SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection.

Autor:	Adhikari SD; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI.; Department of Statistics and Probability, Michigan State University, East Lansing, MI., Steele NG; Department of Surgery, Henry Ford Pancreatic Cancer Center, Henry Ford Hospital, Detroit, MI.; Department of Pathology, Wayne State University, Detroit, MI.; Department of Oncology, Wayne State University, Detroit, MI.; Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI., Theisen B; Department of Pathology, Henry Ford Health, Detroit, MI., Wang J; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI., Cui Y; Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Aug 25. Date of Electronic Publication: 2024 Aug 25.
DOI:	10.1101/2024.08.23.609477
Abstrakt:	Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose. However, in diverse datasets with many SVGs, this approach may not ensure accurate results. Instead, grouping SVGs by expression patterns and using all SVG groups in downstream analysis can improve accuracy. Furthermore, classifying SVGs in this manner is akin to identifying cell type marker genes, offering valuable biological insights. The challenge lies in accurately categorizing SVGs into relevant clusters, aggravated by the absence of prior knowledge regarding the number and spectrum of spatial gene patterns. Addressing this challenge, we propose SPACE, SPatially variable gene clustering Adjusting for Cell type Effect, a framework that classifies SVGs based on their spatial patterns by adjusting for confounding effects caused by shared cell types, to improve spatial domain detection. This method does not require prior knowledge of gene cluster numbers, spatial patterns, or cell type information. Our comprehensive simulations and real data analyses demonstrate that SPACE is an efficient and promising tool for spatial transcriptomics analysis. Competing Interests: Conflict of Interest Disclosure We do not have any conflicts of interest, and we have not received any financial support for this work that could create potential conflicts of interest.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu