Date on Master's Thesis/Doctoral Dissertation

5-2017

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Bioinformatics and Biostatistics

Degree Program

Biostatistics, PhD

Committee Chair

Rai, Shesh

Committee Co-Chair (if applicable)

Brock, Guy

Committee Member

Bhatnagar, Aruni

Committee Member

Gaskins, Jeremy

Committee Member

Wu, Dongfeng

Author's Keywords

missing values; metabolomics; MNAR; MAR; biostatistics; bioinformatics

Abstract

Despite considerable advances in high throughput technology over the last decade, new challenges have emerged related to the analysis, interpretation, and integration of high-dimensional data. The arrival of omics datasets has contributed to the rapid improvement of systems biology, which seeks the understanding of complex biological systems. Metabolomics is an emerging omics field, where mass spectrometry technologies generate high dimensional datasets. As advances in this area are progressing, the need for better analysis methods to provide correct and adequate results are required. While in other omics sectors such as genomics or proteomics there has and continues to be critical understanding and concern in developing appropriate methods to handle missing values, handling of missing values in metabolomics has been an undervalued step. Missing data are a common issue in all types of medical research and handling missing data has always been a challenge. Since many downstream analyses such as classification methods, clustering methods, and dimension reduction methods require complete datasets, imputation of missing data is a critical and crucial step. The standard approach used is to remove features with one or more missing values or to substitute them with a value such as mean or half minimum substitution. One of the major issues from the missing data in metabolomics is due to a limit of detection, and thus sophisticated methods are needed to incorporate different origins of missingness. This dissertation contributes to the knowledge of missing value imputation methods with three separate but related research projects. The first project consists of a novel missing value imputation method based on a modification of the k nearest neighbor method which accounts for truncation at the minimum value/limit of detection. The approach assumes that the data follows a truncated normal distribution with the truncation point at the detection limit. The aim of the second project arises from the limitation in the first project. While the novel approach is useful, estimation of the truncated mean and standard deviation is problematic in small sample sizes (N < 10). In this project, we develop a Bayesian model for imputing missing values with small sample sizes. The Bayesian paradigm has generally been utilized in the omics field as it exploits the data accessible from related components to acquire data to stabilize parameter estimation. The third project is based on the motivation to determine the impact of missing value imputation on down-stream analyses and whether ranking of imputation methods correlates well with the biological implications of the imputation.

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