Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.

Autor: Bhaskar D; School of Engineering, Brown University, Providence, RI, USA.; Center for Biomedical Engineering, Brown University, Providence, RI, USA.; Data Science Institute, Brown University, Providence, RI, USA.; Department of Genetics, Yale School of Medicine, New Haven, CT, USA., Zhang WY; Data Science Institute, Brown University, Providence, RI, USA.; Division of Applied Mathematics, Brown University, Providence, RI, USA.; Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA., Volkening A; Department of Mathematics, Purdue University, West Lafayette, IN, USA., Sandstede B; Data Science Institute, Brown University, Providence, RI, USA.; Division of Applied Mathematics, Brown University, Providence, RI, USA., Wong IY; School of Engineering, Brown University, Providence, RI, USA. ian_wong@brown.edu.; Center for Biomedical Engineering, Brown University, Providence, RI, USA. ian_wong@brown.edu.; Data Science Institute, Brown University, Providence, RI, USA. ian_wong@brown.edu.; Legorreta Cancer Center, Brown University, Providence, RI, USA. ian_wong@brown.edu.
Jazyk: angličtina
Zdroj: NPJ systems biology and applications [NPJ Syst Biol Appl] 2023 Sep 14; Vol. 9 (1), pp. 43. Date of Electronic Publication: 2023 Sep 14.
DOI: 10.1038/s41540-023-00302-8
Abstrakt: Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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