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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
Descripción
Sumario: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.