Statistical Series: Opportunities and challenges of sperm motility subpopulation analysis

被引：100

作者：

Martinez-Pastor, Felipe ^{[1
]}

Jorge Tizado, E.

Julian Garde, J. ^{[2
,3
]}

Anel, Luis ^{[1
]}

de Paz, Paulino ^{[1
]}

机构：

[1] Univ Leon, INDEGSAL, E-24071 Leon, Spain

[2] CSIC UCLM JCCM, Natl Wildlife Res Inst IREC, Biol Reprod Grp, Albacete 02071, Spain

[3] Inst Reg Dev IDR, Albacete 02071, Spain

来源：

THERIOGENOLOGY | 2011年 / 75卷 / 05期

关键词：

CASA; Automated semen analysis; Sperm subpopulations; Multivariate analysis; Cluster analysis; MULTIVARIATE CLUSTER-ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; DEER EPIDIDYMAL SPERM; GAZELLA-DAMA-MHORR; VARIABLE-SELECTION; REGRESSION-ANALYSES; PATTERN-ANALYSIS; DOG SPERMATOZOA; GENE-EXPRESSION; BOAR;

D O I：

10.1016/j.theriogenology.2010.11.034

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：783 / 795

页数：13

共 87 条

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