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Zhou Z, Zhang R, Zhou A, Lv J, Chen S, Zou H, Zhang G, Lin T, Wang Z, Zhang Y, Weng S, Han X, Liu Z. Proteomics appending a complementary dimension to precision oncotherapy. Comput Struct Biotechnol J 2024; 23:1725-1739. [PMID: 38689716 PMCID: PMC11058087 DOI: 10.1016/j.csbj.2024.04.044] [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: 02/06/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.
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Affiliation(s)
- Zhaokai Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China
| | - Ruiqi Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Aoyang Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Jinxiang Lv
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Haijiao Zou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Ting Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhan Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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2
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Kobel CM, Merkesvik J, Burgos IMT, Lai W, Øyås O, Pope PB, Hvidsten TR, Aho VTE. Integrating host and microbiome biology using holo-omics. Mol Omics 2024. [PMID: 38963125 DOI: 10.1039/d4mo00017j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Holo-omics is the use of omics data to study a host and its inherent microbiomes - a biological system known as a "holobiont". A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host-microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host-microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host-microbiome interplay to make meaningful interpretations and biotechnological applications.
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Affiliation(s)
- Carl M Kobel
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
| | - Jenny Merkesvik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | | | - Wanxin Lai
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Ove Øyås
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
| | - Phillip B Pope
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology (QUT), Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Torgeir R Hvidsten
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Velma T E Aho
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
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3
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Mattessich M, Reyna J, Aron E, Ay F, Kilmer M, Kleinstein SH, Konstorum A. nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Non-linear Iterative Partial Least Squares. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597819. [PMID: 38915554 PMCID: PMC11195050 DOI: 10.1101/2024.06.07.597819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Motivation With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data. Results We introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors. Availability nipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.
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Affiliation(s)
- Max Mattessich
- Department of Applied Mathematics, Northwestern University
| | - Joaquin Reyna
- Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology
- Bioinformatics and Systems Biology PhD Program, University of California, San Diego
| | - Edel Aron
- Program in Computational Biology and Bioinformatics, Yale University
| | - Ferhat Ay
- Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology
- Bioinformatics and Systems Biology PhD Program, University of California, San Diego
- Department of Pediatrics, University of California, San Diego
| | | | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University
- Department of Pathology and Department of Immunobiology, Yale School of Medicine
| | - Anna Konstorum
- Department of Pathology and Department of Immunobiology, Yale School of Medicine
- Center for Computing Sciences, Institute for Defense Analyses
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4
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Aminu M, Hong L, Vokes N, Schmidt ST, Saad M, Zhu B, Le X, Tina C, Sheshadri A, Wang B, Jaffray D, Futreal A, Lee JJ, Byers LA, Gibbons D, Heymach J, Chen K, Cheng C, Zhang J, Wu J. Joint multi-omics discriminant analysis with consistent representation learning using PANDA. RESEARCH SQUARE 2024:rs.3.rs-4353037. [PMID: 38798352 PMCID: PMC11118856 DOI: 10.21203/rs.3.rs-4353037/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.
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Affiliation(s)
- Muhammad Aminu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lingzhi Hong
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Natalie Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Stephanie T. Schmidt
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maliazurina Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bo Zhu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cascone Tina
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bo Wang
- Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - David Jaffray
- Office of the Chief Technology and Digital Officer, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Andy Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lauren A. Byers
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Don Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chao Cheng
- Department of Medicine, Institution of Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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5
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Gygi JP, Maguire C, Patel RK, Shinde P, Konstorum A, Shannon CP, Xu L, Hoch A, Jayavelu ND, Haddad EK, Reed EF, Kraft M, McComsey GA, Metcalf JP, Ozonoff A, Esserman D, Cairns CB, Rouphael N, Bosinger SE, Kim-Schulze S, Krammer F, Rosen LB, van Bakel H, Wilson M, Eckalbar WL, Maecker HT, Langelier CR, Steen H, Altman MC, Montgomery RR, Levy O, Melamed E, Pulendran B, Diray-Arce J, Smolen KK, Fragiadakis GK, Becker PM, Sekaly RP, Ehrlich LI, Fourati S, Peters B, Kleinstein SH, Guan L. Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality. J Clin Invest 2024; 134:e176640. [PMID: 38690733 PMCID: PMC11060740 DOI: 10.1172/jci176640] [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: 10/26/2023] [Accepted: 03/12/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUNDPatients hospitalized for COVID-19 exhibit diverse clinical outcomes, with outcomes for some individuals diverging over time even though their initial disease severity appears similar to that of other patients. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity.METHODSWe performed deep immunophenotyping and conducted longitudinal multiomics modeling, integrating 10 assays for 1,152 Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study participants and identifying several immune cascades that were significant drivers of differential clinical outcomes.RESULTSIncreasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, formation of neutrophil extracellular traps, and T cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma Igs and B cells and dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to failure of viral clearance in patients with fatal illness.CONCLUSIONOur longitudinal multiomics profiling study revealed temporal coordination across diverse omics that potentially explain the disease progression, providing insights that can inform the targeted development of therapies for patients hospitalized with COVID-19, especially those who are critically ill.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNIH (5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); NIAID, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and National Science Foundation (DMS2310836).
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Affiliation(s)
| | - Cole Maguire
- The University of Texas at Austin, Austin, Texas, USA
| | | | - Pramod Shinde
- La Jolla Institute for Immunology, La Jolla, California, USA
| | | | - Casey P. Shannon
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, Canada
- Prevention of Organ Failure (PROOF) Centre of Excellence, Providence Research, Vancouver, British Columbia, Canada
| | - Leqi Xu
- Yale School of Public Health, New Haven, Connecticut, USA
| | - Annmarie Hoch
- Clinical and Data Coordinating Center (CDCC) and
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Elias K. Haddad
- Drexel University, Tower Health Hospital, Philadelphia, Pennsylvania, USA
| | - IMPACC Network
- The Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) Network is detailed in Supplemental Acknowledgments
| | - Elaine F. Reed
- David Geffen School of Medicine at the UCLA, Los Angeles, California, USA
| | - Monica Kraft
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Grace A. McComsey
- Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Jordan P. Metcalf
- Oklahoma University Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Al Ozonoff
- Clinical and Data Coordinating Center (CDCC) and
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Charles B. Cairns
- Drexel University, Tower Health Hospital, Philadelphia, Pennsylvania, USA
| | | | | | | | - Florian Krammer
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Ignaz Semmelweis Institute, Interuniversity Institute for Infection Research, Medical University of Vienna, Vienna, Austria
| | - Lindsey B. Rosen
- National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Harm van Bakel
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | - Hanno Steen
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - Ofer Levy
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Bali Pulendran
- Stanford University School of Medicine, Palo Alto, California, USA
| | - Joann Diray-Arce
- Clinical and Data Coordinating Center (CDCC) and
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kinga K. Smolen
- Precision Vaccines Program, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Patrice M. Becker
- National Institute of Allergy and Infectious Diseases (NIAID), NIH, Bethesda, Maryland, USA
| | - Rafick P. Sekaly
- Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | | | - Slim Fourati
- Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Bjoern Peters
- La Jolla Institute for Immunology, La Jolla, California, USA
- Department of Medicine, UCSD, La Jolla, California, USA
| | | | - Leying Guan
- Yale School of Public Health, New Haven, Connecticut, USA
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6
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Shinde P, Soldevila F, Reyna J, Aoki M, Rasmussen M, Willemsen L, Kojima M, Ha B, Greenbaum JA, Overton JA, Guzman-Orozco H, Nili S, Orfield S, Gygi JP, da Silva Antunes R, Sette A, Grant B, Olsen LR, Konstorum A, Guan L, Ay F, Kleinstein SH, Peters B. A multi-omics systems vaccinology resource to develop and test computational models of immunity. CELL REPORTS METHODS 2024; 4:100731. [PMID: 38490204 PMCID: PMC10985234 DOI: 10.1016/j.crmeth.2024.100731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/04/2024] [Accepted: 02/20/2024] [Indexed: 03/17/2024]
Abstract
Systems vaccinology studies have identified factors affecting individual vaccine responses, but comparing these findings is challenging due to varying study designs. To address this lack of reproducibility, we established a community resource for comparing Bordetella pertussis booster responses and to host annual contests for predicting patients' vaccination outcomes. We report here on our experiences with the "dry-run" prediction contest. We found that, among 20+ models adopted from the literature, the most successful model predicting vaccination outcome was based on age alone. This confirms our concerns about the reproducibility of conclusions between different vaccinology studies. Further, we found that, for newly trained models, handling of baseline information on the target variables was crucial. Overall, multiple co-inertia analysis gave the best results of the tested modeling approaches. Our goal is to engage community in these prediction challenges by making data and models available and opening a public contest in August 2024.
