2024-12-23-chelebian24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Learned morphological features guide cell type assignment of deconvolved spatial transcriptomics	Spatial transcriptomics enables to study the relationship between gene expression and tissue organization. Despite many recent advancements, existing sequencing-based methods have a spatial resolution that limits identification of individual cells. To address this, several cell type deconvolution methods have been proposed to integrate spatial gene expression with single-cell and single-nucleus RNA sequencing, producing per spot cell typing. However, these methods often overlook the contribution of morphology, which means cell identities are randomly assigned to the nuclei within a spot. In this paper, we introduce MHAST, a morphology-guided hierarchical permutation-based framework which efficiently reassigns cell types in spatial transcriptomics. We validate our method on simulated data, synthetic data, and a use case on the broadly used Tangram cell type deconvolution method with Visium data. We show that deconvolution-based cell typing using morphological tissue features from self-supervised deep learning lead to a more accurate annotation of the cells.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chelebian24a	0	Learned morphological features guide cell type assignment of deconvolved spatial transcriptomics	220	233	220-233	220	false	Chelebian, Eduard and Avenel, Christophe and Leon, Julio and Hon, Chung-Chau and Wahlby, Carolina	given family Eduard Chelebian given family Christophe Avenel given family Julio Leon given family Chung-Chau Hon given family Carolina Wahlby	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/chelebian24a/chelebian24a.pdf