Cargando…
TinGa: fast and flexible trajectory inference with Growing Neural Gas
MOTIVATION: During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355244/ https://www.ncbi.nlm.nih.gov/pubmed/32657409 http://dx.doi.org/10.1093/bioinformatics/btaa463 |
_version_ | 1783558235498741760 |
---|---|
author | Todorov, Helena Cannoodt, Robrecht Saelens, Wouter Saeys, Yvan |
author_facet | Todorov, Helena Cannoodt, Robrecht Saelens, Wouter Saeys, Yvan |
author_sort | Todorov, Helena |
collection | PubMed |
description | MOTIVATION: During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state-of-the-art methods only perform well for certain trajectory types but not others. RESULTS: In this work, we present TinGa, a new TI model that is fast and flexible, and that is based on Growing Neural Graphs. We performed an extensive comparison of TinGa to five state-of-the-art methods for TI on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa improves the state-of-the-art by producing accurate models (comparable to or an improvement on the state-of-the-art) on the whole spectrum of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest execution times, showing that our method is thus one of the most versatile methods up to date. AVAILABILITY AND IMPLEMENTATION: R scripts for running TinGa, comparing it to top existing methods and generating the figures of this article are available at https://github.com/Helena-todd/TinGa. |
format | Online Article Text |
id | pubmed-7355244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73552442020-07-16 TinGa: fast and flexible trajectory inference with Growing Neural Gas Todorov, Helena Cannoodt, Robrecht Saelens, Wouter Saeys, Yvan Bioinformatics Comparative and Functional Genomics MOTIVATION: During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state-of-the-art methods only perform well for certain trajectory types but not others. RESULTS: In this work, we present TinGa, a new TI model that is fast and flexible, and that is based on Growing Neural Graphs. We performed an extensive comparison of TinGa to five state-of-the-art methods for TI on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa improves the state-of-the-art by producing accurate models (comparable to or an improvement on the state-of-the-art) on the whole spectrum of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest execution times, showing that our method is thus one of the most versatile methods up to date. AVAILABILITY AND IMPLEMENTATION: R scripts for running TinGa, comparing it to top existing methods and generating the figures of this article are available at https://github.com/Helena-todd/TinGa. Oxford University Press 2020-07 2020-07-13 /pmc/articles/PMC7355244/ /pubmed/32657409 http://dx.doi.org/10.1093/bioinformatics/btaa463 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Comparative and Functional Genomics Todorov, Helena Cannoodt, Robrecht Saelens, Wouter Saeys, Yvan TinGa: fast and flexible trajectory inference with Growing Neural Gas |
title | TinGa: fast and flexible trajectory inference with Growing Neural Gas |
title_full | TinGa: fast and flexible trajectory inference with Growing Neural Gas |
title_fullStr | TinGa: fast and flexible trajectory inference with Growing Neural Gas |
title_full_unstemmed | TinGa: fast and flexible trajectory inference with Growing Neural Gas |
title_short | TinGa: fast and flexible trajectory inference with Growing Neural Gas |
title_sort | tinga: fast and flexible trajectory inference with growing neural gas |
topic | Comparative and Functional Genomics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355244/ https://www.ncbi.nlm.nih.gov/pubmed/32657409 http://dx.doi.org/10.1093/bioinformatics/btaa463 |
work_keys_str_mv | AT todorovhelena tingafastandflexibletrajectoryinferencewithgrowingneuralgas AT cannoodtrobrecht tingafastandflexibletrajectoryinferencewithgrowingneuralgas AT saelenswouter tingafastandflexibletrajectoryinferencewithgrowingneuralgas AT saeysyvan tingafastandflexibletrajectoryinferencewithgrowingneuralgas |