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Computational methods for trajectory inference from single-cell transcriptomics

(2016) EUROPEAN JOURNAL OF IMMUNOLOGY. 46(11). p.2496-2506
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Abstract
Recent developments in single-cell transcriptomics have opened new opportunities for studying dynamic processes in immunology in a high throughput and unbiased manner. Starting from a mixture of cells in different stages of a developmental process, unsupervised trajectory inference algorithms aim to automatically reconstruct the underlying developmental path that cells are following. In this review, we break down the strategies used by this novel class of methods, and organize their components into a common framework, highlighting several practical advantages and disadvantages of the individual methods. We also give an overview of new insights these methods have already provided regarding the wiring and gene regulation of cell differentiation. As the trajectory inference field is still in its infancy, we propose several future developments that will ultimately lead to a global and data-driven way of studying immune cell differentiation.
Keywords
GENE-EXPRESSION ANALYSIS, STEM-CELLS, RNA-SEQ, FATE DECISIONS, MASS, CYTOMETRY, EPIGENETIC LANDSCAPE, NETWORK MOTIFS, DIFFUSION MAPS, BONE-MARROW, T-CELL, Bioinformatics, Cell differentiation, Single-cell transcriptomics

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MLA
Cannoodt, Robrecht, et al. “Computational Methods for Trajectory Inference from Single-Cell Transcriptomics.” EUROPEAN JOURNAL OF IMMUNOLOGY, vol. 46, no. 11, 2016, pp. 2496–506, doi:10.1002/eji.201646347.
APA
Cannoodt, R., Saelens, W., & Saeys, Y. (2016). Computational methods for trajectory inference from single-cell transcriptomics. EUROPEAN JOURNAL OF IMMUNOLOGY, 46(11), 2496–2506. https://doi.org/10.1002/eji.201646347
Chicago author-date
Cannoodt, Robrecht, Wouter Saelens, and Yvan Saeys. 2016. “Computational Methods for Trajectory Inference from Single-Cell Transcriptomics.” EUROPEAN JOURNAL OF IMMUNOLOGY 46 (11): 2496–2506. https://doi.org/10.1002/eji.201646347.
Chicago author-date (all authors)
Cannoodt, Robrecht, Wouter Saelens, and Yvan Saeys. 2016. “Computational Methods for Trajectory Inference from Single-Cell Transcriptomics.” EUROPEAN JOURNAL OF IMMUNOLOGY 46 (11): 2496–2506. doi:10.1002/eji.201646347.
Vancouver
1.
Cannoodt R, Saelens W, Saeys Y. Computational methods for trajectory inference from single-cell transcriptomics. EUROPEAN JOURNAL OF IMMUNOLOGY. 2016;46(11):2496–506.
IEEE
[1]
R. Cannoodt, W. Saelens, and Y. Saeys, “Computational methods for trajectory inference from single-cell transcriptomics,” EUROPEAN JOURNAL OF IMMUNOLOGY, vol. 46, no. 11, pp. 2496–2506, 2016.
@article{8510906,
  abstract     = {{Recent developments in single-cell transcriptomics have opened new opportunities for studying dynamic processes in immunology in a high throughput and unbiased manner. Starting from a mixture of cells in different stages of a developmental process, unsupervised trajectory inference algorithms aim to automatically reconstruct the underlying developmental path that cells are following. In this review, we break down the strategies used by this novel class of methods, and organize their components into a common framework, highlighting several practical advantages and disadvantages of the individual methods. We also give an overview of new insights these methods have already provided regarding the wiring and gene regulation of cell differentiation. As the trajectory inference field is still in its infancy, we propose several future developments that will ultimately lead to a global and data-driven way of studying immune cell differentiation.}},
  author       = {{Cannoodt, Robrecht and Saelens, Wouter and Saeys, Yvan}},
  issn         = {{0014-2980}},
  journal      = {{EUROPEAN JOURNAL OF IMMUNOLOGY}},
  keywords     = {{GENE-EXPRESSION ANALYSIS,STEM-CELLS,RNA-SEQ,FATE DECISIONS,MASS,CYTOMETRY,EPIGENETIC LANDSCAPE,NETWORK MOTIFS,DIFFUSION MAPS,BONE-MARROW,T-CELL,Bioinformatics,Cell differentiation,Single-cell transcriptomics}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{2496--2506}},
  title        = {{Computational methods for trajectory inference from single-cell transcriptomics}},
  url          = {{http://doi.org/10.1002/eji.201646347}},
  volume       = {{46}},
  year         = {{2016}},
}

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