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Affiliation(s)
- Pramod Shinde
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Ferran Soldevila
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Joaquin Reyna
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, San Diego, CA, USA
| | - Minori Aoki
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mikkel Rasmussen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lisa Willemsen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mari Kojima
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Brendan Ha
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jason A Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - James A Overton
- Knocean Inc., 107 Quebec Avenue, Toronto, Ontario M6P 2T3, Canada
| | - Hector Guzman-Orozco
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Somayeh Nili
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Shelby Orfield
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jeremy P Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Ricardo da Silva Antunes
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Barry Grant
- Department of Molecular Biology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Anna Konstorum
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ferhat Ay
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Steven H Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA.
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7
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RPJ, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLoS Comput Biol 2024; 20:e1011814. [PMID: 38527092 PMCID: PMC10994553 DOI: 10.1371/journal.pcbi.1011814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/04/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, Denver, Colorado, United States of America
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Rachel PJ Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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8
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Selle M, Kircher M, Schwennen C, Visscher C, Jung K. Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images. BMC Med Inform Decis Mak 2024; 24:49. [PMID: 38355504 PMCID: PMC10865689 DOI: 10.1186/s12911-024-02457-8] [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: 03/24/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted. METHODS We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space. RESULTS MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA. CONCLUSIONS MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.
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Affiliation(s)
- Michael Selle
- Institute of Animal Genomics, University of Veterinary Medicine Hannover, Hannover, Germany.
| | - Magdalena Kircher
- Institute of Animal Genomics, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Cornelia Schwennen
- Institute for Animal Nutrition, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Christian Visscher
- Institute for Animal Nutrition, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Klaus Jung
- Institute of Animal Genomics, University of Veterinary Medicine Hannover, Hannover, Germany.
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9
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Ji Q, Jiang X, Wang M, Xin Z, Zhang W, Qu J, Liu GH. Multimodal Omics Approaches to Aging and Age-Related Diseases. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:56-71. [PMID: 38605908 PMCID: PMC11003952 DOI: 10.1007/s43657-023-00125-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 04/13/2024]
Abstract
Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.
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Affiliation(s)
- Qianzhao Ji
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Xiaoyu Jiang
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Minxian Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zijuan Xin
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
- Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053 China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
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10
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Mu R, Li S, Zhang Y, Li Y, Zhu Y, Zhao F, Si H, Li Z. Microbiota and Metabolite Profiles in the Feces of Juvenile Sika Deer ( Cervus nippon) from Birth to Weaning. Animals (Basel) 2024; 14:432. [PMID: 38338075 PMCID: PMC10854736 DOI: 10.3390/ani14030432] [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: 12/11/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
The gut microbiota establishment in young ruminants has a profound impact on their adult production performance. However, the critical phase for the succession of the gut microbial composition and metabolic profiles of juvenile sika deer still needs to be further investigated. Here, we analyzed the fecal microbiota and metabolites of juvenile sika deer during the birth (D1), transition (D42), and rumination (D70) periods based on 16S rRNA sequencing and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). The results showed that the fecal bacteria and metabolites composition were significantly different in D1 compared to D42 and D70, and the number of OTUs and the Shannon index were significantly higher in D70 than in D1 (p < 0.05). The relative abundances of Lactobacillus, Lactococcus, and Lachnoclostridium showed a significant increase in D1 compared to D42 and D70, whereas the relative abundances of Ruminococcaceae UCG-005, Ruminococcaceae UCG-010, Ruminococcaceae UCG-014, Christensenellaceae R-7, and Eubacterium coprostanoligenes group were significantly decreased in D1 compared to D42 and D70 (p < 0.05). The amounts of serine, phenylalanine, aspartic acid, ornithine, citrulline, creatine, isoleucine, galactose, and ribose in the feces were significantly higher in D1 compared to D42 and D70. In contrast, the concentrations of cortexolone, resveratrol, piceatannol, fumaric acid, alpha-ketoglutarate, glycerol, uracil-5-carboxylic acid, and maleic acid were significantly decreased in D1. The enrichment analysis showed that amino acid metabolism and carbohydrate metabolism were significantly changed in D1 compared to D42 and D70. The glycine, serine and threonine metabolism; alanine, aspartate and glutamate metabolism; arginine biosynthesis; glyoxylate and dicarboxylate metabolism; citrate cycle; and pyruvate metabolism were significantly enriched across the three periods (p < 0.05). In conclusion, our results suggested that the birth-transition period is a critical phase for the gut bacterial community and metabolic function shift in juvenile sika deer.
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Affiliation(s)
- Ruina Mu
- Joint International Research Laboratory of Modern Agricultural Technology, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Songze Li
- Joint International Research Laboratory of Modern Agricultural Technology, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Yunxi Zhang
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Yuqian Li
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Yuhang Zhu
- Joint International Research Laboratory of Modern Agricultural Technology, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Fei Zhao
- Joint International Research Laboratory of Modern Agricultural Technology, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Huazhe Si
- Joint International Research Laboratory of Modern Agricultural Technology, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
| | - Zhipeng Li
- Joint International Research Laboratory of Modern Agricultural Technology, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
- College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
- Jilin Provincial Engineering Research Center for Efficient Breeding and Product Development of Sika Deer, Jilin Agricultural University, Changchun 130118, China
- Key Laboratory of Animal Production, Product Quality and Security, Ministry of Education, Jilin Agricultural University, Changchun 130118, China
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11
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Wang J, Liao N, Du X, Chen Q, Wei B. A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks. BMC Genomics 2024; 25:86. [PMID: 38254021 PMCID: PMC10802018 DOI: 10.1186/s12864-024-09985-7] [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: 11/03/2023] [Accepted: 01/07/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes. RESULTS The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases. CONCLUSION MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.
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Affiliation(s)
- Jiahui Wang
- School of Computer and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin City, 541004, Guangxi Zhuang Autonomous Region, China
| | - Nanqing Liao
- School of Medical, Guangxi University, No. 100 East University Road, Nanning, 530004, Guangxi, China
| | - Xiaofei Du
- School of Computer and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin City, 541004, Guangxi Zhuang Autonomous Region, China
| | - Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, No. 100 East University Road, Nanning, 530004, Guangxi, China.
| | - Bizhong Wei
- School of Computer and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin City, 541004, Guangxi Zhuang Autonomous Region, China.
