Pub Date : 2023-11-28DOI: 10.1146/annurev-statistics-040522-020722
Sean L. Simpson, Heather M. Shappell, Mohsen Bahrami
The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks—a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Statistical Brain Network Analysis","authors":"Sean L. Simpson, Heather M. Shappell, Mohsen Bahrami","doi":"10.1146/annurev-statistics-040522-020722","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-020722","url":null,"abstract":"The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks—a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"80 14","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138449678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.1146/annurev-statistics-040722-060248
Federica Bianchi, Edoardo Filippi-Mazzola, Alessandro Lomi, Ernst C. Wit
Advances in information technology have increased the availability of time-stamped relational data, such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the events frequently contains information that requires new models for the analysis of continuous-time interactions, subject to both endogenous and exogenous influences. The introduction of the relational event model (REM) has been a major development that has stimulated new questions and led to further methodological developments. In this review, we track the intellectual history of the REM, define its core properties, and discuss why and how it has been considered useful in empirical research. We describe how the demands of novel applications have stimulated methodological, computational, and inferential advancements.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Relational Event Modeling","authors":"Federica Bianchi, Edoardo Filippi-Mazzola, Alessandro Lomi, Ernst C. Wit","doi":"10.1146/annurev-statistics-040722-060248","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040722-060248","url":null,"abstract":"Advances in information technology have increased the availability of time-stamped relational data, such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the events frequently contains information that requires new models for the analysis of continuous-time interactions, subject to both endogenous and exogenous influences. The introduction of the relational event model (REM) has been a major development that has stimulated new questions and led to further methodological developments. In this review, we track the intellectual history of the REM, define its core properties, and discuss why and how it has been considered useful in empirical research. We describe how the demands of novel applications have stimulated methodological, computational, and inferential advancements.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"80 15","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138449677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-22DOI: 10.1146/annurev-statistics-040522-094556
Ronald B. Geskus
The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and what assumptions do I need to make? The main quantities in a competing risks analysis are the cause-specific cumulative incidence, the cause-specific hazard, and the subdistribution hazard. We describe their nonparametric estimation, give an overview of regression models for each of these quantities, and explain their difference in interpretation. We discuss the proper analysis in relation to the type of study question, and we suggest software in R and Stata. Our focus is on competing risks analysis in medical research, but methods can equally be applied in other fields like social science, engineering, and economics.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Competing Risks: Concepts, Methods, and Software","authors":"Ronald B. Geskus","doi":"10.1146/annurev-statistics-040522-094556","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-094556","url":null,"abstract":"The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and what assumptions do I need to make? The main quantities in a competing risks analysis are the cause-specific cumulative incidence, the cause-specific hazard, and the subdistribution hazard. We describe their nonparametric estimation, give an overview of regression models for each of these quantities, and explain their difference in interpretation. We discuss the proper analysis in relation to the type of study question, and we suggest software in R and Stata. Our focus is on competing risks analysis in medical research, but methods can equally be applied in other fields like social science, engineering, and economics.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"27 8","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.1146/annurev-statistics-040120-030919
Genevera I. Allen, Luqin Gan, Lili Zheng
New technologies have led to vast troves of large and complex data sets across many scientific domains and industries. People routinely use machine learning techniques not only to process, visualize, and make predictions from these big data, but also to make data-driven discoveries. These discoveries are often made using interpretable machine learning, or machine learning models and techniques that yield human-understandable insights. In this article, we discuss and review the field of interpretable machine learning, focusing especially on the techniques, as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using interpretable machine learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation both from a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven discoveries.