TinGa

TinGa is a trajectory inference (TI) method for modeling cell developmental dynamics, particularly in single-cell transcriptomics. It is based on Growing Neural Graphs and offers a fast and flexible approach to TI.

In an extensive comparison with five state-of-the-art TI methods on 250 datasets, including synthetic and real data, TinGa demonstrated its versatility by producing accurate models across a wide range of data complexity, from simple linear datasets to complex, disconnected graphs. Moreover, TinGa achieved the fastest execution times among the compared methods.

Topic

Transcriptomics;Machine learning;Cell biology

Detail

  • Operation: Trajectory visualization;Standardisation and normalisation

  • Software interface: Command-line user interface

  • Language: R

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: Flemish Government.

  • Input: -

  • Output: -

  • Contact: Helena Todorov helena.todorov@irc.vib-ugent.be ,Yvan Saeys yvan.saeys@ugent.be

  • Collection: -

  • Maturity: -

Publications

  • TinGa: fast and flexible trajectory inference with Growing Neural Gas.
  • Todorov H, et al. TinGa: fast and flexible trajectory inference with Growing Neural Gas. TinGa: fast and flexible trajectory inference with Growing Neural Gas. 2020; 36:i66-i74. doi: 10.1093/bioinformatics/btaa463
  • https://doi.org/10.1093/BIOINFORMATICS/BTAA463
  • PMID: 32657409
  • PMC: PMC7355244

Download and documentation

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