Annals of Applied Statistics最新文献_第8页

A COMPOSITIONAL MODEL TO ASSESS EXPRESSION CHANGES FROM SINGLE-CELL RNA-SEQ DATA. 从单细胞rna-seq数据中评估表达变化的组成模型。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2021-06-01 DOI: 10.1214/20-aoas1423

Xiuyu Ma, Keegan Korthauer, Christina Kendziorski, Michael A Newton

{"title":"A COMPOSITIONAL MODEL TO ASSESS EXPRESSION CHANGES FROM SINGLE-CELL RNA-SEQ DATA.","authors":"Xiuyu Ma, Keegan Korthauer, Christina Kendziorski, Michael A Newton","doi":"10.1214/20-aoas1423","DOIUrl":"https://doi.org/10.1214/20-aoas1423","url":null,"abstract":"On the problem of scoring genes for evidence of changes in the distribution of single-cell expression, we introduce an empirical Bayesian mixture approach and evaluate its operating characteristics in a range of numerical experiments. The proposed approach leverages cell-subtype structure revealed in cluster analysis in order to boost gene-level information on expression changes. Cell clustering informs gene-level analysis through a specially-constructed prior distribution over pairs of multinomial probability vectors; this prior meshes with available model-based tools that score patterns of differential expression over multiple subtypes. We derive an explicit formula for the posterior probability that a gene has the same distribution in two cellular conditions, allowing for a gene-specific mixture over subtypes in each condition. Advantage is gained by the compositional structure of the model not only in which a host of gene-specific mixture components are allowed but also in which the mixing proportions are constrained at the whole cell level. This structure leads to a novel form of information sharing through which the cell-clustering results support gene-level scoring of differential distribution. The result, according to our numerical experiments, is improved sensitivity compared to several standard approaches for detecting distributional expression changes.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"15 2","pages":"880-901"},"PeriodicalIF":1.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275512/pdf/nihms-1901161.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9762402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY. 基于模型的特征选择和 rna-seq 数据聚类，用于无监督亚型发现。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2021-03-01 Epub Date: 2021-03-18 DOI: 10.1214/20-aoas1407

David K Lim, Naim U Rashid, Joseph G Ibrahim

{"title":"MODEL-BASED FEATURE SELECTION AND CLUSTERING OF RNA-SEQ DATA FOR UNSUPERVISED SUBTYPE DISCOVERY.","authors":"David K Lim, Naim U Rashid, Joseph G Ibrahim","doi":"10.1214/20-aoas1407","DOIUrl":"10.1214/20-aoas1407","url":null,"abstract":"Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and the quadratic penalty method with a Smoothly-Clipped Absolute Deviation (SCAD) penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership, even in the presence of batch effects. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"15 1","pages":"481-508"},"PeriodicalIF":1.8,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386505/pdf/nihms-1716637.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9546884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH. 暴露混合物与健康的综合统计方法。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-12-01 DOI: 10.1214/20-AOAS1364

Brian J Reich, Yawen Guan, Denis Fourches, Joshua L Warren, Stefanie E Sarnat, Howard H Chang

{"title":"INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH.","authors":"Brian J Reich, Yawen Guan, Denis Fourches, Joshua L Warren, Stefanie E Sarnat, Howard H Chang","doi":"10.1214/20-AOAS1364","DOIUrl":"https://doi.org/10.1214/20-AOAS1364","url":null,"abstract":"Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 4","pages":"1945-1963"},"PeriodicalIF":1.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914338/pdf/nihms-1780774.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10265042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

LOG-CONTRAST REGRESSION WITH FUNCTIONAL COMPOSITIONAL PREDICTORS: LINKING PRETERM INFANT'S GUT MICROBIOME TRAJECTORIES TO NEUROBEHAVIORAL OUTCOME. 功能成分预测因子的对数对比回归:将早产儿肠道微生物组轨迹与神经行为结果联系起来。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-09-01 Epub Date: 2020-09-18 DOI: 10.1214/20-aoas1357

