SECANT: a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics

Author:

Wang Xinjun12ORCID,Xu Zhongli34ORCID,Hu Haoran1ORCID,Zhou Xueping1,Zhang Yanfu5,Lafyatis Robert6ORCID,Chen Kong6,Huang Heng5,Ding Ying1ORCID,Duerr Richard H6ORCID,Chen Wei13ORCID

Affiliation:

1. Department of Biostatistics, University of Pittsburgh , Pittsburgh, PA 15213, USA

2. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center , New York, NY 10065, USA

3. Department of Pediatrics, University of Pittsburgh , Pittsburgh, PA 15224, USA

4. School of Medicine, Tsinghua University , Beijing 100084, China

5. Department of Electrical and Computer Engineering, University of Pittsburgh , Pittsburgh, PA 15261, USA

6. Department of Medicine, University of Pittsburgh , Pittsburgh, PA 15261, USA

Abstract

Abstract The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein abundance and RNA expression simultaneously at single cell resolution. Although CITE-seq and other similar technologies have gained enormous popularity, novel methods for analyzing this type of single cell multi-omics data are in urgent need. A limited number of available tools utilize data-driven approach, which may undermine the biological importance of surface protein data. In this study, we developed SECANT, a biology-guided SEmi-supervised method for Clustering, classification, and ANnoTation of single-cell multi-omics. SECANT is used to analyze CITE-seq data, or jointly analyze CITE-seq and scRNA-seq data. The novelties of SECANT include (1) using confident cell type label identified from surface protein data as guidance for cell clustering, (2) providing general annotation of confident cell types for each cell cluster, (3) utilizing cells with uncertain or missing cell type label to increase performance, and (4) accurate prediction of confident cell types for scRNA-seq data. Besides, as a model-based approach, SECANT can quantify the uncertainty of the results through easily interpretable posterior probability, and our framework can be potentially extended to handle other types of multi-omics data. We successfully demonstrated the validity and advantages of SECANT via simulation studies and analysis of public and in-house datasets from multiple tissues. We believe this new method will be complementary to existing tools for characterizing novel cell types and make new biological discoveries using single-cell multi-omics data.

Funder

National Institutes of Health

National Cancer Institute

Publisher

Oxford University Press (OUP)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3