Repository to explore various types of spatial transcriptomics and proteomics datasets

Types of spatial data being generated

Spatial image-based and sequencing-based transcriptomics methods

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

Spatial proteomics

Spatial data infrastructure

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/

Datasets

Analysis of 10x Visium mouse brain tissue (strain C57BL/6)

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.

Useful resources

Contributors

Stephanie C. Hicks [@stephaniehicks]

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repo to explore spatial data in R/Bioconductor and Python