ArticleBioinformatics (Oxford, England)2020
TinGa: fast and flexible trajectory inference with Growing Neural Gas.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Glial-to-mesenchymal transition of tumor Schwann cells drives the genetic burden in MPNSTs from neurofibromatosis type 1 mouse model.Science advances · 2025Article
- Article
- Cell-connectivity-guided trajectory inference from single-cell data.Bioinformatics (Oxford, England) · 2023Article
- Smart Home Automation-Based Hand Gesture Recognition Using Feature Fusion and Recurrent Neural Network.Sensors (Basel, Switzerland) · 2023Article
- LVPT: Lazy Velocity Pseudotime Inference Method.Biomolecules · 2023Article
- Article
- Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care.Biomolecules · 2022Review
- Inference of trajectory presence by tree dimension and subset specificity by subtree cover.PLoS computational biology · 2022Article
- Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases.Frontiers in cell and developmental biology · 2022Review
- Generalized and scalable trajectory inference in single-cell omics data with VIA.Nature communications · 2021Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
motivationDuring 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.
resultsIn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.