@article {Subedi2023-fl,
	title = {Single-cell pair-wise relationships untangled by composite embedding model},
	journal = {iScience},
	volume = {26},
	number = {2},
	year = {2023},
	month = {feb},
	pages = {106025},
	abstract = {In multicellular 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 tumor microenvironments consolidating multiple breast cancer datasets and found seven frequently observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumor heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.},
	keywords = {Cancer systems biology, machine learning, Transcriptomics},
	author = {Subedi, Sishir and Park, Yongjin P}
}
