Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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.
I: Communications Biology , Bind 5, 510, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
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