An active learning approach for clustering single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for cluster...

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Veröffentlicht in:	Laboratory investigation Jg. 102; H. 3; S. 227 - 235
Hauptverfasser:	Lin, Xiang, Liu, Haoran, Wei, Zhi, Roy, Senjuti Basu, Gao, Nan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 01.03.2022 Nature Publishing Group US Nature Publishing Group
Schlagworte:	38/39 45/91 631/1647/48 631/208/199 631/208/514/1949 Active learning Algorithms Animals Annotations Biological effects Biologists Cells, Cultured Cluster Analysis Clustering Data analysis Gene Expression Profiling - methods Gene sequencing Humans Kidney - cytology Kidney - metabolism Labelling Labels Laboratory Medicine Learning Leukocytes, Mononuclear - cytology Leukocytes, Mononuclear - metabolism Machine learning Medicine Medicine & Public Health Neurons - cytology Neurons - metabolism Nucleotide sequence Pathology Reproducibility of Results Ribonucleic acid RNA RNA-Seq - methods Single-Cell Analysis - methods Unsupervised Machine Learning Urinary Bladder - cytology Urinary Bladder - metabolism
ISSN:	0023-6837, 1530-0307, 1530-0307
Online-Zugang:	Volltext
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