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

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Sheikhi Kiasari, M. (2021). Distance Based Linear Regression Model And Its Application To Microbiome Association Studies. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

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.

Details
Author

Sheikhi Kiasari, Masoumeh

Title

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

Physical Description

1 online resource (69 pages) : PDF

Date

2021

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

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.

Genre

doctoral dissertations

Subjects--Topics

Mathematics
Statistics

Degree

Ph.D.

Subject Area

Applied Mathematics

Advisor(s)

Li, Shaoyu
Sun, Yanqing

Committee Members

Christou, Eliana
Kazemi, Donna

Degree Note

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

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Rights Holder Information

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Identifier

SheikhiKiasari_uncc_0694D_12970

Permalink

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