Computational methods for trajectory inference from single-cell transcriptomics.
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.
Data Mining and Modelling for Biomedicine group, VIB Inflammation Research Center, Ghent, Belgium.|Department of Internal Medicine, Ghent University, Ghent, Belgium.|Center for Medical Genetics, Ghent University, Ghent, Belgium.|Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
Robrecht
R
Cannoodt
Data Mining and Modelling for Biomedicine group, VIB Inflammation Research Center, Ghent, Belgium.|Department of Internal Medicine, Ghent University, Ghent, Belgium.
Wouter
W
Saelens
Data Mining and Modelling for Biomedicine group, VIB Inflammation Research Center, Ghent, Belgium. yvan.saeys@ugent.be.|Department of Internal Medicine, Ghent University, Ghent, Belgium. yvan.saeys@ugent.be.
Yvan
Y
Saeys
D000465:Algorithms; D002454:Cell Differentiation / Q000235:genetics; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D059010:Single-Cell Analysis / Q000379:methods*
Bioinformatics; Cell differentiation; Single-cell transcriptomics