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12
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Tong L, Shi W, Isgut M, Zhong Y, Lais P, Gloster L, Sun J, Swain A, Giuste F, Wang MD. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng 2024; 17:80-97. [PMID: 37824325 DOI: 10.1109/rbme.2023.3324264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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13
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RP, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574780. [PMID: 38260498 PMCID: PMC10802464 DOI: 10.1101/2024.01.09.574780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Rachel Pj Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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14
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Alahmad A, Harir M, Fochesato S, Tulumello J, Walker A, Barakat M, Ndour PMS, Schmitt-Kopplin P, Cournac L, Laplaze L, Heulin T, Achouak W. Unraveling the interplay between root exudates, microbiota, and rhizosheath formation in pearl millet. MICROBIOME 2024; 12:1. [PMID: 38167150 PMCID: PMC10763007 DOI: 10.1186/s40168-023-01727-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/19/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND The rhizosheath, a cohesive soil layer firmly adhering to plant roots, plays a vital role in facilitating water and mineral uptake. In pearl millet, rhizosheath formation is genetically controlled and influenced by root exudates. Here, we investigated the impact of root exudates on the microbiota composition, interactions, and assembly processes, and rhizosheath structure in pearl millet using four distinct lines with contrasting soil aggregation abilities. RESULTS Utilizing 16S rRNA gene and ITS metabarcoding for microbiota profiling, coupled with FTICR-MS metabonomic analysis of metabolite composition in distinct plant compartments and root exudates, we revealed substantial disparities in microbial diversity and interaction networks. The ß-NTI analysis highlighted bacterial rhizosphere turnover driven primarily by deterministic processes, showcasing prevalent homogeneous selection in root tissue (RT) and root-adhering soil (RAS). Conversely, fungal communities were more influenced by stochastic processes. In bulk soil assembly, a combination of deterministic and stochastic mechanisms shapes composition, with deterministic factors exerting a more pronounced role. Metabolic profiles across shoots, RT, and RAS in different pearl millet lines mirrored their soil aggregation levels, emphasizing the impact of inherent plant traits on microbiota composition and unique metabolic profiles in RT and exudates. Notably, exclusive presence of antimicrobial compounds, including DIMBOA and H-DIMBOA, emerged in root exudates and RT of low aggregation lines. CONCLUSIONS This research underscores the pivotal influence of root exudates in shaping the root-associated microbiota composition across pearl millet lines, entwined with their soil aggregation capacities. These findings underscore the interconnectedness of root exudates and microbiota, which jointly shape rhizosheath structure, deepening insights into soil-plant-microbe interactions and ecological processes shaping rhizosphere microbial communities. Deciphering plant-microbe interactions and their contribution to soil aggregation and microbiota dynamics holds promise for the advancement of sustainable agricultural strategies. Video Abstract.
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Affiliation(s)
- Abdelrahman Alahmad
- CEA, CNRS, BIAM, Lab Microbial Ecology of the Rhizosphere (LEMiRE), Aix Marseille Univ, 13108, Saint-Paul-Lez-Durance, France
- UniLaSalle, SFR NORVEGE FED 4277, AGHYLE Rouen UP 2018.C101, 3 Rue du Tronquet, 76130, Mont-Saint- Aignan, France
| | - Mourad Harir
- Research Unit Analytical BioGeoChemistry, Helmholtz Munich, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
- Chair Analytl Food Chem, Technical University of Munich, 85354, Freising, Weihenstephan, Germany
| | - Sylvain Fochesato
- CEA, CNRS, BIAM, Lab Microbial Ecology of the Rhizosphere (LEMiRE), Aix Marseille Univ, 13108, Saint-Paul-Lez-Durance, France
| | - Joris Tulumello
- CEA, CNRS, BIAM, Lab Microbial Ecology of the Rhizosphere (LEMiRE), Aix Marseille Univ, 13108, Saint-Paul-Lez-Durance, France
| | - Alesia Walker
- Research Unit Analytical BioGeoChemistry, Helmholtz Munich, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - Mohamed Barakat
- CEA, CNRS, BIAM, Lab Microbial Ecology of the Rhizosphere (LEMiRE), Aix Marseille Univ, 13108, Saint-Paul-Lez-Durance, France
| | - Papa Mamadou Sitor Ndour
- CIRAD, INRAE, Eco&Sols, Université de Montpellier, Institut Agro, IRD FR, Montpellier, France
- UCEIV-ULCO, 50 Rue Ferdinand Buisson, 62228, Calais, France
- LMI IESOL, Centre de Recherche, ISRA-IRD de Bel Air, Dakar, Senegal
| | - Philippe Schmitt-Kopplin
- Research Unit Analytical BioGeoChemistry, Helmholtz Munich, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
- Chair Analytl Food Chem, Technical University of Munich, 85354, Freising, Weihenstephan, Germany
| | - Laurent Cournac
- CIRAD, INRAE, Eco&Sols, Université de Montpellier, Institut Agro, IRD FR, Montpellier, France
- LMI IESOL, Centre de Recherche, ISRA-IRD de Bel Air, Dakar, Senegal
| | - Laurent Laplaze
- UMR DIADE, Université de Montpellier, IRD, CIRAD, Montpellier, France
- LMI LAPSE, Centre de Recherche, ISRA-IRD de Bel Air, Dakar, Senegal
| | - Thierry Heulin
- CEA, CNRS, BIAM, Lab Microbial Ecology of the Rhizosphere (LEMiRE), Aix Marseille Univ, 13108, Saint-Paul-Lez-Durance, France.
| | - Wafa Achouak
- CEA, CNRS, BIAM, Lab Microbial Ecology of the Rhizosphere (LEMiRE), Aix Marseille Univ, 13108, Saint-Paul-Lez-Durance, France.
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15
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Sharma V, Singh A, Chauhan S, Sharma PK, Chaudhary S, Sharma A, Porwal O, Fuloria NK. Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer. Curr Drug Deliv 2024; 21:870-886. [PMID: 37670704 DOI: 10.2174/1567201821666230905090621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/08/2023] [Accepted: 03/24/2023] [Indexed: 09/07/2023]
Abstract
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
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Affiliation(s)
- Vishal Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Amit Singh
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Sanjana Chauhan
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Pramod Kumar Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Shubham Chaudhary
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Astha Sharma
- Department of Pharmacy, Galgotias University, Greater Noida, Uttar Pradesh, 201310, India
| | - Omji Porwal
- Department of Pharmacognosy, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
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16
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Michel A, Minocher R, Niehoff PP, Li Y, Nota K, Gadhvi MA, Su J, Iyer N, Porter A, Ngobobo-As-Ibungu U, Binyinyi E, Nishuli Pekeyake R, Parducci L, Caillaud D, Guschanski K. Isolated Grauer's gorilla populations differ in diet and gut microbiome. Mol Ecol 2023; 32:6523-6542. [PMID: 35976262 DOI: 10.1111/mec.16663] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/30/2022]
Abstract
The animal gut microbiome has been implicated in a number of key biological processes, ranging from digestion to behaviour, and has also been suggested to facilitate local adaptation. Yet studies in wild animals rarely compare multiple populations that differ ecologically, which is the level at which local adaptation may occur. Further, few studies simultaneously characterize diet and gut microbiome from the same sample, despite their probable interdependence. Here, we investigate the interplay between diet and gut microbiome in three geographically isolated populations of the critically endangered Grauer's gorilla (Gorilla beringei graueri), which we show to be genetically differentiated. We find population- and social group-specific dietary and gut microbial profiles and covariation between diet and gut microbiome, despite the presence of core microbial taxa. There was no detectable effect of age, and only marginal effects of sex and genetic relatedness on the microbiome. Diet differed considerably across populations, with the high-altitude population consuming a lower diversity of plants compared to low-altitude populations, consistent with plant availability constraining dietary choices. The observed pattern of covariation between diet and gut microbiome is probably a result of long-term social and environmental factors. Our study suggests that the gut microbiome is sufficiently plastic to support flexible food selection and hence contribute to local adaptation.
