Interested in spatial statistics for spatial omics data? Check out our new resource: pasta. We show how different technologies lead to different data modalities give and overview of point-pattern and lattice-based spatial analysis. Feedback welcome! arxiv.org/abs/2412.01561 - ThreadSky

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Interested in spatial statistics for spatial omics data? Check out our new resource: pasta.

We show how different technologies lead to different data modalities give and overview of point-pattern and lattice-based spatial analysis. Feedback welcome!

https://arxiv.org/abs/2412.01561

Comments

samuelgunz.bsky.social•85 days ago

Spatial omics data can be classified into imaging-based and high throughput sequencing (HTS)-methods that differ in resolution and the number of features targeted.

samuelgunz.bsky.social•85 days ago

Imaging-based data can be viewed as a point pattern either in terms of transcript locations or cell centroids. Alternatively, the segmented cell outlines can be interpreted as an irregular lattice. HTS-based approaches are most often recorded on a regular lattice.

samuelgunz.bsky.social•85 days ago

This offers two streams of analysis: point pattern- or lattice-based analysis.

samuelgunz.bsky.social•85 days ago

Point pattern-based analysis offers an event-based view of the data. It allows us to study the processes that lead to an pattern that is e.g., clustered.

samuelgunz.bsky.social•85 days ago

In lattice-based analysis we assume that the locations were fixed at the time of sampling and study the associated features at each location accounting for spatial relationships. This offers an observation-based on the data.

samuelgunz.bsky.social•85 days ago

We give an overview of established methods for the analysis of both lattice- and point pattern-based data and discuss common challenges. More information can be found in the accompanying website: https://robinsonlabuzh.github.io/pasta/

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