Implementation of novel statistical procedures and other advanced approaches to improve analysis of CASA data
M. Ramón A and F. Martínez-Pastor B C
+ Author Affiliations
- Author Affiliations
A CERSYRA-IRIAF, Junta de Comunidades de Castilla-La Mancha, Valdepeñas, Spain.
B INDEGSAL and Department of Molecular Biology (Cell Biology), Universidad de León, 24071 León, Spain.
C Corresponding author. Email: felipe.martinez@unileon.es
Reproduction, Fertility and Development 30(6) 860-866 https://doi.org/10.1071/RD17479
Submitted: 9 November 2017 Accepted: 14 March 2018 Published: 23 April 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.
Additional keywords: clustering, computer-aided sperm analyses, spermatozoon, subpopulations, support vector machines (SVM).
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