SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downs...

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Bibliographic Details
Published in:PLoS computational biology 2021-06, Vol.17 (6), p.e1009118-e1009118
Main Authors: Qi, Jing, Zhou, Yang, Zhao, Zicen, Jin, Shuilin
Format: Article
Language:English
Subjects:
Animals
Biology and Life Sciences
Cluster Analysis
Clustering
Computational Biology
Computer and Information Sciences
Computer Simulation
Data Interpretation, Statistical
Data Visualization
Databases, Nucleic Acid - statistics & numerical data
Datasets
Dropouts
Gene expression
Gene Expression Profiling - statistics & numerical data
Gene sequencing
Genetic research
Genetic Techniques - statistics & numerical data
Heterogeneity
Humans
Methods
Noise
Physical Sciences
Research and analysis methods
Ribonucleic acid
RNA
RNA sequencing
RNA, Messenger - genetics
RNA, Messenger - isolation & purification
RNA-Seq - statistics & numerical data
Single-Cell Analysis - statistics & numerical data
Software
Statistical methods
Statistics
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Summary:The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009118