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Affiliation(s)
- Alice Michel
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
- Department of Anthropology, University of California, Davis, California, USA
| | - Riana Minocher
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
- Department of Human Behavior, Ecology and Culture, Max-Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Peter-Philip Niehoff
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Yuhong Li
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
- Conservation Ecology Group, Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Kevin Nota
- Plant Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Maya A Gadhvi
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Jiancheng Su
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
| | - Neetha Iyer
- Department of Anthropology, University of California, Davis, California, USA
| | - Amy Porter
- Department of Anthropology, University of California, Davis, California, USA
| | | | - Escobar Binyinyi
- The Dian Fossey Gorilla Fund International, Kinshasa, Democratic Republic of the Congo
| | - Radar Nishuli Pekeyake
- Institut Congolais pour la Conservation de la Nature, Kinshasa, Democratic Republic of the Congo
| | - Laura Parducci
- Department of Human Behavior, Ecology and Culture, Max-Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Environmental Biology, Sapienza University of Rome, Rome, Italy
| | - Damien Caillaud
- Department of Anthropology, University of California, Davis, California, USA
| | - Katerina Guschanski
- Animal Ecology, Department of Ecology and Genetics, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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17
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Makrodimitris S, Pronk B, Abdelaal T, Reinders M. An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics. Brief Bioinform 2023; 25:bbad416. [PMID: 38018908 PMCID: PMC10685331 DOI: 10.1093/bib/bbad416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/30/2023] Open
Abstract
Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly.
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Affiliation(s)
- Stavros Makrodimitris
- Delft Bioinformatics Lab, Delft University of Technology, Street, Postcode, State, Country
- Department of Medical Oncology, Erasmus University Medical Center, Street, Postcode, State, Country
- Department of Clinical Genetics, Erasmus University Medical Center, Street, Postcode, State, Country
| | - Bram Pronk
- Delft Bioinformatics Lab, Delft University of Technology, Street, Postcode, State, Country
| | - Tamim Abdelaal
- Delft Bioinformatics Lab, Delft University of Technology, Street, Postcode, State, Country
- Department of Radiology, Leiden University Medical Center, Street, Postcode, State, Country
- Leiden Computational Biology Center, Leiden University Medical Center, Street, Postcode, State, Country
| | - Marcel Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Street, Postcode, State, Country
- Leiden Computational Biology Center, Leiden University Medical Center, Street, Postcode, State, Country
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18
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Bukva M, Dobra G, Gyukity-Sebestyen E, Boroczky T, Korsos MM, Meckes DG, Horvath P, Buzas K, Harmati M. Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis. Cell Commun Signal 2023; 21:333. [PMID: 37986165 PMCID: PMC10658864 DOI: 10.1186/s12964-023-01344-5] [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: 08/17/2023] [Accepted: 09/27/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods. METHODS On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas. RESULTS Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes. CONCLUSION Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.
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Affiliation(s)
- Matyas Bukva
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6720, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Gabriella Dobra
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6720, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Edina Gyukity-Sebestyen
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Timea Boroczky
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, Albert Szent-Györgyi Medical School, University of Szeged, 6720, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Marietta Margareta Korsos
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
| | - David G Meckes
- Department of Biomedical Sciences, Florida State University College of Medicine, Tallahassee, FL, 32306, USA
| | - Peter Horvath
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Krisztina Buzas
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary
| | - Maria Harmati
- Department of Immunology, Albert Szent-Györgyi Medical School, Faculty of Science and Informatics, University of Szeged, 6726, Szeged, Hungary.
- Laboratory of Microscopic Image Analysis and Machine Learning, Institute of Biochemistry, Biological Research Centre, Hungarian Research Network (HUN-REN), Szeged, 6726, Hungary.
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19
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Gygi JP, Maguire C, Patel RK, Shinde P, Konstorum A, Shannon CP, Xu L, Hoch A, Jayavelu ND, Network I, Haddad EK, Reed EF, Kraft M, McComsey GA, Metcalf J, Ozonoff A, Esserman D, Cairns CB, Rouphael N, Bosinger SE, Kim-Schulze S, Krammer F, Rosen LB, van Bakel H, Wilson M, Eckalbar W, Maecker H, Langelier CR, Steen H, Altman MC, Montgomery RR, Levy O, Melamed E, Pulendran B, Diray-Arce J, Smolen KK, Fragiadakis GK, Becker PM, Augustine AD, Sekaly RP, Ehrlich LIR, Fourati S, Peters B, Kleinstein SH, Guan L. Integrated longitudinal multi-omics study identifies immune programs associated with COVID-19 severity and mortality in 1152 hospitalized participants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565292. [PMID: 37986828 PMCID: PMC10659275 DOI: 10.1101/2023.11.03.565292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Hospitalized COVID-19 patients exhibit diverse clinical outcomes, with some individuals diverging over time even though their initial disease severity appears similar. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. In this study, we carried out deep immunophenotyping and conducted longitudinal multi-omics modeling integrating ten distinct assays on a total of 1,152 IMPACC participants and identified several immune cascades that were significant drivers of differential clinical outcomes. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, NETosis, and T-cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma immunoglobulins and B cells, as well as dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to the failure of viral clearance in patients with fatal illness. Our longitudinal multi-omics profiling study revealed novel temporal coordination across diverse omics that potentially explain disease progression, providing insights that inform the targeted development of therapies for hospitalized COVID-19 patients, especially those critically ill.
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20
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Piton G, Allison SD, Bahram M, Hildebrand F, Martiny JBH, Treseder KK, Martiny AC. Life history strategies of soil bacterial communities across global terrestrial biomes. Nat Microbiol 2023; 8:2093-2102. [PMID: 37798477 DOI: 10.1038/s41564-023-01465-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/08/2023] [Indexed: 10/07/2023]
Abstract
The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Our findings provide a trait-based framework to compare life history strategies of soil bacteria.
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Affiliation(s)
- Gabin Piton
- Department of Earth System Science, University of California, Irvine, Irvine, CA, USA.
- Eco&Sols, University Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France.
| | - Steven D Allison
- Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
| | - Mohammad Bahram
- Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
- Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
| | - Jennifer B H Martiny
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
| | - Kathleen K Treseder
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
| | - Adam C Martiny
- Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
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21
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Mengelkoch S, Miryam Schüssler-Fiorenza Rose S, Lautman Z, Alley JC, Roos LG, Ehlert B, Moriarity DP, Lancaster S, Snyder MP, Slavich GM. Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations. Brain Behav Immun 2023; 114:475-487. [PMID: 37543247 PMCID: PMC11195542 DOI: 10.1016/j.bbi.2023.07.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/04/2023] [Accepted: 07/30/2023] [Indexed: 08/07/2023] Open
Abstract
The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
| | | | - Ziv Lautman
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jenna C Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Lydia G Roos
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Benjamin Ehlert
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Daniel P Moriarity
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
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22
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Kim D, Tran A, Kim HJ, Lin Y, Yang JYH, Yang P. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. NPJ Syst Biol Appl 2023; 9:51. [PMID: 37857632 PMCID: PMC10587078 DOI: 10.1038/s41540-023-00312-6] [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: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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Affiliation(s)
- Daniel Kim
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Andy Tran
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Yingxin Lin
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
| | - Pengyi Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
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23
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Downing T, Angelopoulos N. A primer on correlation-based dimension reduction methods for multi-omics analysis. J R Soc Interface 2023; 20:20230344. [PMID: 37817584 PMCID: PMC10565429 DOI: 10.1098/rsif.2023.0344] [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: 06/15/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.
