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Lett TA, Vaidya N, Jia T, Polemiti E, Banaschewski T, Bokde ALW, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brüh R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Paus T, Poustka L, Stringaris A, Waller L, Zhang Z, Robinson L, Winterer J, Zhang Y, King S, Smolka MN, Whelan R, Schmidt U, Sinclair J, Walter H, Feng J, Robbins TW, Desrivières S, Marquand A, Schumann G. A framework for a brain-derived nosology of psychiatric disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.07.24306980. [PMID: 38766134 PMCID: PMC11100856 DOI: 10.1101/2024.05.07.24306980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.
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2
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Wang M, Bissonnette N, Laterrière M, Dudemaine PL, Gagné D, Roy JP, Sirard MA, Ibeagha-Awemu EM. DNA methylation haplotype block signatures responding to Staphylococcus aureus subclinical mastitis and association with production and health traits. BMC Biol 2024; 22:65. [PMID: 38486242 PMCID: PMC10941392 DOI: 10.1186/s12915-024-01843-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/09/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND DNA methylation has been documented to play vital roles in diseases and biological processes. In bovine, little is known about the regulatory roles of DNA methylation alterations on production and health traits, including mastitis. RESULTS Here, we employed whole-genome DNA methylation sequencing to profile the DNA methylation patterns of milk somatic cells from sixteen cows with naturally occurring Staphylococcus aureus (S. aureus) subclinical mastitis and ten healthy control cows. We observed abundant DNA methylation alterations, including 3,356,456 differentially methylated cytosines and 153,783 differential methylation haplotype blocks (dMHBs). The DNA methylation in regulatory regions, including promoters, first exons and first introns, showed global significant negative correlations with gene expression status. We identified 6435 dMHBs located in the regulatory regions of differentially expressed genes and significantly correlated with their corresponding genes, revealing their potential effects on transcriptional activities. Genes harboring DNA methylation alterations were significantly enriched in multiple immune- and disease-related pathways, suggesting the involvement of DNA methylation in regulating host responses to S. aureus subclinical mastitis. In addition, we found nine discriminant signatures (differentiates cows with S. aureus subclinical mastitis from healthy cows) representing the majority of the DNA methylation variations related to S. aureus subclinical mastitis. Validation of seven dMHBs in 200 cows indicated significant associations with mammary gland health (SCC and SCS) and milk production performance (milk yield). CONCLUSIONS In conclusion, our findings revealed abundant DNA methylation alterations in milk somatic cells that may be involved in regulating mammary gland defense against S. aureus infection. Particularly noteworthy is the identification of seven dMHBs showing significant associations with mammary gland health, underscoring their potential as promising epigenetic biomarkers. Overall, our findings on DNA methylation alterations offer novel insights into the regulatory mechanisms of bovine subclinical mastitis, providing further avenues for the development of effective control measures.
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Affiliation(s)
- Mengqi Wang
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
- Department of Animal Science, Laval University, Quebec, QC, Canada
| | - Nathalie Bissonnette
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
| | - Mario Laterrière
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec, QC, Canada
| | - Pier-Luc Dudemaine
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
| | - David Gagné
- Quebec Research and Development Centre, Agriculture and Agri-Food Canada, Quebec, QC, Canada
| | - Jean-Philippe Roy
- Department of Clinical Sciences, Université de Montréal, St-Hyacinthe, QC, Canada
| | | | - Eveline M Ibeagha-Awemu
- Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada.
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3
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Hernandez-Baixauli J, Chomiciute G, Tracey H, Mora I, Cortés-Espinar AJ, Ávila-Román J, Abasolo N, Palacios-Jordan H, Foguet-Romero E, Suñol D, Galofré M, Alcaide-Hidalgo JM, Baselga-Escudero L, del Bas JM, Mulero M. Exploring Metabolic and Gut Microbiome Responses to Paraquat Administration in Male Wistar Rats: Implications for Oxidative Stress. Antioxidants (Basel) 2024; 13:67. [PMID: 38247491 PMCID: PMC10812659 DOI: 10.3390/antiox13010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
In this study, we examined the metabolic and gut microbiome responses to paraquat (PQ) in male Wistar rats, focusing on oxidative stress effects. Rats received a single intraperitoneal injection of PQ at 15 and 30 mg/kg, and various oxidative stress parameters (i.e., MDA, SOD, ROS, 8-isoprostanes) were assessed after three days. To explore the omic profile, GC-qTOF and UHPLC-qTOF were performed to assess the plasma metabolome; 1H-NMR was used to assess the urine metabolome; and shotgun metagenomics sequencing was performed to study the gut microbiome. Our results revealed reductions in body weight and tissue changes, particularly in the liver, were observed, suggesting a systemic effect of PQ. Elevated lipid peroxidation and reactive oxygen species levels in the liver and plasma indicated the induction of oxidative stress. Metabolic profiling revealed changes in the tricarboxylic acid cycle, accumulation of ketone body, and altered levels of key metabolites, such as 3-hydroxybutyric acid and serine, suggesting intricate links between energy metabolism and redox reactions. Plasma metabolomic analysis revealed alterations in mitochondrial metabolism, nicotinamide metabolism, and tryptophan degradation. The gut microbiome showed shifts, with higher PQ doses influencing microbial populations (e.g., Escherichia coli and Akkermansia muciniphila) and metagenomic functions (pyruvate metabolism, fermentation, nucleotide and amino acid biosynthesis). Overall, this study provides comprehensive insights into the complex interplay between PQ exposure, metabolic responses, and gut microbiome dynamics. These findings enhance our understanding of the mechanisms behind oxidative stress-induced metabolic alterations and underscore the connections between xenobiotic exposure, gut microbiota, and host metabolism.
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Affiliation(s)
- Julia Hernandez-Baixauli
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (G.C.); (H.T.); (J.M.A.-H.); (L.B.-E.)
- Laboratory of Metabolism and Obesity, Vall d’Hebron-Institut de Recerca, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Gertruda Chomiciute
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (G.C.); (H.T.); (J.M.A.-H.); (L.B.-E.)
| | - Harry Tracey
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (G.C.); (H.T.); (J.M.A.-H.); (L.B.-E.)
- Department of Medical Sciences, School of Medicine, University of Girona, 17004 Girona, Spain
- School of Science, RMIT University, Bundoora, VIC 3000, Australia
| | - Ignasi Mora
- Brudy Technology S.L., 08006 Barcelona, Spain;
| | - Antonio J. Cortés-Espinar
- Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43007 Tarragona, Spain;
| | - Javier Ávila-Román
- Molecular and Applied Pharmacology Group (FARMOLAP), Department of Pharmacology, Universidad de Sevilla, 41012 Sevilla, Spain;
| | - Nerea Abasolo
- Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, 43204 Reus, Spain; (N.A.); (H.P.-J.); (E.F.-R.)
| | - Hector Palacios-Jordan
- Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, 43204 Reus, Spain; (N.A.); (H.P.-J.); (E.F.-R.)
| | - Elisabet Foguet-Romero
- Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, 43204 Reus, Spain; (N.A.); (H.P.-J.); (E.F.-R.)
| | - David Suñol
- Eurecat, Centre Tecnològic de Catalunya, Digital Health, 08005 Barcelona, Spain; (D.S.); (M.G.)
| | - Mar Galofré
- Eurecat, Centre Tecnològic de Catalunya, Digital Health, 08005 Barcelona, Spain; (D.S.); (M.G.)
| | - Juan María Alcaide-Hidalgo
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (G.C.); (H.T.); (J.M.A.-H.); (L.B.-E.)
| | - Laura Baselga-Escudero
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (G.C.); (H.T.); (J.M.A.-H.); (L.B.-E.)
