Rabadan Lab @ Columbia University

More details and examples in the paper genomebiology.biomedcentral.…. Don't forget to check out the package github.com/JiayuSuPKU/Smooth… and start your favorite *spatially informed* analysis right away!

[8/n] Finally a shoutout to the amazing team especially JB, @alexanderfuxi and my supervisors @david_a_knowles @RabadanLab! Excited to have the first major piece of my PhD out and it’s been an incredible journey so far. Hopefully more to come in 2024!🤞

[7/n] Most importantly, what new biology can we learn?👨‍🔬In colon cancer, we discover distinct spatial localization patterns of plasma cell subtypes (IgG+ in lesion and IgA+ in mucosa), which is reported of clinical interests, and link them to fibroblast-related matrix remodeling.

[6/n] Example 2⃣: Slide-seqV2 data is often too sparse to blend into single-cell atlases. Extending SCVI, we provide the first *spatially aware* joint embeddings of spatial and single-cell human prostate data, removing batch effects and reducing ambiguity in label transfer.

[5/n] When is spatial modeling useful? Always, but even more so with noisy data. Example 1⃣: When using deconvolution to map transcriptomic cell types to epigenomic CUT&Tag data, biologically coherent embryonic compartmentalization is revealed only under spatial regularization.

[4/n] Rethinking spatial analysis. Spatial omics data are still omics data and thus need not be treated as fundamentally different. Our solution? A new pretraining-finetuning paradigm where one model can accommodate data of varying and even zero spatial structures. Example in DR:

[3/n] How to represent spatial dependency? 🧠Through *spatial priors*, Smoother encourages coherence of any spatial variable of interest in a unified, task-independent mechanism. The separation of prior from models also allows the joint analysis of spatial and non-spatial data.

[2/n] What makes spatial data special? 🗺️Sample relatedness. Adjacent spots are more similar, and the similarity decreases with distance. Such patterns are observed across technologies, modalities, and tissues (even in tumor microenvironment) and are NOT technical artifacts.

Frustrated that you must reinvent the wheel when applying your favorite single-cell analysis tools to #SpatialOmics data? 📢Introducing a new framework to bridge the two worlds! Check out our latest work Smoother on spatial modeling at doi.org/10.1186/s13059-023-0…! Highlights [1/n]