Dharmesh D Bhuva

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 - Genome Biology Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools inc...

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/