title: Statistical Series: Opportunities and challenges of sperm motility subpopulation analysis.
abstract: Computer-assisted sperm analysis (CASA) allows assessing the motility of individual spermatozoa, generating huge datasets. These datasets can be analyzed using data mining techniques such as cluster analysis, to group the spermatozoa in subpopulations with biological meaning. This review considers the use of statistical techniques for clustering CASA data, their challenges and possibilities. There are many clustering approaches potentially useful for grouping sperm motility data, but some options may be more appropriate than others. Future development should focus not only in improvements of subpopulation analysis, but also in finding consistent biological meanings for these subpopulations.
authors.affiliation: INDEGSAL, University of León, 24071 León, Spain. fmarp@unileon.es
authors.forename: Felipe
authors.initials: F
authors.lastname: Martínez-Pastor
authors.forename: E Jorge
authors.initials: EJ
authors.lastname: Tizado
authors.forename: J Julian
authors.initials: JJ
authors.lastname: Garde
authors.forename: Luis
authors.initials: L
authors.lastname: Anel
authors.forename: Paulino
authors.initials: P
authors.lastname: de Paz
mesh_terms: D000818:Animals; D003201:Computers; D005298:Fertility; D006801:Humans; D008297:Male; D015203:Reproducibility of Results; D055101:Semen Analysis / Q000379:methods / Q000706:statistics & numerical data* / Q000639:trends; D013081:Sperm Motility; D013094:Spermatozoa / Q000145:classification / Q000502:physiology