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

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

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

Author(s) -

Johannes Smolander,

Sini Junttila,

Mikko S. Venäläinen,

Laura L. Elo

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab831

Subject(s) - rna seq , inference , trajectory , computer science , algorithm , data mining , artificial intelligence , transcriptome , biology , gene expression , gene , genetics , physics , astronomy

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.

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