| | - Josep M. del Bas
- Eurecat, Centre Tecnològic de Catalunya, Àrea Biotecnologia, 43204 Reus, Spain
| | - Miquel Mulero
- Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43007 Tarragona, Spain;
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Park S, Ceulemans E, Van Deun K. Logistic regression with sparse common and distinctive covariates. Behav Res Methods 2023; 55:4143-4174. [PMID: 36781701 PMCID: PMC10700465 DOI: 10.3758/s13428-022-02011-2] [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: 10/10/2022] [Indexed: 02/15/2023]
Abstract
Having large sets of predictor variables from multiple sources concerning the same individuals is becoming increasingly common in behavioral research. On top of the variable selection problem, predicting a categorical outcome using such data gives rise to an additional challenge of identifying the processes at play underneath the predictors. These processes are of particular interest in the setting of multi-source data because they can either be associated individually with a single data source or jointly with multiple sources. Although many methods have addressed the classification problem in high dimensionality, the additional challenge of distinguishing such underlying predictor processes from multi-source data has not received sufficient attention. To this end, we propose the method of Sparse Common and Distinctive Covariates Logistic Regression (SCD-Cov-logR). The method is a multi-source extension of principal covariates regression that combines with generalized linear modeling framework to allow classification of a categorical outcome. In a simulation study, SCD-Cov-logR resulted in outperformance compared to related methods commonly used in behavioral sciences. We also demonstrate the practical usage of the method under an empirical dataset.
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Affiliation(s)
- S Park
- Tilburg University, Tilburg, Netherlands.
| | | | - K Van Deun
- Tilburg University, Tilburg, Netherlands
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5
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Du X, Jiang X, Lin J. Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data. PSYCHOMETRIKA 2023; 88:975-1001. [PMID: 37268759 DOI: 10.1007/s11336-023-09918-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 03/23/2023] [Indexed: 06/04/2023]
Abstract
Multi-source functional block-wise missing data arise more commonly in medical care recently with the rapid development of big data and medical technology, hence there is an urgent need to develop efficient dimension reduction to extract important information for classification under such data. However, most existing methods for classification problems consider high-dimensional data as covariates. In the paper, we propose a novel multinomial imputed-factor Logistic regression model with multi-source functional block-wise missing data as covariates. Our main contribution is to establishing two multinomial factor regression models by using the imputed multi-source functional principal component scores and imputed canonical scores as covariates, respectively, where the missing factors are imputed by both the conditional mean imputation and the multiple block-wise imputation approaches. Specifically, the univariate FPCA is carried out for the observable data of each data source firstly to obtain the univariate principal component scores and the eigenfunctions. Then, the block-wise missing univariate principal component scores instead of the block-wise missing functional data are imputed by the conditional mean imputation method and the multiple block-wise imputation method, respectively. After that, based on the imputed univariate factors, the multi-source principal component scores are constructed by using the relationship between the multi-source principal component scores and the univariate principal component scores; and at the same time, the canonical scores are obtained by the multiple-set canonial correlation analysis. Finally, the multinomial imputed-factor Logistic regression model is established with the multi-source principal component scores or the canonical scores as factors. Numerical simulations and real data analysis on ADNI data show the proposed method works well.
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Affiliation(s)
- Xiuli Du
- College of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, China.
| | - Xiaohu Jiang
- College of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, China
| | - Jinguan Lin
- Institute of Statistics and Data Science, Nanjing Audit University, Nanjing, 211815, China
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6
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Gygi JP, Kleinstein SH, Guan L. Predictive overfitting in immunological applications: Pitfalls and solutions. Hum Vaccin Immunother 2023; 19:2251830. [PMID: 37697867 PMCID: PMC10498807 DOI: 10.1080/21645515.2023.2251830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/27/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research.
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Affiliation(s)
- Jeremy P. Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Steven H. Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Leying Guan
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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7
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Bensalma F, Mezghani N, Cagnin A, Fuente A, Lenoir L, Hagemeister N. Multimodal data analysis of knee osteoarthritis assessment: factors selection for conservative care decision making. Comput Methods Biomech Biomed Engin 2023; 26:450-459. [PMID: 35472257 DOI: 10.1080/10255842.2022.2066973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
When assessing a patient with knee osteoarthritis (OA), a number of factors are considered to guide treatment plan, namely, demographic, radiographic, clinical, musculoskeletal, and biomechanical factors. The aim of this study is to identify which of these factors are the most related to each other to potentially better prioritize the modifiable factors to be addressed as they may influence treatment outcomes. We investigated a multimodal canonical correlation analysis to evaluate associations between these factors. The analysis was performed on 415 OA patients who were not candidates for knee arthroplasty, to identify factors that are associated to the patients' clinical conditions.
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Affiliation(s)
- F Bensalma
- Research Center LICEF institute, TELUQ, Montréal, Canada.,Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada
| | - N Mezghani
- Research Center LICEF institute, TELUQ, Montréal, Canada.,Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada
| | - A Cagnin
- Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada.,LIO, École de technologie supérieure, Montréal, Canada
| | | | | | - N Hagemeister
- Laboratoire de recherche en imagerie et orthopédie (LIO), Research Centre of the centre hospitalier de l'université de Montréal (CRCHUM), Montréal, Canada.,LIO, École de technologie supérieure, Montréal, Canada
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8
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Flores JE, Claborne DM, Weller ZD, Webb-Robertson BJM, Waters KM, Bramer LM. Missing data in multi-omics integration: Recent advances through artificial intelligence. Front Artif Intell 2023; 6:1098308. [PMID: 36844425 PMCID: PMC9949722 DOI: 10.3389/frai.2023.1098308] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
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Affiliation(s)
- Javier E. Flores
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Daniel M. Claborne
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Zachary D. Weller
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Katrina M. Waters
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States,*Correspondence: Lisa M. Bramer ✉
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9
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Bu X, Gao Y, Liang K, Bao W, Chen Y, Guo L, Gong Q, Lu H, Caffo B, Mori S, Huang X. Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder. J Child Psychol Psychiatry 2023; 64:244-253. [PMID: 36000340 PMCID: PMC10087687 DOI: 10.1111/jcpp.13689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/11/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Attention deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder. Integrity of white matter microstructure plays a key role in the neural mechanism of ADHD presentations. However, the relationships between specific behavioural dimensions and white matter microstructure are less well known. This study aimed to identify associations between white matter and a broad set of clinical features across children and adolescent with and without ADHD using a data-driven multivariate approach. METHOD We recruited a total of 130 children (62 controls and 68 ADHD) and employed regularized generalized canonical correlation analysis to characterize the associations between white matter and a comprehensive set of clinical measures covering three domains, including symptom, cognition and behaviour. We further applied linear discriminant analysis to integrate these associations to explore potential developmental effects. RESULTS We delineated two brain-behaviour dimensional associations in each domain resulting a total of six multivariate patterns of white matter microstructural alterations linked to hyperactivity-impulsivity and mild affected; executive functions and working memory; externalizing behaviour and social withdrawal, respectively. Apart from executive function and externalizing behaviour sharing similar white matter patterns, all other dimensions linked to a specific pattern of white matter microstructural alterations. The multivariate dimensional association scores showed an overall increase and normalization with age in ADHD group while remained stable in controls. CONCLUSIONS We found multivariate neurobehavioral associations exist across ADHD and controls, which suggested that multiple white matter patterns underlie ADHD heterogeneity and provided neural bases for more precise diagnosis and individualized treatment.
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Affiliation(s)
- Xuan Bu
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMDUSA
| | - Yingxue Gao
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Kaili Liang
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Weijie Bao
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Ying Chen
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Lanting Guo
- Department of PsychiatryWest China Hospital of Sichuan UniversityChengduChina
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMDUSA
| | - Brian Caffo
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMDUSA
| | - Xiaoqi Huang
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
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10
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Ni Y, He J, Chalise P. Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization. Stat Appl Genet Mol Biol 2023; 22:sagmb-2022-0047. [PMID: 37937887 DOI: 10.1515/sagmb-2022-0047] [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] [Received: 10/09/2022] [Accepted: 09/25/2023] [Indexed: 11/09/2023]
Abstract
Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.
