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OrgDyn: feature- and model-based characterization of spatial and temporal organoid dynamics
Author(s) -
Zaki Hasnain,
Andrew K. Fraser,
Dan Georgess,
Alex Seok Choi,
Paul Macklin,
Joel S. Bader,
Shelly R. Peyton,
Andrew J. Ewald,
Paul K. Newton
Publication year - 2020
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/btaa096
Subject(s) - computer science , organoid , pipeline (software) , modular design , feature (linguistics) , pattern recognition (psychology) , artificial intelligence , mit license , time point , data mining , computational biology , license , biology , linguistics , philosophy , genetics , aesthetics , programming language , operating system
Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature- and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease.

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