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Affiliation(s)
- Tim Downing
- Pirbright Institute, Pirbright, Surrey, UK
- Department of Biotechnology, Dublin City University, Dublin, Ireland
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24
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Powell J, Powell S, Mather MW, Beck L, Nelson A, Palmowski P, Porter A, Coxhead J, Hedley A, Scott J, Rostron AJ, Hellyer TP, Zaidi F, Davey T, Garnett JP, Agbeko R, Ward C, Stewart CJ, Taggart CC, Brodlie M, Simpson AJ. Tracheostomy in children is associated with neutrophilic airway inflammation. Thorax 2023; 78:1019-1027. [PMID: 36808087 PMCID: PMC10511973 DOI: 10.1136/thorax-2022-219557] [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: 08/25/2022] [Accepted: 12/29/2022] [Indexed: 02/22/2023]
Abstract
BACKGROUND Tracheostomies in children are associated with significant morbidity, poor quality of life, excess healthcare costs and excess mortality. The underlying mechanisms facilitating adverse respiratory outcomes in tracheostomised children are poorly understood. We aimed to characterise airway host defence in tracheostomised children using serial molecular analyses. METHODS Tracheal aspirates, tracheal cytology brushings and nasal swabs were prospectively collected from children with a tracheostomy and controls. Transcriptomic, proteomic and metabolomic methods were applied to characterise the impact of tracheostomy on host immune response and the airway microbiome. RESULTS Children followed up serially from the time of tracheostomy up to 3 months postprocedure (n=9) were studied. A further cohort of children with a long-term tracheostomy were also enrolled (n=24). Controls (n=13) comprised children without a tracheostomy undergoing bronchoscopy. Long-term tracheostomy was associated with airway neutrophilic inflammation, superoxide production and evidence of proteolysis when compared with controls. Reduced airway microbial diversity was established pre-tracheostomy and sustained thereafter. CONCLUSIONS Long-term childhood tracheostomy is associated with a inflammatory tracheal phenotype characterised by neutrophilic inflammation and the ongoing presence of potential respiratory pathogens. These findings suggest neutrophil recruitment and activation as potential exploratory targets in seeking to prevent recurrent airway complications in this vulnerable group of patients.
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Affiliation(s)
- Jason Powell
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Paediatric Otolaryngology, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Steven Powell
- Department of Paediatric Otolaryngology, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Michael W Mather
- Department of Paediatric Otolaryngology, Great North Children's Hospital, Newcastle upon Tyne, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Lauren Beck
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Andrew Nelson
- Department of Applied Science, Northumbria University, Newcastle upon Tyne, UK
| | - Pawel Palmowski
- Protein and Proteome Facility, Newcastle University, Newcastle upon Tyne, UK
| | - Andrew Porter
- Protein and Proteome Facility, Newcastle University, Newcastle upon Tyne, UK
| | - Jonathan Coxhead
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ann Hedley
- Bioinformatics Support Unit, Newcastle University, Newcastle upon Tyne, UK
| | - Jonathan Scott
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Anthony J Rostron
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Thomas P Hellyer
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Fatima Zaidi
- Discovery and Translational Science, Metabolon, Morrisville, North Carolina, USA
| | - Tracey Davey
- Electron Microscopy Research Services, Newcastle University, Newcastle upon Tyne, UK
| | - James P Garnett
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rachel Agbeko
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Paediatric Intensive Care, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Chris Ward
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Christopher J Stewart
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Malcolm Brodlie
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Paediatric Respiratory Medicine, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - A John Simpson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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25
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2023:10.1038/s41587-023-01934-1. [PMID: 37679543 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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26
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Shinde P, Soldevila F, Reyna J, Aoki M, Rasmussen M, Willemsen L, Kojima M, Ha B, Greenbaum JA, Overton JA, Guzman-Orozco H, Nili S, Orfield S, Gygi JP, da Silva Antunes R, Sette A, Grant B, Olsen LR, Konstorum A, Guan L, Ay F, Kleinstein SH, Peters B. A systems vaccinology resource to develop and test computational models of immunity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.28.555193. [PMID: 37693565 PMCID: PMC10491180 DOI: 10.1101/2023.08.28.555193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Computational models that predict an individual's response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such 'dry run' for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.
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Affiliation(s)
- Pramod Shinde
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Ferran Soldevila
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Joaquin Reyna
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, CA, USA
| | - Minori Aoki
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mikkel Rasmussen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lisa Willemsen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mari Kojima
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Brendan Ha
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jason A Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - James A Overton
- Knocean Inc., 107 Quebec Ave. Toronto, Ontario, M6P 2T3, Canada
| | - Hector Guzman-Orozco
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Somayeh Nili
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Shelby Orfield
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jeremy P. Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Ricardo da Silva Antunes
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Barry Grant
- Department of Molecular Biology, School of Biological Sciences, University of California San Diego, La Jolla, California, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Anna Konstorum
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ferhat Ay
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Steven H. Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
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27
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Flynn E, Almonte-Loya A, Fragiadakis GK. Single-Cell Multiomics. Annu Rev Biomed Data Sci 2023; 6:313-337. [PMID: 37159875 PMCID: PMC11146013 DOI: 10.1146/annurev-biodatasci-020422-050645] [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] [Indexed: 05/11/2023]
Abstract
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.
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Affiliation(s)
- Emily Flynn
- CoLabs, University of California, San Francisco, California, USA;
| | - Ana Almonte-Loya
- CoLabs, University of California, San Francisco, California, USA;
- Biomedical Informatics Program, University of California, San Francisco, California, USA
| | - Gabriela K Fragiadakis
- CoLabs, University of California, San Francisco, California, USA;
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
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28
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Gygi JP, Kleinstein SH, Guan L. Predictive overfitting in immunological applications: Pitfalls and solutions. Hum Vaccin Immunother 2023; 19:2251830. [PMID: 37697867 PMCID: PMC10498807 DOI: 10.1080/21645515.2023.2251830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/27/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
Overfitting describes the phenomenon where a highly predictive model on the training data generalizes poorly to future observations. It is a common concern when applying machine learning techniques to contemporary medical applications, such as predicting vaccination response and disease status in infectious disease or cancer studies. This review examines the causes of overfitting and offers strategies to counteract it, focusing on model complexity reduction, reliable model evaluation, and harnessing data diversity. Through discussion of the underlying mathematical models and illustrative examples using both synthetic data and published real datasets, our objective is to equip analysts and bioinformaticians with the knowledge and tools necessary to detect and mitigate overfitting in their research.
