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Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, Luo Z, Qing T, Qiu C, Zhao LJ, Liu A, Jiang L, Zhang X, Shen H, Zhou W, Deng HW. Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data. Comput Biol Med 2024; 179:108813. [PMID: 38955127 PMCID: PMC11324385 DOI: 10.1016/j.compbiomed.2024.108813] [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: 03/13/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.
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Affiliation(s)
- Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA, 30060, USA
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chong Wu
- Department of Biostatistics, University of Texas MD Anderson, Pickens Academic Tower, 1400 Pressler St., Houston, TX, 77030, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Wu Li
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Tian Qing
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Lan Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Anqi Liu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Lindong Jiang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Xiao Zhang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, 49931, USA.
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
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Zhao C, Liu A, Zhang X, Cao X, Ding Z, Sha Q, Shen H, Deng HW, Zhou W. CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data. Comput Biol Med 2024; 170:108058. [PMID: 38295477 PMCID: PMC10959569 DOI: 10.1016/j.compbiomed.2024.108058] [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: 08/23/2023] [Revised: 12/30/2023] [Accepted: 01/26/2024] [Indexed: 02/02/2024]
Abstract
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data.
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Affiliation(s)
- Chen Zhao
- Department of Computer Science, Kennesaw State University, Marietta, GA, 30060, USA
| | - Anqi Liu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Xiao Zhang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Zhengming Ding
- Department of Computer Science, Tulane University, New Orleans, LA, 70118, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA.
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, 49931, USA.
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3
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Zhao C, Keyak JH, Cao X, Sha Q, Wu L, Luo Z, Zhao LJ, Tian Q, Serou M, Qiu C, Su KJ, Shen H, Deng HW, Zhou W. Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load. Front Endocrinol (Lausanne) 2023; 14:1261088. [PMID: 38075049 PMCID: PMC10710145 DOI: 10.3389/fendo.2023.1261088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023] Open
Abstract
Background Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.
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Affiliation(s)
- Chen Zhao
- Department of Applied Computing, Michigan Technological University, Houghton, MI, United States
| | - Joyce H. Keyak
- Department of Radiological Sciences, Department of Biomedical Engineering, Department of Mechanical and Aerospace Engineering, and Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, United States
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
| | - Li Wu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Lan-Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Qing Tian
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Michael Serou
- Department of Radiology, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, United States
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, United States
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, Houghton, MI, United States
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, United States
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4
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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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5
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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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6
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Zheng T, Ge H, Li J, Wang L. Unsupervised multi-view representation learning with proximity guided representation and generalized canonical correlation analysis. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01821-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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7
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Dahiya N, Fan Y, Bignardi S, Sandhu R, Yezzi A. Dependently Coupled Principal Component Analysis for Bivariate Inversion Problems. PROCEEDINGS OF THE ... IAPR INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION. INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION 2021; 2020:10.1109/icpr48806.2021.9413305. [PMID: 34350427 PMCID: PMC8330695 DOI: 10.1109/icpr48806.2021.9413305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction in various problem domains, including data compression, image processing, visualization, exploratory data analysis, pattern recognition, time-series prediction, and machine learning. Often, data is presented in a correlated paired manner such that there exist observable and correlated unobservable measurements. Unfortunately, traditional PCA techniques generally fail to optimally capture the leverageable correlations between such paired data as it does not yield a maximally correlated basis between the observable and unobservable counterparts. This instead is the objective of Canonical Correlation Analysis (and the more general Partial Least Squares methods); however, such techniques are still symmetric in maximizing correlation (covariance for PLSR) over all choices of the basis for both datasets without differentiating between observable and unobservable variables (except for the regression phase of PLSR). Further, these methods deviate from PCA's formulation objective to minimize approximation error, seeking instead to maximize correlation or covariance. While these are sensible optimization objectives, they are not equivalent to error minimization. We therefore introduce a new method of leveraging PCA between paired datasets in a dependently coupled manner, which is optimal with respect to approximation error during training. We generate a dependently coupled paired basis for which we relax orthogonality constraints in decomposing unreliable unobservable measurements. In doing so, this allows us to optimally capture the variations of the observable data while conditionally minimizing the expected prediction error for the unobservable component. We show preliminary results that demonstrate improved learning of our proposed method compared to that of traditional techniques.
