Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data

Research output: Contribution to journalJournal articlepeer-review

Standard

Chord : an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data. / Xiong, Ke-Xu; Zhou, Han-Lin; Lin, Cong; Yin, Jian-Hua; Kristiansen, Karsten; Yang, Huan-Ming; Li, Gui-Bo.

In: Communications Biology , Vol. 5, 510, 2022.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Xiong, K-X, Zhou, H-L, Lin, C, Yin, J-H, Kristiansen, K, Yang, H-M & Li, G-B 2022, 'Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data', Communications Biology , vol. 5, 510. https://doi.org/10.1038/s42003-022-03476-9

APA

Xiong, K-X., Zhou, H-L., Lin, C., Yin, J-H., Kristiansen, K., Yang, H-M., & Li, G-B. (2022). Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data. Communications Biology , 5, [510]. https://doi.org/10.1038/s42003-022-03476-9

Vancouver

Xiong K-X, Zhou H-L, Lin C, Yin J-H, Kristiansen K, Yang H-M et al. Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data. Communications Biology . 2022;5. 510. https://doi.org/10.1038/s42003-022-03476-9

Author

Xiong, Ke-Xu ; Zhou, Han-Lin ; Lin, Cong ; Yin, Jian-Hua ; Kristiansen, Karsten ; Yang, Huan-Ming ; Li, Gui-Bo. / Chord : an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data. In: Communications Biology . 2022 ; Vol. 5.

Bibtex

@article{b6be38d82e7a4fe2bd85e86e0c914f9b,
title = "Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data",
abstract = "High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem.",
keywords = "Algorithms, Machine Learning, Sequence Analysis, RNA/methods, Single-Cell Analysis/methods, Software",
author = "Ke-Xu Xiong and Han-Lin Zhou and Cong Lin and Jian-Hua Yin and Karsten Kristiansen and Huan-Ming Yang and Gui-Bo Li",
note = "{\textcopyright} 2022. The Author(s).",
year = "2022",
doi = "10.1038/s42003-022-03476-9",
language = "English",
volume = "5",
journal = "Communications Biology",
issn = "2399-3642",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Chord

T2 - an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data

AU - Xiong, Ke-Xu

AU - Zhou, Han-Lin

AU - Lin, Cong

AU - Yin, Jian-Hua

AU - Kristiansen, Karsten

AU - Yang, Huan-Ming

AU - Li, Gui-Bo

N1 - © 2022. The Author(s).

PY - 2022

Y1 - 2022

N2 - High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem.

AB - High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of these methods may yield satisfactory performance in some datasets but lack stability in others; thus, it is difficult to regard a single method as the gold standard which can be applied to all types of scenarios. It is a difficult and time-consuming task for researchers to choose the most appropriate software. We here propose Chord which implements a machine learning algorithm that integrates multiple doublet detection methods to address these issues. Chord had higher accuracy and stability than the individual approaches on different datasets containing real and synthetic data. Moreover, Chord was designed with a modular architecture port, which has high flexibility and adaptability to the incorporation of any new tools. Chord is a general solution to the doublet detection problem.

KW - Algorithms

KW - Machine Learning

KW - Sequence Analysis, RNA/methods

KW - Single-Cell Analysis/methods

KW - Software

U2 - 10.1038/s42003-022-03476-9

DO - 10.1038/s42003-022-03476-9

M3 - Journal article

C2 - 35637301

VL - 5

JO - Communications Biology

JF - Communications Biology

SN - 2399-3642

M1 - 510

ER -

ID: 310501069