김찬우 (서울대학교, University of Washington) | 한빛사논문 > 한빛사

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서울대학교, University of Washington

Nucleic Acids Res., Published: 14 April 2022 | https://doi.org/10.1093/nar/gkac216 MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering

Chanwoo Kim^1,2,†, Hanbin Lee^3,†, Juhee Jeong⁴, Keehoon Jung^4,5,6,* and Buhm Han^4,7,*

¹Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea, ²Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA, ³Department of Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea, ⁴Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, Republic of Korea, ⁵Department of Anatomy and Cell Biology, Seoul National University College of Medicine, Seoul, Republic of Korea, ⁶Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, Republic of Korea and ⁷Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea

^*To whom correspondence should be addressed.
^†These authors contributed equally

Abstract

The standard analysis pipeline for single-cell RNA-seq data consists of sequential steps initiated by clustering the cells. An innate limitation of this pipeline is that an imperfect clustering result can irreversibly affect the succeeding steps. For example, there can be cell types not well distinguished by clustering because they largely share the global structure, such as the anterior primitive streak and mid primitive streak cells. If one searches differentially expressed genes (DEGs) solely based on clustering, marker genes for distinguishing these types will be missed. Moreover, clustering depends on many parameters and can often be subjective to manual decisions. To overcome these limitations, we propose MarcoPolo, a method that identifies informative DEGs independently of prior clustering. MarcoPolo sorts out genes by evaluating if the distributions are bimodal, if similar expression patterns are observed in other genes, and if the expressing cells are proximal in a low-dimensional space. Using real datasets with FACS-purified cell labels, we demonstrate that MarcoPolo recovers marker genes better than competing methods. Notably, MarcoPolo finds key genes that can distinguish cell types that are not distinguishable by the standard clustering. MarcoPolo is built in a convenient software package that provides analysis results in an HTML file.

논문정보

형식Research article
게재일2022년 04월 (BRIC 등록일 2022-04-19)
연구진국내(교신)+국외 연구진
분야 의약학 > 유전 및 유전체의학

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