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

Venäläinen Mikko S.; Junttila Sini; Smolander Johannes; Elo Laura L.

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

Venäläinen Mikko S.
Junttila Sini
Smolander Johannes
Elo Laura L.
Katso/Avaa
Publisher´s PDF (3.691Mb)
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Oxford University Press
doi:10.1093/bioinformatics/btab831
URI
https://doi.org/10.1093/bioinformatics/btab831
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022020818105
Tiivistelmä

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.

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