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Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks
BACKGROUND: Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcription...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7607865/ https://www.ncbi.nlm.nih.gov/pubmed/33138772 http://dx.doi.org/10.1186/s12864-020-07144-2 |
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author | Klimm, Florian Toledo, Enrique M. Monfeuga, Thomas Zhang, Fang Deane, Charlotte M. Reinert, Gesine |
author_facet | Klimm, Florian Toledo, Enrique M. Monfeuga, Thomas Zhang, Fang Deane, Charlotte M. Reinert, Gesine |
author_sort | Klimm, Florian |
collection | PubMed |
description | BACKGROUND: Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS: In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS: The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12864-020-07144-2). |
format | Online Article Text |
id | pubmed-7607865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76078652020-11-03 Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks Klimm, Florian Toledo, Enrique M. Monfeuga, Thomas Zhang, Fang Deane, Charlotte M. Reinert, Gesine BMC Genomics Research Article BACKGROUND: Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. RESULTS: In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. CONCLUSIONS: The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12864-020-07144-2). BioMed Central 2020-11-02 /pmc/articles/PMC7607865/ /pubmed/33138772 http://dx.doi.org/10.1186/s12864-020-07144-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Article Klimm, Florian Toledo, Enrique M. Monfeuga, Thomas Zhang, Fang Deane, Charlotte M. Reinert, Gesine Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks |
title | Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks |
title_full | Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks |
title_fullStr | Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks |
title_full_unstemmed | Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks |
title_short | Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks |
title_sort | functional module detection through integration of single-cell rna sequencing data with protein–protein interaction networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7607865/ https://www.ncbi.nlm.nih.gov/pubmed/33138772 http://dx.doi.org/10.1186/s12864-020-07144-2 |
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