Use of multivariate statistics to identify unreliable data obtained using CASA.

Use of multivariate statistics to identify unreliable data obtained using CASA. Syst Biol Reprod Med. 2013 Feb 8; Authors: Martínez LB, Crispín RH, Mendoza MM, Gallegos OH, Martínez AA Abstract In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering re...
Source: Systems Biology in Reproductive Medicine - Category: Reproduction Medicine Authors: Tags: Syst Biol Reprod Med Source Type: research