Zhe Sun, Wanli Xu, Xiaomei Cong, Gen Li, Kun Chen

{"title":"LOG-CONTRAST REGRESSION WITH FUNCTIONAL COMPOSITIONAL PREDICTORS: LINKING PRETERM INFANT'S GUT MICROBIOME TRAJECTORIES TO NEUROBEHAVIORAL OUTCOME.","authors":"Zhe Sun, Wanli Xu, Xiaomei Cong, Gen Li, Kun Chen","doi":"10.1214/20-aoas1357","DOIUrl":"https://doi.org/10.1214/20-aoas1357","url":null,"abstract":"The neonatal intensive care unit (NICU) experience is known to be one of the most crucial factors that drive preterm infant's neurodevelopmental and health outcome. It is hypothesized that stressful early life experience of very preterm neonate is imprinting gut microbiome by the regulation of the so-called brain-gut axis, and consequently, certain microbiome markers are predictive of later infant neurodevelopment. To investigate, a preterm infant study was conducted; infant fecal samples were collected during the infants' first month of postnatal age, resulting in functional compositional microbiome data, and neurobehavioral outcomes were measured when infants reached 36-38 weeks of post-menstrual age. To identify potential microbiome markers and estimate how the trajectories of gut microbiome compositions during early postnatal stage impact later neurobehavioral outcomes of the preterm infants, we innovate a sparse log-contrast regression with functional compositional predictors. The functional simplex structure is strictly preserved, and the functional compositional predictors are allowed to have sparse, smoothly varying, and accumulating effects on the outcome through time. Through a pragmatic basis expansion step, the problem boils down to a linearly constrained sparse group regression, for which we develop an efficient algorithm and obtain theoretical performance guarantees. Our approach yields insightful results in the preterm infant study. The identified microbiome markers and the estimated time dynamics of their impact on the neurobehavioral outcome shed lights on the linkage between stress accumulation in early postnatal stage and neurodevelpomental process of infants.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 3","pages":"1535-1556"},"PeriodicalIF":1.8,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218926/pdf/nihms-1601428.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39100587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Inferring a consensus problem list using penalized multistage models for ordered data. 利用有序数据的惩罚性多阶段模型推断共识问题列表。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-09-01 Epub Date: 2020-09-18 DOI: 10.1214/20-aoas1361

Philip S Boonstra, John C Krauss

{"title":"Inferring a consensus problem list using penalized multistage models for ordered data.","authors":"Philip S Boonstra, John C Krauss","doi":"10.1214/20-aoas1361","DOIUrl":"10.1214/20-aoas1361","url":null,"abstract":"A patient's medical problem list describes his or her current health status and aids in the coordination and transfer of care between providers. Because a problem list is generated once and then subsequently modified or updated, what is not usually observable is the provider-effect. That is, to what extent does a patient's problem in the electronic medical record actually reflect a consensus communication of that patient's current health status? To that end, we report on and analyze a unique interview-based design in which multiple medical providers independently generate problem lists for each of three patient case abstracts of varying clinical difficulty. Due to the uniqueness of both our data and the scientific objectives of our analysis, we apply and extend so-called multistage models for ordered lists and equip the models with variable selection penalties to induce sparsity. Each problem has a corresponding non-negative parameter estimate, interpreted as a relative log-odds ratio, with larger values suggesting greater importance and zero values suggesting unimportant problems. We use these fitted penalized models to quantify and report the extent of consensus. We conduct a simulation study to evaluate the performance of our methodology and then analyze the motivating problem list data. For the three case abstracts, the proportions of problems with model-estimated non-zero log-odds ratios were 10/28, 16/47, and 13/30. Physicians exhibited consensus on the highest ranked problems in the first and last case abstracts but agreement quickly deteriorated; in contrast, physicians broadly disagreed on the relevant problems for the middle - and most difficult - case abstract.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 3","pages":"1557-1580"},"PeriodicalIF":1.8,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345315/pdf/nihms-1696242.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39291448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STATISTICAL METHODS FOR ANALYSIS OF COMBINED CATEGORICAL BIOMARKER DATA FROM MULTIPLE STUDIES. 用于分析来自多项研究的综合分类生物标志物数据的统计方法。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-09-01 Epub Date: 2020-09-18 DOI: 10.1214/20-aoas1337

