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Statistical modeling and inference for complex-structured count data with applications in genomics and social science

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dc.contributor.authorCao, Meng, author
dc.contributor.authorZhou, Wen, advisor
dc.contributor.authorBreidt, F. Jay, advisor
dc.contributor.authorEstep, Don, committee member
dc.contributor.authorMeyer, Mary C., committee member
dc.contributor.authorPeers, Graham, committee member
dc.date.accessioned2020-06-22T11:53:48Z
dc.date.available2020-06-22T11:53:48Z
dc.date.issued2020
dc.description.abstractThis dissertation describes models, estimation methods, and testing procedures for count data that build upon classic generalized linear models, including Gaussian, Poisson, and negative binomial regression. The methodological extensions proposed in this dissertation are motivated by complex structures for count data arising in three important classes of scientific problems, from both genomics and sociological contexts. Complexities include large scale, temporal dependence, zero-inflation and other mixture features, and group structure. The first class of problems involves count data that are collected from longitudinal RNA sequencing (RNA-seq) experiments, where the data consist of tens of thousands of short time series of counts, with replicate time series under treatment and under control. In order to determine if the time course differs between treatment and control, we consider two questions: 1) whether the treatment affects the geometric attributes of the temporal profiles and 2) whether any treatment effect varies over time. To answer the first question, we determine whether there has been a fundamental change in shape by modeling the transformed count data for genes at each time point using a Gaussian distribution, with the mean temporal profile generated by spline models, and introduce a measurement that quantifies the average minimum squared distance between the locations of peaks (or valleys) of each gene's temporal profile across experimental conditions. We then develop a testing framework based on a permutation procedure. Via simulation studies, we show that the proposed test achieves good power while controlling the false discovery rate. We also apply the test to data collected from a light physiology experiment on maize. To answer the second question, we model the time series of counts for each gene by a Gaussian-Negative Binomial model and introduce a new testing procedure that enjoys the optimality property of maximum average power. The test allows not only identification of traditional differentially expressed genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and expose its good performance via simulation studies. The procedure reveals interesting biological insights when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of a marine diatom. The second class of problems involves analyzing group-structured sRNA data that consist of independent replicates of counts for each sRNA across experimental conditions. Most existing methods—for both normalization and differential expression—are designed for non-group structured data. These methods may fail to provide correct normalization factors or fail to control FDR. They may lack power and may not be able to make inference on group effects. To address these challenges simultaneously, we introduce an inferential procedure using a group-based negative binomial model and a bootstrap testing method. This procedure not only provides a group-based normalization factor, but also enables group-based differential expression analysis. Our method shows good performance in both simulation studies and analysis of experimental data on roundworm. The last class of problems is motivated by the study of sensitive behaviors. These problems involve mixture-distributed count data that are collected by a quantitative randomized response technique (QRRT) which guarantees respondent anonymity. We propose a Poisson regression method based on maximum likelihood estimation computed via the EM algorithm. This method allows assessment of the importance of potential drivers of different quantities of non-compliant behavior. The method is illustrated with a case study examining potential drivers of non-compliance with hunting regulations in Sierra Leone.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierCao_colostate_0053A_15968.pdf
dc.identifier.urihttps://hdl.handle.net/10217/208560
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectgeneralized linear model
dc.subjectrandomized response technique
dc.subjectsmall RNA
dc.subjectlongitudinal
dc.subjectcount data
dc.subjectRNA-seq
dc.titleStatistical modeling and inference for complex-structured count data with applications in genomics and social science
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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