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Henao JD, Lauber M, Azevedo M, Grekova A, Theis F, List M, Ogris C, Schubert B. Multi-omics regulatory network inference in the presence of missing data. Brief Bioinform 2023; 24:bbad309. [PMID: 37670505 PMCID: PMC10516394 DOI: 10.1093/bib/bbad309] [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: 12/08/2022] [Revised: 05/06/2023] [Accepted: 05/29/2023] [Indexed: 09/07/2023] Open
Abstract
A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.
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Affiliation(s)
- Juan D Henao
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Michael Lauber
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising
| | - Manuel Azevedo
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Anastasiia Grekova
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Fabian Theis
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
- Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising
| | - Christoph Ogris
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
| | - Benjamin Schubert
- Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
- Department of Mathematics, Technical University of Munich, 85748 Garching bei München, Germany
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2
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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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3
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Einav T, Ma R. Using interpretable machine learning to extend heterogeneous antibody-virus datasets. CELL REPORTS METHODS 2023; 3:100540. [PMID: 37671020 PMCID: PMC10475791 DOI: 10.1016/j.crmeth.2023.100540] [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: 12/19/2022] [Revised: 05/14/2023] [Accepted: 06/30/2023] [Indexed: 09/07/2023]
Abstract
A central challenge in biology is to use existing measurements to predict the outcomes of future experiments. For the rapidly evolving influenza virus, variants examined in one study will often have little to no overlap with other studies, making it difficult to discern patterns or unify datasets. We develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We validate this method using hemagglutination inhibition data from seven studies and predict 2,000,000 new values ± uncertainties. Our analysis quantifies the transferability between vaccination and infection studies in humans and ferrets, shows that serum potency is negatively correlated with breadth, and provides a tool for pandemic preparedness. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."
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Affiliation(s)
- Tal Einav
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Rong Ma
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
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4
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Gentry AE, Kirkpatrick RM, Peterson RE, Webb BT. Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery. Front Genet 2023; 14:1162690. [PMID: 37547462 PMCID: PMC10399453 DOI: 10.3389/fgene.2023.1162690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction: The availability of large-scale biobanks linking genetic data, rich phenotypes, and biological measures is a powerful opportunity for scientific discovery. However, real-world collections frequently have extensive missingness. While missing data prediction is possible, performance is significantly impaired by block-wise missingness inherent to many biobanks. Methods: To address this, we developed Missingness Adapted Group-wise Informed Clustered (MAGIC)-LASSO which performs hierarchical clustering of variables based on missingness followed by sequential Group LASSO within clusters. Variables are pre-filtered for missingness and balance between training and target sets with final models built using stepwise inclusion of features ranked by completeness. This research has been conducted using the UK Biobank (n > 500 k) to predict unmeasured Alcohol Use Disorders Identification Test (AUDIT) scores. Results: The phenotypic correlation between measured and predicted total score was 0.67 while genetic correlations between independent subjects was high >0.86. Discussion: Phenotypic and genetic correlations in real data application, as well as simulations, demonstrate the method has significant accuracy and utility for increasing power for genetic loci discovery.
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Affiliation(s)
- Amanda Elswick Gentry
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Robert M. Kirkpatrick
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Roseann E. Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
| | - Bradley T. Webb
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, United States
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5
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Wang H, Li Q, Liu Y. Regularized Buckley-James method for right-censored outcomes with block-missing multimodal covariates. Stat (Int Stat Inst) 2022; 11:e515. [PMID: 37854542 PMCID: PMC10583730 DOI: 10.1002/sta4.515] [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: 07/06/2022] [Accepted: 10/10/2022] [Indexed: 10/20/2023]
Abstract
High-dimensional data with censored outcomes of interest are prevalent in medical research. To analyze such data, the regularized Buckley-James estimator has been successfully applied to build accurate predictive models and conduct variable selection. In this paper, we consider the problem of parameter estimation and variable selection for the semiparametric accelerated failure time model for high-dimensional block-missing multimodal neuroimaging data with censored outcomes. We propose a penalized Buckley-James method that can simultaneously handle block-wise missing covariates and censored outcomes. This method can also perform variable selection. The proposed method is evaluated by simulations and applied to a multimodal neuroimaging dataset and obtains meaningful results.
