Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Qi, Jing, Zhou, Yang, Zhao, Zicen, Jin, Shuilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266063/
https://www.ncbi.nlm.nih.gov/pubmed/34138847
http://dx.doi.org/10.1371/journal.pcbi.1009118
_version_ 1783719864263770112
author Qi, Jing
Zhou, Yang
Zhao, Zicen
Jin, Shuilin
author_facet Qi, Jing
Zhou, Yang
Zhao, Zicen
Jin, Shuilin
author_sort Qi, Jing
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8266063
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-82660632021-07-19 SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data Qi, Jing Zhou, Yang Zhao, Zicen Jin, Shuilin PLoS Comput Biol Research Article 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. Public Library of Science 2021-06-17 /pmc/articles/PMC8266063/ /pubmed/34138847 http://dx.doi.org/10.1371/journal.pcbi.1009118 Text en © 2021 Qi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Qi, Jing
Zhou, Yang
Zhao, Zicen
Jin, Shuilin
SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
title SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
title_full SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
title_fullStr SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
title_full_unstemmed SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
title_short SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
title_sort sdimpute: a statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell rna-seq data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266063/
https://www.ncbi.nlm.nih.gov/pubmed/34138847
http://dx.doi.org/10.1371/journal.pcbi.1009118
work_keys_str_mv AT qijing sdimputeastatisticalblockimputationmethodbasedoncelllevelandgenelevelinformationfordropoutsinsinglecellrnaseqdata
AT zhouyang sdimputeastatisticalblockimputationmethodbasedoncelllevelandgenelevelinformationfordropoutsinsinglecellrnaseqdata
AT zhaozicen sdimputeastatisticalblockimputationmethodbasedoncelllevelandgenelevelinformationfordropoutsinsinglecellrnaseqdata
AT jinshuilin sdimputeastatisticalblockimputationmethodbasedoncelllevelandgenelevelinformationfordropoutsinsinglecellrnaseqdata