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Affiliation(s)
- Yonghui Ni
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Jianghua He
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Prabhakar Chalise
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
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11
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Athieniti E, Spyrou GM. A guide to multi-omics data collection and integration for translational medicine. Comput Struct Biotechnol J 2022; 21:134-149. [PMID: 36544480 PMCID: PMC9747357 DOI: 10.1016/j.csbj.2022.11.050] [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: 06/23/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/02/2022] Open
Abstract
The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.
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12
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Haghighi M, Caicedo JC, Cimini BA, Carpenter AE, Singh S. High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations. Nat Methods 2022; 19:1550-1557. [PMID: 36344834 PMCID: PMC10012424 DOI: 10.1038/s41592-022-01667-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 09/28/2022] [Indexed: 11/09/2022]
Abstract
Cells can be perturbed by various chemical and genetic treatments and the impact on gene expression and morphology can be measured via transcriptomic profiling and image-based assays, respectively. The patterns observed in these high-dimensional profile data can power a dozen applications in drug discovery and basic biology research, but both types of profiles are rarely available for large-scale experiments. Here, we provide a collection of four datasets with both gene expression and morphological profile data useful for developing and testing multimodal methodologies. Roughly a thousand features are measured for each of the two data types, across more than 28,000 chemical and genetic perturbations. We define biological problems that use the shared and complementary information in these two data modalities, provide baseline analysis and evaluation metrics for multi-omic applications, and make the data resource publicly available ( https://broad.io/rosetta/ ).
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Affiliation(s)
| | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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13
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Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma. Sci Rep 2022; 12:15425. [PMID: 36104347 PMCID: PMC9475034 DOI: 10.1038/s41598-022-19019-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractMulti-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.
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14
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Overcoming convergence problems in PLS path modelling. Comput Stat 2022. [DOI: 10.1007/s00180-022-01204-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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15
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Hernandez-Baixauli J, Abasolo N, Palacios-Jordan H, Foguet-Romero E, Suñol D, Galofré M, Caimari A, Baselga-Escudero L, Del Bas JM, Mulero M. Imbalances in TCA, Short Fatty Acids and One-Carbon Metabolisms as Important Features of Homeostatic Disruption Evidenced by a Multi-Omics Integrative Approach of LPS-Induced Chronic Inflammation in Male Wistar Rats. Int J Mol Sci 2022; 23:ijms23052563. [PMID: 35269702 PMCID: PMC8910732 DOI: 10.3390/ijms23052563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/21/2022] [Accepted: 02/23/2022] [Indexed: 12/02/2022] Open
Abstract
Chronic inflammation is an important risk factor in a broad variety of physical and mental disorders leading to highly prevalent non-communicable diseases (NCDs). However, there is a need for a deeper understanding of this condition and its progression to the disease state. For this reason, it is important to define metabolic pathways and complementary biomarkers associated with homeostatic disruption in chronic inflammation. To achieve that, male Wistar rats were subjected to intraperitoneal and intermittent injections with saline solution or increasing lipopolysaccharide (LPS) concentrations (0.5, 5 and 7.5 mg/kg) thrice a week for 31 days. Biochemical and inflammatory parameters were measured at the end of the study. To assess the omics profile, GC-qTOF and UHPLC-qTOF were performed to evaluate plasma metabolome; 1H-NMR was used to evaluate urine metabolome; additionally, shotgun metagenomics sequencing was carried out to characterize the cecum microbiome. The chronicity of inflammation in the study was evaluated by the monitoring of monocyte chemoattractant protein-1 (MCP-1) during the different weeks of the experimental process. At the end of the study, together with the increased levels of MCP-1, levels of interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α) and prostaglandin E2 (PGE2) along with 8-isoprostanes (an indicative of oxidative stress) were significantly increased (p-value < 0.05). The leading features implicated in the current model were tricarboxylic acid (TCA) cycle intermediates (i.e., alpha-ketoglutarate, aconitic acid, malic acid, fumaric acid and succinic acid); lipids such as specific cholesterol esters (ChoEs), lysophospholipids (LPCs) and phosphatidylcholines (PCs); and glycine, as well as N, N-dimethylglycine, which are related to one-carbon (1C) metabolism. These metabolites point towards mitochondrial metabolism through TCA cycle, β-oxidation of fatty acids and 1C metabolism as interconnected pathways that could reveal the metabolic effects of chronic inflammation induced by LPS administration. These results provide deeper knowledge concerning the impact of chronic inflammation on the disruption of metabolic homeostasis.
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Affiliation(s)
- Julia Hernandez-Baixauli
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (A.C.); (L.B.-E.)
| | - Nerea Abasolo
- Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, 43204 Reus, Spain; (N.A.); (H.P.-J.); (E.F.-R.)
| | - Hector Palacios-Jordan
- Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, 43204 Reus, Spain; (N.A.); (H.P.-J.); (E.F.-R.)
| | - Elisabet Foguet-Romero
- Eurecat, Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, 43204 Reus, Spain; (N.A.); (H.P.-J.); (E.F.-R.)
| | - David Suñol
- Eurecat, Centre Tecnològic de Catalunya, Digital Health, 08005 Barcelona, Spain; (D.S.); (M.G.)
| | - Mar Galofré
- Eurecat, Centre Tecnològic de Catalunya, Digital Health, 08005 Barcelona, Spain; (D.S.); (M.G.)
| | - Antoni Caimari
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (A.C.); (L.B.-E.)
| | - Laura Baselga-Escudero
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (A.C.); (L.B.-E.)
| | - Josep M Del Bas
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (A.C.); (L.B.-E.)
- Correspondence: (J.M.D.B.); (M.M.)
| | - Miquel Mulero
- Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Nutrigenomics Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Correspondence: (J.M.D.B.); (M.M.)
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16
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Thomas DB, Harmer AMT, Giovanardi S, Holvast EJ, McGoverin CM, Tenenhaus A. Constructing a multiple‐part morphospace using a multiblock method. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Daniel B. Thomas
- School of Natural Sciences Massey University Auckland New Zealand
| | | | | | - Emma J. Holvast
- School of Natural Sciences Massey University Auckland New Zealand
| | - Cushla M. McGoverin
- Department of Physics University of Auckland Auckland New Zealand
- The Dodd‐Walls Centre for Photonic and Quantum Technologies Auckland New Zealand
| | - Arthur Tenenhaus
- Laboratoire des Signaux et Systèmes CentraleSupelec Université Paris‐Saclay Gif‐sur‐Yvette France
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17
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Vrijheid M, Basagaña X, Gonzalez JR, Jaddoe VWV, Jensen G, Keun HC, McEachan RRC, Porcel J, Siroux V, Swertz MA, Thomsen C, Aasvang GM, Andrušaitytė S, Angeli K, Avraam D, Ballester F, Burton P, Bustamante M, Casas M, Chatzi L, Chevrier C, Cingotti N, Conti D, Crépet A, Dadvand P, Duijts L, van Enckevort E, Esplugues A, Fossati S, Garlantezec R, Gómez Roig MD, Grazuleviciene R, Gützkow KB, Guxens M, Haakma S, Hessel EVS, Hoyles L, Hyde E, Klanova J, van Klaveren JD, Kortenkamp A, Le Brusquet L, Leenen I, Lertxundi A, Lertxundi N, Lionis C, Llop S, Lopez-Espinosa MJ, Lyon-Caen S, Maitre L, Mason D, Mathy S, Mazarico E, Nawrot T, Nieuwenhuijsen M, Ortiz R, Pedersen M, Perelló J, Pérez-Cruz M, Philippat C, Piler P, Pizzi C, Quentin J, Richiardi L, Rodriguez A, Roumeliotaki T, Sabin Capote JM, Santiago L, Santos S, Siskos AP, Strandberg-Larsen K, Stratakis N, Sunyer J, Tenenhaus A, Vafeiadi M, Wilson RC, Wright J, Yang T, Slama R. Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview. Environ Epidemiol 2021; 5:e166. [PMID: 34934888 PMCID: PMC8683140 DOI: 10.1097/ee9.0000000000000166] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 06/28/2021] [Indexed: 11/26/2022] Open
Abstract
Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.
