Analysis of Metabolomics Data via Mixed Models

Ren, Austin Mu Qing

Analysis of Metabolomics Data via Mixed Models

dc.contributor.advisor	de Leon, Alexander R.
dc.contributor.advisor	Kopciuk, Karen Arlene
dc.contributor.author	Ren, Austin Mu Qing
dc.contributor.committeemember	Vogel, Hans J.
dc.contributor.committeemember	Sajobi, Tolulope T.
dc.date	2020-11
dc.date.accessioned	2020-08-17T22:03:01Z
dc.date.available	2020-08-17T22:03:01Z
dc.date.issued	2020-08
dc.description.abstract	Generalized linear mixed models have been widely studied and used in many different disciplines, yet very little application of them can be found with metabolomics data analysis. Traditional methods of cancer classification used to determine disease severity, such as biopsies, can be harmful to the health of the patients. Classification based on metabolomics data analysis demonstrates a main advantage as it only requires non-invasive procedures such as the drawing of a small amount of blood from patients. However, data analysis in cancer research often requires the handling of multiple correlated measurements of disease severity. The methods that are most commonly used with metabolomics data, such as partial least squares discriminant analysis, were traditionally designed to handle univariate data only, and can be very challenging to work with when applied to data with multiple correlated outcomes. Therefore, different methods should be considered for metabolomics data analysis in cancer classification. In this thesis, we proposed bivariate generalized linear mixed models with binary outcomes using the probit link function for the analysis of metabolomics data. The models were specifically designed to handle multiple correlated outcomes via the inclusion of subject-specific random intercepts. Random slopes were not included in the models to reduce complexity. We specifically designed three settings for the random intercept models: shared, independent, and correlated between the outcomes. An extensive number of simulations were carried out to test our models' parameters, including: standard deviation and correlation of the distribution of the random intercepts, correlation between the covariates as well as correlation between the covariates and the outcomes, the proportion of data missing among the covariates, misspecified distribution of the random intercepts, and misspecified conditional correlation between the outcomes. In addition, we also incorporated the nearest neighbors algorithm as a missing values imputation method and LASSO as a feature selection method to our mixed models in order to handle the common issues of high dimensional covariates and missing values in metabolomics data. Finally, our proposed mixed models were applied to a real dataset with prostate cancer patients to evaluate our models' performance on outcome predictions.	en_US
dc.identifier.citation	Ren, A. M. (2020). Analysis of metabolomics data via mixed models (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/38084
dc.identifier.uri	http://hdl.handle.net/1880/112397
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Mixed Models	en_US
dc.subject	Metabolomics	en_US
dc.subject	NMR	en_US
dc.subject	Prostate Cancer	en_US
dc.subject	LASSO	en_US
dc.subject	PLS	en_US
dc.subject	KNN	en_US
dc.subject	Cancer Diagnostic	en_US
dc.subject.classification	Education--Health	en_US
dc.subject.classification	Education--Sciences	en_US
dc.subject.classification	Oncology	en_US
dc.subject.classification	Biochemistry	en_US
dc.subject.classification	Statistics	en_US
dc.title	Analysis of Metabolomics Data via Mixed Models	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Mathematics & Statistics	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US