deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues

IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Genomics, Proteomics & Bioinformatics Pub Date : 2023-04-01 DOI:10.1016/j.gpb.2022.04.001
Guangsheng Pei , Fangfang Yan , Lukas M. Simon , Yulin Dai , Peilin Jia , Zhongming Zhao
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Abstract

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas. Here, we present decoding Cell type Specificity (deCS), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait–cell type associations in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for deCS, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.

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deCS:人类组织中单细胞RNA测序数据的系统细胞类型注释工具。
单细胞RNA测序(scRNA-seq)正在彻底改变复杂和动态细胞机制的研究。然而,细胞类型注释仍然是一个主要挑战,因为它在很大程度上依赖于先验知识和手动管理,这是繁琐和主观的。越来越多的scRNA-seq数据集,以及大量已发表的遗传学研究,促使我们建立一个全面的人类细胞类型参考图谱。在这里,我们提出了解码细胞类型特异性(deCS),这是一种自动细胞类型注释方法,通过全面收集人类细胞类型表达谱和标记基因来增强。我们使用deCS对来自不同组织类型的scRNA-seq数据进行注释,并系统评估了不同条件下的注释准确性,包括参考面板、测序深度和特征选择策略。我们的结果表明,扩展引用对于提高注释准确性至关重要。与许多现有的最先进的注释工具相比,deCS显著减少了计算时间,提高了精度。deCS可以整合到标准scRNA-seq分析管道中,以增强细胞类型注释。最后,我们证明了deCS在识别51个人类复杂性状中的性状-细胞类型关联方面的广泛用途,为疾病发病机制的细胞机制提供了深入的见解。deCS的所有文档,包括源代码、用户手册、演示数据和教程,都可以在https://github.com/bsml320/deCS.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Genomics, Proteomics & Bioinformatics
Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
14.30
自引率
4.20%
发文量
844
审稿时长
61 days
期刊介绍: Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.
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