Spatially resolved omics technologies are revolutionising our understanding of biological tissues by introducing new ways to characterise tissue architectures and identify cell-cell interactions. However, handling spatial omics datasets remains challenging due to the large heterogeneity of profiling technologies, resulting in fragmented file formats, and the plethora of arbitrary ways to store analysis results further exacerbates this fragmentation. Additionally, the data volume often exceeds the memory capacity of standard computing environments. These challenges make it difficult to construct scalable, interoperable workflows and to share them reproducibly with the scientific community.

In this webinar, we will present a software solution—the SpatialData framework—that we developed in Python to address these data representation challenges. Specifically, we will demonstrate how our software infrastructure can be used to:

• Represent data from the most commonly available assays in a unified way
• Manipulate, query, and compute statistics on the data
• Visualise and annotate the data interactively
• Prepare the data for sharing to maximise interoperability

Who is this course for?

This webinar is suitable for any researcher in life sciences who is interested in exploring spatial omics and learning about relevant methods and resources . No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.

This event is part of a webinar series focusing on concepts, resources, and recent advancements in the field of spatial omics. For details on all topics covered in this series and registration information, please visit the following link: Advances in spatial omics: exploring concepts, innovations, and resources.

Outcomes

By the end of the webinar you will be able to:

• Describe how to ingest common or custom spatial omics datasets into a flexible, unified multimodal representation.
• Identify how to query, process, visualise, and annotate the data, and how to introduce alternative views and custom representations.
• Discover how to prepare the data for sharing and access it outside the Python programming language.

DOI: 10.6019/TOL.SpatialData-w.2024.00001.1

This webinar took place on 25 September 2024. Please click the 'Watch video' button to view the recording. 

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