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	scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data
	Zhou, Songqi1; Li, Yang2; Wu, Wenyuan1; Li, Li1
	2024-01-22
摘要	Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.
关键词	multi-task learning scRNA-seq cell annotation protein prediction low-dimensional embedding
DOI	10.1093/bib/bbad523
发表期刊	BRIEFINGS IN BIOINFORMATICS
ISSN	1467-5463
卷号	25期号:2页码:12
通讯作者	Wu, Wenyuan(wuwenyuan@cigit.ac.cn) ; Li, Li(lili@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:001177227400039
语种	英语