CSS: cluster similarity spectrum integration of single-cell genomics data
Abstract It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-09-01
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| Series: | Genome Biology |
| Online Access: | http://link.springer.com/article/10.1186/s13059-020-02147-4 |
| _version_ | 1818314466873311232 |
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| author | Zhisong He Agnieska Brazovskaja Sebastian Ebert J. Gray Camp Barbara Treutlein |
| author_facet | Zhisong He Agnieska Brazovskaja Sebastian Ebert J. Gray Camp Barbara Treutlein |
| author_sort | Zhisong He |
| collection | DOAJ |
| description | Abstract It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, cluster similarity spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals. |
| first_indexed | 2024-12-13T08:50:06Z |
| format | Article |
| id | doaj.art-1fbfc43aa64b4e3d818a549ba57178f8 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-13T08:50:06Z |
| publishDate | 2020-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-1fbfc43aa64b4e3d818a549ba57178f82022-12-21T23:53:23ZengBMCGenome Biology1474-760X2020-09-0121112110.1186/s13059-020-02147-4CSS: cluster similarity spectrum integration of single-cell genomics dataZhisong He0Agnieska Brazovskaja1Sebastian Ebert2J. Gray Camp3Barbara Treutlein4Department of Biosystems Science and Engineering, ETH ZürichMax Planck Institute for Evolutionary AnthropologyMax Planck Institute for Evolutionary AnthropologyInstitute of Molecular and Clinical OphthalmologyDepartment of Biosystems Science and Engineering, ETH ZürichAbstract It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, cluster similarity spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals.http://link.springer.com/article/10.1186/s13059-020-02147-4 |
| spellingShingle | Zhisong He Agnieska Brazovskaja Sebastian Ebert J. Gray Camp Barbara Treutlein CSS: cluster similarity spectrum integration of single-cell genomics data Genome Biology |
| title | CSS: cluster similarity spectrum integration of single-cell genomics data |
| title_full | CSS: cluster similarity spectrum integration of single-cell genomics data |
| title_fullStr | CSS: cluster similarity spectrum integration of single-cell genomics data |
| title_full_unstemmed | CSS: cluster similarity spectrum integration of single-cell genomics data |
| title_short | CSS: cluster similarity spectrum integration of single-cell genomics data |
| title_sort | css cluster similarity spectrum integration of single cell genomics data |
| url | http://link.springer.com/article/10.1186/s13059-020-02147-4 |
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