This was a very nice collaboration and we thank everyone involved: @samuelgunz.bsky.social, @helucro.bsky.social, Izaskun Mallona, @maltekuehl.com, Reinhard Furrer, and @markrobinsonca.bsky.social

The paper is accompanied by a collection of vignettes written in both R and python to make these analyses accessible to interested researchers.

robinsonlabuzh.github.io/pasta/

In our paper, we discuss spatial omics technologies in terms of the type of data they produce. These are either lattice-based or point pattern-based data. We continue by discussing exploratory spatial statistics methods guided by biological use-cases for both data modalities.

We thank everyone involed: @samuelgunz.bsky.social, @helucro.bsky.social , Izaskun Mallona, @maltekuehl.com, Reinhard Furrer, @markrobinsonca.bsky.social and all Robinsonlab members

The accompanying webpage was updated and shows examples in R and Python, extending the usability of our framework.

robinsonlabuzh.github.io/pasta/

The second focus is on technological details in both point-pattern and lattice-based analyses. Two main points we discuss is the confounding between inhomogeneity and clustering in point pattern analysis and correct definition of the neighbourhood interactions for lattice-based analysis.

Next, the focus of the revised paper is on concrete biological applications. We re-analysed a Xenium breast cancer data set. We show that we can recapitulate the main findings of this paper and add a straight-forward quantification of results.

www.nature.com/articles/s41...

First, we show the differences of lattice-based and point-pattern based analysis. In addition to the prior setup, we added concrete biological questions that can be answered with either of the two analysis streams.