses of spatial data

  • Spatial differential expression
  • Cell-neighbour analysis
  • Deconvolution of pixel-based spatial data
  • Multi-modality integration


Package 1



480 €

Should you have any further questions, please send an email to

Cancellation Policy:


> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.


Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.