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Trecarten S, Fongang B, Liss M. Current Trends and Challenges of Microbiome Research in Prostate Cancer. Curr Oncol Rep 2024; 26:477-487. [PMID: 38573440 DOI: 10.1007/s11912-024-01520-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE OF REVIEW The role of the gut microbiome in prostate cancer is an emerging area of research interest. However, no single causative organism has yet been identified. The goal of this paper is to examine the role of the microbiome in prostate cancer and summarize the challenges relating to methodology in specimen collection, sequencing technology, and interpretation of results. RECENT FINDINGS Significant heterogeneity still exists in methodology for stool sampling/storage, preservative options, DNA extraction, and sequencing database selection/in silico processing. Debate persists over primer choice in amplicon sequencing as well as optimal methods for data normalization. Statistical methods for longitudinal microbiome analysis continue to undergo refinement. While standardization of methodology may help yield more consistent results for organism identification in prostate cancer, this is a difficult task due to considerable procedural variation at each step in the process. Further reproducibility and methodology research is required.
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Affiliation(s)
- Shaun Trecarten
- Department of Urology, UT Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA
| | - Bernard Fongang
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Biochemistry and Structural Biology, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Michael Liss
- Department of Urology, UT Health San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, USA.
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2
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Prataviera F, Ortega EMM, Cordeiro GM, Cancho VG. The exponentiated power exponential semiparametric regression model. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2020.1788585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Fábio Prataviera
- Departamento de ciências Exatas, ESALQ, Universidade de São Paulo, Piracicaba, Brazil
| | - Edwin M. M. Ortega
- Departamento de ciências Exatas, ESALQ, Universidade de São Paulo, Piracicaba, Brazil
| | - Gauss M. Cordeiro
- Departamento de Estatística, Universidade Federal de Pernambuco, Brazil
| | - Vicente G. Cancho
- Departamento de Matemática Aplicada e Estatística, ICMC, Universidade de São Paulo, Piracicaba, Brazil
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Kodikara S, Ellul S, Lê Cao KA. Statistical challenges in longitudinal microbiome data analysis. Brief Bioinform 2022; 23:bbac273. [PMID: 35830875 PMCID: PMC9294433 DOI: 10.1093/bib/bbac273] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/28/2022] [Accepted: 06/12/2022] [Indexed: 11/13/2022] Open
Abstract
The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
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Affiliation(s)
- Saritha Kodikara
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia
| | - Susan Ellul
- Murdoch Children’s Research Institute and Department of Paediatrics, University of Melbourne, Bouverie Street, 3052, Victoria, Australia
| | - Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia
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4
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A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms. Sci Rep 2021; 11:19944. [PMID: 34620921 PMCID: PMC8497483 DOI: 10.1038/s41598-021-98110-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/24/2021] [Indexed: 11/12/2022] Open
Abstract
Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.
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5
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Revers A, Zhang X, Zwinderman AH. A Bayesian Negative Binomial Hierarchical Model for Identifying Diet-Gut Microbiome Associations. Front Microbiol 2021; 12:711861. [PMID: 34690956 PMCID: PMC8529249 DOI: 10.3389/fmicb.2021.711861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/20/2021] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota composition plays an important role in human health. Long-term diet intervention may shape human gut microbiome. Therefore, many studies focus on discovering links between long-term diets and gut microbiota composition. This study aimed to incorporate the phylogenetic relationships between the operational taxonomic units (OTUs) into the diet-microbe association analysis, using a Bayesian hierarchical negative binomial (NB) model. We regularized the dispersion parameter of the negative binomial distribution by assuming a mean-dispersion association. A simulation study showed that, if over-dispersion is present in the microbiome data, our approach performed better in terms of mean squared error (MSE) of the slope-estimates compared to the standard NB regression model or a Bayesian hierarchical NB model without including the phylogenetic relationships. Data of the Healthy Life in an Urban Setting (HELIUS) study showed that for some phylogenetic families the (posterior) variances of the slope-estimates were decreasing when including the phylogenetic relationships into the analyses. In contrast, when OTUs of the same family were not similarly affected by the food item, some bias was introduced, leading to larger (posterior) variances of the slope-estimates. Overall, the Bayesian hierarchical NB model, with a dependency between the mean and dispersion parameters, proved to be a robust method for analyzing diet-microbe associations.
