• insta-seq: https://www.biorxiv.org/content/10.1101/722819v1
• bar-seq: https://www.biorxiv.org/content/10.1101/294637v2

• 4i: https://www.ncbi.nlm.nih.gov/pubmed/30072512
• cycif: https://www.cycif.org/
• codex: https://www.akoyabio.com/application/files/7015/6625/6771/CODEX_Brochure_Aug_2019_WEB.pdf

There are various types of proposed spatial data infrastructure to store and analyze spatial data in R/Bioconductor or Python:

• starfish schema (docs available here))
  • Pros: has a schema for a spatially-localized gene expression matrix that supports with transcriptomics and proteomics and spatial sequencing methods
  • Cons:
• Giotto data structure (R package available)
  • Pros:
  • Cons:
• Spaniel data infrastructure (Bioconductor package available) -- stores the processed data (count matrix) from spatial transcriptomics in a SingleCellExperiment object, with x/y coordinates in the colData. The image is then read in separately.
  • Pros:
  • Cons: (1) might be better to define a slot rather than storing as metadata (to enable validity checks) and (2) like SingleCellExperiment, make a package dedicated to the data structure, and leaving plotting functions to downstream packages
• SpatialCellExperiment package (available on GitHub).
  • Pros:
  • Cons:
• https://akoyabio.github.io/phenoptr/

This Visium data come from the 10x website and there is a short description provided:

"10x Genomics obtained fresh frozen mouse brain tissue (Strain C57BL/6)from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols - Tissue Preparation Guide (Demonstrated Protocol CG000240). Tissue sections of 10 µm thickness from a slice of the coronal plane were placed on Visium Gene Expression Slides."

Code and analysis available here.

• presentation from Lars Borm (Linnarsson lab)
• lit review by Ambrose
• slides from Ruben Dries on giotto

• Stephanie C. Hicks [@stephaniehicks]