ArticleBioinformatics (Oxford, England)2015
A generic methodological framework for studying single cell motility in high-throughput time-lapse data.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.Bioinformatics (Oxford, England) · 2024Article
- A machine learning approach to discover migration modes and transition dynamics of heterogeneous dendritic cells.Frontiers in immunology · 2023Article
- Emerging machine learning approaches to phenotyping cellular motility and morphodynamics.Physical biology · 2021Review
- Comparative Study of Contact Repulsion in Control and Mutant Macrophages Using a Novel Interaction Detection.Journal of imaging · 2020Article
- Macrosight: A Novel Framework to Analyze the Shape and Movement of Interacting Macrophages Using MatlabJournal of imaging · 2019Article
- Sharing and reusing cell image data.Molecular biology of the cell · 2018Article
- Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance.PLoS computational biology · 2018Article
- An end-to-end software solution for the analysis of high-throughput single-cell migration data.Scientific reports · 2017Article
- Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.Integrative biology : quantitative biosciences from nano to macro · 2016Article
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Authors and funding
6 authors.
Funding
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Abstract
motivationMotility is a fundamental cellular attribute, which plays a major part in processes ranging from embryonic development to metastasis. Traditionally, single cell motility is often studied by live cell imaging. Yet, such studies were so far limited to low throughput. To systematically study cell motility at a large scale, we need robust methods to quantify cell trajectories in live cell imaging data.
resultsThe primary contribution of this article is to present Motility study Integrated Workflow (MotIW), a generic workflow for the study of single cell motility in high-throughput time-lapse screening data. It is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner. AVAILABILITY AND IMPLEMENTATION: Python code and examples are available online (http://cbio.ensmp.fr/∼aschoenauer/motiw.html)
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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.