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  CSS: Cluster similarity spectrum integration of single-cell genomics data

He, Z., Brazovskaja, A., Ebert, S., Camp, J. G., & Treutlein, B. (2020). CSS: Cluster similarity spectrum integration of single-cell genomics data. Genome Biology, 21: 224. doi:10.1186/s13059-020-02147-4.

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© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
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 Creators:
He, Zhisong, Author
Brazovskaja, Agnieska1, 2, Author                 
Ebert, Sebastian1, Author           
Camp, J. Grey, Author
Treutlein, Barbara1, Author                 
Affiliations:
1Single Cell Genomics, Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society, ou_2173644              
2The Leipzig School of Human Origins (IMPRS), Max Planck Institute for Evolutionary Anthropology, Max Planck Society, Deutscher Platz 6, 04103 Leipzig, DE, ou_1497688              

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 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.

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Language(s): eng - English
 Dates: 2020-09
 Publication Status: Issued
 Pages: 21
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1186/s13059-020-02147-4
BibTex Citekey: He2020
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Title: Genome Biology
Source Genre: Journal
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Publ. Info: Springer Nature
Pages: - Volume / Issue: 21 Sequence Number: 224 Start / End Page: - Identifier: ISSN: 1474-760X