WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition.
Brief Bioinform
; 22(5)2021 09 02.
Article
in English
| MEDLINE | ID: covidwho-1528156
ABSTRACT
The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method. WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations and improved the clustering of cells, performing impressively for applications with sparse datasets. Overall, this study shows a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their single-cell RNA sequencing datasets. The source code of WEDGE has been released at https//github.com/QuKunLab/WEDGE.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Computational Biology
/
Gene Expression Profiling
/
Single-Cell Analysis
/
RNA-Seq
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Bib
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