Appendix A — Related resources

In this chapter, we highlight several related resources from the Bioconductor and other communities.

A.1 Data preprocessing procedures

Details on data preprocessing procedures for spatially-resolved transcriptomics data from the 10x Genomics Visium platform are provided in the following online book (using tools outside R and Bioconductor):

A.2 Resources for other spatially-resolved platforms

Workflows and other resources for other spatially-resolved platforms:


Analysis workflow for IMC data

  • Online book providing a workflow highlighting the use of common R/Bioconductor packages to analyze single-cell data obtained from segmented imaging mass cytometry (IMC) images. Examples focus on IMC data and can also be applied to images obtained by other highly-multiplexed imaging technologies, e.g. CODEX, MIBI, and mIF.
  • Authors: Nils Eling, Vito Zanotelli, Michelle Daniel, Daniel Schulz, Jonas Windhager


VectraPolarisData

  • Bioconductor data package providing two multiplex single-cell imaging datasets collected on Vectra Polaris and Vectra 3 instruments.
  • Authors: Julia Wrobel, Tusharkanti Ghosh

A.3 Data structures

SpatialExperiment

  • S4 class for storing spatially-resolved transcriptomics (SRT) data, which is used as the basis for the examples in this book. See Chapter [Bioconductor data classes].
  • Authors: Dario Righelli, Lukas M. Weber, Helena L. Crowell, Brenda Pardo, Leonardo Collado-Torres, Shila Ghazanfar, Aaron T. L. Lun, Stephanie C. Hicks, Davide Risso


SpatialFeatureExperiment

  • S4 class extending SpatialExperiment to incorporate geometries and geometry operations with the sf R package.
  • Authors: Lambda Moses, Lior Pachter

A.4 Statistical concepts

Modern Statistics for Modern Biology