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Computational approaches for high-throughput single-cell data analysis

Helena Todorov (UGent) and Yvan Saeys (UGent)
(2019) FEBS JOURNAL. 286(8). p.1451-1467
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Abstract
During the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way.
Keywords
REGULATORY NETWORK INFERENCE, RNA-SEQUENCING DATA, FLOW-CYTOMETRY DATA, GENE-EXPRESSION, DIFFERENTIAL EXPRESSION, MESSENGER-RNA, AUTOMATED, IDENTIFICATION, SEQ DATA, TRANSCRIPTOME, VISUALIZATION, bioinformatics, computational tools, proteome, single cell, transcriptome

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Citation

Please use this url to cite or link to this publication:

Chicago
Todorov, Helena, and Yvan Saeys. 2019. “Computational Approaches for High-throughput Single-cell Data Analysis.” Febs Journal 286 (8): 1451–1467.
APA
Todorov, H., & Saeys, Y. (2019). Computational approaches for high-throughput single-cell data analysis. FEBS JOURNAL, 286(8), 1451–1467.
Vancouver
1.
Todorov H, Saeys Y. Computational approaches for high-throughput single-cell data analysis. FEBS JOURNAL. Hoboken: Wiley; 2019;286(8):1451–67.
MLA
Todorov, Helena, and Yvan Saeys. “Computational Approaches for High-throughput Single-cell Data Analysis.” FEBS JOURNAL 286.8 (2019): 1451–1467. Print.
@article{8617544,
  abstract     = {During the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way.},
  author       = {Todorov, Helena and Saeys, Yvan},
  issn         = {1742-464X},
  journal      = {FEBS JOURNAL},
  keywords     = {REGULATORY NETWORK INFERENCE,RNA-SEQUENCING DATA,FLOW-CYTOMETRY DATA,GENE-EXPRESSION,DIFFERENTIAL EXPRESSION,MESSENGER-RNA,AUTOMATED,IDENTIFICATION,SEQ DATA,TRANSCRIPTOME,VISUALIZATION,bioinformatics,computational tools,proteome,single cell,transcriptome},
  language     = {eng},
  number       = {8},
  pages        = {1451--1467},
  publisher    = {Wiley},
  title        = {Computational approaches for high-throughput single-cell data analysis},
  url          = {http://dx.doi.org/10.1111/febs.14613},
  volume       = {286},
  year         = {2019},
}

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