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Shu H, Qu Z. CDPA: Common and distinctive pattern analysis between high-dimensional datasets. Electron J Stat 2022; 16:2475-2517. [DOI: 10.1214/22-ejs2008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Hai Shu
- Department of Biostatistics, School of Global Public Health, New York University
| | - Zhe Qu
- Department of Mathematics, School of Science and Engineering, Tulane University
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Shu H, Qu Z, Zhu H. D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2022; 23:169. [PMID: 35983506 PMCID: PMC9380864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Modern biomedical studies often collect multi-view data, that is, multiple types of data measured on the same set of objects. A popular model in high-dimensional multi-view data analysis is to decompose each view's data matrix into a low-rank common-source matrix generated by latent factors common across all data views, a low-rank distinctive-source matrix corresponding to each view, and an additive noise matrix. We propose a novel decomposition method for this model, called decomposition-based generalized canonical correlation analysis (D-GCCA). The D-GCCA rigorously defines the decomposition on theL 2 space of random variables in contrast to the Euclidean dot product space used by most existing methods, thereby being able to provide the estimation consistency for the low-rank matrix recovery. Moreover, to well calibrate common latent factors, we impose a desirable orthogonality constraint on distinctive latent factors. Existing methods, however, inadequately consider such orthogonality and may thus suffer from substantial loss of undetected common-source variation. Our D-GCCA takes one step further than generalized canonical correlation analysis by separating common and distinctive components among canonical variables, while enjoying an appealing interpretation from the perspective of principal component analysis. Furthermore, we propose to use the variable-level proportion of signal variance explained by common or distinctive latent factors for selecting the variables most influenced. Consistent estimators of our D-GCCA method are established with good finite-sample numerical performance, and have closed-form expressions leading to efficient computation especially for large-scale data. The superiority of D-GCCA over state-of-the-art methods is also corroborated in simulations and real-world data examples.
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Affiliation(s)
- Hai Shu
- Department of Biostatistics, New York University, New York, NY 10003, USA
| | - Zhe Qu
- Department of Mathematics, Tulane University, New Orleans, LA 70118, USA
| | - Hongtu Zhu
- Department of Biostatistics, Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Tarazona S, Arzalluz-Luque A, Conesa A. Undisclosed, unmet and neglected challenges in multi-omics studies. NATURE COMPUTATIONAL SCIENCE 2021; 1:395-402. [PMID: 38217236 DOI: 10.1038/s43588-021-00086-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/17/2021] [Indexed: 01/15/2024]
Abstract
Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.
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Affiliation(s)
- Sonia Tarazona
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Angeles Arzalluz-Luque
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Ana Conesa
- Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida, Gainesville, FL, USA.
- Genetics Institute, University of Florida, Gainesville, FL, USA.
- Institute for Integrative Systems Biology, Spanish National Research Council, Valencia, Spain.
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Planell N, Lagani V, Sebastian-Leon P, van der Kloet F, Ewing E, Karathanasis N, Urdangarin A, Arozarena I, Jagodic M, Tsamardinos I, Tarazona S, Conesa A, Tegner J, Gomez-Cabrero D. STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline. Front Genet 2021; 12:620453. [PMID: 33747045 PMCID: PMC7970106 DOI: 10.3389/fgene.2021.620453] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/20/2021] [Indexed: 12/13/2022] Open
Abstract
Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.
