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...
Autores principales: | , , , |
---|---|
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 |