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Affiliation(s)
- Jeremy P. Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Steven H. Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Leying Guan
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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29
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Nõlvak H, Truu M, Tiirik K, Devarajan AK, Peeb A, Truu J. The effect of synthetic silver nanoparticles on the antibiotic resistome and the removal efficiency of antibiotic resistance genes in a hybrid filter system treating municipal wastewater. WATER RESEARCH 2023; 237:119986. [PMID: 37098287 DOI: 10.1016/j.watres.2023.119986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023]
Abstract
Engineered nanoparticles, including silver nanoparticles (AgNPs), are released into the environment mainly through wastewater treatment systems. Knowledge of the impact of AgNPs on the abundance and removal efficiency of antibiotic resistance genes (ARGs) in wastewater treatment facilities, including constructed wetlands (CWs), is essential in the context of public health. This study evaluated the effect of increased (100-fold) collargol (protein-coated AgNPs) and ionic Ag+ in municipal wastewater on the structure, abundance, and removal efficiency of the antibiotic resistome, integron-integrase genes, and pathogens in a hybrid CW using quantitative PCR and metagenomic approaches. The abundance of ARGs in wastewater and the removal efficiency of ARGs in the hybrid system were significantly affected by higher Ag concentrations, especially with collargol treatment, resulting in an elevated ARG discharge of system effluent into the environment. The accumulated Ag in the filters had a more profound effect on the absolute and relative abundance of ARGs in the treated water than the Ag content in the water. This study recorded significantly enhanced relative abundance values for tetracycline (tetA, tetC, tetQ), sulfonamide (sul1, sul2), and aminoglycoside (aadA) resistance genes, which are frequently found on mobile genetic elements in collargol- and, to a lesser extent, AgNO3-treated subsystems. Elevated plasmid and integron-integrase gene levels, especially intI1, in response to collargol presence indicated the substantial role of AgNPs in promoting horizontal gene transfer in the treatment system. The pathogenic segment of the prokaryotic community was similar to a typical sewage community, and strong correlations between pathogen and ARG proportions were recorded in vertical subsurface flow filters. Furthermore, the proportion of Salmonella enterica was positively related to the Ag content in these filter effluents. The effect of AgNPs on the nature and characteristics of prominent resistance genes carried by mobile genetic elements in CWs requires further investigation.
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Affiliation(s)
- Hiie Nõlvak
- Institute of Molecular and Cell Biology, University of Tartu, Riia 23, Tartu 51010, Estonia.
| | - Marika Truu
- Institute of Molecular and Cell Biology, University of Tartu, Riia 23, Tartu 51010, Estonia
| | - Kertu Tiirik
- Institute of Molecular and Cell Biology, University of Tartu, Riia 23, Tartu 51010, Estonia
| | - Arun Kumar Devarajan
- Institute of Molecular and Cell Biology, University of Tartu, Riia 23, Tartu 51010, Estonia
| | - Angela Peeb
- Institute of Molecular and Cell Biology, University of Tartu, Riia 23, Tartu 51010, Estonia
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Riia 23, Tartu 51010, Estonia
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30
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Flores JE, Claborne DM, Weller ZD, Webb-Robertson BJM, Waters KM, Bramer LM. Missing data in multi-omics integration: Recent advances through artificial intelligence. Front Artif Intell 2023; 6:1098308. [PMID: 36844425 PMCID: PMC9949722 DOI: 10.3389/frai.2023.1098308] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
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Affiliation(s)
- Javier E. Flores
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Daniel M. Claborne
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Zachary D. Weller
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Katrina M. Waters
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States,*Correspondence: Lisa M. Bramer ✉
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31
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Tarfeen N, Nisa KU, Ali S, Yatoo AM, Shah AM, Sabba A, Maqbool R, Ahmad MB. Utility of proteomics and phosphoproteomics in the tailored medication of cancer. Proteomics 2023. [DOI: 10.1016/b978-0-323-95072-5.00006-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
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32
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Zhi Y, Li M, Lv G. Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation. Front Immunol 2023; 14:1058296. [PMID: 36798139 PMCID: PMC9927650 DOI: 10.3389/fimmu.2023.1058296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.
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Affiliation(s)
- Yao Zhi
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Mingqian Li
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
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33
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Ni Y, He J, Chalise P. Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization. Stat Appl Genet Mol Biol 2023; 22:sagmb-2022-0047. [PMID: 37937887 DOI: 10.1515/sagmb-2022-0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 09/25/2023] [Indexed: 11/09/2023]
Abstract
Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.
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Affiliation(s)
- Yonghui Ni
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Jianghua He
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Prabhakar Chalise
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
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34
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Ray A, Das J, Wenzel SE. Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning. Cell Rep Med 2022; 3:100857. [PMID: 36543110 PMCID: PMC9798025 DOI: 10.1016/j.xcrm.2022.100857] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/24/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022]
Abstract
There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.
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Affiliation(s)
- Anuradha Ray
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 3459 Fifth Avenue, MUH 628 NW, Pittsburgh, PA 15213, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| | - Jishnu Das
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Sally E Wenzel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 3459 Fifth Avenue, MUH 628 NW, Pittsburgh, PA 15213, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Environmental Medicine and Occupational Health, School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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35
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Devarajan AK, Truu M, Gopalasubramaniam SK, Muthukrishanan G, Truu J. Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits. Front Microbiol 2022; 13:1058772. [PMID: 36590400 PMCID: PMC9797599 DOI: 10.3389/fmicb.2022.1058772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Agricultural application of plant-beneficial bacteria to improve crop yield and alleviate the stress caused by environmental conditions, pests, and pathogens is gaining popularity. However, before using these bacterial strains in plant experiments, their environmental stress responses and plant health improvement potential should be examined. In this study, we explored the applicability of three unsupervised machine learning-based data integration methods, including principal component analysis (PCA) of concatenated data, multiple co-inertia analysis (MCIA), and multiple kernel learning (MKL), to select osmotic stress-tolerant plant growth-promoting (PGP) bacterial strains isolated from the rice phyllosphere. The studied datasets consisted of direct and indirect PGP activity measurements and osmotic stress responses of eight bacterial strains previously isolated from the phyllosphere of drought-tolerant rice cultivar. The production of phytohormones, such as indole-acetic acid (IAA), gibberellic acid (GA), abscisic acid (ABA), and cytokinin, were used as direct PGP traits, whereas the production of hydrogen cyanide and siderophore and antagonistic activity against the foliar pathogens Pyricularia oryzae and Helminthosporium oryzae were evaluated as measures of indirect PGP activity. The strains were subjected to a range of osmotic stress levels by adding PEG 6000 (0, 11, 21, and 32.6%) to their growth medium. The results of the osmotic stress response experiments showed that all bacterial strains accumulated endogenous proline and glycine betaine (GB) and exhibited an increase in growth, when osmotic stress levels were increased to a specific degree, while the production of IAA and GA considerably decreased. The three applied data integration methods did not provide a similar grouping of the strains. Especially deviant was the ordination of microbial strains based on the PCA of concatenated data. However, all three data integration methods indicated that the strains Bacillus altitudinis PB46 and B. megaterium PB50 shared high similarity in PGP traits and osmotic stress response. Overall, our results indicate that data integration methods complement the single-table data analysis approach and improve the selection process for PGP microbial strains.
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Affiliation(s)
- Arun Kumar Devarajan
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia,*Correspondence: Arun Kumar Devarajan,
| | - Marika Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Sabarinathan Kuttalingam Gopalasubramaniam
- Department of Plant Pathology, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Killikulam, Tuticorin, India,Sabarinathan Kuttalingam Gopalasubramaniam,
| | - Gomathy Muthukrishanan
- Department of Soil Science and Agricultural Chemistry, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Killikulam, Tuticorin, India
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
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Athieniti E, Spyrou GM. A guide to multi-omics data collection and integration for translational medicine. Comput Struct Biotechnol J 2022; 21:134-149. [PMID: 36544480 PMCID: PMC9747357 DOI: 10.1016/j.csbj.2022.11.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/02/2022] Open
Abstract
The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.
