Excited to share a new algorithm that we have been working on over the last year. 💡 idea is to extend mutual nearest neighbors for #spatial data. We call it spatial mutual nearest neighbors (spatialMNN) 😄 Thank you @haowen-zhou.bsky.social @pratibha-panwar.bsky.social who led this work! 👏 🧬🖥️🧪

If you didn't join #multiomics2024, here is a nice illustrative summary of my talk on normalisation in spatial txomics data. TL;DR - Use SpaNorm, the only spatially aware normalisation method out there! We are improving as we learn more so stay tuned for updates! Preprint doi.org/10.1101/2024...

You need to be careful with how you approach library size normalisation in spatial txomics, or what you could end up eliminating organs / meaningful structures. -- Dharmesh Bhuva 2/

Many sources of variation in spatial -omics: - Tissue structure / library sizes - Images captured for each FOV (Field of View) separately - Antibody-binding affinity differences - Cells overlapping in z-axis - Partial cells captured - Background intensity - Instrument noise @bhuvad.bsky.social 3/

Library size confounds biology in spatial transcriptomics data. Single cell RNA-seq tools & ideologies will NOT translate to spatial molecular data! genomebiology.biomedcentral.com/articles/10.... @bhuvad.bsky.social #multiomics2024 4/