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SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data

Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the SCRABBLE algorithm to address this problem. SCRABBLE leverages bulk data as a constraint and reduces unwante...

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Detalles Bibliográficos
Autores principales: Peng, Tao, Zhu, Qin, Yin, Penghang, Tan, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501316/
https://www.ncbi.nlm.nih.gov/pubmed/31060596
http://dx.doi.org/10.1186/s13059-019-1681-8
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author Peng, Tao
Zhu, Qin
Yin, Penghang
Tan, Kai
author_facet Peng, Tao
Zhu, Qin
Yin, Penghang
Tan, Kai
author_sort Peng, Tao
collection PubMed
description Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the SCRABBLE algorithm to address this problem. SCRABBLE leverages bulk data as a constraint and reduces unwanted bias towards expressed genes during imputation. Using both simulation and several types of experimental data, we demonstrate that SCRABBLE outperforms the existing methods in recovering dropout events, capturing true distribution of gene expression across cells, and preserving gene-gene relationship and cell-cell relationship in the data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1681-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-65013162019-05-10 SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data Peng, Tao Zhu, Qin Yin, Penghang Tan, Kai Genome Biol Method Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the SCRABBLE algorithm to address this problem. SCRABBLE leverages bulk data as a constraint and reduces unwanted bias towards expressed genes during imputation. Using both simulation and several types of experimental data, we demonstrate that SCRABBLE outperforms the existing methods in recovering dropout events, capturing true distribution of gene expression across cells, and preserving gene-gene relationship and cell-cell relationship in the data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1681-8) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-06 /pmc/articles/PMC6501316/ /pubmed/31060596 http://dx.doi.org/10.1186/s13059-019-1681-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Peng, Tao
Zhu, Qin
Yin, Penghang
Tan, Kai
SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
title SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
title_full SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
title_fullStr SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
title_full_unstemmed SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
title_short SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
title_sort scrabble: single-cell rna-seq imputation constrained by bulk rna-seq data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501316/
https://www.ncbi.nlm.nih.gov/pubmed/31060596
http://dx.doi.org/10.1186/s13059-019-1681-8
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