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Affiliation(s)
- Martine Vrijheid
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Corresponding Author. Address: ISGlobal, Institute for Global Health, C. Doctor Aiguader 88, 08003 Barcelona, Spain. E-mail: (M. Vrijheid)
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan R. Gonzalez
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Genon Jensen
- Health & Environment Alliance (HEAL), Brussels, Belgium
| | - Hector C. Keun
- Department of Surgery & Cancer and Department of Metabolism, Digestion & Reproduction, Imperial College London, London, United Kingdom
| | - Rosemary R. C. McEachan
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Joana Porcel
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Valerie Siroux
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Morris A. Swertz
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Cathrine Thomsen
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunn Marit Aasvang
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Sandra Andrušaitytė
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Karine Angeli
- French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Risk Assessment Department, Maisons-Alfort, France
| | - Demetris Avraam
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Ferran Ballester
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
- Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
| | - Paul Burton
- Population Health Sciences Institute, Newcastle University, Newcastle, United Kingdom
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Maribel Casas
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Cécile Chevrier
- University Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail)—UMR_S 1085, Rennes, France
| | | | - David Conti
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Amélie Crépet
- French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Risk Assessment Department, Maisons-Alfort, France
| | - Payam Dadvand
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Liesbeth Duijts
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Esther van Enckevort
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Ana Esplugues
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
- Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
| | - Serena Fossati
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Ronan Garlantezec
- CHU de Rennes, University Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail)—UMR_S 1085, Rennes, France
| | - María Dolores Gómez Roig
- Institut de Recerca Sant Joan de Déu (IR-SJD), Barcelona, Spain
- Maternal and Child Health and Development Network II (SAMID II), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- BCNatal—Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Sant Joan de Déu, Barcelona, Spain
| | | | - Kristine B. Gützkow
- Department of Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Mònica Guxens
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Department of Child and Adolescence Psychiatry, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sido Haakma
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Ellen V. S. Hessel
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Lesley Hoyles
- Department of Biosciences, Nottingham Trent University, Nottingham, United Kingdom
| | - Eleanor Hyde
- University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Jana Klanova
- RECETOX Centre, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jacob D. van Klaveren
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Andreas Kortenkamp
- Brunel University London, College of Health, Medicine and Life Sciences, Uxbridge, United Kingdom
| | - Laurent Le Brusquet
- University Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France
| | - Ivonne Leenen
- Health & Environment Alliance (HEAL), Brussels, Belgium
| | - Aitana Lertxundi
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- University of Basque Country UPV/EHU, Basque Country, Bilbao, Spain
- Biodonostia, Research Health Institute, Donostia-San Sebastian, Spain
| | - Nerea Lertxundi
- University of Basque Country UPV/EHU, Basque Country, Bilbao, Spain
- Biodonostia, Research Health Institute, Donostia-San Sebastian, Spain
| | - Christos Lionis
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Sabrina Llop
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
| | - Maria-Jose Lopez-Espinosa
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de València, València, Spain
- Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain
| | - Sarah Lyon-Caen
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Lea Maitre
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Dan Mason
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Sandrine Mathy
- University Grenoble Alpes, CNRS, INRAE, Grenoble INP, GAEL, Grenoble, France
| | - Edurne Mazarico
- Institut de Recerca Sant Joan de Déu (IR-SJD), Barcelona, Spain
- Maternal and Child Health and Development Network II (SAMID II), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- BCNatal—Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Tim Nawrot
- Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium
- Centre for Health and Environment, Leuven University, Leuven, Belgium
| | - Mark Nieuwenhuijsen
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Rodney Ortiz
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Marie Pedersen
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | | | - Míriam Pérez-Cruz
- Institut de Recerca Sant Joan de Déu (IR-SJD), Barcelona, Spain
- Maternal and Child Health and Development Network II (SAMID II), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- BCNatal—Barcelona Center for Maternal Fetal and Neonatal Medicine, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Claire Philippat
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Pavel Piler
- RECETOX Centre, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Costanza Pizzi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | - Joane Quentin
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
| | - Lorenzo Richiardi
- Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy
| | | | - Theano Roumeliotaki
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | | | | | - Susana Santos
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Alexandros P. Siskos
- Department of Surgery & Cancer and Department of Metabolism, Digestion & Reproduction, Imperial College London, London, United Kingdom
| | | | - Nikos Stratakis
- ISGlobal, Barcelona, Spain
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Jordi Sunyer
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Arthur Tenenhaus
- University Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, France
| | - Marina Vafeiadi
- Department of Social Medicine, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Rebecca C. Wilson
- Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Tiffany Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford,United Kingdom
| | - Remy Slama
- University Grenoble Alpes, Inserm, CNRS, IAB (Institute for Advanced Biosciences) Joint Research Center, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Grenoble, France
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18
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Chadaeva I, Ponomarenko P, Kozhemyakina R, Suslov V, Bogomolov A, Klimova N, Shikhevich S, Savinkova L, Oshchepkov D, Kolchanov NA, Markel A, Ponomarenko M. Domestication Explains Two-Thirds of Differential-Gene-Expression Variance between Domestic and Wild Animals; The Remaining One-Third Reflects Intraspecific and Interspecific Variation. Animals (Basel) 2021; 11:2667. [PMID: 34573632 PMCID: PMC8465180 DOI: 10.3390/ani11092667] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/04/2021] [Accepted: 09/06/2021] [Indexed: 12/19/2022] Open
Abstract
Belyaev's concept of destabilizing selection during domestication was a major achievement in the XX century. Its practical value has been realized in commercial colors of the domesticated fox that never occur in the wild and has been confirmed in a wide variety of pet breeds. Many human disease models involving animals allow to test drugs before human testing. Perhaps this is why investigators doing transcriptomic profiling of domestic versus wild animals have searched for breed-specific patterns. Here we sequenced hypothalamic transcriptomes of tame and aggressive rats, identified their differentially expressed genes (DEGs), and, for the first time, applied principal component analysis to compare them with all the known DEGs of domestic versus wild animals that we could find. Two principal components, PC1 and PC2, respectively explained 67% and 33% of differential-gene-expression variance (hereinafter: log2 value) between domestic and wild animals. PC1 corresponded to multiple orthologous DEGs supported by homologs; these DEGs kept the log2 value sign from species to species and from tissue to tissue (i.e., a common domestication pattern). PC2 represented stand-alone homologous DEG pairs reversing the log2 value sign from one species to another and from tissue to tissue (i.e., representing intraspecific and interspecific variation).
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Mikhail Ponomarenko
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia; (I.C.); (P.P.); (R.K.); (V.S.); (A.B.); (N.K.); (S.S.); (L.S.); (D.O.); (N.A.K.); (A.M.)
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19
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Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 2021; 19:3735-3746. [PMID: 34285775 PMCID: PMC8258788 DOI: 10.1016/j.csbj.2021.06.030] [Citation(s) in RCA: 124] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 12/25/2022] Open
Abstract
Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- Corresponding author.
