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Comparison of Scanpy-based algorithms to remove the batch effect from single-cell RNA-seq data

Authors
  • Li, Jiaqi1
  • Yu, Chengxuan1
  • Ma, Lifeng1
  • Wang, Jingjing1, 2
  • Guo, Guoji1, 2, 3, 4, 4
  • 1 The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China , Hangzhou (China)
  • 2 Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, 310058, China , Hangzhou (China)
  • 3 The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China , Hangzhou (China)
  • 4 Zhejiang University, Hangzhou, 310058, China , Hangzhou (China)
Type
Published Article
Journal
Cell Regeneration
Publisher
Springer Singapore
Publication Date
Jul 06, 2020
Volume
9
Issue
1
Identifiers
DOI: 10.1186/s13619-020-00041-9
Source
Springer Nature
License
Green

Abstract

With the development of single-cell RNA sequencing (scRNA-seq) technology, analysts need to integrate hundreds of thousands of cells with multiple experimental batches. It is becoming increasingly difficult for users to select the best integration methods to remove batch effects. Here, we compared the advantages and limitations of four commonly used Scanpy-based batch-correction methods using two representative and large-scale scRNA-seq datasets. We quantitatively evaluated batch-correction performance and efficiency. Furthermore, we discussed the performance differences among the evaluated methods at the algorithm level.

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