Implementation of novel statistical procedures and other advanced approaches to improve analysis of CASA data.

摘要: Computer-aided sperm analysis (CASA) produces a wealth of data that is frequently ignored. The use multiparametric statistical methods can help explore these datasets, unveiling the subpopulation structure samples. In this review we analyse significance internal heterogeneity samples and its relevance. We also provide brief description tools used for extracting subpopulations from namely unsupervised clustering (with non-hierarchical, hierarchical two-step methods) most advanced supervised methods, based on machine learning. former method has allowed exploration patterns in many species, whereas latter offering further possibilities, especially considering functional studies practical analysis. consider novel approaches, such as geometric morphometrics or imaging flow cytometry. Finally, although provided by CASA systems provides valuable information applying analyses, there are several caveats. Protocols capturing analysing motility morphometry should be standardised adapted to each experiment, algorithms open order allow comparison results between laboratories. Moreover, must aware new technology could change paradigm studying morphology.