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20
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Guo B, Holscher HD, Auvil LS, Welge ME, Bushell CB, Novotny JA, Baer DJ, Burd NA, Khan NA, Zhu R. Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09310-w] [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]
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21
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Luong NDM, Membré JM, Coroller L, Zagorec M, Poirier S, Chaillou S, Desmonts MH, Werner D, Cariou V, Guillou S. Application of a path-modelling approach for deciphering causality relationships between microbiota, volatile organic compounds and off-odour profiles during meat spoilage. Int J Food Microbiol 2021; 348:109208. [PMID: 33940536 DOI: 10.1016/j.ijfoodmicro.2021.109208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/26/2021] [Accepted: 04/18/2021] [Indexed: 12/01/2022]
Abstract
Microbiological spoilage of meat is considered as a process which involves mainly bacterial metabolism leading to degradation of meat sensory qualities. Studying spoilage requires the collection of different types of experimental data encompassing microbiological, physicochemical and sensorial measurements. Within this framework, the objective herein was to carry out a multiblock path modelling workflow to decipher causality relationships between different types of spoilage-related responses: composition of microbiota, volatilome and off-odour profiles. Analyses were performed with the Path-ComDim approach on a large-scale dataset collected on fresh turkey sausages. This approach enabled to quantify the importance of causality relationships determined a priori between each type of responses as well as to identify important responses involved in spoilage, then to validate causality assumptions. Results were very promising: the data integration confirmed and quantified the causality between data blocks, exhibiting the dynamical nature of spoilage, mainly characterized by the evolution of off-odour profiles caused by the production of volatile organic compounds such as ethanol or ethyl acetate. This production was possibly associated with several bacterial species like Lactococcus piscium, Leuconostoc gelidum, Psychrobacter sp. or Latilactobacillus fuchuensis. Likewise, the production of acetoin and diacetyl in meat spoilage was highlighted. The Path-ComDim approach illustrated here with meat spoilage can be applied to other large-scale and heterogeneous datasets associated with pathway scenarios and represents a promising key tool for deciphering causality in complex biological phenomena.
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Affiliation(s)
| | | | - Louis Coroller
- Univ Brest, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne (LUBEM), UMT Alter'ix, Quimper, France.
| | | | - Simon Poirier
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, F78352 Jouy-en-Josas, France.
| | - Stéphane Chaillou
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, F78352 Jouy-en-Josas, France.
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22
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Multiblock metabolomics: An approach to elucidate whole-body metabolism with multiblock principal component analysis. Comput Struct Biotechnol J 2021; 19:1956-1965. [PMID: 33995897 PMCID: PMC8086023 DOI: 10.1016/j.csbj.2021.04.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/20/2021] [Accepted: 04/04/2021] [Indexed: 12/16/2022] Open
Abstract
“Multiblock metabolomics” elucidates the global metabolic network in a whole body. “Multiblock metabolomics” combines LC/MS-based metabolomics with multiblock PCA. “Multiblock metabolomics” highlights and elicits organ-specific metabolism. TGs with less unsaturated fatty acids were highly accumulated in the diabetic liver.
Principal component analysis (PCA) is a useful tool for omics analysis to identify underlying factors and visualize relationships between biomarkers. However, this approach is limited in addressing life complexity and further improvement is required. This study aimed to develop a new approach that combines mass spectrometry-based metabolomics with multiblock PCA to elucidate the whole-body global metabolic network, thereby generating comparable metabolite maps to clarify the metabolic relationships among several organs. To evaluate the newly developed method, Zucker diabetic fatty (ZDF) rats (n = 6) were used as type 2 diabetic models and Sprague Dawley (SD) rats (n = 6) as controls. Metabolites in the heart, kidney, and liver were analyzed by capillary electrophoresis and liquid chromatography mass spectrometry, respectively, and the detected metabolites were analyzed by multiblock PCA. More than 300 metabolites were detected in the heart, kidney, and liver. When the metabolites obtained from the three organs were analyzed with multiblock PCA, the score and loading maps obtained were highly synchronized and their metabolism patterns were visually comparable. A significant finding in this study was the different expression patterns in lipid metabolism among the three organs; notably triacylglycerols with polyunsaturated fatty acids or less unsaturated fatty acids showed specific accumulation patterns depending on the organs.
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Key Words
- AMP, adenosine monophosphate
- Biomarkers
- CE/MS, capillary electrophoresis mass spectrometry
- CV, coefficient of variation
- ESI, electrospray ionization
- FABP, fatty acid-binding protein
- GC/MS, gas chromatography mass spectrometry
- LC/MS, liquid chromatography mass spectrometry
- Mass spectrometry
- Metabolomics
- Multiblock PCA
- PCA, principal component analysis
- PPAR, peroxisome proliferator-activated receptor
- QC, quality control
- SD, Sprague Dawley
- TCA, tricarboxylic acid. CoA, coenzyme A
- TG, triacylglycerol
- Type 2 Diabetes
- UPLC, ultra-performance liquid chromatography
- ZDF, Zucker diabetic fatty
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23
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Wen Y, Song X, Yan B, Yang X, Wu L, Leng D, He S, Bo X. Multi-dimensional data integration algorithm based on random walk with restart. BMC Bioinformatics 2021; 22:97. [PMID: 33639858 PMCID: PMC7912853 DOI: 10.1186/s12859-021-04029-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 02/15/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The accumulation of various multi-omics data and computational approaches for data integration can accelerate the development of precision medicine. However, the algorithm development for multi-omics data integration remains a pressing challenge. RESULTS Here, we propose a multi-omics data integration algorithm based on random walk with restart (RWR) on multiplex network. We call the resulting methodology Random Walk with Restart for multi-dimensional data Fusion (RWRF). RWRF uses similarity network of samples as the basis for integration. It constructs the similarity network for each data type and then connects corresponding samples of multiple similarity networks to create a multiplex sample network. By applying RWR on the multiplex network, RWRF uses stationary probability distribution to fuse similarity networks. We applied RWRF to The Cancer Genome Atlas (TCGA) data to identify subtypes in different cancer data sets. Three types of data (mRNA expression, DNA methylation, and microRNA expression data) are integrated and network clustering is conducted. Experiment results show that RWRF performs better than single data type analysis and previous integrative methods. CONCLUSIONS RWRF provides powerful support to users to decipher the cancer molecular subtypes, thus may benefit precision treatment of specific patients in clinical practice.
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Affiliation(s)
- Yuqi Wen
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China
| | - Xinyu Song
- Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China
| | - Bowei Yan
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China
| | - Xiaoxi Yang
- Experimental Center, Beijing Friendship Hospital, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Lianlian Wu
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China.,Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China
| | - Dongjin Leng
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China
| | - Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China.
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China.
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24
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Revilla L, Mayorgas A, Corraliza AM, Masamunt MC, Metwaly A, Haller D, Tristán E, Carrasco A, Esteve M, Panés J, Ricart E, Lozano JJ, Salas A. Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis. PLoS One 2021; 16:e0246367. [PMID: 33556098 PMCID: PMC7870068 DOI: 10.1371/journal.pone.0246367] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 01/18/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn's disease. RESULTS We present here a method to identify interactions based on canonical correlation analysis. We show that the model is the most important factor to identify relationships between blocks using a dataset of Crohn's disease patients with longitudinal sampling. First the analysis was tested in two previously published datasets: a glioma and a Crohn's disease and ulcerative colitis dataset where we describe how to select the optimum parameters. Using such parameters, we analyzed our Crohn's disease data set. We selected the model with the highest inner average variance explained to identify relationships between transcriptome, gut microbiome and clinically relevant variables. Adding the clinically relevant variables improved the average variance explained by the model compared to multiple co-inertia analysis. CONCLUSIONS The methodology described herein provides a general framework for identifying interactions between sets of omic data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.
