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Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing./
Author:	Grantham, Neal Steven.
Description:	1 online resource (105 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355634488

Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing.
Grantham, Neal Steven.

Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing. - 1 online resource (105 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2017.

Includes bibliographical references

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. In this dissertation, we present a collection of models for the analysis of high-dimensional, spatially-distributed microbiome data in the context of two growing research areas within the microbiome domain. First, we contribute to the developing field of designed experiments for microbiome data where the primary goal is to detect treatment effects on microbial taxa while accounting for heterogeneous sources of variability. Second, we investigate the potential for microbiome data to guide forensic inquiry, finding that the microbial profile of a sample of ambient dust can be used to geolocate the spatial source of that dust with high precision.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355634488Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing.
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100 1 $a Grantham, Neal Steven. $3 1184458
245 1 0 $a Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing.
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300 $a 1 online resource (105 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Brian J. Reich.
502 $a Thesis (Ph.D.)--North Carolina State University, 2017.
504 $a Includes bibliographical references
520 $a Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. In this dissertation, we present a collection of models for the analysis of high-dimensional, spatially-distributed microbiome data in the context of two growing research areas within the microbiome domain. First, we contribute to the developing field of designed experiments for microbiome data where the primary goal is to detect treatment effects on microbial taxa while accounting for heterogeneous sources of variability. Second, we investigate the potential for microbiome data to guide forensic inquiry, finding that the microbial profile of a sample of ambient dust can be used to geolocate the spatial source of that dust with high precision.
520 $a Initially, we explore the use of a hierarchical Bayesian latent factor model to capture complex relationships among variables of interest. In particular, we develop a model in Chapter 2 which induces dependence between the value of a partially-observed spatial covariate and its missingness status. The model is motivated by a spatial pollution dataset which, while not microbiome-related, presents similar modeling challenges due to high-dimensionality in the spatial covariate. Satellite-measured aerosol optical depth (AOD) is a low-cost surrogate with excellent spatiotemporal coverage and spatial regression models have established that including AOD as a covariate improves spatial interpolation of fine particulate matter (PM2.5). However, AOD is often missing, and our analysis reveals that the conditions that lead to missing AOD are also conducive to high AOD. Therefore, naive interpolation that ignores informative missingness may lead to bias. Our joint model for PM2.5 and AOD accounts for informatively missing AOD and an analysis of daily PM 2.5 in the Southeastern United States reveals statisticallysignificant informative missingness and relationships between PM2.5 and AOD in many seasons after accounting for meteorological and land-use variables.
520 $a Contemporary analyses of microbial data work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In Chapter 3, we propose a Bayesian mixed-effects model for the analysis of high-dimensional microibome data from designed experiments, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2x2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of Andropogon gerardii, a perennial bunchgrass, as part of the global Nutrient Network research initiative.
520 $a There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. In Chapter 4, we demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a Bayesian discriminant analysis (BDA) model that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts. (Abstract shortened by ProQuest.).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $b Statistics. $3 1184459
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10758872 $z click for full text (PQDT)