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    Citas

    Título
    Implementation of novel statistical procedures and other advanced approaches to improve analysis of CASA data
    Autor
    Ramón Fernández, Manuel
    Martínez Pastor, FelipeAutoridad BuleriaORCID
    Facultad/Centro
    Facultad de Ciencias Biologicas y Ambientales
    Área de conocimiento
    Biologia Celular
    Datos de la obra
    Reproduction, Fertility and Development, 2018, vol. 30 n. 6
    Editor
    CSIRO
    Fecha
    2018
    Abstract
    Computer-aided sperm analysis (CASA) produces a wealth of data that is frequently ignored. The use of multiparametric statistical methods can help explore these datasets, unveiling the subpopulation structure of sperm samples. In this review we analyse the significance of the internal heterogeneity of sperm samples and its relevance. We also provide a brief description of the statistical tools used for extracting sperm subpopulations from the datasets, namely unsupervised clustering (with non-hierarchical, hierarchical and two-step methods) and the most advanced supervised methods, based on machine learning. The former method has allowed exploration of subpopulation patterns in many species, whereas the latter offering further possibilities, especially considering functional studies and the practical use of subpopulation analysis. We also consider novel approaches, such as the use of geometric morphometrics or imaging flow cytometry. Finally, although the data provided by CASA systems provides valuable information on sperm samples by applying clustering analyses, there are several caveats. Protocols for capturing and analysing motility or morphometry should be standardised and adapted to each experiment, and the algorithms should be open in order to allow comparison of results between laboratories. Moreover, we must be aware of new technology that could change the paradigm for studying sperm motility and morphology.
    Materia
    Veterinaria
    Palabras clave
    Clustering
    Computer-aided sperm analyses
    Spermatozoon
    Subpopulations
    Support vector machines (SVM)
    Peer review
    SI
    URI
    http://hdl.handle.net/10612/10722
    Versión del editor
    http://www.publish.csiro.au/rd/RD17479
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