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Affiliation(s)
- Lluís Revilla
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Aida Mayorgas
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Ana M. Corraliza
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Maria C. Masamunt
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Amira Metwaly
- Chair of Nutrition and Immunology, Technical University of Munich, Freising-Weihenstephan, Germany
| | - Dirk Haller
- Chair of Nutrition and Immunology, Technical University of Munich, Freising-Weihenstephan, Germany
- ZIEL Institute for Food and Health, Technical University of Munich, Freising-Weihenstephan, Germany
| | - Eva Tristán
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
- Department of Gastroenterology, Hospital Universitari Mútua Terrassa, Barcelona, Spain
| | - Anna Carrasco
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
- Department of Gastroenterology, Hospital Universitari Mútua Terrassa, Barcelona, Spain
| | - Maria Esteve
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
- Department of Gastroenterology, Hospital Universitari Mútua Terrassa, Barcelona, Spain
| | - Julian Panés
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Elena Ricart
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Juan J. Lozano
- Centro de Investigación Biomédica en Red de Enfermedades Hepática y Digestivas (CIBERehd), Barcelona, Spain
| | - Azucena Salas
- Department of Gastroenterology, IDIBAPS, Hospital Clínic, Barcelona, Spain
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25
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Avants BB, Tustison NJ, Stone JR. Similarity-driven multi-view embeddings from high-dimensional biomedical data. NATURE COMPUTATIONAL SCIENCE 2021; 1:143-152. [PMID: 33796865 PMCID: PMC8009088 DOI: 10.1038/s43588-021-00029-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 01/19/2021] [Indexed: 12/31/2022]
Abstract
Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.
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Affiliation(s)
- Brian B Avants
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA
| | - James R Stone
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA
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26
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Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat Commun 2021; 12:124. [PMID: 33402734 PMCID: PMC7785750 DOI: 10.1038/s41467-020-20430-7] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 12/02/2020] [Indexed: 01/08/2023] Open
Abstract
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook—multi-omics mix (momix)—to foster reproducibility, and support users and future developers. Advances in omics technology have resulted in the generation of multi-view data for cancer samples. Here, the authors compare dimensionality reduction techniques using simulated and TCGA data and identify the features of the methods with superior performance.
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27
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Hwang H, Cho G. Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling. PSYCHOMETRIKA 2020; 85:947-972. [PMID: 33346884 DOI: 10.1007/s11336-020-09733-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/07/2020] [Accepted: 11/17/2020] [Indexed: 06/12/2023]
Abstract
Partial least squares path modeling has been widely used for component-based structural equation modeling, where constructs are represented by weighted composites or components of observed variables. This approach remains a limited-information method that carries out two separate stages sequentially to estimate parameters (component weights, loadings, and path coefficients), indicating that it has no single optimization criterion for estimating the parameters at once. In general, limited-information methods are known to provide less efficient parameter estimates than full-information ones. To address this enduring issue, we propose a full-information method for partial least squares path modeling, termed global least squares path modeling, where a single least squares criterion is consistently minimized via a simple iterative algorithm to estimate all the parameters simultaneously. We evaluate the relative performance of the proposed method through the analyses of simulated and real data. We also show that from algorithmic perspectives, the proposed method can be seen as a block-wise special case of another full-information method for component-based structural equation modeling-generalized structured component analysis.
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Affiliation(s)
- Heungsun Hwang
- Department of Psychology, McGill University, 2001 McGill College Avenue, Montreal, QC H3A 1G1, Canada.
| | - Gyeongcheol Cho
- Department of Psychology, McGill University, 2001 McGill College Avenue, Montreal, QC H3A 1G1, Canada
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28
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Wozniak JM, Mills RH, Olson J, Caldera JR, Sepich-Poore GD, Carrillo-Terrazas M, Tsai CM, Vargas F, Knight R, Dorrestein PC, Liu GY, Nizet V, Sakoulas G, Rose W, Gonzalez DJ. Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Signatures. Cell 2020; 182:1311-1327.e14. [PMID: 32888495 PMCID: PMC7494005 DOI: 10.1016/j.cell.2020.07.040] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/11/2020] [Accepted: 07/29/2020] [Indexed: 12/15/2022]
Abstract
Staphylococcus aureus bacteremia (SaB) causes significant disease in humans, carrying mortality rates of ∼25%. The ability to rapidly predict SaB patient responses and guide personalized treatment regimens could reduce mortality. Here, we present a resource of SaB prognostic biomarkers. Integrating proteomic and metabolomic techniques enabled the identification of >10,000 features from >200 serum samples collected upon clinical presentation. We interrogated the complexity of serum using multiple computational strategies, which provided a comprehensive view of the early host response to infection. Our biomarkers exceed the predictive capabilities of those previously reported, particularly when used in combination. Last, we validated the biological contribution of mortality-associated pathways using a murine model of SaB. Our findings represent a starting point for the development of a prognostic test for identifying high-risk patients at a time early enough to trigger intensive monitoring and interventions.
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Affiliation(s)
- Jacob M Wozniak
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert H Mills
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Joshua Olson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - J R Caldera
- Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Gregory D Sepich-Poore
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Marvic Carrillo-Terrazas
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chih-Ming Tsai
- Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Fernando Vargas
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Rob Knight
- Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - George Y Liu
- Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Victor Nizet
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - George Sakoulas
- Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Warren Rose
- School of Pharmacy, School of Medicine and Public Health University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Medicine, School of Medicine and Public Health University of Wisconsin-Madison, Madison, WI 53705, USA
| | - David J Gonzalez
- Department of Pharmacology, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative to Halt Antibiotic-Resistant Microbes, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA.
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Cantini L, Kairov U, de Reyniès A, Barillot E, Radvanyi F, Zinovyev A. Assessing reproducibility of matrix factorization methods in independent transcriptomes. Bioinformatics 2020; 35:4307-4313. [PMID: 30938767 PMCID: PMC6821374 DOI: 10.1093/bioinformatics/btz225] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 03/20/2019] [Accepted: 04/01/2019] [Indexed: 12/26/2022] Open
Abstract
Motivation Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others. Results We systematically test widely used MF methods on several transcriptomic datasets collected from the same cancer type (14 colorectal, 8 breast and 4 ovarian cancer transcriptomic datasets). Inspired by concepts of evolutionary bioinformatics, we design a novel framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the MF methods for their ability to produce generalizable components. We show that a particular protocol of application of independent component analysis (ICA), accompanied by a stabilization procedure, leads to a significant increase in the between-datasets reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other standard methods. We developed a user-friendly tool for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors associated to biological processes or to technological artifacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping. Availability and implementation The RBH construction tool is available from http://goo.gl/DzpwYp Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Laura Cantini
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM U900, F-75005 Paris, France.,CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, F-75006 Paris, France.,Computational Systems Biology Team, Institut de Biologie de l'École Normale Supérieure, CNRS UMR8197, INSERM U1024, École Normale Supérieure, PSL Research University, Paris, France
| | - Ulykbek Kairov
- Laboratory of Bioinformatics and Systems Biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
| | - Aurélien de Reyniès
- Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM U900, F-75005 Paris, France.,CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, F-75006 Paris, France
| | - François Radvanyi
- Institut Curie, PSL Research University, CNRS, UMR144, Equipe Labellisée Ligue Contre le Cancer, Paris, France.,Sorbonne Universités, UPMC Université Paris 06, CNRS, UMR144, Paris
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005 Paris, France.,INSERM U900, F-75005 Paris, France.,CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, F-75006 Paris, France.,Lobachevsky University, Nizhny Novgorod, Russia
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30
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Leung MHY, Tong X, Bastien P, Guinot F, Tenenhaus A, Appenzeller BMR, Betts RJ, Mezzache S, Li J, Bourokba N, Breton L, Clavaud C, Lee PKH. Changes of the human skin microbiota upon chronic exposure to polycyclic aromatic hydrocarbon pollutants. MICROBIOME 2020; 8:100. [PMID: 32591010 PMCID: PMC7320578 DOI: 10.1186/s40168-020-00874-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 05/20/2020] [Indexed: 05/25/2023]
Abstract
BACKGROUND Polycyclic aromatic hydrocarbons (PAHs) are of environmental and public health concerns and contribute to adverse skin attributes such as premature skin aging and pigmentary disorder. However, little information is available on the potential roles of chronic urban PAH pollutant exposure on the cutaneous microbiota. Given the roles of the skin microbiota have on healthy and undesirable skin phenotypes and the relationships between PAHs and skin properties, we hypothesize that exposure of PAHs may be associated with changes in the cutaneous microbiota. In this study, the skin microbiota of over two hundred Chinese individuals from two cities in China with varying exposure levels of PAHs were characterized by bacterial and fungal amplicon and shotgun metagenomics sequencing. RESULTS Skin site and city were strong parameters in changing microbial communities and their assembly processes. Reductions of bacterial-fungal microbial network structural integrity and stability were associated with skin conditions (acne and dandruff). Multivariate analysis revealed associations between abundances of Propionibacterium and Malassezia with host properties and pollutant exposure levels. Shannon diversity increase was correlated to exposure levels of PAHs in a dose-dependent manner. Shotgun metagenomics analysis of samples (n = 32) from individuals of the lowest and highest exposure levels of PAHs further highlighted associations between the PAHs quantified and decrease in abundances of skin commensals and increase in oral bacteria. Functional analysis identified associations between levels of PAHs and abundance of microbial genes of metabolic and other pathways with potential importance in host-microbe interactions as well as degradation of aromatic compounds. CONCLUSIONS The results in this study demonstrated the changes in composition and functional capacities of the cutaneous microbiota associated with chronic exposure levels of PAHs. Findings from this study will aid the development of strategies to harness the microbiota in protecting the skin against pollutants. Video Abstract.
