SPRUCE: Single-cell Pairwise Relationships Untangled by Composite Embedding model

Subedi, S.; Park, Y.

doi:10.1101/2022.09.16.508327

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bioRxiv · 10.1101/2022.09.16.508327

SPRUCE: Single-cell Pairwise Relationships Untangled by Composite Embedding model

Subedi, S.·Park, Y.

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Abstract

In multi-cellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPRUCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumour microenvironments consolidating multiple breast cancer data sets and found seven frequently-observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumour heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.

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Subedi, S., Park, Y.. 2022-09-19. SPRUCE: Single-cell Pairwise Relationships Untangled by Composite Embedding model. https://doi.org/10.1101/2022.09.16.508327

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