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Needhamsen M, Khoonsari PE, Zheleznyakova GY, Piket E, Hagemann-Jensen M, Han Y, Gierlich J, Ekman D, Jagodic M. Integration of small RNAs from plasma and cerebrospinal fluid for classification of multiple sclerosis. Front Genet 2022; 13:1042483. [DOI: 10.3389/fgene.2022.1042483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/21/2022] [Indexed: 11/18/2022] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune, neurological disease, commonly presenting with a relapsing-remitting form, that later converts to a secondary progressive stage, referred to as RRMS and SPMS, respectively. Early treatment slows disease progression, hence, accurate and early diagnosis is crucial. Recent advances in large-scale data processing and analysis have progressed molecular biomarker development. Here, we focus on small RNA data derived from cell-free cerebrospinal fluid (CSF), cerebrospinal fluid cells, plasma and peripheral blood mononuclear cells as well as CSF cell methylome data, from people with RRMS (n = 20), clinically/radiologically isolated syndrome (CIS/RIS, n = 2) and neurological disease controls (n = 14). We applied multiple co-inertia analysis (MCIA), an unsupervised and thereby unbiased, multivariate method for simultaneous data integration and found that the top latent variable classifies RRMS status with an Area Under the Receiver Operating Characteristics (AUROC) score of 0.82. Variable selection based on Lasso regression reduced features to 44, derived from the small RNAs from plasma (20), CSF cells (8) and cell-free CSF (16), with a marginal reduction in AUROC to 0.79. Samples from SPMS patients (n = 6) were subsequently projected on the latent space and differed significantly from RRMS and controls. On contrary, we found no differences between relapse and remission or between inflammatory and non-inflammatory disease controls, suggesting that the latent variable is not prone to inflammatory signals alone, but could be MS-specific. Hence, we here showcase that integration of small RNAs from plasma and CSF can be utilized to distinguish RRMS from SPMS and neurological disease controls.
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A multi-marker integrative analysis reveals benefits and risks of bariatric surgery. Sci Rep 2022; 12:18877. [PMID: 36344536 PMCID: PMC9640526 DOI: 10.1038/s41598-022-23241-6] [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: 05/17/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
Abstract
Bariatric surgery (BS) is an effective intervention for severe obesity and associated comorbidities. Although several studies have addressed the clinical and metabolic effects of BS, an integrative analysis of the complex body response to surgery is still lacking. We conducted a longitudinal data study with 36 patients with severe obesity who were tested before, 6 and 12 months after restrictive BS for more than one hundred blood biomarkers, including clinical, oxidative stress and metabolic markers, peptide mediators and red blood cell membrane lipids. By using a synthetic data-driven modeling based on principal component and correlation analyses, we provided evidence that, besides the early, well-known glucose metabolism- and weight loss-associated beneficial effects of BS, a tardive, weight-independent increase of the hepatic cholesterol metabolism occurs that is associated with potentially detrimental inflammatory and metabolic effects. Canonical correlation analysis indicated that oxidative stress is the most predictive feature of the BS-induced changes of both glucose and lipids metabolism. Our results show the power of multi-level correlation analysis to uncover the network of biological pathways affected by BS. This approach highlighted potential health risks of restrictive BS that are disregarded with the current practice to use weight loss as surrogate of BS success.
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Zhang R, Zhang C, Yu C, Dong J, Hu J. Integration of multi-omics technologies for crop improvement: Status and prospects. FRONTIERS IN BIOINFORMATICS 2022; 2:1027457. [PMID: 36438626 PMCID: PMC9689701 DOI: 10.3389/fbinf.2022.1027457] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 08/03/2023] Open
Abstract
With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.
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Maghsoudi Z, Nguyen H, Tavakkoli A, Nguyen T. A comprehensive survey of the approaches for pathway analysis using multi-omics data integration. Brief Bioinform 2022; 23:6761962. [PMID: 36252928 PMCID: PMC9677478 DOI: 10.1093/bib/bbac435] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/26/2022] [Accepted: 09/08/2022] [Indexed: 02/07/2023] Open
Abstract
Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.
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Affiliation(s)
- Zeynab Maghsoudi
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Ha Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Alireza Tavakkoli
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Tin Nguyen
- Corresponding author: Tin Nguyen, Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA. Tel.: +1-775-784-6619;
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Heumos S, Dehn S, Bräutigam K, Codrea MC, Schürch CM, Lauer UM, Nahnsen S, Schindler M. Multiomics surface receptor profiling of the NCI-60 tumor cell panel uncovers novel theranostics for cancer immunotherapy. Cancer Cell Int 2022; 22:311. [PMID: 36221114 PMCID: PMC9555072 DOI: 10.1186/s12935-022-02710-y] [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: 07/25/2022] [Accepted: 08/30/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized cancer therapy. However, therapeutic targeting of inhibitory T cell receptors such as PD-1 not only initiates a broad immune response against tumors, but also causes severe adverse effects. An ideal future stratified immunotherapy would interfere with cancer-specific cell surface receptors only. METHODS To identify such candidates, we profiled the surface receptors of the NCI-60 tumor cell panel via flow cytometry. The resulting surface receptor expression data were integrated into proteomic and transcriptomic NCI-60 datasets applying a sophisticated multiomics multiple co-inertia analysis (MCIA). This allowed us to identify surface profiles for skin, brain, colon, kidney, and bone marrow derived cell lines and cancer entity-specific cell surface receptor biomarkers for colon and renal cancer. RESULTS For colon cancer, identified biomarkers are CD15, CD104, CD324, CD326, CD49f, and for renal cancer, CD24, CD26, CD106 (VCAM1), EGFR, SSEA-3 (B3GALT5), SSEA-4 (TMCC1), TIM1 (HAVCR1), and TRA-1-60R (PODXL). Further data mining revealed that CD106 (VCAM1) in particular is a promising novel immunotherapeutic target for the treatment of renal cancer. CONCLUSION Altogether, our innovative multiomics analysis of the NCI-60 panel represents a highly valuable resource for uncovering surface receptors that could be further exploited for diagnostic and therapeutic purposes in the context of cancer immunotherapy.
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Affiliation(s)
- Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, 72076, Tübingen, Germany.,Biomedical Data Science, Dept. of Computer Science, University of Tübingen, 72076, Tübingen, Germany
| | - Sandra Dehn
- Institute for Medical Virology and Epidemiology of Viral Diseases, University Hospital Tübingen, Tübingen, Germany
| | | | - Marius C Codrea
- Quantitative Biology Center (QBiC), University of Tübingen, 72076, Tübingen, Germany
| | - Christian M Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Ulrich M Lauer
- Department of Internal Medicine VIII, Medical Oncology and Pneumology, Virotherapy Center Tübingen (VCT), Medical University Hospital Tübingen, 72076, Tübingen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Tübingen, 72076, Tübingen, Germany
| | - Sven Nahnsen
- Quantitative Biology Center (QBiC), University of Tübingen, 72076, Tübingen, Germany.,Biomedical Data Science, Dept. of Computer Science, University of Tübingen, 72076, Tübingen, Germany
| | - Michael Schindler
- Institute for Medical Virology and Epidemiology of Viral Diseases, University Hospital Tübingen, Tübingen, Germany.
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ElKarami B, Alkhateeb A, Qattous H, Alshomali L, Shahrrava B. Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network. Cancer Inform 2022; 21:11769351221124205. [PMID: 36187912 PMCID: PMC9523837 DOI: 10.1177/11769351221124205] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 08/14/2022] [Indexed: 11/15/2022] Open
Abstract
Introduction Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics data for biomedical applications, including disease prediction and disease subtypes, biomarker prediction, and others. Methods In this paper, we introduce a multi-omics data integration method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients. Results The model proposed near perfection with accuracy above 99% with all other performance measurements at the same level. The proposed model outperformed the state-of-art iSOM-GSN model that constructs the GSN map based on the self-organizing map. Conclusion The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SOM. The proposed model can also be applied to build a multi-omics prediction model for other types of cancer.
