Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Spatial transcriptomics technologies offer spatially-resolved gene expression data.
• These technologies generate complex and variable data, requiring sophisticated computational analysis.
• Existing analysis workflows often involve multiple computational methods and software packages.

Purpose of the Study:

• To provide a comprehensive, accessible, and reproducible resource for spatial omics data analysis.
• To demonstrate the application of Bioconductor in R for spatial transcriptomics.
• To facilitate the integration of spatial omics data analysis with Python.

Main Methods:

• Development of a freely accessible, open-source online book.
• Inclusion of reproducible code examples and datasets.
• Focus on multi-step workflows for spatial omics data analysis using Bioconductor in R.

Main Results:

• A continuously updated and tested online resource is now available.
• The book offers practical guidance on analyzing diverse spatial omics data.
• Interoperability between R and Python for spatial omics analysis is demonstrated.

Conclusions:

• The online book serves as a valuable, up-to-date resource for researchers in spatial omics.
• It simplifies complex data analysis workflows, promoting reproducibility.
• Enhanced accessibility to spatial transcriptomics data analysis is achieved through this resource.