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Ali M, Yang T, He H, Zhang Y. Plant biotechnology research with single-cell transcriptome: recent advancements and prospects. PLANT CELL REPORTS 2024; 43:75. [PMID: 38381195 DOI: 10.1007/s00299-024-03168-0] [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: 09/12/2023] [Accepted: 02/05/2024] [Indexed: 02/22/2024]
Abstract
KEY MESSAGE Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.
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Affiliation(s)
- Muhammad Ali
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China
- Peking University-Institute of Advanced Agricultural Sciences, Weifang, China
| | - Tianxia Yang
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding (MOE), China Agricultural University, Beijing, China
| | - Hai He
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China
| | - Yu Zhang
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China.
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2
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Zong W, Seney ML, Ketchesin KD, Gorczyca MT, Liu AC, Esser KA, Tseng GC, McClung CA, Huo Z. Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model. Stat Med 2023; 42:3236-3258. [PMID: 37265194 PMCID: PMC10425922 DOI: 10.1002/sim.9803] [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: 01/03/2023] [Revised: 03/27/2023] [Accepted: 05/09/2023] [Indexed: 06/03/2023]
Abstract
Circadian clocks are 24-h endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub ( https://github.com/circaPower/circaPower).
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Affiliation(s)
- Wei Zong
- Department of Biostatistics, University of Pittsburgh, PA, USA
| | - Marianne L. Seney
- Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, University of Pittsburgh, PA, USA
| | - Kyle D. Ketchesin
- Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, University of Pittsburgh, PA, USA
| | - Michael T. Gorczyca
- Department of Computational and Systems Biology, University of Pittsburgh, PA, USA
| | - Andrew C. Liu
- Department of Physiology and Aging, University of Florida, FL, USA
| | - Karyn A. Esser
- Department of Physiology and Aging, University of Florida, FL, USA
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, PA, USA
| | - Colleen A. McClung
- Translational Neuroscience Program, Department of Psychiatry, Center for Neuroscience, University of Pittsburgh, PA, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Florida, FL, USA
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3
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Rahnenführer J, De Bin R, Benner A, Ambrogi F, Lusa L, Boulesteix AL, Migliavacca E, Binder H, Michiels S, Sauerbrei W, McShane L. Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges. BMC Med 2023; 21:182. [PMID: 37189125 DOI: 10.1186/s12916-023-02858-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/03/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. METHODS Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. RESULTS The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. CONCLUSIONS This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.
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Affiliation(s)
| | | | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Federico Ambrogi
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Scientific Directorate, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Lara Lusa
- Department of Mathematics, Faculty of Mathematics, Natural Sciences and Information Technology, University of Primorksa, Koper, Slovenia
- Institute of Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany
| | | | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Stefan Michiels
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France
| | - Willi Sauerbrei
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Lisa McShane
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
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4
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Ma Y, Sun Z, Zeng P, Zhang W, Lin Z. JSNMF enables effective and accurate integrative analysis of single-cell multiomics data. Brief Bioinform 2022; 23:6563185. [PMID: 35380624 DOI: 10.1093/bib/bbac105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/25/2022] [Accepted: 03/02/2022] [Indexed: 01/18/2023] Open
Abstract
The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular heterogeneity from different layers of transcriptional regulation. However, the datasets generated from these technologies tend to have high levels of noise, making data analysis challenging. Here, we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile toolkit for the integrative analysis of transcriptomic and epigenomic data profiled from the same cell. JSNMF enables data visualization and clustering of the cells and also facilitates downstream analysis, including the characterization of markers and functional pathway enrichment analysis. The core of JSNMF is an unsupervised method based on JSNMF, where it assumes different latent variables for the two molecular modalities, and integrates the information of transcriptomic and epigenomic data with consensus graph fusion, which better tackles the distinct characteristics and levels of noise across different molecular modalities in single-cell multiomics data. We applied JSNMF to single-cell multiomics datasets from different tissues and different technologies. The results demonstrate the superior performance of JSNMF in clustering and data visualization of the cells. JSNMF also allows joint analysis of multiple single-cell multiomics experiments and single-cell multiomics data with more than two modalities profiled on the same cell. JSNMF also provides rich biological insight on the markers, cell-type-specific region-gene associations and the functions of the identified cell subpopulation.
