Topological Data Analysis of C. elegans Locomotion and Behavior

Thomas, Ashleigh; Bates, Kathleen; Elchesen, Alex; Hartsock, Iryna; Lu, Hang; Bubenik, Peter

doi:10.3389/frai.2021.668395

Cited by 4 publications

(2 citation statements)

References 30 publications

(42 reference statements)

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“…As an alternative to methods mentioned above, we propose to use topological data analysis (TDA) to examine pattern formation. TDA is a fast-growing field which provides methods for summarizing the shape of complex data 14 and has found useful applications in several fields, including cosmology 15 , material science 16 , neuroscience 17 , anomaly detection 18 , and C. elegans behavior 19 . Persistence homology, the central tool of TDA, tracks the appearance and disappearance of structural features (e.g.…”

Section: Introductionmentioning

confidence: 99%

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Hartsock,

Park,

Toppen

et al. 2024

Preprint

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Understanding the multicellular organization of stem cells is vital for determining the mechanisms that coordinate cell fate decision-making during differentiation; these mechanisms range from neighbor-to-neighbor communication to tissue-level biochemical gradients. Current methods for quantifying multicellular patterning cannot capture the spatial properties of cell colonies across all scales and typically rely on human annotation or a priori selection of parameters. We present a computational pipeline that utilizes topological data analysis to generate quantitative, multiscale descriptors which capture the shape of data extracted from multichannel microscopy images. By applying our pipeline to certain stem cell colonies, we detected subtle differences in patterning that reflect distinct biological markers and progressive stages of differentiation. These results yield insight into directed cellular movement and morphogen-mediated, neighbor-to-neighbor signaling. Because of its broad applicability to immunofluorescence microscopy images, our pipeline is well-positioned to serve as a general-purpose tool for the quantitative study of multicellular pattern formation.

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Section: Introductionmentioning

confidence: 99%

Topological data analysis of pattern formation of human induced pluripotent stem cell colonies

Hartsock,

Park,

Toppen

et al. 2024

Preprint

Self Cite

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“…The first type contains 75 records of 900 samples, and the second type has 105 records with the same time series length. As with previous datasets, there are other works that used time series from this one [54,55].…”

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confidence: 99%

Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis

Kouka,

Cuesta-Frau,

Moltó-Gallego

2024

Entropy

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Slope Entropy (SlpEn) is a novel method recently proposed in the field of time series entropy estimation. In addition to the well-known embedded dimension parameter, m, used in other methods, it applies two additional thresholds, denoted as δ and γ, to derive a symbolic representation of a data subsequence. The original paper introducing SlpEn provided some guidelines for recommended specific values of these two parameters, which have been successfully followed in subsequent studies. However, a deeper understanding of the role of these thresholds is necessary to explore the potential for further SlpEn optimisations. Some works have already addressed the role of δ, but in this paper, we extend this investigation to include the role of γ and explore the impact of using an asymmetric scheme to select threshold values. We conduct a comparative analysis between the standard SlpEn method as initially proposed and an optimised version obtained through a grid search to maximise signal classification performance based on SlpEn. The results confirm that the optimised version achieves higher time series classification accuracy, albeit at the cost of significantly increased computational complexity.

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