Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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A semi-automatic cell type annotation method for single-cell RNA sequencing dataset

  • Kim, Wan (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Yoon, Sung Min (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Kim, Sangsoo (Department of Bioinformatics and Life Science, Soongsil University)
  • Received : 2020.03.11
  • Accepted : 2020.03.27
  • Published : 2020.09.30
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Abstract

Single-cell RNA sequencing (scRNA-seq) has been widely applied to provide insights into the cell-by-cell expression difference in a given bulk sample. Accordingly, numerous analysis methods have been developed. As it involves simultaneous analyses of many cell and genes, efficiency of the methods is crucial. The conventional cell type annotation method is laborious and subjective. Here we propose a semi-automatic method that calculates a normalized score for each cell type based on user-supplied cell type-specific marker gene list. The method was applied to a publicly available scRNA-seq data of mouse cardiac non-myocyte cell pool. Annotating the 35 t-stochastic neighbor embedding clusters into 12 cell types was straightforward, and its accuracy was evaluated by constructing co-expression network for each cell type. Gene Ontology analysis was congruent with the annotated cell type and the corollary regulatory network analysis showed upstream transcription factors that have well supported literature evidences. The source code is available as an R script upon request.

Keywords

Acknowledgement

This work is based the Master's degree thesis of SMY at Soongsil University, Seoul, Korea. We acknowledge the financial support from the Soongsil University Research Fund. The computational resources were kindly provided by Korea Institute of Science and Technology Information (GSDC & KREONET).

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