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Affiliation(s)
- Yuanyuan Ma
- School of Computer & Information Engineering, Anyang Normal University, Anyang Henan, China.,Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zexuan Sun
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Pengcheng Zeng
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wenyu Zhang
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zhixiang Lin
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
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5
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Meng L, Avram D, Tseng G, Huo Z. Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lingsong Meng
- Department of Biostatistics University of Florida Gainesville USA
| | - Dorina Avram
- Department of Immunology H. Lee Moffitt Cancer Center and Research Institute Tampa USA
| | - George Tseng
- Department of Biostatistics University of Pittsburgh Pittsburgh USA
| | - Zhiguang Huo
- Department of Biostatistics University of Florida Gainesville USA
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6
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De Vito R, Bellio R, Trippa L, Parmigiani G. Bayesian multistudy factor analysis for high-throughput biological data. Ann Appl Stat 2021. [DOI: 10.1214/21-aoas1456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | - Ruggero Bellio
- Department of Economics and Statistics, University of Udine
| | - Lorenzo Trippa
- Department of Data Science, Dana Farber Cancer Institute
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7
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Ye Z, Ke H, Chen S, Cruz-Cano R, He X, Zhang J, Dorgan J, Milton DK, Ma T. Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer Studies. Front Genet 2021; 12:651546. [PMID: 34276766 PMCID: PMC8283696 DOI: 10.3389/fgene.2021.651546] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/28/2021] [Indexed: 01/21/2023] Open
Abstract
With the increasing availability and dropping cost of high-throughput technology in recent years, many-omics datasets have accumulated in the public domain. Combining multiple transcriptomic studies on related hypothesis via meta-analysis can improve statistical power and reproducibility over single studies. For differential expression (DE) analysis, biomarker categorization by DE pattern across studies is a natural but critical task following biomarker detection to help explain between study heterogeneity and classify biomarkers into categories with potentially related functionality. In this paper, we propose a novel meta-analysis method to categorize biomarkers by simultaneously considering the concordant pattern and the biological and statistical significance across studies. Biomarkers with the same DE pattern can be analyzed together in downstream pathway enrichment analysis. In the presence of different types of transcripts (e.g., mRNA, miRNA, and lncRNA, etc.), integrative analysis including miRNA/lncRNA target enrichment analysis and miRNA-mRNA and lncRNA-mRNA causal regulatory network analysis can be conducted jointly on all the transcripts of the same category. We applied our method to two Pan-cancer transcriptomic study examples with single or multiple types of transcripts available. Targeted downstream analysis identified categories of biomarkers with unique functionality and regulatory relationships that motivate new hypothesis in Pan-cancer analysis.
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Affiliation(s)
- Zhenyao Ye
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Shuo Chen
- Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Raul Cruz-Cano
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Xin He
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Joanne Dorgan
- Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Donald K Milton
- Maryland Institute for Applied Environmental Health, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
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8
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Seyfferth C, Renema J, Wendrich JR, Eekhout T, Seurinck R, Vandamme N, Blob B, Saeys Y, Helariutta Y, Birnbaum KD, De Rybel B. Advances and Opportunities in Single-Cell Transcriptomics for Plant Research. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:847-866. [PMID: 33730513 PMCID: PMC7611048 DOI: 10.1146/annurev-arplant-081720-010120] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.
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Affiliation(s)
- Carolin Seyfferth
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jim Renema
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jos R Wendrich
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Thomas Eekhout
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Ruth Seurinck
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
| | - Niels Vandamme
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
| | - Bernhard Blob
- The Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
- Viikki Plant Science Centre, HiLIFE/Organismal and Evolutionary Biology Research Program, Institute of Biotechnology, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
| | - Yrjo Helariutta
- The Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
- Viikki Plant Science Centre, HiLIFE/Organismal and Evolutionary Biology Research Program, Institute of Biotechnology, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
| | - Kenneth D Birnbaum
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA;
| | - Bert De Rybel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
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9
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Saegusa T, Zhao Z, Ke H, Ye Z, Xu Z, Chen S, Ma T. Detecting survival-associated biomarkers from heterogeneous populations. Sci Rep 2021; 11:3203. [PMID: 33547332 PMCID: PMC7865037 DOI: 10.1038/s41598-021-82332-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023] Open
Abstract
Detection of prognostic factors associated with patients' survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers. However, new challenges arise from heterogeneity in study populations and designs when jointly analyzing the multiple studies. For example, patients from different cohorts show different demographic characteristics and risk profiles. Existing high-dimensional variable selection methods for survival analysis, however, are restricted to single study analysis. We propose a novel Cox model based two-stage variable selection method called "Cox-TOTEM" to detect survival-associated biomarkers common in multiple genomic studies. Simulations showed our method greatly improved the sensitivity of variable selection as compared to the separate applications of existing methods to each study, especially when the signals are weak or when the studies are heterogeneous. An application of our method to TCGA transcriptomic data identified essential survival associated genes related to the common disease mechanism of five Pan-Gynecologic cancers.
