Statistical Series: Opportunities and challenges of sperm motility subpopulation analysis
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 t...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/143987 |
| Acesso em linha: | http://hdl.handle.net/10261/143987 |
| Access Level: | acceso abierto |
| Palavra-chave: | Sperm subpopulations Cluster analysis Multivariate analysis Automated semen analysis CASA |
| Resumo: | 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. |
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