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Abstract
We have applied a statistical protocol based on principal
component analysis, clustering methods and discriminant
analysis for the identification of sperm subpopulations in
CASA data. Samples were obtained from the cauda epididymis
of 11 Iberian red deers, and cryopreserved following a
standard protocol. Motility by CASA was analyzed just
after sperm recovery, just before freezing and after
thawing, and eight motility descriptors for each
individual spermatozoon were recorded. Sperm viability and
acrosomal status were also assessed. Subpopulation
analysis was performed in four sequential steps: principal
component analysis using the 8 motility descriptors;
non-hierarchical clustering analysis (k-means) using the
first two principal components; hierarchical clustering
analysis (UPGMA); and selection of the final number of
clusters. Three clusters were obtained for each motility
analysis: slow and non-linear; rapid and linear; and
rapid, high ALH, non-linear. We detected variations in the
clusters between treatments (initial, pre-freezing and
post-thawed). Indeed, motility increased and linearity
decreased in the pre-freezing analysis. A discriminant
analysis isolated three descriptors that were used again
in the same statistical analysis, giving four clusters
that resembled the pattern found in the first
classification. We also performed a clustering analysis of
the males according to pre-freezing/post-thawed variation
of total motility, viability and acrosomal status. The
proportion of the linear subpopulations in the
pre-freezing treatment, in both clustering analyses,
correlated positively with post-thawed viability recovery.
Our results show that clustering analysis of CASA data
gives useful and practical information that is not
obtained by conventional sperm analysis.
Key words:
Gamete Biology •
Epididymis •
Sperm •
Sperm motility and transport
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