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Affiliation(s)
- Takumi Saegusa
- grid.164295.d0000 0001 0941 7177Department of Mathematics, University of Maryland, College Park, MD 20742 USA
| | - Zhiwei Zhao
- grid.164295.d0000 0001 0941 7177Department of Mathematics, University of Maryland, College Park, MD 20742 USA
| | - Hongjie Ke
- grid.164295.d0000 0001 0941 7177Department of Mathematics, University of Maryland, College Park, MD 20742 USA
| | - Zhenyao Ye
- grid.164295.d0000 0001 0941 7177Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20740 USA
| | - Zhongying Xu
- grid.21925.3d0000 0004 1936 9000Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Shuo Chen
- grid.411024.20000 0001 2175 4264Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - Tianzhou Ma
- grid.164295.d0000 0001 0941 7177Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20740 USA
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10
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Furuhata H, Araki K, Ogawa T. Event Surrogate from Clinical Pathway Completion to Daily Meal for Availability Extension Using Standard Electronic Medical Records: a Retrospective Cohort Study. J Med Syst 2021; 45:33. [PMID: 33547499 DOI: 10.1007/s10916-021-01714-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 01/20/2021] [Indexed: 11/24/2022]
Abstract
This study aimed to improve generalizability of our previous study that analyzed clinical pathway (CP) completion. Although our previous study demonstrated that CP completion can reduce the length of hospital stay, it is possible for few medical organizations to extract the implementation of treatment registered on CP from typical electronic medical records. Therefore, we have defined a prospective event for event substitution, called meal completion (MC), in which patients can take their meal daily. Data were collected from April 2013 to March 2018 from the electronic medical records of the University of Miyazaki Hospital. We used propensity score matching to extract records from 8033 patients. Patients were further divided into the MC and non-MC groups; 2577 patients in each group were available for data analysis. The numbers of patients with CP completion were 646 (28.1%) in the MC group and 411 (18.2%) in the non-MC group. The P value of the chi-square test was <0.001. According to this result, there was the causation from MC to increase in CP completion. Additionally, it was possible to consider the inclusion relationship in all treatments (universal set), treatments registered on CP (subset of all treatments), and meals (subset of treatments registered on CP). In conclusion, MC can substitute for CP completion because the demonstration is appropriate for the Prentice criterion, which is often used for the evaluation of a surrogate endpoint.
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Affiliation(s)
- Hiroki Furuhata
- Department of Hospital Institutional Research, University of Miyazaki Hospital, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan. .,Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan.
| | - Kenji Araki
- Department of Hospital Institutional Research, University of Miyazaki Hospital, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan
| | - Taisuke Ogawa
- Department of Hospital Institutional Research, University of Miyazaki Hospital, 5200 Kibara Kiyotake-cho, Miyazaki, 8891692, Japan
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11
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Abbasi L, Rojhani-Shirazi Z, Razeghi M, Raeisi-Shahraki H. Kinematic cluster analysis of the crouch gait pattern in children with spastic diplegic cerebral palsy using sparse K-means method. Clin Biomech (Bristol, Avon) 2021; 81:105248. [PMID: 33340832 DOI: 10.1016/j.clinbiomech.2020.105248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 12/01/2020] [Accepted: 12/07/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Crouch gait pattern is a common gait pattern in children with diplegic cerebral palsy with excessive knee flexion throughout stance phase. Few studies have grouped this pattern of gait and usually have examined only the features of gait in the sagittal plane and mostly lower extremities without considering pelvis and trunk behavior. Studies usually categorize the gait pattern according to important variables from the researcher's point of view. Sparse K-means is high dimensional clustering methods that perform clustering and variable selection simultaneously even with low sample size and large number of variables. Our aim was to define existing clusters of crouch gait pattern in children with spastic diplegic cerebral palsy. METHODS Cluster analysis was applied on the lower extremity, pelvis and trunk gait kinematics data of 64 limbs of children with crouch gait pattern and 64 limbs of typically developing children. Eighty-nine kinematic variables were used as input variables for clustering. FINDINGS Four clusters of crouch gait pattern were defined. Sparse K-means identified influential variables and identified the knee and hip flexion as a major factor in clustering. Kinematic of the trunk, pelvis and ankle was determined in each cluster. Trunk and pelvis kinematic features were strongly correlated with the knee and hip joint flexion severity. INTERPRETATION Obtained clusters were confirmed observationally. With increasing knee flexion, the kinematic of the trunk and pelvis were further away from the patterns of typically developing individuals. The clusters ranking appear to be reasonable based on the crouch severity.
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Affiliation(s)
- Leila Abbasi
- Department of Physiotherapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Zahra Rojhani-Shirazi
- Department of Physiotherapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Mohsen Razeghi
- Department of Physiotherapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hadi Raeisi-Shahraki
- Department of Epidemiology and Biostatistics, School of Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
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12
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Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction. Nat Commun 2020; 11:3274. [PMID: 32612268 PMCID: PMC7330047 DOI: 10.1038/s41467-020-16905-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 05/29/2020] [Indexed: 01/22/2023] Open
Abstract
Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs—the reference panel and the chain-type designs—true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data. It is not clear which designs, other than completely randomized ones, are valid for scRNA-seq experiments so that batch effects can be adjusted. Here the authors show that under flexible reference panel and chain-type designs, biological variability can also be separated from batch effects, at least by BUSseq.