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Interpretable Machine Learning for Discovery: Statistical Challenges and Opportunities","authors":"Genevera I. Allen, Luqin Gan, Lili Zheng","doi":"10.1146/annurev-statistics-040120-030919","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040120-030919","url":null,"abstract":"New technologies have led to vast troves of large and complex data sets across many scientific domains and industries. People routinely use machine learning techniques not only to process, visualize, and make predictions from these big data, but also to make data-driven discoveries. These discoveries are often made using interpretable machine learning, or machine learning models and techniques that yield human-understandable insights. In this article, we discuss and review the field of interpretable machine learning, focusing especially on the techniques, as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using interpretable machine learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation both from a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven discoveries.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"59 9","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.1146/annurev-statistics-033121-114601
Guido W. Imbens
Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data. This fundamental problem has been known and studied for many years in many disciplines. In the past thirty years, however, the amount of empirical as well as methodological research in this area has increased dramatically, and so has its scope. It has become more interdisciplinary, and the focus has been more specifically on methods for credibly estimating causal effects in a wide range of both experimental and observational settings. This work has greatly impacted empirical work in the social and biomedical sciences. In this article, I review some of this work and discuss open questions.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Causal Inference in the Social Sciences","authors":"Guido W. Imbens","doi":"10.1146/annurev-statistics-033121-114601","DOIUrl":"https://doi.org/10.1146/annurev-statistics-033121-114601","url":null,"abstract":"Knowledge of causal effects is of great importance to decision makers in a wide variety of settings. In many cases, however, these causal effects are not known to the decision makers and need to be estimated from data. This fundamental problem has been known and studied for many years in many disciplines. In the past thirty years, however, the amount of empirical as well as methodological research in this area has increased dramatically, and so has its scope. It has become more interdisciplinary, and the focus has been more specifically on methods for credibly estimating causal effects in a wide range of both experimental and observational settings. This work has greatly impacted empirical work in the social and biomedical sciences. In this article, I review some of this work and discuss open questions.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"59 8","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-17DOI: 10.1146/annurev-statistics-040522-021241
Ling Zhou, Ziyang Gong, Pengcheng Xiang
Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Distributed Computing and Inference for Big Data","authors":"Ling Zhou, Ziyang Gong, Pengcheng Xiang","doi":"10.1146/annurev-statistics-040522-021241","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-021241","url":null,"abstract":"Data are distributed across different sites due to computing facility limitations or data privacy considerations. Conventional centralized methods—those in which all datasets are stored and processed in a central computing facility—are not applicable in practice. Therefore, it has become necessary to develop distributed learning approaches that have good inference or predictive accuracy while remaining free of individual data or obeying policies and regulations to protect privacy. In this article, we introduce the basic idea of distributed learning and conduct a selected review on various distributed learning methods, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. This categorization can help evaluate newly proposed methods from different aspects. Moreover, we provide up-to-date descriptions of the existing theoretical results that cover statistical equivalency and computational efficiency under different statistical learning frameworks. Finally, we provide existing software implementations and benchmark datasets, and we discuss future research opportunities.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"59 7","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-06DOI: 10.1146/annurev-statistics-040522-093748
Javier Carrón Duque, Domenico Marinucci
This review is devoted to recent developments in the statistical analysis of spherical data, strongly motivated by applications in cosmology. We start from a brief discussion of cosmological questions and motivations, arguing that most cosmological observables are spherical random fields. Then, we introduce some mathematical background on spherical random fields, including spectral representations and the construction of needlet and wavelet frames. We then focus on some specific issues, including tools and algorithms for map reconstruction (i.e., separating the different physical components that contribute to the observed field), geometric tools for testing the assumptions of Gaussianity and isotropy, and multiple testing methods to detect contamination in the field due to point sources. Although these tools are introduced in the cosmological context, they can be applied to other situations dealing with spherical data. Finally, we discuss more recent and challenging issues, such as the analysis of polarization data, which can be viewed as realizations of random fields taking values in spin fiber bundles.