Distance Based Linear Regression Model And Its Application To Microbiome Association Studies

1 online resource (69 pages) : PDF

2021

University of North Carolina at Charlotte

In the past few decades, pairwise distance based statistical methods have beendeveloped to identify spatial and/or temporal clusters of disease, study the association between the dissimilarity of ecological communities and distance in geographical locations. With emergence of high-throughput technologies, pairwise distance base methods are widely used in the analysis of genetics and genomics data, especially when the data structure fails the fundamental assumptions of classical multivariate analysis, including independency and normality. However, much of existing knowledge has been around non-parametric or semi-parametric estimations usually employing permutation techniques to assess statistical significance, which are known to be computationally expensive and sensitive to the choice of permutation. Majority of this thesis focuses on linear regression of pairwise distance matrices. We consider the pairwise correlation structure between the distances and investigate the large sample properties of the ordinary least squares estimator of the model coefficients. Extensive simulations are conducted to evaluate the performance of our method with finite sample size. Another major component of the thesis is the human microbiome data analysis. We analyze the integrative Human Microbiome Project (iHMP) data set of composition of microbial communities in the digestive tracts of humans by using multiple statistical methods, including our proposed method. The results are presented and interpreted. Existing challenges and future works are also discussed.

doctoral dissertations

Mathematics
Statistics

Ph.D.

Applied Mathematics

Li, Shaoyu
Sun, Yanqing

Christou, Eliana
Kazemi, Donna

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2021.

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SheikhiKiasari_uncc_0694D_12970

http://hdl.handle.net/20.500.13093/etd:2989