A comparison of single-cell trajectory inference methods
Ghent University · VIB-UGent Center for Inflammation Research · +6 more institutions
Abstract
Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we…
Citation impact
- FWCI
- 73.91
- Percentile
- 100%
- References
- 61
Authors
4- WSWouter SaelensCorresponding
Ghent University, VIB-UGent Center for Inflammation Research
- RCRobrecht Cannoodt
Ghent University Hospital, Ghent University, VIB-UGent Center for Inflammation Research
- HTHelena Todorov
Université Claude Bernard Lyon 1, École Normale Supérieure de Lyon, Centre National de la Recherche Scientifique, Inserm, Ghent University, VIB-UGent Center for Inflammation Research
- YSYvan Saeys
University College Ghent, Ghent University, VIB-UGent Center for Inflammation Research
Topics & keywords
- Computer science
- Inference
- Scalability
- Benchmark (surveying)
- Trajectory
- Pipeline (software)
- Data mining
- Usability