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Affiliation(s)
- Navdeep Dahiya
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA
| | - Yifei Fan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA
| | - Samuel Bignardi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA
| | - Romeil Sandhu
- Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA
| | - Anthony Yezzi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA
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8
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Ben-Othman R, Cai B, Liu AC, Varankovich N, He D, Blimkie TM, Lee AH, Gill EE, Novotny M, Aevermann B, Drissler S, Shannon CP, McCann S, Marty K, Bjornson G, Edgar RD, Lin DTS, Gladish N, Maclsaac J, Amenyogbe N, Chan Q, Llibre A, Collin J, Landais E, Le K, Reiss SM, Koff WC, Havenar-Daughton C, Heran M, Sangha B, Walt D, Krajden M, Crotty S, Sok D, Briney B, Burton DR, Duffy D, Foster LJ, Mohn WW, Kobor MS, Tebbutt SJ, Brinkman RR, Scheuermann RH, Hancock REW, Kollmann TR, Sadarangani M. Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults. Front Immunol 2020; 11:580373. [PMID: 33250895 PMCID: PMC7672042 DOI: 10.3389/fimmu.2020.580373] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 09/24/2020] [Indexed: 12/26/2022] Open
Abstract
Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches-NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.
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Affiliation(s)
- Rym Ben-Othman
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada.,Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
| | - Bing Cai
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Aaron C Liu
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Natallia Varankovich
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Daniel He
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Travis M Blimkie
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Amy H Lee
- Simon Fraser University, Burnaby, BC, Canada
| | - Erin E Gill
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Mark Novotny
- Department of Informatics, J. Craig Venter Institute (La Jolla), La Jolla, CA, United States
| | - Brian Aevermann
- Department of Informatics, J. Craig Venter Institute (La Jolla), La Jolla, CA, United States
| | | | - Casey P Shannon
- Prevention of Organ Failure (PROOF) Centre of Excellence and Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, BC, Canada
| | - Sarah McCann
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Kim Marty
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Gordean Bjornson
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Rachel D Edgar
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - David Tse Shen Lin
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Nicole Gladish
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Julia Maclsaac
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Nelly Amenyogbe
- Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
| | - Queenie Chan
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alba Llibre
- Translational Immunology Lab, Institut Pasteur, Paris, France
| | - Joyce Collin
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Elise Landais
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA, United States
| | - Khoa Le
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA, United States
| | - Samantha M Reiss
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Wayne C Koff
- Human Vaccines Project, New York, NY, United States
| | - Colin Havenar-Daughton
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Manraj Heran
- Department of Radiology, BC Children's Hospital, Vancouver, BC, Canada
| | - Bippan Sangha
- Department of Radiology, BC Children's Hospital, Vancouver, BC, Canada
| | - David Walt
- Wyss Institute at Harvard University, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Mel Krajden
- British Columbia Centre for Disease Control, Vancouver, BC, Canada
| | - Shane Crotty
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Devin Sok
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States.,IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA, United States
| | - Bryan Briney
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Dennis R Burton