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Affiliation(s)
- Marcus H. Y. Leung
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | - Xinzhao Tong
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | | | - Florent Guinot
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Arthur Tenenhaus
- CentraleSupelec-L2S-Laboratoire des signaux et systèmes, Brain and Spine Institute, Université Paris-Sud, Orsay, France
| | | | | | | | - Jing Li
- L’Oréal Research and Innovation, Pudong, China
| | | | - Lionel Breton
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Cécile Clavaud
- L’Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Patrick K. H. Lee
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
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31
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Gloaguen A, Philippe C, Frouin V, Gennari G, Dehaene-Lambertz G, Le Brusquet L, Tenenhaus A. Multiway generalized canonical correlation analysis. Biostatistics 2020; 23:240-256. [PMID: 32451525 DOI: 10.1093/biostatistics/kxaa010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/07/2020] [Accepted: 01/20/2020] [Indexed: 11/13/2022] Open
Abstract
Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).
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Affiliation(s)
- Arnaud Gloaguen
- Laboratoire des Signaux et Systèmes (L2S), CNRS-CentraleSupélec, Université Paris-Saclay, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette cedex, France and Université Paris-Saclay, CEA, Neurospin, 91191, Gif-sur-Yvette, France
| | - Cathy Philippe
- Université Paris-Saclay, CEA, Neurospin, 91191, Gif-sur-Yvette, France
| | - Vincent Frouin
- Université Paris-Saclay, CEA, Neurospin, 91191, Gif-sur-Yvette, France
| | - Giulia Gennari
- Cognitive Neuroimaging Unit, CEA, INSERM U992, NeuroSpin Center, 91191 Gif-sur-Yvette, France
| | | | - Laurent Le Brusquet
- Laboratoire des Signaux et Systèmes (L2S), CNRS-CentraleSupélec, Université Paris-Saclay, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette cedex, France
| | - Arthur Tenenhaus
- Laboratoire des Signaux et Systèmes (L2S), CNRS-CentraleSupélec, Université Paris-Saclay, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette cedex, France and Institut du Cerveau, INSERM U1127, CNRS UMR 7225, Sorbonne Universitè, F-75013, Paris, France
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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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Min EJ, Long Q. Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data. BMC Bioinformatics 2020; 21:141. [PMID: 32293260 PMCID: PMC7157996 DOI: 10.1186/s12859-020-3455-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 03/13/2020] [Indexed: 01/28/2023] Open
Abstract
Background Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. Results Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease. Conclusion Proposed sparse mCIA achieves simultaneous model estimation and feature selection and yields analysis results that are more interpretable than the existing mCIA. Furthermore, proposed structured sparse mCIA can effectively incorporate prior network information among genes, resulting in improved feature selection and enhanced interpretability.
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Affiliation(s)
- Eun Jeong Min
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, USA.
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Fan Z, Zhou Y, Ressom HW. MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery. Metabolites 2020; 10:metabo10040144. [PMID: 32276350 PMCID: PMC7241240 DOI: 10.3390/metabo10040144] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/26/2020] [Accepted: 04/05/2020] [Indexed: 01/03/2023] Open
Abstract
The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.
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Affiliation(s)
- Ziling Fan
- Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC 20057, USA;
| | - Yuan Zhou
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA;
| | - Habtom W. Ressom
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA;
- Correspondence:
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35
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Varghese RS, Zhou Y, Barefoot M, Chen Y, Di Poto C, Balla AK, Oliver E, Sherif ZA, Kumar D, Kroemer AH, Tadesse MG, Ressom HW. Identification of miRNA-mRNA associations in hepatocellular carcinoma using hierarchical integrative model. BMC Med Genomics 2020; 13:56. [PMID: 32228601 PMCID: PMC7106691 DOI: 10.1186/s12920-020-0706-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 03/19/2020] [Indexed: 02/07/2023] Open
Abstract
Background The established role miRNA-mRNA regulation of gene expression has in oncogenesis highlights the importance of integrating miRNA with downstream mRNA targets. These findings call for investigations aimed at identifying disease-associated miRNA-mRNA pairs. Hierarchical integrative models (HIM) offer the opportunity to uncover the relationships between disease and the levels of different molecules measured in multiple omic studies. Methods The HIM model we formulated for analysis of mRNA-seq and miRNA-seq data can be specified with two levels: (1) a mechanistic submodel relating mRNAs to miRNAs, and (2) a clinical submodel relating disease status to mRNA and miRNA, while accounting for the mechanistic relationships in the first level. Results mRNA-seq and miRNA-seq data were acquired by analysis of tumor and normal liver tissues from 30 patients with hepatocellular carcinoma (HCC). We analyzed the data using HIM and identified 157 significant miRNA-mRNA pairs in HCC. The majority of these molecules have already been independently identified as being either diagnostic, prognostic, or therapeutic biomarker candidates for HCC. These pairs appear to be involved in processes contributing to the pathogenesis of HCC involving inflammation, regulation of cell cycle, apoptosis, and metabolism. For further evaluation of our method, we analyzed miRNA-seq and mRNA-seq data from TCGA network. While some of the miRNA-mRNA pairs we identified by analyzing both our and TCGA data are previously reported in the literature and overlap in regulation and function, new pairs have been identified that may contribute to the discovery of novel targets. Conclusion The results strongly support the hypothesis that miRNAs are important regulators of mRNAs in HCC. Furthermore, these results emphasize the biological relevance of studying miRNA-mRNA pairs.
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Affiliation(s)
- Rency S Varghese
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Yuan Zhou
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Megan Barefoot
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Yifan Chen
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Cristina Di Poto
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA
| | | | - Everett Oliver
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Zaki A Sherif
- Department of Biochemistry & Molecular Biology, College of Medicine, Howard University, Washington DC, USA
| | - Deepak Kumar
- Department of Pharmaceutical Sciences, North Carolina Central University, Durham, NC, USA
| | | | - Mahlet G Tadesse
- Department of Mathematics and Statistics, Georgetown University, Washington DC, USA
| | - Habtom W Ressom
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Room 175, Building D, 4000 Reservoir Rd NW, Washington, DC, 20057, USA.
