scHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering

In this study, spatiotemporal embedding and cell graphs are extracted to capture spatial dynamics at the molecular level. In order to enhance the accuracy of cell type detection, this study proposes the scHybridBERT architecture to conduct multi-view modeling of scRNA-seq data using extracted spatiotemporal patterns. In this scHybridBERT method, graph learning models are employed to deal with cell graphs and the Performer model employs spatiotemporal embeddings. Experimental outcomes about benchmark scRNA-seq datasets indicate that the proposed scHybridBERT method is able to enhance the accuracy of single-cell clustering tasks by integrating spatiotemporal embeddings and cell graphs.PMID:38517692 | PMC:PMC10959234 | DOI:10.1093/bib/bbae018

Source: Briefings in Bioinformatics - March 22, 2024 Category: Bioinformatics Authors: Zhang Wei Wu Chenjun Xing Feiyang Jiang Mingfeng Zhang Yixuan Liu Qi Shi Zhuoxing Dai Qi Source Type: research