Open AccessscShaper: 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) - computer science , trajectory , inference , algorithm , representation (politics) , set (abstract data type) , hyperparameter , benchmarking , data mining , artificial intelligence , physics , marketing , astronomy , politics , political science , law , business , programming language
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.