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States
| | - Darragh Duffy
- Translational Immunology Lab, Institut Pasteur, Paris, France
| | - Leonard J Foster
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - William W Mohn
- Department of Microbiology and Immunology, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Michael S Kobor
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Scott J Tebbutt
- Prevention of Organ Failure (PROOF) Centre of Excellence and Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, BC, Canada.,Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ryan R Brinkman
- Terry Fox Laboratory, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute (La Jolla), La Jolla, CA, United States.,Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, United States
| | - Robert E W Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, Canada
| | - Tobias R Kollmann
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada.,Telethon Kids Institute, University of Western Australia, Nedlands, WA, Australia
| | - Manish Sadarangani
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada
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9
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Bae E, Hur JW, Kim J, Kwon JS, Lee J, Lee SH, Lim CY. Multi-group analysis using generalized additive kernel canonical correlation analysis. Sci Rep 2020; 10:12624. [PMID: 32724222 PMCID: PMC7387565 DOI: 10.1038/s41598-020-69575-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 07/13/2020] [Indexed: 11/09/2022] Open
Abstract
Multivariate analysis has been widely used and one of the popular multivariate analysis methods is canonical correlation analysis (CCA). CCA finds the linear combination in each group that maximizes the Pearson correlation. CCA has been extended to a kernel CCA for nonlinear relationships and generalized CCA that can consider more than two groups. We propose an extension of CCA that allows multi-group and nonlinear relationships in an additive fashion for a better interpretation, which we termed as Generalized Additive Kernel Canonical Correlation Analysis (GAKCCA). In addition to exploring multi-group relationship with nonlinear extension, GAKCCA can reveal contribution of variables in each group; which enables in-depth structural analysis. A simulation study shows that GAKCCA can distinguish a relationship between groups and whether they are correlated or not. We applied GAKCCA to real data on neurodevelopmental status, psychosocial factors, clinical problems as well as neurophysiological measures of individuals. As a result, it is shown that the neurophysiological domain has a statistically significant relationship with the neurodevelopmental domain and clinical domain, respectively, which was not revealed in the ordinary CCA.
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Affiliation(s)
- Eunseong Bae
- Department of Statistics, University of California, Davis, CA, USA
| | - Ji-Won Hur
- Department of Psychology, Korea University, Seoul, Korea
| | - Jinyoung Kim
- Department of Brain & Cognitive Sciences, Seoul National University, Seoul, Korea
| | - Jun Soo Kwon
- Department of Brain & Cognitive Sciences, Seoul National University, Seoul, Korea
- Department of Psychiatry, Seoul National University, Seoul, Korea
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Sang-Hun Lee
- Department of Brain & Cognitive Sciences, Seoul National University, Seoul, Korea
| | - Chae Young Lim
- Department of Statistics, Seoul National University, Seoul, Korea.
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10
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de Cheveigné A, Di Liberto GM, Arzounian D, Wong DDE, Hjortkjær J, Fuglsang S, Parra LC. Multiway canonical correlation analysis of brain data. Neuroimage 2018; 186:728-740. [PMID: 30496819 DOI: 10.1016/j.neuroimage.2018.11.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 10/11/2018] [Accepted: 11/16/2018] [Indexed: 01/12/2023] Open
Abstract
Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.
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Affiliation(s)
- Alain de Cheveigné
- Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France; UCL Ear Institute, London, United Kingdom.