Chao Cheng, Molin Wang

{"title":"STATISTICAL METHODS FOR ANALYSIS OF COMBINED CATEGORICAL BIOMARKER DATA FROM MULTIPLE STUDIES.","authors":"Chao Cheng, Molin Wang","doi":"10.1214/20-aoas1337","DOIUrl":"10.1214/20-aoas1337","url":null,"abstract":"In the analysis of pooled data from multiple studies involving a biomarker exposure, the biomarker measurements can vary across laboratories and usually require calibration to a reference assay prior to pooling. Previous researches consider the measurements from a reference laboratory as the gold standard, even though measurements in the reference laboratory are not necessarily closer to the underlying truth in reality. In this paper we do not treat any laboratory measurements as the gold standard, and we develop two statistical methods, the exact calibration and cut-off calibration methods, for the analysis of aggregated categorical biomarker data. We compare the performance of both methods for estimating the biomarker-disease relationship under a random sample or controls-only calibration design. Our findings include: (1) the exact calibration method provides significantly less biased estimates and more accurate confidence intervals than the other method; (2) the cut-off calibration method could yield estimates with minimal bias and valid confidence intervals under small measurement errors and/or small exposure effects; (3) controls-only calibration design can result in additional bias, but the bias is minimal if the exposure effects and/or disease prevalences are small. Finally, we illustrate the methods in an application evaluating the relationship between circulating vitamin D levels and colorectal cancer risk in a pooling project.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 3","pages":"1146-1163"},"PeriodicalIF":1.8,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903924/pdf/nihms-1669923.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25407136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TORUS GRAPHS FOR MULTIVARIATE PHASE COUPLING ANALYSIS. 用于多元相位耦合分析的环形图。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2020-06-01 Epub Date: 2020-06-29 DOI: 10.1214/19-aoas1300

Natalie Klein, Josue Orellana, Scott L Brincat, Earl K Miller, Robert E Kass

{"title":"TORUS GRAPHS FOR MULTIVARIATE PHASE COUPLING ANALYSIS.","authors":"Natalie Klein, Josue Orellana, Scott L Brincat, Earl K Miller, Robert E Kass","doi":"10.1214/19-aoas1300","DOIUrl":"10.1214/19-aoas1300","url":null,"abstract":"Angular measurements are often modeled as circular random variables, where there are natural circular analogues of moments, including correlation. Because a product of circles is a torus, a d-dimensional vector of circular random variables lies on a d-dimensional torus. For such vectors we present here a class of graphical models, which we call torus graphs, based on the full exponential family with pairwise interactions. The topological distinction between a torus and Euclidean space has several important consequences. Our development was motivated by the problem of identifying phase coupling among oscillatory signals recorded from multiple electrodes in the brain: oscillatory phases across electrodes might tend to advance or recede together, indicating coordination across brain areas. The data analyzed here consisted of 24 phase angles measured repeatedly across 840 experimental trials (replications) during a memory task, where the electrodes were in 4 distinct brain regions, all known to be active while memories are being stored or retrieved. In realistic numerical simulations, we found that a standard pairwise assessment, known as phase locking value, is unable to describe multivariate phase interactions, but that torus graphs can accurately identify conditional associations. Torus graphs generalize several more restrictive approaches that have appeared in various scientific literatures, and produced intuitive results in the data we analyzed. Torus graphs thus unify multivariate analysis of circular data and present fertile territory for future research.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 2","pages":"635-660"},"PeriodicalIF":1.3,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9812283/pdf/nihms-1716022.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10503612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression. 在多重响应回归中检测热点的全局-局部方法

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-06-01 Epub Date: 2020-06-29 DOI: 10.1214/20-AOAS1332

Hélène Ruffieux, Anthony C Davison, Jörg Hager, Jamie Inshaw, Benjamin P Fairfax, Sylvia Richardson, Leonardo Bottolo

{"title":"A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.","authors":"Hélène Ruffieux, Anthony C Davison, Jörg Hager, Jamie Inshaw, Benjamin P Fairfax, Sylvia Richardson, Leonardo Bottolo","doi":"10.1214/20-AOAS1332","DOIUrl":"10.1214/20-AOAS1332","url":null,"abstract":"We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many genes and may initiate decisive functional mechanisms underlying disease endpoints. Existing hierarchical regression approaches designed to model hotspots suffer from two limitations: their discrimination of hotspots is sensitive to the choice of top-level scale parameters for the propensity of predictors to be hotspots, and they do not scale to large predictor and response vectors, for example, of dimensions 103-105 in genetic applications. We address these shortcomings by introducing a flexible hierarchical regression framework that is tailored to the detection of hotspots and scalable to the above dimensions. Our proposal implements a fully Bayesian model for hotspots based on the horseshoe shrinkage prior. Its global-local formulation shrinks noise globally and, hence, accommodates the highly sparse nature of genetic analyses while being robust to individual signals, thus leaving the effects of hotspots unshrunk. Inference is carried out using a fast variational algorithm coupled with a novel simulated annealing procedure that allows efficient exploration of multimodal distributions.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 2","pages":"905-928"},"PeriodicalIF":1.8,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612176/pdf/EMS140637.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10039384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACCOUNTING FOR DEPENDENT ERRORS IN PREDICTORS AND TIME-TO-EVENT OUTCOMES USING ELECTRONIC HEALTH RECORDS, VALIDATION SAMPLES, AND MULTIPLE IMPUTATION. 利用电子健康记录、验证样本和多重估算，计算预测因子和事件发生时间结果的因果误差。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-06-01 Epub Date: 2020-06-29 DOI: 10.1214/20-aoas1343