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Min EJ, Safo SE, Long Q. Penalized co-inertia analysis with applications to -omics data. Bioinformatics 2019; 35:1018-1025. [PMID: 30165424 DOI: 10.1093/bioinformatics/bty726] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 04/01/2018] [Accepted: 08/23/2018] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Co-inertia analysis (CIA) is a multivariate statistical analysis method that can assess relationships and trends in two sets of data. Recently CIA has been used for an integrative analysis of multiple high-dimensional omics data. However, for classical CIA, all elements in the loading vectors are nonzero, presenting a challenge for the interpretation when analyzing omics data. For other multivariate statistical methods such as canonical correlation analysis (CCA), penalized least squares (PLS), various approaches have been proposed to produce sparse loading vectors via l1-penalization/constraint. We propose a novel CIA method that uses l1-penalization to induce sparsity in estimators of loading vectors. Our method simultaneously conducts model fitting and variable selection. Also, we propose another CIA method that incorporates structure/network information such as those from functional genomics, besides using sparsity penalty so that one can get biologically meaningful and interpretable results. RESULTS Extensive simulations demonstrate that our proposed penalized CIA methods achieve the best or close to the best performance compared to the existing CIA method in terms of feature selection and recovery of true loading vectors. Also, we apply our methods to the integrative analysis of gene expression data and protein abundance data from the NCI-60 cancer cell lines. Our analysis of the NCI-60 cancer cell line data reveals meaningful variables for cancer diseases and biologically meaningful results that are consistent with previous studies. AVAILABILITY AND IMPLEMENTATION Our algorithms are implemented as an R package which is freely available at: https://www.med.upenn.edu/long-lab/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eun Jeong Min
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandra E Safo
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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Newton RU, Christophersen CT, Fairman CM, Hart NH, Taaffe DR, Broadhurst D, Devine A, Chee R, Tang CI, Spry N, Galvão DA. Does exercise impact gut microbiota composition in men receiving androgen deprivation therapy for prostate cancer? A single-blinded, two-armed, randomised controlled trial. BMJ Open 2019; 9:e024872. [PMID: 30987986 PMCID: PMC6500366 DOI: 10.1136/bmjopen-2018-024872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
INTRODUCTION A potential link exists between prostate cancer (PCa) disease and treatment and increased inflammatory levels from gut dysbiosis. This study aims to examine if exercise favourably alters gut microbiota in men receiving androgen deprivation therapy (ADT) for PCa. Specifically, this study will explore whether: (1) exercise improves the composition of gut microbiota and increases the abundance of bacteria associated with health promotion and (2) whether gut health correlates with favourable inflammatory status, bowel function, continence and nausea among patients participating in the exercise intervention. METHODS AND ANALYSIS A single-blinded, two-armed, randomised controlled trial will explore the influence of a 3-month exercise programme (3 days/week) for men with high-risk localised PCa receiving ADT. Sixty patients will be randomly assigned to either exercise intervention or usual care. The primary endpoint (gut health and function assessed via feacal samples) and secondary endpoints (self-reported quality of life via standardised questionnaires, blood biomarkers, body composition and physical fitness) will be measured at baseline and following the intervention. A variety of statistical methods will be used to understand the covariance between microbial diversity and metabolomics profile across time and intervention. An intention-to-treat approach will be utilised for the analyses with multiple imputations followed by a secondary sensitivity analysis to ensure data robustness using a complete cases approach. ETHICS AND DISSEMINATION Ethics approval was obtained from the Human Research Ethics Committee of Edith Cowan University (ID: 19827 NEWTON). Findings will be reported in peer-reviewed publications and scientific conferences in addition to working with national support groups to translate findings for the broader community. If exercise is shown to result in favourable changes in gut microbial diversity, composition and metabolic profile, and reduce gastrointestinal complications in PCa patients receiving ADT, this study will form the basis of a future phase III trial. TRIAL REGISTRATION NUMBER ANZCTR12618000280202.
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Affiliation(s)
- Robert U Newton
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Claus T Christophersen
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- School of Molecular and Life Science, Curtin University - Perth City Campus, Perth, Western Australia, Australia
| | - Ciaran M Fairman
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Nicolas H Hart
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Institute for Health Research, University of Notre Dame Australia, Perth, Western Australia, Australia
| | - Dennis R Taaffe
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Queensland, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - David Broadhurst
- School of Science, Edith Cowan University, Perth, Western Australia, Australia
- Centre for Integrative Metabolomics and Computational Biology, Edith Cowan University, Perth, Western Australia, Australia
| | - Amanda Devine
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Centre for Integrative Metabolomics and Computational Biology, Edith Cowan University, Perth, Western Australia, Australia
| | - Raphael Chee
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Department of Radiation Oncology, Genesis Cancer Care, Perth, Western Australia, Australia
| | - Colin I Tang
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Nigel Spry
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
- Department of Radiation Oncology, Genesis Cancer Care, Perth, Western Australia, Australia
| | - Daniel A Galvão
- Exercise Medicine Research Institute, Edith Cowan University, Perth, Western Australia, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
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Smolinska A, Engel J, Szymanska E, Buydens L, Blanchet L. General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences. DATA HANDLING IN SCIENCE AND TECHNOLOGY 2019. [DOI: 10.1016/b978-0-444-63984-4.00003-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Jun I, Choi W, Park M. Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis. Genomics Inform 2018; 16:e33. [PMID: 30602094 PMCID: PMC6440675 DOI: 10.5808/gi.2018.16.4.e33] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 12/26/2018] [Indexed: 11/20/2022] Open
Abstract
Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.
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Affiliation(s)
- Inyoung Jun
- Department of Statistics, Korea University, Seoul 02841, Korea
| | | | - Mira Park
- Department of Preventive Medicine, Eulji University School of Medicine, Daejeon 34824, Korea
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Vilor-Tejedor N, Alemany S, Cáceres A, Bustamante M, Pujol J, Sunyer J, González JR. Strategies for integrated analysis in imaging genetics studies. Neurosci Biobehav Rev 2018; 93:57-70. [PMID: 29944960 DOI: 10.1016/j.neubiorev.2018.06.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 04/30/2018] [Accepted: 06/15/2018] [Indexed: 02/06/2023]
Abstract
Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.
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Affiliation(s)
- Natàlia Vilor-Tejedor
- Barcelona Research Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Barcelona Beta Brain Research Center (BBRC) - Pasqual Maragall Foundation, Barcelona, Spain.
| | - Silvia Alemany
- Barcelona Research Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Alejandro Cáceres
- Barcelona Research Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Mariona Bustamante
- Barcelona Research Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jesús Pujol
- MRI Research Unit, Hospital del Mar, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM G21, Barcelona, Spain
| | - Jordi Sunyer
- Barcelona Research Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Juan R González
- Barcelona Research Institute for Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
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Garali I, Adanyeguh IM, Ichou F, Perlbarg V, Seyer A, Colsch B, Moszer I, Guillemot V, Durr A, Mochel F, Tenenhaus A. A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia. Brief Bioinform 2017; 19:1356-1369. [DOI: 10.1093/bib/bbx060] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Indexed: 11/14/2022] Open
Affiliation(s)
- Imene Garali
- Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France
| | | | - Farid Ichou
- ICANalytics department, institute of cardiometabolism and nutrition, Paris, France
| | - Vincent Perlbarg
- Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France
| | - Alexandre Seyer
- SpectMet platform of the MedDay Pharmaceuticals company, Paris, France
| | | | - Ivan Moszer
- Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France
| | - Vincent Guillemot
- Institut Pasteur, Statistical Genetics group, Bioinformatics/Biostatistics Core Facility
| | | | - Fanny Mochel
- University Pierre and Marie Curie (UPMC) and the Pitié-Salpêtriére University Hospital
| | - Arthur Tenenhaus
- Bioinformatics and Biostatistics Core Facility of the Brain and Spine Institute, La Pitié-Salpêtriére Hospital, Paris, France
- L2S Laboratory at CentraleSupélec, France
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