Spatial transcriptomics data analysis: theory and practice
This book will guide you through the practical steps of the in-person tutorial IP2 for the ISMB/ECCB 2023 conference in Lyon named: “Spatial transcriptomics data analysis: theory and practice”.
Recent technological advances have led to the application of RNA Sequencing in situ. This allows for whole-transcriptome characterisation, at approaching single-cell resolution, while retaining the spatial information inherent in the intact tissue. Since tissues are congregations of intercommunicating cells, identifying local and global patterns of spatial association is imperative to elucidate the processes which underlie tissue function. Performing spatial data analysis requires particular considerations of the distinct properties of data with a spatial dimension, which gives rise to an association with a different set of statistical and inferential considerations.
In this comprehensive tutorial, we will introduce users to spatial transcriptomics (STx) technologies and current pipelines of STx data analysis inside the Bioconductor framework. Furthermore, we will introduce attendees to the underlying features of spatial data analysis and how they can effectively utilise space to extract in-depth information from STx datasets.
Participants in this tutorial will gain understanding of the core technologies for undertaking a spatial transcriptomics experiment, and the common tools used for the analysis of this data. In particular, participants will appreciate the strengths of geospatial data analysis methods in relation to this type of data. Specific learning objectives will include:
- Describe and discuss core technologies for spatial transcriptomics
- Make use of key computational technologies to process and analyse STx data
- Apply an analysis strategy to obtain derived results and data visualisations
- Appreciate the principles underlying spatial data analysis
- Understand some of the methods available for spatial data analysis
- Apply said methods to an example STx data set