A comparison of single-cell trajectory inference methods

By Wouter Saelens, Robrecht Cannoodt, H. Todorov, Yvan Saeys. Nature Biotechnology, 2019

1,398 citations recorded by the source corpus.

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 develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline (https://benchmark.dynverse.org) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets. The authors comprehensively benchmark the accuracy, scalability, stability and usability of 45 single-cell trajectory inference methods.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8044b4714eb27c12020326d3540d6b94652c6b35

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Citations in the corpus

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics (2020, methodology): “Existing methods attempt to infer a trajectory within one timepoint (Saelens et al., 2019; La Manno et al., 2018), or interpolate linearly between two timepoints (Yang & Uhler, 2019; Schiebinger et al., 2019), but TrajectoryNet can interpolate non-linearly using information from more than two…”
stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, background): “In vitro cell state trajectories can be built from snapshots of single-cell transcriptional proﬁles 25 .”
Mapping Transcriptomic Vector Fields of Single Cells (2022, positive): “Chief among them are pseudotime-based methods (Bendall et al., 2014; Haghverdi et al., 2016; Saelens et al., 2019; Trapnell et al., 2014) first developed to infer the order of biological progression by learning a graph manifold of single cells based on transcriptome similarity.”
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (2022, methodology): “We apply trajectory inference (pseudotem-poral ordering) [40], which is commonly used in single-cell transcriptomics, to a subset of lncRNA with their RNA-FM embedding as input.”
Trajectory-based differential expression analysis for single-cell sequencing data (2019, methodology): “These trajectory-based methods improve upon discrete clusterbased approaches by (1) exploiting the continuous expression resolution along the trajectory and (2) comparing lineages using a single test based on entire gene expression profiles.”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, result): “In contrast to the extensive evaluations of clustering, differential expression, and trajectory inference methods [10–12], there is currently only a single attempt comparing methods to”