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Affiliation(s)
- Nuria Planell
- Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
| | - Vincenzo Lagani
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia
- Gnosis Data Analysis P.C., Heraklion, Greece
| | - Patricia Sebastian-Leon
- Department of Genomic and Systems Reproductive Medicine, IVI-RMA (Instituto Valenciano de Infertilidad – Reproductive Medicine Associates) IVI Foundation, Valencia, Spain
| | - Frans van der Kloet
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Ewoud Ewing
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Nestoras Karathanasis
- Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece
- Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Arantxa Urdangarin
- Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
| | - Imanol Arozarena
- Cancer Signalling Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Health Research Institute of Navarre (IdiSNA), Pamplona, Spain
| | - Maja Jagodic
- Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Ioannis Tsamardinos
- Gnosis Data Analysis P.C., Heraklion, Greece
- Computer Science Department, University of Crete, Heraklion, Greece
| | - Sonia Tarazona
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, València, Spain
| | - Ana Conesa
- Microbiology and Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States
- Genetics Institute, University of Florida, Gainesville, FL, United States
| | - Jesper Tegner
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Science for Life Laboratory, Solna, Sweden
| | - David Gomez-Cabrero
- Translational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Mucosal & Salivary Biology DivisionKing’s College London Dental Institute, London, United Kingdom
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Pang Z, Chen J, Wang T, Gao C, Li Z, Guo L, Xu J, Cheng Y. Linking Plant Secondary Metabolites and Plant Microbiomes: A Review. FRONTIERS IN PLANT SCIENCE 2021; 12:621276. [PMID: 33737943 PMCID: PMC7961088 DOI: 10.3389/fpls.2021.621276] [Citation(s) in RCA: 196] [Impact Index Per Article: 65.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 02/08/2021] [Indexed: 05/09/2023]
Abstract
Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
- CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jia Chen
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
| | - Tuhong Wang
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
| | - Chunsheng Gao
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
| | - Zhimin Li
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
| | - Litao Guo
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
| | - Jianping Xu
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
- Department of Biology, McMaster University, Hamilton, ON, Canada
| | - Yi Cheng
- Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha, China
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Shu H, Wang X, Zhu H. D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets. J Am Stat Assoc 2020; 115:292-306. [PMID: 33311817 DOI: 10.1080/01621459.2018.1543599] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix. Existing decomposition methods often focus on the orthogonality between the common and distinctive matrices, but inadequately consider the more necessary orthogonal relationship between the two distinctive matrices. The latter guarantees that no more shared information is extractable from the distinctive matrices. We propose decomposition-based canonical correlation analysis (D-CCA), a novel decomposition method that defines the common and distinctive matrices from the L 2 space of random variables rather than the conventionally used Euclidean space, with a careful construction of the orthogonal relationship between distinctive matrices. D-CCA represents a natural generalization of the traditional canonical correlation analysis. The proposed estimators of common and distinctive matrices are shown to be consistent and have reasonably better performance than some state-of-the-art methods in both simulated data and the real data analysis of breast cancer data obtained from The Cancer Genome Atlas.
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Affiliation(s)
- Hai Shu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Xiao Wang
- Department of Statistics, Purdue University
| | - Hongtu Zhu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center.,Department of Biostatistics, The University of North Carolina at Chapel Hill
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Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset. Metabolites 2020; 10:metabo10070295. [PMID: 32709053 PMCID: PMC7407932 DOI: 10.3390/metabo10070295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/14/2020] [Accepted: 07/15/2020] [Indexed: 11/16/2022] Open
Abstract
Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has a few number of samples and a lack of statistical power, we studied a small metabolomic multiblock dataset containing six blocks (i.e., tissue types), only including common metabolites. We used a single model multiblock analysis method called the joint and unique multiblock analysis (JUMBA) and compared it to a commonly used method, concatenated principal component analysis (PCA). These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships, and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples.
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Alinaghi M, Bertram HC, Brunse A, Smilde AK, Westerhuis JA. Common and distinct variation in data fusion of designed experimental data. Metabolomics 2019; 16:2. [PMID: 31797165 PMCID: PMC6890597 DOI: 10.1007/s11306-019-1622-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/25/2019] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept. OBJECTIVES In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets. METHODS Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same underlying experimental design. RESULTS Using two simulated cases, the result of simultaneous component analysis (SCA), penalized exponential simultaneous component analysis (P-ESCA) and ANOVA-simultaneous component analysis (ASCA) are compared with the proposed method. Furthermore, real metabolomics data obtained from NMR analysis of two different brains tissues (hypothalamus and midbrain) from the same piglets with an underlying experimental design is investigated by PE-ASCA. CONCLUSIONS This method provides an improved understanding of the common and distinct variation in response to different experimental factors.
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Affiliation(s)
| | | | - Anders Brunse
- Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Age K Smilde
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Johan A Westerhuis
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
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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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Bouhaddani SE, Uh HW, Jongbloed G, Hayward C, Klarić L, Kiełbasa SM, Houwing-Duistermaat J. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics 2018; 19:371. [PMID: 30309317 PMCID: PMC6182835 DOI: 10.1186/s12859-018-2371-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 09/11/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. RESULTS We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. CONCLUSIONS We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages("OmicsPLS").
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Affiliation(s)
- Said el Bouhaddani
- Dept. of Biomedical Data Sciences, LUMC, Albinusdreef 2, Leiden, 2300 RC The Netherlands
- Delft Institute of Applied Mathematics, EEMCS, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE The Netherlands
| | - Hae-Won Uh
- Department of Biostatistics and Research Support, UMC Utrecht, div. Julius Centre, Huispost Str. 6.131, Utrecht, 3508 GA The Netherlands
| | - Geurt Jongbloed
- Delft Institute of Applied Mathematics, EEMCS, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE The Netherlands
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU Scotland
| | - Lucija Klarić
- Genos Glycobiology Laboratory, Zagreb, 10000 Croatia
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU Scotland
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH8 9DX Scotland
| | - Szymon M. Kiełbasa
- Dept. of Biomedical Data Sciences, LUMC, Albinusdreef 2, Leiden, 2300 RC The Netherlands
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