| | - Giovanni M Di Liberto
- Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - Dorothée Arzounian
- Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - Daniel D E Wong
- Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - Jens Hjortkjær
- Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
| | - Søren Fuglsang
- Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Denmark
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11
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Lorenzon R, Mariotti-Ferrandiz E, Aheng C, Ribet C, Toumi F, Pitoiset F, Chaara W, Derian N, Johanet C, Drakos I, Harris S, Amselem S, Berenbaum F, Benveniste O, Bodaghi B, Cacoub P, Grateau G, Amouyal C, Hartemann A, Saadoun D, Sellam J, Seksik P, Sokol H, Salem JE, Vicaut E, Six A, Rosenzwajg M, Bernard C, Klatzmann D. Clinical and multi-omics cross-phenotyping of patients with autoimmune and autoinflammatory diseases: the observational TRANSIMMUNOM protocol. BMJ Open 2018; 8:e021037. [PMID: 30166293 PMCID: PMC6119447 DOI: 10.1136/bmjopen-2017-021037] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 07/02/2018] [Accepted: 07/17/2018] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Autoimmune and autoinflammatory diseases (AIDs) represent a socioeconomic burden as the second cause of chronic illness in Western countries. In this context, the TRANSIMMUNOM clinical protocol is designed to revisit the nosology of AIDs by combining basic, clinical and information sciences. Based on classical and systems biology analyses, it aims to uncover important phenotypes that cut across diagnostic groups so as to discover biomarkers and identify novel therapeutic targets. METHODS AND ANALYSIS TRANSIMMUNOM is an observational clinical protocol that aims to cross-phenotype a set of 19 AIDs, six related control diseases and healthy volunteers . We assembled a multidisciplinary cohort management team tasked with (1) selecting informative biological (routine and omics type) and clinical parameters to be captured, (2) standardising the sample collection and shipment circuit, (3) selecting omics technologies and benchmarking omics data providers, (4) designing and implementing a multidisease electronic case report form and an omics database and (5) implementing supervised and unsupervised data analyses. ETHICS AND DISSEMINATION The study was approved by the institutional review board of Pitié-Salpêtrière Hospital (ethics committee Ile-De-France 48-15) and done in accordance with the Declaration of Helsinki and good clinical practice. Written informed consent is obtained from all participants before enrolment in the study. TRANSIMMUNOM's project website provides information about the protocol (https://www.transimmunom.fr/en/) including experimental set-up and tool developments. Results will be disseminated during annual scientific committees appraising the project progresses and at national and international scientific conferences. DISCUSSION Systems biology approaches are increasingly implemented in human pathophysiology research. The TRANSIMMUNOM study applies such approach to the pathophysiology of AIDs. We believe that this translational systems immunology approach has the potential to provide breakthrough discoveries for better understanding and treatment of AIDs. TRIAL REGISTRATION NUMBER NCT02466217; Pre-results.
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Affiliation(s)
- Roberta Lorenzon
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | | | - Caroline Aheng
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Claire Ribet
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Ferial Toumi
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Fabien Pitoiset
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Wahiba Chaara
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Nicolas Derian
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Catherine Johanet
- Immunology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UFR 967, Sorbonne Université, Paris, France
| | - Iannis Drakos
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Sophie Harris
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Serge Amselem
- Laboratoire de génétique, UMR S933, Sorbonne Université, INSERM, Paris, France
| | - Francis Berenbaum
- Rheumatology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S938, Sorbonne Université, INSERM, Paris, France
| | - Olivier Benveniste
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR 974, Sorbonne Université, Paris, France
| | - Bahram Bodaghi
- Département Hospitalo-Universitaire Vision and Handicaps ‘ViewMaintain’, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - Patrice Cacoub
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Gilles Grateau
- Internal Medicine Department, Tenon Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S933, Sorbonne Université, INSERM, Paris, France
| | - Chloe Amouyal
- Diabetology Department, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - Agnes Hartemann
- Diabetology Department, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - David Saadoun
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Jeremie Sellam
- Rheumatology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S938, Sorbonne Université, INSERM, Paris, France
| | - Philippe Seksik
- Gastroenterology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- GRC-UPMC 03, Sorbonne Université, Paris, France
| | - Harry Sokol
- Gastroenterology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- GRC-UPMC 03, Sorbonne Université, Paris, France
| | - Joe-Elie Salem
- CIC-1421, Pharmacology Department, INSERM, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Eric Vicaut
- Unité de recherche clinique, UMR 942, Saint-Louis Lariboisière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Adrien Six
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michelle Rosenzwajg
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Claude Bernard
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - David Klatzmann
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
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12
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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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13
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Mishra S, Mishra D. Enhanced gene ranking approaches using modified trace ratio algorithm for gene expression data. INFORMATICS IN MEDICINE UNLOCKED 2016. [DOI: 10.1016/j.imu.2016.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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