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Geometric Methods for Cosmological Data on the Sphere","authors":"Javier Carrón Duque, Domenico Marinucci","doi":"10.1146/annurev-statistics-040522-093748","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-093748","url":null,"abstract":"This review is devoted to recent developments in the statistical analysis of spherical data, strongly motivated by applications in cosmology. We start from a brief discussion of cosmological questions and motivations, arguing that most cosmological observables are spherical random fields. Then, we introduce some mathematical background on spherical random fields, including spectral representations and the construction of needlet and wavelet frames. We then focus on some specific issues, including tools and algorithms for map reconstruction (i.e., separating the different physical components that contribute to the observed field), geometric tools for testing the assumptions of Gaussianity and isotropy, and multiple testing methods to detect contamination in the field due to point sources. Although these tools are introduced in the cosmological context, they can be applied to other situations dealing with spherical data. Finally, we discuss more recent and challenging issues, such as the analysis of polarization data, which can be viewed as realizations of random fields taking values in spin fiber bundles.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"11 3","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71473809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1146/annurev-statistics-040622-023838
Paul J. Northrop
Rainfall is the main input to most hydrological systems. To assess flood risk for a catchment area, hydrologists use models that require long series of subdaily, perhaps even subhourly, rainfall data, ideally from locations that cover the area. If historical data are not sufficient for this purpose, an alternative is to simulate synthetic data from a suitably calibrated model. We review stochastic models that have a mechanistic structure, intended to mimic physical features of the rainfall processes, and are constructed using stationary point processes. We describe models for temporal and spatial-temporal rainfall and consider how they can be fitted to data. We provide an example application using a temporal model and an illustration of data simulated from a spatial-temporal model. We discuss how these models can contribute to the simulation of future rainfall that reflects our changing climate.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Stochastic Models of Rainfall","authors":"Paul J. Northrop","doi":"10.1146/annurev-statistics-040622-023838","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040622-023838","url":null,"abstract":"Rainfall is the main input to most hydrological systems. To assess flood risk for a catchment area, hydrologists use models that require long series of subdaily, perhaps even subhourly, rainfall data, ideally from locations that cover the area. If historical data are not sufficient for this purpose, an alternative is to simulate synthetic data from a suitably calibrated model. We review stochastic models that have a mechanistic structure, intended to mimic physical features of the rainfall processes, and are constructed using stationary point processes. We describe models for temporal and spatial-temporal rainfall and consider how they can be fitted to data. We provide an example application using a temporal model and an illustration of data simulated from a spatial-temporal model. We discuss how these models can contribute to the simulation of future rainfall that reflects our changing climate.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"88 23","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71435578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1146/annurev-statistics-040722-043616
Mushan Li, Yanyuan Ma
The issues caused by measurement errors have been recognized for almost 90 years, and research in this area has flourished since the 1980s. We review some of the classical methods in both density estimation and regression problems with measurement errors. In both problems, we consider when the original error-free model is parametric, nonparametric, and semiparametric, in combination with different error types. We also summarize and explain some new approaches, including recent developments and challenges in the high-dimensional setting.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"An Update on Measurement Error Modeling","authors":"Mushan Li, Yanyuan Ma","doi":"10.1146/annurev-statistics-040722-043616","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040722-043616","url":null,"abstract":"The issues caused by measurement errors have been recognized for almost 90 years, and research in this area has flourished since the 1980s. We review some of the classical methods in both density estimation and regression problems with measurement errors. In both problems, we consider when the original error-free model is parametric, nonparametric, and semiparametric, in combination with different error types. We also summarize and explain some new approaches, including recent developments and challenges in the high-dimensional setting.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"20 14","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1146/annurev-statistics-040522-120734
Christine B. Peterson, Satabdi Saha, Kim-Anh Do
The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
{"title":"Analysis of Microbiome Data","authors":"Christine B. Peterson, Satabdi Saha, Kim-Anh Do","doi":"10.1146/annurev-statistics-040522-120734","DOIUrl":"https://doi.org/10.1146/annurev-statistics-040522-120734","url":null,"abstract":"The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.","PeriodicalId":48855,"journal":{"name":"Annual Review of Statistics and Its Application","volume":"20 16","pages":""},"PeriodicalIF":7.9,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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