scPriorGraph: constructing biosemantic cell–cell graphs with prior gene set selection for cell type identification from scRNA-seq data

Autor:	Xiyue Cao, Yu-An Huang, Zhu-Hong You, Xuequn Shang, Lun Hu, Peng-Wei Hu, Zhi-An Huang
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Single-cell RNA sequencing Cell-type identification Dual-channel graph neural network Pathway Ligand-receptor network Graph augmentation Biology (General) QH301-705.5 Genetics QH426-470
Zdroj:	Genome Biology, Vol 25, Iss 1, Pp 1-29 (2024)
Druh dokumentu:	article
ISSN:	1474-760X
DOI:	10.1186/s13059-024-03357-w
Popis:	Abstract Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/56b87e6f343b4ebfb94715675a4aa99b Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