scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data.
Bioinformatics
; 38(5): 1328-1335, 2022 02 07.
Article
em En
| MEDLINE
| ID: mdl-34888622
ABSTRACT
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 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. AVAILABILITY AND IMPLEMENTATION scShaper is available as an R package at https//github.com/elolab/scshaper. The test data are available at https//doi.org/10.5281/zenodo.5734488. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Análise da Expressão Gênica de Célula Única
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article