Mark J Giganti, Pamela A Shaw, Guanhua Chen, Sally S Bebawy, Megan M Turner, Timothy R Sterling, Bryan E Shepherd

{"title":"ACCOUNTING FOR DEPENDENT ERRORS IN PREDICTORS AND TIME-TO-EVENT OUTCOMES USING ELECTRONIC HEALTH RECORDS, VALIDATION SAMPLES, AND MULTIPLE IMPUTATION.","authors":"Mark J Giganti, Pamela A Shaw, Guanhua Chen, Sally S Bebawy, Megan M Turner, Timothy R Sterling, Bryan E Shepherd","doi":"10.1214/20-aoas1343","DOIUrl":"10.1214/20-aoas1343","url":null,"abstract":"Data from electronic health records (EHR) are prone to errors, which are often correlated across multiple variables. The error structure is further complicated when analysis variables are derived as functions of two or more error-prone variables. Such errors can substantially impact estimates, yet we are unaware of methods that simultaneously account for errors in covariates and time-to-event outcomes. Using EHR data from 4217 patients, the hazard ratio for an AIDS-defining event associated with a 100 cell/mm3 increase in CD4 count at ART initiation was 0.74 (95%CI: 0.68-0.80) using unvalidated data and 0.60 (95%CI: 0.53-0.68) using fully validated data. Our goal is to obtain unbiased and efficient estimates after validating a random subset of records. We propose fitting discrete failure time models to the validated subsample and then multiply imputing values for unvalidated records. We demonstrate how this approach simultaneously addresses dependent errors in predictors, time-to-event outcomes, and inclusion criteria. Using the fully validated dataset as a gold standard, we compare the mean squared error of our estimates with those from the unvalidated dataset and the corresponding subsample-only dataset for various subsample sizes. By incorporating reasonably sized validated subsamples and appropriate imputation models, our approach had improved estimation over both the naive analysis and the analysis using only the validation subsample.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 2","pages":"1045-1061"},"PeriodicalIF":1.8,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523695/pdf/nihms-1619324.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9946384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A COMPARISON OF PRINCIPAL COMPONENT METHODS BETWEEN MULTIPLE PHENOTYPE REGRESSION AND MULTIPLE SNP REGRESSION IN GENETIC ASSOCIATION STUDIES. 遗传关联研究中多表型回归与多SNP回归主成分方法的比较。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2020-03-01 DOI: 10.1214/19-aoas1312

Zhonghua Liu, Ian Barnett, Xihong Lin

{"title":"A COMPARISON OF PRINCIPAL COMPONENT METHODS BETWEEN MULTIPLE PHENOTYPE REGRESSION AND MULTIPLE SNP REGRESSION IN GENETIC ASSOCIATION STUDIES.","authors":"Zhonghua Liu, Ian Barnett, Xihong Lin","doi":"10.1214/19-aoas1312","DOIUrl":"https://doi.org/10.1214/19-aoas1312","url":null,"abstract":"Principal component analysis (PCA) is a popular method for dimension reduction in unsupervised multivariate analysis. However, existing ad hoc uses of PCA in both multivariate regression (multiple outcomes) and multiple regression (multiple predictors) lack theoretical justification. The differences in the statistical properties of PCAs in these two regression settings are not well understood. In this paper we provide theoretical results on the power of PCA in genetic association testings in both multiple phenotype and SNP-set settings. The multiple phenotype setting refers to the case when one is interested in studying the association between a single SNP and multiple phenotypes as outcomes. The SNP-set setting refers to the case when one is interested in studying the association between multiple SNPs in a SNP set and a single phenotype as the outcome. We demonstrate analytically that the properties of the PC-based analysis in these two regression settings are substantially different. We show that the lower order PCs, that is, PCs with large eigenvalues, are generally preferred and lead to a higher power in the SNP-set setting, while the higher-order PCs, that is, PCs with small eigenvalues, are generally preferred in the multiple phenotype setting. We also investigate the power of three other popular statistical methods, the Wald test, the variance component test and the minimum <math><mi>p</mi></math>-value test, in both multiple phenotype and SNP-set settings. We use theoretical power, simulation studies, and two real data analyses to validate our findings.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"14 1","pages":"433-451"},"PeriodicalIF":1.8,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313330/pdf/nihms-1906054.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9750854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4