TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level

注释 计算机科学 杠杆(统计) 管道(软件) 电池类型 公制(单位) 数据类型 计算生物学 人工智能 机器学习 数据挖掘 细胞 生物 遗传学 经济 程序设计语言 运营管理
作者
Yan Liu,Wei Guo,Chen Li,Long-Chen Shen,Robin B. Gasser,Jiangning Song,Dijun Chen,Dong‐Jun Yu
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:24 (3)
标识
DOI:10.1093/bib/bbad132
摘要

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.
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