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2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions

SUMMARY: We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both...

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Detalles Bibliográficos
Autores principales: Zhu, Kaiyi, Anastassiou, Dimitris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267828/
https://www.ncbi.nlm.nih.gov/pubmed/32108864
http://dx.doi.org/10.1093/bioinformatics/btaa148
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author Zhu, Kaiyi
Anastassiou, Dimitris
author_facet Zhu, Kaiyi
Anastassiou, Dimitris
author_sort Zhu, Kaiyi
collection PubMed
description SUMMARY: We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols. AVAILABILITY AND IMPLEMENTATION: The R package of 2DImpute is freely available at GitHub (https://github.com/zky0708/2DImpute). CONTACT: d.anastassiou@columbia.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-72678282020-06-09 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions Zhu, Kaiyi Anastassiou, Dimitris Bioinformatics Applications Notes SUMMARY: We developed 2DImpute, an imputation method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data. It features preventing excessive correction by predicting the false zeros and imputing their values by making use of the interrelationships between both genes and cells in the expression matrix. We showed that 2DImpute outperforms several leading imputation methods by applying it on datasets from various scRNA-seq protocols. AVAILABILITY AND IMPLEMENTATION: The R package of 2DImpute is freely available at GitHub (https://github.com/zky0708/2DImpute). CONTACT: d.anastassiou@columbia.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-06 2020-02-28 /pmc/articles/PMC7267828/ /pubmed/32108864 http://dx.doi.org/10.1093/bioinformatics/btaa148 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Zhu, Kaiyi
Anastassiou, Dimitris
2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
title 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
title_full 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
title_fullStr 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
title_full_unstemmed 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
title_short 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions
title_sort 2dimpute: imputation in single-cell rna-seq data from correlations in two dimensions
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267828/
https://www.ncbi.nlm.nih.gov/pubmed/32108864
http://dx.doi.org/10.1093/bioinformatics/btaa148
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