Date of Award


Degree Type


Degree Name

Master of Science (MS)


Mathematics and Statistics

Committee Chair(s)

John R. Stevens


John R. Stevens


Adele Cutler


Guifang Fu



Statistical Methods for Assessing Individual Oocyte Viability Through Gene Expression Profiles


Michael O. Bishop

Utah State University, 2017

Major Professor: Dr. John R. Stevens

Department: Mathematics and Statistics

Oocytes are the precursor cells to the female gamete, or egg. While reproduction may vary from species to species, within humans and most domesticated animals, the oocyte maturation process is fairly similar. As an oocyte matures, there are various processes that take place, all of which have an effect on the viability of the individual oocyte. Barring outside damage that may come to the oocyte, one of the primary reasons for non-viability is that of abnormal gene expression. Within this project, we focus on two oocyte maturation techniques: in vivo (IVV) derived oocytes (our gold-standard) and in vitro matured (IVM) oocytes. A great disparity exists between the viability rates of the two origination techniques, and this disparity has led to low yields and inefficiency in the fields of cloning, fertility treatments, as well as personalized medicine.

Within our project we use existing swine oocyte gene expression profile data as a proxy measure of viability, based on the similarity to IVV oocytes. Four statistical techniques for assessing the individual oocyte viability are proposed and compared, including: a weighted root mean squared deviation (wRMSD) approach, a distance kernel p-value approach, a distance tolerance interval approach, and a classification tree method. The relative performance of these four measures is discussed.