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

Motivation: 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 generalizable methods. Results: We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudo-time 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.

Automated summary

The scShaper is a new trajectory inference method that generates a continuous smooth pseudo-time using an ensemble approach. It accurately infers various trigonometric trajectories and outperforms other methods in terms of accuracy of cell sorting and differentially expressed genes. The scShaper is a fast method with a few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering.