Orchestrating Spatial Transcriptomics Analysis with Bioconductor

  1. Helena L. Crowell
  2. Yixing Dong
  3. Ilaria Billato
  4. Peiying Cai
  5. Martin Emons
  6. Samuel Gunz
  7. Boyi Guo
  8. Mengbo Li
  9. Alexandru Mahmoud
  10. Artür Manukyan
  11. Hervé Pagès
  12. Pratibha Panwar
  13. Shreya Rao
  14. Callum J. Sargeant
  15. Lori Shepherd Kern
  16. Marcel Ramos
  17. Jieran Sun
  18. Michael Totty
  19. Vincent J. Carey
  20. Yunshun Chen
  21. Leonardo Collado-Torres
  22. Shila Ghazanfar
  23. Kasper D. Hansen
  24. Keri Martinowich
  25. Kristen R. Maynard
  26. Ellis Patrick
  27. Dario Righelli
  28. Davide Risso
  29. Simone Tiberi
  30. Levi Waldron
  31. Raphael Gottardo
  32. Mark D. Robinson
  33. Stephanie C. Hicks
  34. Lukas M. Weber
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Abstract

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

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