Workshop: Analysis and Visualisation of Spatial Transcriptomics Data

Thursday June 4th 2026 | 11.30am - 1.00pm | Room 50

Unlock the spatial dimension of gene expression and turn complex tissue data into powerful biological insights.

This hands-on workshop will cover the conceptual foundations and practical implementation of spatial transcriptomics analysis in Python. Participants will explore key principles of quality control, data structures, and core analysis workflows, and learn how to generate and critically interpret spatial and single-cell visualizations.

Using pre-processed datasets and the scverse ecosystem (Scanpy, AnnData, Squidpy), we will walk through how analytical choices influence biological conclusions and how spatial data complements scRNA-seq to answer complex biological questions. 

PharmAI roundtable

Moderators

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Speaker profile image for Martin Hemberg

Martin Hemberg

Associate Professor of Neurology, Harvard Medical School
Speaker profile image for Luca Zanella

Luca Zanella

Research Scientist and Course Director, Columbia University

    Learning outcomes

    By the end of the workshop, you'll be able to: 

    • Interpret key quality control metrics from spatial transcriptomics data. 

    • Perform a basic downstream spatial data analysis in Python. 

    • Generate and critically interpret common single-cell and spatial visualizations. 

    • Understand how analysis choices influence visual outputs and conclusions. 

    • Demonstrate awareness of advanced scverse tools for extended analyses. 

    Who should attend

    Researchers with molecular biology experience, some exposure to single-cell analysis, and basic Python proficiency.

    Event format

    This is a hands-on workshop using core tools such as Scanpy, AnnData, Squidpy and scverse ecosystem. Pre-processed spatial data provided.

    Participants to bring their own laptops.  

    What the workshop will focus on 

    What can spatial transcriptomics answer? 

    • How scRNA-seq and spatial data support biological discovery and validation 

    • Examples of biological questions: 

    • Understanding tissue architecture and cellular neighborhoods 

    • Identifying tissue domains 

    • Inferring cell-cell communication 

    • Technology overview: spot-based vs single-cell resolution technologies 

    Understanding the Data: QC and Data Structure 

    • How AnnData stores gene expression and spatial information (coordinates, morphology and histology images, segmentation masks) 

    • How to assess data quality and perform spatial QC 

    Core Spatial Analysis Workflow 

    • The purpose and impact of each main analysis step 

    • Identification of “highly variable genes” and “spatially variable genes”; identification of cell types and spatial domains  

    Visualizing Gene Expression and Cell Identity  

    • How to visualize gene expression meaningfully and overlaying gene expression and cluster labels onto images 

    • Neighborhood analysis 

    • Avoiding common pitfalls 

    Register your interest

     Please be aware that there are only a limited amount of places available. Attendance is limited to approved Festival attendees only. Please select this roundtable when registering for your place.

    All registrations will be reviewed by the organising committee and approved participants will receive confirmation and event details.

    Please contact info@frontlinegenomics.com if you have any questions.
     

    Register here (opens in a new tab)