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Affiliation(s)
- Haodong Wang
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA
| | - Quefeng Li
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, 27516, North Carolina, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, 27516, North Carolina, USA
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, 27599-7264, North Carolina, USA
- Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, 27514, North Carolina, USA
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, 27514, North Carolina, USA
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6
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Yuan C, Fang F, Ni L. Mallows model averaging with effective model size in fragmentary data prediction. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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7
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Lin C, Peng J, Qin Y, Li Y, Yang Y. Optimal integrating learning for split questionnaire design type data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2118753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Cunjie Lin
- Center for Applied Statistics, Renmin University of China, Beijing, China
- School of Statistics, Renmin University of China, Beijing, China
| | - Jingfu Peng
- School of Statistics, Renmin University of China, Beijing, China
| | - Yichen Qin
- Department of Operations, Business Analytics, and Information Systems, University of Cincinnati, OH, USA
| | - Yang Li
- Center for Applied Statistics, Renmin University of China, Beijing, China
- School of Statistics, Renmin University of China, Beijing, China
| | - Yuhong Yang
- School of Statistics, University of Minnesota, MN, USA
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8
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Li J, Zhang Q, Chen S, Fang K. Weighted multiple blockwise imputation method for high-dimensional regression with blockwise missing data. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2109636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Jingmao Li
- Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, People's Republic of China
| | - Qingzhao Zhang
- Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, People's Republic of China
- The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, People's Republic of China
| | - Song Chen
- College of Micro-Finance, Taizhou University, Taizhou, People's Republic of China
| | - Kuangnan Fang
- Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, People's Republic of China
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9
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Li J, Yu G, Li Q, Liu Y. Sample-wise Combined Missing Effect Model with Penalization. J Comput Graph Stat 2022; 32:263-274. [PMID: 37274355 PMCID: PMC10237115 DOI: 10.1080/10618600.2022.2070172] [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: 12/27/2020] [Accepted: 04/11/2022] [Indexed: 10/18/2022]
Abstract
Modern high-dimensional statistical inference often faces the problem of missing data. In recent decades, many studies have focused on this topic and provided strategies including complete-sample analysis and imputation procedures. However, complete-sample analysis discards information of incomplete samples, while imputation procedures have accumulative errors from each single imputation. In this paper, we propose a new method, Sample-wise COmbined missing effect Model with penalization (SCOM), to deal with missing data occurring in predictors. Instead of imputing the predictors, SCOM estimates the combined effect caused by all missing data for each incomplete sample. SCOM makes full use of all available data. It is robust with respect to various missing mechanisms. Theoretical studies show the oracle inequality for the proposed estimator, and the consistency of variable selection and combined missing effect selection. Simulation studies and an application to the Residential Building Data also illustrate the effectiveness of the proposed SCOM.
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Affiliation(s)
- Jialu Li
- School of Mathematics and Statistics, Beijing Institute of Technology
| | - Guan Yu
- Department of Biostatistics, State University of New York at Buffalo
| | - Qizhai Li
- LSC, NCMIS, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences
| | - Yufeng Liu
- Department of Statistics and Operations Research, Carolina Center for Genome Science, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
- Department of Genetics, Carolina Center for Genome Science, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
- Department of Biostatistics, Carolina Center for Genome Science, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
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10
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Okyay S, Adar N. Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores. PeerJ 2022; 10:e13425. [PMID: 35642196 PMCID: PMC9148556 DOI: 10.7717/peerj.13425] [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: 01/05/2022] [Accepted: 04/21/2022] [Indexed: 01/14/2023] Open
Abstract
Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.
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Affiliation(s)
- Savas Okyay
- Computer Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey,Computer Engineering, Eskisehir Technical University, Eskisehir, Turkey
| | - Nihat Adar
- Computer Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey
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11
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Dai X, Li L. Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis. J Am Stat Assoc 2022; 118:1796-1810. [PMID: 37771509 PMCID: PMC10530774 DOI: 10.1080/01621459.2021.2013851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 11/23/2021] [Indexed: 10/19/2022]
Abstract
Multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combine the merits of the interpretability attributed to a simple association model with the flexibility achieved by a highly adaptive nonlinear model. In this article, we propose an orthogonalized kernel debiased machine learning approach, which is built upon the Neyman orthogonality and a form of decomposition orthogonality, for multimodal data analysis. We target the setting that naturally arises in almost all multimodal studies, where there is a primary modality of interest, plus additional auxiliary modalities. We establish the root-N-consistency and asymptotic normality of the estimated primary parameter, the semi-parametric estimation efficiency, and the asymptotic validity of the confidence band of the predicted primary modality effect. Our proposal enjoys, to a good extent, both model interpretability and model flexibility. It is also considerably different from the existing statistical methods for multimodal data integration, as well as the orthogonality-based methods for high-dimensional inferences. We demonstrate the efficacy of our method through both simulations and an application to a multimodal neuroimaging study of Alzheimer's disease.
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Affiliation(s)
| | - Lexin Li
- University of California at Berkeley
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12
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Abstract
Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis.
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13
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Zhou J, Sun WW, Zhang J, Li L. Partially Observed Dynamic Tensor Response Regression. J Am Stat Assoc 2021; 118:424-439. [PMID: 37333062 PMCID: PMC10274377 DOI: 10.1080/01621459.2021.1938082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 05/22/2021] [Accepted: 05/25/2021] [Indexed: 10/21/2022]
Abstract
In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient nonconvex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in the tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations and two real applications, including a neuroimaging dementia study and a digital advertising study.
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Affiliation(s)
- Jie Zhou
- Department of Management Science, University of Miami Herbert Business School, Miami, FL
| | - Will Wei Sun
- Krannert School of Management, Purdue University, West Lafayette, IN
| | - Jingfei Zhang
- Department of Management Science, University of Miami Herbert Business School, Miami, FL
| | - Lexin Li
- Division of Biostatistics, University of California, Berkeley, Berkeley, CA
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