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Joint learning of multiple gene networks from single-cell gene expression data
Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527714/ https://www.ncbi.nlm.nih.gov/pubmed/33033579 http://dx.doi.org/10.1016/j.csbj.2020.09.004 |
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author | Wu, Nuosi Yin, Fu Ou-Yang, Le Zhu, Zexuan Xie, Weixin |
author_facet | Wu, Nuosi Yin, Fu Ou-Yang, Le Zhu, Zexuan Xie, Weixin |
author_sort | Wu, Nuosi |
collection | PubMed |
description | Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance. |
format | Online Article Text |
id | pubmed-7527714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-75277142020-10-07 Joint learning of multiple gene networks from single-cell gene expression data Wu, Nuosi Yin, Fu Ou-Yang, Le Zhu, Zexuan Xie, Weixin Comput Struct Biotechnol J Research Article Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance. Research Network of Computational and Structural Biotechnology 2020-09-10 /pmc/articles/PMC7527714/ /pubmed/33033579 http://dx.doi.org/10.1016/j.csbj.2020.09.004 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Wu, Nuosi Yin, Fu Ou-Yang, Le Zhu, Zexuan Xie, Weixin Joint learning of multiple gene networks from single-cell gene expression data |
title | Joint learning of multiple gene networks from single-cell gene expression data |
title_full | Joint learning of multiple gene networks from single-cell gene expression data |
title_fullStr | Joint learning of multiple gene networks from single-cell gene expression data |
title_full_unstemmed | Joint learning of multiple gene networks from single-cell gene expression data |
title_short | Joint learning of multiple gene networks from single-cell gene expression data |
title_sort | joint learning of multiple gene networks from single-cell gene expression data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527714/ https://www.ncbi.nlm.nih.gov/pubmed/33033579 http://dx.doi.org/10.1016/j.csbj.2020.09.004 |
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