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PanoView: An iterative clustering method for single-cell RNA sequencing data
Single-cell RNA-sequencing (scRNA-seq) provides new opportunities to gain a mechanistic understanding of many biological processes. Current approaches for single cell clustering are often sensitive to the input parameters and have difficulty dealing with cell types with different densities. Here, we...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742414/ https://www.ncbi.nlm.nih.gov/pubmed/31469823 http://dx.doi.org/10.1371/journal.pcbi.1007040 |
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author | Hu, Ming-Wen Kim, Dong Won Liu, Sheng Zack, Donald J. Blackshaw, Seth Qian, Jiang |
author_facet | Hu, Ming-Wen Kim, Dong Won Liu, Sheng Zack, Donald J. Blackshaw, Seth Qian, Jiang |
author_sort | Hu, Ming-Wen |
collection | PubMed |
description | Single-cell RNA-sequencing (scRNA-seq) provides new opportunities to gain a mechanistic understanding of many biological processes. Current approaches for single cell clustering are often sensitive to the input parameters and have difficulty dealing with cell types with different densities. Here, we present Panoramic View (PanoView), an iterative method integrated with a novel density-based clustering, Ordering Local Maximum by Convex hull (OLMC), that uses a heuristic approach to estimate the required parameters based on the input data structures. In each iteration, PanoView will identify the most confident cell clusters and repeat the clustering with the remaining cells in a new PCA space. Without adjusting any parameter in PanoView, we demonstrated that PanoView was able to detect major and rare cell types simultaneously and outperformed other existing methods in both simulated datasets and published single-cell RNA-sequencing datasets. Finally, we conducted scRNA-Seq analysis of embryonic mouse hypothalamus, and PanoView was able to reveal known cell types and several rare cell subpopulations. |
format | Online Article Text |
id | pubmed-6742414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67424142019-09-20 PanoView: An iterative clustering method for single-cell RNA sequencing data Hu, Ming-Wen Kim, Dong Won Liu, Sheng Zack, Donald J. Blackshaw, Seth Qian, Jiang PLoS Comput Biol Research Article Single-cell RNA-sequencing (scRNA-seq) provides new opportunities to gain a mechanistic understanding of many biological processes. Current approaches for single cell clustering are often sensitive to the input parameters and have difficulty dealing with cell types with different densities. Here, we present Panoramic View (PanoView), an iterative method integrated with a novel density-based clustering, Ordering Local Maximum by Convex hull (OLMC), that uses a heuristic approach to estimate the required parameters based on the input data structures. In each iteration, PanoView will identify the most confident cell clusters and repeat the clustering with the remaining cells in a new PCA space. Without adjusting any parameter in PanoView, we demonstrated that PanoView was able to detect major and rare cell types simultaneously and outperformed other existing methods in both simulated datasets and published single-cell RNA-sequencing datasets. Finally, we conducted scRNA-Seq analysis of embryonic mouse hypothalamus, and PanoView was able to reveal known cell types and several rare cell subpopulations. Public Library of Science 2019-08-30 /pmc/articles/PMC6742414/ /pubmed/31469823 http://dx.doi.org/10.1371/journal.pcbi.1007040 Text en © 2019 Hu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hu, Ming-Wen Kim, Dong Won Liu, Sheng Zack, Donald J. Blackshaw, Seth Qian, Jiang PanoView: An iterative clustering method for single-cell RNA sequencing data |
title | PanoView: An iterative clustering method for single-cell RNA sequencing data |
title_full | PanoView: An iterative clustering method for single-cell RNA sequencing data |
title_fullStr | PanoView: An iterative clustering method for single-cell RNA sequencing data |
title_full_unstemmed | PanoView: An iterative clustering method for single-cell RNA sequencing data |
title_short | PanoView: An iterative clustering method for single-cell RNA sequencing data |
title_sort | panoview: an iterative clustering method for single-cell rna sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742414/ https://www.ncbi.nlm.nih.gov/pubmed/31469823 http://dx.doi.org/10.1371/journal.pcbi.1007040 |
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