Use of multivariate statistics to identify unreliable data obtained using CASA

Area of Science:

• Animal Science
• Reproductive Biology
• Data Science

Background:

• Computer Assisted Sperm Analysis (CASA) systems are crucial for objective semen evaluation.
• Operator variability can introduce errors in CASA data, impacting study reliability.
• Standardized methods are needed to identify and mitigate operator-induced data unreliability.

Purpose of the Study:

• To identify unreliable data in boar semen motility parameters obtained from a pilot study.
• To assess the impact of operator inexperience with CASA systems on data quality.
• To develop objective methods for evaluating CASA operator skill levels.

Main Methods:

• Multivariate graphical and statistical analysis of CASA data from sixteen boar semen samples.
• Application of Chernoff faces for visualizing individual measurements.
• Principal Component Analysis (PCA) for data dimensionality reduction and pre-processing.
• Hierarchical clustering to group similar data points.

Main Results:

• Twelve individual semen measurements exhibited abnormal features via Chernoff faces.
• PCA indicated that the first two principal components explained 63.08% of the dataset variance.
• Hierarchical clustering formed three distinct groups, with one cluster containing measurements linked to operator inexperience.

Conclusions:

• Operator inexperience with CASA systems is a likely source of unreliable boar semen data.
• The developed multivariate approach can objectively evaluate CASA operator skill.
• This methodology offers a valuable tool for quality control in CASA-based semen analysis.