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scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data
BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hin...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505295/ https://www.ncbi.nlm.nih.gov/pubmed/31068142 http://dx.doi.org/10.1186/s12864-019-5747-5 |
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author | Ye, Wenbin Ji, Guoli Ye, Pengchao Long, Yuqi Xiao, Xuesong Li, Shuchao Su, Yaru Wu, Xiaohui |
author_facet | Ye, Wenbin Ji, Guoli Ye, Pengchao Long, Yuqi Xiao, Xuesong Li, Shuchao Su, Yaru Wu, Xiaohui |
author_sort | Ye, Wenbin |
collection | PubMed |
description | BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data. RESULTS: We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools. CONCLUSIONS: scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5747-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6505295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65052952019-05-10 scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data Ye, Wenbin Ji, Guoli Ye, Pengchao Long, Yuqi Xiao, Xuesong Li, Shuchao Su, Yaru Wu, Xiaohui BMC Genomics Methodology Article BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data. RESULTS: We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools. CONCLUSIONS: scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-019-5747-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-08 /pmc/articles/PMC6505295/ /pubmed/31068142 http://dx.doi.org/10.1186/s12864-019-5747-5 Text en © The Author(s). 2019 Open Access This 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 | Methodology Article Ye, Wenbin Ji, Guoli Ye, Pengchao Long, Yuqi Xiao, Xuesong Li, Shuchao Su, Yaru Wu, Xiaohui scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data |
title | scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data |
title_full | scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data |
title_fullStr | scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data |
title_full_unstemmed | scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data |
title_short | scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data |
title_sort | scnpf: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell rna-seq data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505295/ https://www.ncbi.nlm.nih.gov/pubmed/31068142 http://dx.doi.org/10.1186/s12864-019-5747-5 |
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