Imputation method for dropout in single-cell transcriptome data / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 778-783, 2023.
Article
Dans Chinois
| WPRIM
| ID: wpr-1008899
ABSTRACT
Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Analyse de regroupements
/
Transcriptome
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2023
Type:
Article
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