Quality assessment of boar semen by multivariate analysis of flow cytometric data

TY - JOUR

T1 - Quality assessment of boar semen by multivariate analysis of flow cytometric data

AU - Babamoradi, Hamid

AU - Amigo Rubio, Jose Manuel

AU - van der Berg, Franciscus Winfried J

AU - Petersen, Morten Rønn

AU - Satake, Nana

AU - Boe-Hansen, Gry

PY - 2015/3/5

Y1 - 2015/3/5

N2 - Flow cytometry (FCM) has become very powerful over the last decades, enabling multi-parametric measurements of up to thousands of cells per second. This generates massive amounts of data on individual cell characteristics that need to be analyzed in an efficient manner from both physiological and chemical points of view. In this study, a methodology of analysis for FCM data was comprehensively studied to assess quality changes in semen extracted from boars. The proposed methodology combines new automated multi-dimensional data normalization, a density-based clustering method for identification of cell populations, and multivariate methods for post-analysis of the identified populations, enabling the exploratory evaluation and prediction/classification of subpopulations within the experimental data set. The performance of the suggested methodology was compared with the performance of an existing automated clustering method.

AB - Flow cytometry (FCM) has become very powerful over the last decades, enabling multi-parametric measurements of up to thousands of cells per second. This generates massive amounts of data on individual cell characteristics that need to be analyzed in an efficient manner from both physiological and chemical points of view. In this study, a methodology of analysis for FCM data was comprehensively studied to assess quality changes in semen extracted from boars. The proposed methodology combines new automated multi-dimensional data normalization, a density-based clustering method for identification of cell populations, and multivariate methods for post-analysis of the identified populations, enabling the exploratory evaluation and prediction/classification of subpopulations within the experimental data set. The performance of the suggested methodology was compared with the performance of an existing automated clustering method.

U2 - 10.1016/j.chemolab.2015.02.008

DO - 10.1016/j.chemolab.2015.02.008

M3 - Journal article

SN - 0169-7439

VL - 142

SP - 219

EP - 230

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

ER -