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Affiliation(s)
- Alma Revers
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Xiang Zhang
- Theoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Utrecht, Netherlands
| | - Aeilko H. Zwinderman
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, Netherlands
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6
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Shuler K, Verbanic S, Chen IA, Lee J. A Bayesian nonparametric analysis for zero‐inflated multivariate count data with application to microbiome study. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kurtis Shuler
- Sandia National Laboratories in Albuquerque Albuquerque NM USA
| | - Samuel Verbanic
- Department of Chemical and Biomolecular Engineering University of California Los Angeles Los Angeles CA USA
| | - Irene A. Chen
- Department of Chemical and Biomolecular Engineering University of California Los Angeles Los Angeles CA USA
| | - Juhee Lee
- Department of Statistics University of California Santa Cruz Santa Cruz CA USA
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7
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Prataviera F, Ortega EMM, Cordeiro GM. An extended Maxwell semiparametric regression for censored and uncensored data. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1934021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
| | | | - Gauss M. Cordeiro
- Departamento de Estatística, Universidade Federal de Pernambuco, Recife, Brazil
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8
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Jiang S, Xiao G, Koh AY, Chen Y, Yao B, Li Q, Zhan X. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet 2020; 11:445. [PMID: 32582274 PMCID: PMC7283552 DOI: 10.3389/fgene.2020.00445] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/14/2020] [Indexed: 12/19/2022] Open
Abstract
The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to construct microbial networks from the microbiome sequencing count data. As microbiome count data are high-dimensional and suffer from uneven sampling depth, over-dispersion, and zero-inflation, these characteristics can bias the network estimation and require specialized analytical tools. Here we propose a general framework, HARMONIES, Hybrid Approach foR MicrobiOme Network Inferences via Exploiting Sparsity, to infer a sparse microbiome network. HARMONIES first utilizes a zero-inflated negative binomial (ZINB) distribution to model the skewness and excess zeros in the microbiome data, as well as incorporates a stochastic process prior for sample-wise normalization. This approach infers a sparse and stable network by imposing non-trivial regularizations based on the Gaussian graphical model. In comprehensive simulation studies, HARMONIES outperformed four other commonly used methods. When using published microbiome data from a colorectal cancer study, it discovered a novel community with disease-enriched bacteria. In summary, HARMONIES is a novel and useful statistical framework for microbiome network inference, and it is available at https://github.com/shuangj00/HARMONIES.
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Affiliation(s)
- Shuang Jiang
- Department of Statistical Science, Southern Methodist University, Dallas, TX, United States.,Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Andrew Y Koh
- Departments of Pediatrics, Departments of Microbiology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Yingfei Chen
- Lyda Hill Department of Bioinformatics, Bioinformatics High Performance Computing, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Bo Yao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
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9
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Xia Y. Correlation and association analyses in microbiome study integrating multiomics in health and disease. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 171:309-491. [PMID: 32475527 DOI: 10.1016/bs.pmbts.2020.04.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.
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Affiliation(s)
- Yinglin Xia
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States.
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10
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Verbanic S, Shen Y, Lee J, Deacon JM, Chen IA. Microbial predictors of healing and short-term effect of debridement on the microbiome of chronic wounds. NPJ Biofilms Microbiomes 2020; 6:21. [PMID: 32358500 PMCID: PMC7195478 DOI: 10.1038/s41522-020-0130-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 03/24/2020] [Indexed: 02/06/2023] Open
Abstract
Chronic wounds represent a large and growing disease burden. Infection and biofilm formation are two of the leading impediments of wound healing, suggesting an important role for the microbiome of these wounds. Debridement is a common and effective treatment for chronic wounds. We analyzed the bacterial content of the wound surface from 20 outpatients with chronic wounds before and immediately after debridement, as well as healthy skin. Given the large variation observed among different wounds, we introduce a Bayesian statistical method that models patient-to-patient variability and identify several genera that were significantly enriched in wounds vs. healthy skin. We found no difference between the microbiome of the original wound surface and that exposed by a single episode of sharp debridement, suggesting that this debridement did not directly alter the wound microbiome. However, we found that aerobes and especially facultative anaerobes were significantly associated with wounds that did not heal within 6 months. The facultative anaerobic genus Enterobacter was significantly associated with lack of healing. The results suggest that an abundance of facultative anaerobes is a negative prognostic factor in the chronic wound microbiome, possibly due to the increased robustness of such communities to different metabolic environments.
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Affiliation(s)
- Samuel Verbanic
- Program in Biomolecular Science and Engineering, University of California, Santa Barbara, CA, USA
| | - Yuning Shen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, USA
| | - Juhee Lee
- Department of Statistics, University of California, Santa Cruz, CA, USA
| | - John M Deacon
- Goleta Valley Cottage Hospital, Ridley-Tree Center for Wound Management, Santa Barbara, CA, USA
| | - Irene A Chen
- Program in Biomolecular Science and Engineering, University of California, Santa Barbara, CA, USA. .,Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, USA. .,Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA.
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11
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Xia Y, Sun J, Chen DG. Introductory Overview of Statistical Analysis of Microbiome Data. STATISTICAL ANALYSIS OF MICROBIOME DATA WITH R 2018. [DOI: 10.1007/978-981-13-1534-3_3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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