Imputation method for dropout in single-cell transcriptome data.
10.7507/1001-5515.202301009
- Author:
Chao JIANG
1
;
Longfei HU
2
;
Chunxiang XU
1
;
Qinyu GE
1
;
Xiangwei ZHAO
1
Author Information
1. State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China.
2. Singleron BiotechCo., Ltd, Nanjing 210018, P. R. China.
- Publication Type:Review
- Keywords:
Deep learning;
Dropout;
Low rank matrix completion;
Single-cell RNA sequencing;
Statistical model
- MeSH:
Cluster Analysis;
Transcriptome
- From:
Journal of Biomedical Engineering
2023;40(4):778-783
- CountryChina
- Language:Chinese
-
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.