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13
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Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery. STATISTICS IN BIOSCIENCES 2019. [DOI: 10.1007/s12561-019-09242-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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14
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Kim S, Kang D, Huo Z, Park Y, Tseng GC. Meta-analytic principal component analysis in integrative omics application. Bioinformatics 2019; 34:1321-1328. [PMID: 29186328 DOI: 10.1093/bioinformatics/btx765] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Accepted: 11/22/2017] [Indexed: 12/15/2022] Open
Abstract
Motivation With the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high-dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization. Results In this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta-analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta-analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan-cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework. Availability and implementation An R package MetaPCA is available online. (http://tsenglab.biostat.pitt.edu/software.htm). Contact ctseng@pitt.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- SungHwan Kim
- Department of Statistics, Keimyung University, Daegu 42601, South Korea
| | - Dongwan Kang
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Zhiguang Huo
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yongseok Park
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - George C Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA.,Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
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15
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Huo Z, Song C, Tseng G. BAYESIAN LATENT HIERARCHICAL MODEL FOR TRANSCRIPTOMIC META-ANALYSIS TO DETECT BIOMARKERS WITH CLUSTERED META-PATTERNS OF DIFFERENTIAL EXPRESSION SIGNALS. Ann Appl Stat 2019; 13:340-366. [PMID: 31007807 PMCID: PMC6472949 DOI: 10.1214/18-aoas1188] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Due to the rapid development of high-throughput experimental techniques and fast-dropping prices, many transcriptomic datasets have been generated and accumulated in the public domain. Meta-analysis combining multiple transcriptomic studies can increase the statistical power to detect disease-related biomarkers. In this paper, we introduce a Bayesian latent hierarchical model to perform transcriptomic meta-analysis. This method is capable of detecting genes that are differentially expressed (DE) in only a subset of the combined studies, and the latent variables help quantify homogeneous and heterogeneous differential expression signals across studies. A tight clustering algorithm is applied to detected biomarkers to capture differential meta-patterns that are informative to guide further biological investigation. Simulations and three examples, including a microarray dataset from metabolism-related knockout mice, an RNA-seq dataset from HIV transgenic rats, and cross-platform datasets from human breast cancer, are used to demonstrate the performance of the proposed method.
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Affiliation(s)
- Zhiguang Huo
- Department of Biostatistics University of Florida Gainesville, FL 32611
| | - Chi Song
- Division of Biostatistics College of Public Health The Ohio State University Columbus, OH 43210
| | - George Tseng
- Department of Biostatistics, Human Genetics and Computational Biology University of Pittsburgh Pittsburgh, PA 15261
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16
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Yu Z. Big Data Clustering Analysis Algorithm for Internet of Things Based on K-Means. INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2019. [DOI: 10.4018/ijdst.2019010101] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
To explore the Internet of things logistics system application, an Internet of things big data clustering analysis algorithm based on K-mans was discussed. First of all, according to the complex event relation and processing technology, the big data processing of Internet of things was transformed into the extraction and analysis of complex relational schema, so as to provide support for simplifying the processing complexity of big data in Internet of things (IOT). The traditional K-means algorithm was optimized and improved to make it fit the demand of big data RFID data network. Based on Hadoop cloud cluster platform, a K-means cluster analysis was achieved. In addition, based on the traditional clustering algorithm, a center point selection technology suitable for RFID IOT data clustering was selected. The results showed that the clustering efficiency was improved to some extent. As a result, an RFID Internet of things clustering analysis prototype system is designed and realized, which further tests the feasibility.
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Affiliation(s)
- Zhanqiu Yu
- Anhui Technical College of Industry and Economy, Hefei, China
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17
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Affiliation(s)
- Xiangyu Luo
- Department of Statistics, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong
| | - Yingying Wei
- Department of Statistics, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong
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18
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Ma T, Song C, Tseng GC. Discussant paper on ‘Statistical contributions to bioinformatics: Design, modelling, structure learning and integration’. STAT MODEL 2017. [DOI: 10.1177/1471082x17705992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Tianzhou Ma
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh Pittsburgh, PA, USA
| | - Chi Song
- Division of Biostatistics, College of Public Health, Ohio State University, Columbus, OH, USA
| | - George C. Tseng
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh Pittsburgh, PA, USA
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19
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Huo Z, Tseng G. Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery. Ann Appl Stat 2017; 11:1011-1039. [PMID: 28959370 PMCID: PMC5613668 DOI: 10.1214/17-aoas1033] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is-K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.
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Affiliation(s)
- Zhiguang Huo
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, ennsylvania 15261, USA
| | - George Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, ennsylvania 15261, USA
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