Quantitative assessment of cell population diversity in single-cell landscapes.
Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-10-01
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| Series: | PLoS Biology |
| Online Access: | https://doi.org/10.1371/journal.pbio.2006687 |
| _version_ | 1797818272495697920 |
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| author | Qi Liu Charles A Herring Quanhu Sheng Jie Ping Alan J Simmons Bob Chen Amrita Banerjee Wei Li Guoqiang Gu Robert J Coffey Yu Shyr Ken S Lau |
| author_facet | Qi Liu Charles A Herring Quanhu Sheng Jie Ping Alan J Simmons Bob Chen Amrita Banerjee Wei Li Guoqiang Gu Robert J Coffey Yu Shyr Ken S Lau |
| author_sort | Qi Liu |
| collection | DOAJ |
| description | Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution. |
| first_indexed | 2024-03-13T09:05:38Z |
| format | Article |
| id | doaj.art-4532c001b272488bbf12f068b3538c09 |
| institution | Directory Open Access Journal |
| issn | 1544-9173 1545-7885 |
| language | English |
| last_indexed | 2024-03-13T09:05:38Z |
| publishDate | 2018-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj.art-4532c001b272488bbf12f068b3538c092023-05-28T05:30:43ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852018-10-011610e200668710.1371/journal.pbio.2006687Quantitative assessment of cell population diversity in single-cell landscapes.Qi LiuCharles A HerringQuanhu ShengJie PingAlan J SimmonsBob ChenAmrita BanerjeeWei LiGuoqiang GuRobert J CoffeyYu ShyrKen S LauSingle-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.https://doi.org/10.1371/journal.pbio.2006687 |
| spellingShingle | Qi Liu Charles A Herring Quanhu Sheng Jie Ping Alan J Simmons Bob Chen Amrita Banerjee Wei Li Guoqiang Gu Robert J Coffey Yu Shyr Ken S Lau Quantitative assessment of cell population diversity in single-cell landscapes. PLoS Biology |
| title | Quantitative assessment of cell population diversity in single-cell landscapes. |
| title_full | Quantitative assessment of cell population diversity in single-cell landscapes. |
| title_fullStr | Quantitative assessment of cell population diversity in single-cell landscapes. |
| title_full_unstemmed | Quantitative assessment of cell population diversity in single-cell landscapes. |
| title_short | Quantitative assessment of cell population diversity in single-cell landscapes. |
| title_sort | quantitative assessment of cell population diversity in single cell landscapes |
| url | https://doi.org/10.1371/journal.pbio.2006687 |
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