Introducing ParTIpy, a python package for Pareto Task Inference that scales to large-scale datasets, including single-cell and spatial transcriptomics. 🔗 Manuscript: www.biorxiv.org/content/10.1... 💻 Code: partipy.readthedocs.io

🎉 Such a great work by everyone involved in this major push forward in spatial multiplexing and next-generation pathology. I‘m glad to have been able to contribute to this effort and shed a light on the discovery of sub-cellular to tissue level organization patterns by xAI based on this technology.

The latest version of the Kasumi manuscript is now published in Nature Comms www.nature.com/articles/s41... Kasumi identifies patterns in tissue patches, enabling analysis of disease progression and treatment response while providing insights into spatial coordination at cell-type or marker level

Brilliant session focused entirely on spatial multiomics @theaacr.bsky.social #AUA25 Well said @tanevski.bsky.social "Cancer is a spatial disease-spatialomics is the future of cancer science"! Wonderful composite spatial data from Linghua Wang @mdanderson.bsky.social www.nature.com/articles/s41...

🚨 New preprint: Topography Aware Optimal Transport for Alignment of Spatial Omics Data We present our new alignment framework TOAST www.biorxiv.org/content/10.1...

@chiaraschiller.bsky.social did an amazing job describing the landscape of methods for pairwise-association analysis in immediate spatial neighborhoods. Addressing limitations she proposes COZI and shows its ability to consistently recover directional cell-type associations and generate new insights