ScShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data.

ABSTRACT: Computational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalisable methods. We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering. Supplementary data are available at Bioinformatics online. scShaper is available as an R package at https://github.com/elolab/scshaper. The submitted software version of scShaper and test data are available at https://doi.org/10.5281/zenodo.5734488.

SUBMITTER: Smolander J 

PROVIDER: S-EPMC8825760 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature