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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007040 |
| _version_ | 1819021855985500160 |
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| author | Ming-Wen Hu Dong Won Kim Sheng Liu Donald J Zack Seth Blackshaw Jiang Qian |
| author_facet | Ming-Wen Hu Dong Won Kim Sheng Liu Donald J Zack Seth Blackshaw Jiang Qian |
| author_sort | Ming-Wen Hu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T04:13:45Z |
| format | Article |
| id | doaj.art-d3a830bf473d41f993f0cfef169d5f55 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T04:13:45Z |
| publishDate | 2019-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-d3a830bf473d41f993f0cfef169d5f552022-12-21T19:16:23ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-08-01158e100704010.1371/journal.pcbi.1007040PanoView: An iterative clustering method for single-cell RNA sequencing data.Ming-Wen HuDong Won KimSheng LiuDonald J ZackSeth BlackshawJiang QianSingle-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.https://doi.org/10.1371/journal.pcbi.1007040 |
| spellingShingle | Ming-Wen Hu Dong Won Kim Sheng Liu Donald J Zack Seth Blackshaw Jiang Qian PanoView: An iterative clustering method for single-cell RNA sequencing data. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1007040 |
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