Join us next week for the second free webinar in our spatial transcriptomics series: www.ebi.ac.uk/training/eve...
Daria Lazic (EMBL Heidelberg) presents 'Imaging-based spatial transcriptomics: methods, preprocessing, and quality control' on 25 February | 14:30 UK time.
Omnibenchmark (omnibenchmark.org): transparent, reproducible, extensible and standardized orchestration of solo and collaborative benchmarks arxiv.org/abs/2409.17038 🧬💻🧪
Orchestrating Spatial Transcriptomics Analysis with Bioconductor https://www.biorxiv.org/content/10.1101/2025.11.20.688607v1
I'm very excited to share our latest preprint!
We introduce structure-based analysis of spatial omics data – an approach that focuses on multi-cellular anatomical structures rather than single cells.
We also present sosta to facilitate this type of analysis: bioconductor.org/packages/sos...
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 are excited to share the publication of our paper on exploratory spatial statistics for spatial omics data
academic.oup.com/nar/article/...
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.
Update: We greatly revised our paper and renamed it “Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta”.
We discuss the broad range of exploratory spatial statistics options for spatial Omics technologies and show relevant use cases.
arxiv.org/abs/2412.01561
