Molecules and Cells

  • 제46권2호
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  • Pages.106-119
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  • 2023
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  • 1016-8478(pISSN)
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  • 0219-1032(eISSN)
한국분자세포생물학회 (Korean Society for Molecular and Cellular Biology)
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Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods

  • Yeonjae Ryu (School of Biological Sciences, Seoul National University) ;
  • Geun Hee Han (School of Biological Sciences, Seoul National University) ;
  • Eunsoo Jung (School of Biological Sciences, Seoul National University) ;
  • Daehee Hwang (School of Biological Sciences, Seoul National University)
  • 투고 : 2023.01.10
  • 심사 : 2023.01.19
  • 발행 : 2023.02.28
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초록

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

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과제정보

This study was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF), funded by the Korean government (MSIT) (No. 2019M3A9B6066967).

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