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Integrated single cell data analysis reveals cell specific networks and novel coactivation markers
BACKGROUND: Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5249008/ https://www.ncbi.nlm.nih.gov/pubmed/28105940 http://dx.doi.org/10.1186/s12918-016-0370-4 |
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author | Ghazanfar, Shila Bisogni, Adam J. Ormerod, John T. Lin, David M. Yang, Jean Y. H. |
author_facet | Ghazanfar, Shila Bisogni, Adam J. Ormerod, John T. Lin, David M. Yang, Jean Y. H. |
author_sort | Ghazanfar, Shila |
collection | PubMed |
description | BACKGROUND: Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. RESULTS: We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. CONCLUSION: Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0370-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5249008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52490082017-01-26 Integrated single cell data analysis reveals cell specific networks and novel coactivation markers Ghazanfar, Shila Bisogni, Adam J. Ormerod, John T. Lin, David M. Yang, Jean Y. H. BMC Syst Biol Research BACKGROUND: Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. RESULTS: We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. CONCLUSION: Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0370-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-12-05 /pmc/articles/PMC5249008/ /pubmed/28105940 http://dx.doi.org/10.1186/s12918-016-0370-4 Text en © The Author(s) 2016 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 | Research Ghazanfar, Shila Bisogni, Adam J. Ormerod, John T. Lin, David M. Yang, Jean Y. H. Integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
title | Integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
title_full | Integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
title_fullStr | Integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
title_full_unstemmed | Integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
title_short | Integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
title_sort | integrated single cell data analysis reveals cell specific networks and novel coactivation markers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5249008/ https://www.ncbi.nlm.nih.gov/pubmed/28105940 http://dx.doi.org/10.1186/s12918-016-0370-4 |
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