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Affiliation(s)
- Bashier ElKarami
- Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada
| | - Abedalrhman Alkhateeb
- Software Engineering Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Al-Jubaiha, Amman, Jordan
- Abedalrhman Alkhateeb, Software Engineering Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, P. O. Box 1438, Al-Jubaiha, Amman 11941, Jordan.
| | - Hazem Qattous
- Software Engineering Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Al-Jubaiha, Amman, Jordan
| | - Lujain Alshomali
- Software Engineering Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Al-Jubaiha, Amman, Jordan
| | - Behnam Shahrrava
- Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada
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Brault C, Lazerges J, Doligez A, Thomas M, Ecarnot M, Roumet P, Bertrand Y, Berger G, Pons T, François P, Le Cunff L, This P, Segura V. Interest of phenomic prediction as an alternative to genomic prediction in grapevine. PLANT METHODS 2022; 18:108. [PMID: 36064570 PMCID: PMC9442960 DOI: 10.1186/s13007-022-00940-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. RESULTS For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. CONCLUSION NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
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Affiliation(s)
- Charlotte Brault
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
- Institut Français de la vigne et du vin, 34398, Montpellier, France
| | - Juliette Lazerges
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Agnès Doligez
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Miguel Thomas
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Martin Ecarnot
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
| | - Pierre Roumet
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
| | - Yves Bertrand
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Gilles Berger
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Thierry Pons
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Pierre François
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Loïc Le Cunff
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
- Institut Français de la vigne et du vin, 34398, Montpellier, France
| | - Patrice This
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France
| | - Vincent Segura
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier, 34398, France.
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, 34398, Montpellier, France.
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Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:100978. [PMID: 35936554 PMCID: PMC9354369 DOI: 10.1016/j.xcrp.2022.100978] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.
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Affiliation(s)
- Lauren M. Petrick
- The Bert Strassburger Metabolic Center, Sheba Medical Center, Tel-Hashomer, Israel
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Exposomics Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Shomron
- Faculty of Medicine, Edmond J. Safra Center for Bioinformatics, Sagol School of Neuroscience, Center for Nanoscience and Nanotechnology, Center for Innovation Laboratories (TILabs), Tel Aviv University, Tel Aviv, Israel
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Wang X, Wen Y. A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data. Brief Bioinform 2022; 23:6596990. [PMID: 35649346 PMCID: PMC9310531 DOI: 10.1093/bib/bbac193] [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: 01/25/2022] [Revised: 03/18/2022] [Accepted: 04/27/2022] [Indexed: 11/13/2022] Open
Abstract
With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.
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Affiliation(s)
- Xiaqiong Wang
- Department of Statistics, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
| | - Yalu Wen
- Department of Statistics, University of Auckland, 38 Princes Street, 1010, Auckland, New Zealand
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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Liu Z, Xu J, Que S, Geng L, Zhou L, Mardinoglu A, Zheng S. Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation. J Clin Transl Hepatol 2022; 10:363-373. [PMID: 35528975 PMCID: PMC9039708 DOI: 10.14218/jcth.2021.00219] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/14/2021] [Accepted: 10/07/2021] [Indexed: 12/04/2022] Open
Abstract
Omics data address key issues in liver transplantation (LT) as the most effective therapeutic means for end-stage liver disease. The purpose of this study was to review the current application and future direction for omics in LT. We reviewed the use of multiomics to elucidate the pathogenesis leading to LT and prognostication. Future directions with respect to the use of omics in LT are also described based on perspectives of surgeons with experience in omics. Significant molecules were identified and summarized based on omics, with a focus on post-transplant liver fibrosis, early allograft dysfunction, tumor recurrence, and graft failure. We emphasized the importance omics for clinicians who perform LTs and prioritized the directions that should be established. We also outlined the ideal workflow for omics in LT. In step with advances in technology, the quality of omics data can be guaranteed using an improved algorithm at a lower price. Concerns should be addressed on the translational value of omics for better therapeutic effects in patients undergoing LT.
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Affiliation(s)
- Zhengtao Liu
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Key Laboratory of the diagnosis and treatment of organ Transplantation, CAMS, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Organ Transplantation, Zhejiang Province, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jun Xu
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuping Que
- DingXiang Clinics, Hangzhou, Zhejiang, China
| | - Lei Geng
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lin Zhou
- NHC Key Laboratory of Combined Multi-organ Transplantation, Key Laboratory of the diagnosis and treatment of organ Transplantation, CAMS, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Organ Transplantation, Zhejiang Province, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
- Correspondence to: Adil Mardinoglu, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID: https://orcid.org/0000-0002-4254-6090. Tel: +46-31-772-3140, Fax: +46-31-772-3801, E-mail: ; Shusen Zheng, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China. ORCID: https://orcid.org/0000-0003-1459-8261. Tel/Fax: +86-571-87236570, E-mail:
| | - Shusen Zheng
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Key Laboratory of the diagnosis and treatment of organ Transplantation, CAMS, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Organ Transplantation, Zhejiang Province, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Correspondence to: Adil Mardinoglu, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden. ORCID: https://orcid.org/0000-0002-4254-6090. Tel: +46-31-772-3140, Fax: +46-31-772-3801, E-mail: ; Shusen Zheng, Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China. ORCID: https://orcid.org/0000-0003-1459-8261. Tel/Fax: +86-571-87236570, E-mail:
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Yuan D, Gaynanova I. Double-matched matrix decomposition for multi-view data. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2067860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Vahabi N, Michailidis G. Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Front Genet 2022; 13:854752. [PMID: 35391796 PMCID: PMC8981526 DOI: 10.3389/fgene.2022.854752] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 02/28/2022] [Indexed: 12/26/2022] Open
Abstract
Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.
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Affiliation(s)
- Nasim Vahabi
- Informatics Institute, University of Florida, Gainesville, FL, United States
| | - George Michailidis
- Informatics Institute, University of Florida, Gainesville, FL, United States
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Zenere A, Rundquist O, Gustafsson M, Altafini C. Multi-omics protein-coding units as massively parallel Bayesian networks: empirical validation of causality structure. iScience 2022; 25:104048. [PMID: 35355520 PMCID: PMC8958332 DOI: 10.1016/j.isci.2022.104048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/17/2022] [Accepted: 03/08/2022] [Indexed: 11/29/2022] Open
Abstract
In this article we use high-throughput epigenomics, transcriptomics, and proteomics data to construct fine-graded models of the “protein-coding units” gathering all transcript isoforms and chromatin accessibility peaks associated with more than 4000 genes in humans. Each protein-coding unit has the structure of a directed acyclic graph (DAG) and can be represented as a Bayesian network. The factorization of the joint probability distribution induced by the DAGs imposes a number of conditional independence relationships among the variables forming a protein-coding unit, corresponding to the missing edges in the DAGs. We show that a large fraction of these conditional independencies are indeed verified by the data. Factors driving this verification appear to be the structural and functional annotation of the transcript isoforms, as well as a notion of structural balance (or frustration-free) of the corresponding sample correlation graph, which naturally leads to reduction of correlation (and hence to independence) upon conditioning. Protein coding unit: DAG associated with epigenetic and gene information of a protein DAGs correspond to Bayesian networks Edge absence on a DAG corresponds to conditional independence Multi-omics data (ATAC-seq, RNA-seq and mass-spec) are used for DAG validation
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