1
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Bogan SN, Strader ME, Hofmann GE. Associations between DNA methylation and gene regulation depend on chromatin accessibility during transgenerational plasticity. BMC Biol 2023; 21:149. [PMID: 37365578 DOI: 10.1186/s12915-023-01645-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Epigenetic processes are proposed to be a mechanism regulating gene expression during phenotypic plasticity. However, environmentally induced changes in DNA methylation exhibit little-to-no association with differential gene expression in metazoans at a transcriptome-wide level. It remains unexplored whether associations between environmentally induced differential methylation and expression are contingent upon other epigenomic processes such as chromatin accessibility. We quantified methylation and gene expression in larvae of the purple sea urchin Strongylocentrotus purpuratus exposed to different ecologically relevant conditions during gametogenesis (maternal conditioning) and modeled changes in gene expression and splicing resulting from maternal conditioning as functions of differential methylation, incorporating covariates for genomic features and chromatin accessibility. We detected significant interactions between differential methylation, chromatin accessibility, and genic feature type associated with differential expression and splicing. RESULTS Differential gene body methylation had significantly stronger effects on expression among genes with poorly accessible transcriptional start sites while baseline transcript abundance influenced the direction of this effect. Transcriptional responses to maternal conditioning were 4-13 × more likely when accounting for interactions between methylation and chromatin accessibility, demonstrating that the relationship between differential methylation and gene regulation is partially explained by chromatin state. CONCLUSIONS DNA methylation likely possesses multiple associations with gene regulation during transgenerational plasticity in S. purpuratus and potentially other metazoans, but its effects are dependent on chromatin accessibility and underlying genic features.
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Affiliation(s)
- Samuel N Bogan
- Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, USA.
| | - Marie E Strader
- Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, USA
- Department of Biology, Texas A&M University, College Station, USA
| | - Gretchen E Hofmann
- Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, USA
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2
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Song X, Li R, Wang K, Bai Y, Xiao Y, Wang YP. Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1137-1146. [PMID: 35503837 PMCID: PMC10321021 DOI: 10.1109/tcbb.2022.3172289] [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] [Indexed: 05/04/2023]
Abstract
The imaging genetics approach generates large amount of high dimensional and multi-modal data, providing complementary information for comprehensive study of Schizophrenia, a complex mental disease. However, at the same time, the variety of these data in structures, resolutions, and formats makes their integrative study a forbidding task. In this paper, we propose a novel model called Joint Sparse Collaborative Regression (JSCoReg), which can extract class-specific features from different health conditions/disease classes. We first evaluate the performance of feature selection in terms of Receiver operating characteristic curve and the area under the ROC curve in the simulation experiment. We demonstrate that the JSCoReg model can achieve higher accuracy compared with similar models including Joint Sparse Canonical Correlation Analysis and Sparse Collaborative Regression. We then applied the JSCoReg model to the analysis of schizophrenia dataset collected from the Mind Clinical Imaging Consortium. The JSCoReg enables us to better identify biomarkers associated with schizophrenia, which are verified to be both biologically and statistically significant.
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Affiliation(s)
- Xueli Song
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Rongpeng Li
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Kaiming Wang
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Yuntong Bai
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
| | - Yuzhu Xiao
- School of Sciences, Chang’an University, Xi’an, 710064, China
| | - Yu-ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
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3
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Inference of epigenetic subnetworks by Bayesian regression with the incorporation of prior information. Sci Rep 2022; 12:20224. [PMID: 36418365 PMCID: PMC9684215 DOI: 10.1038/s41598-022-19879-x] [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: 11/17/2020] [Accepted: 09/06/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in gene expression have been thought to play a crucial role in various types of cancer. With the advance of high-throughput experimental techniques, many genome-wide studies are underway to analyze underlying mechanisms that may drive the changes in gene expression. It has been observed that the change could arise from altered DNA methylation. However, the knowledge about the degree to which epigenetic changes might cause differences in gene expression in cancer is currently lacking. By considering the change of gene expression as the response of altered DNA methylation, we introduce a novel analytical framework to identify epigenetic subnetworks in which the methylation status of a set of highly correlated genes is predictive of a set of gene expression. By detecting highly correlated modules as representatives of the regulatory scenario underling the gene expression and DNA methylation, the dependency between DNA methylation and gene expression is explored by a Bayesian regression model with the incorporation of g-prior followed by a strategy of an optimal predictor subset selection. The subsequent network analysis indicates that the detected epigenetic subnetworks are highly biologically relevant and contain many verified epigenetic causal mechanisms. Moreover, a survival analysis indicates that they might be effective prognostic factors associated with patient survival time.
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4
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Zhang Y, Kou C, Wang S, Zhang Y. Genome-wide Differential-based Analysis of the Relationship between DNA Methylation and Gene Expression in Cancer. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190424160046] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Background::
DNA methylation is an epigenetic modification that plays an important
role in regulating gene expression. There is evidence that the hypermethylation of promoter regions
always causes gene silencing. However, how the methylation patterns of other regions in the
genome, such as gene body and 3’UTR, affect gene expression is unknown.
Objective::
The study aimed to fully explore the relationship between DNA methylation and expression
throughout the genome-wide analysis which is important in understanding the function of
DNA methylation essentially.
Method::
In this paper, we develop a heuristic framework to analyze the relationship between the
methylated change in different regions and that of the corresponding gene expression based on differential
analysis.
Results::
To understande the methylated function of different genomic regions, a gene is divided
into seven functional regions. By applying the method in five cancer datasets from the Synapse database,
it was found that methylated regions with a significant difference between cases and controls
were almost uniformly distributed in the seven regions of the genome. Also, the effect of
DNA methylation in different regions on gene expression was different. For example, there was a
higher percentage of positive relationships in 1stExon, gene body and 3’UTR than in TSS1500 and
TSS200. The functional analysis of genes with a significant positive and negative correlation between
DNA methylation and gene expression demonstrated the epigenetic mechanism of cancerassociated
genes.
Conclusion::
Differential based analysis helps us to recognize the change in DNA methylation and
how this change affects the change in gene expression. It provides a basis for further integrating
gene expression and DNA methylation data to identify disease-associated biomarkers.
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Affiliation(s)
- Yuanyuan Zhang
- School of information and control engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Chuanhua Kou
- School of information and control engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Shudong Wang
- College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao, Shandong, China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, China
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5
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Wang DD, Zheng Y, Toledo E, Razquin C, Ruiz-Canela M, Guasch-Ferré M, Yu E, Corella D, Gómez-Gracia E, Fiol M, Estruch R, Ros E, Lapetra J, Fito M, Aros F, Serra-Majem L, Clish CB, Salas-Salvadó J, Liang L, Martínez-González MA, Hu FB. Lipid metabolic networks, Mediterranean diet and cardiovascular disease in the PREDIMED trial. Int J Epidemiol 2019; 47:1830-1845. [PMID: 30428039 DOI: 10.1093/ije/dyy198] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2018] [Indexed: 12/14/2022] Open
Abstract
Background Perturbed lipid metabolic pathways may play important roles in the development of cardiovascular disease (CVD). However, existing epidemiological studies have focused more on discovering individual lipid metabolites for CVD risk prediction rather than assessing metabolic pathways. Methods This study included a subcohort of 787 participants and all 230 incident CVD cases from the PREDIMED trial. Applying a network-based analytical method, we identified lipid subnetworks and clusters from a global network of 200 lipid metabolites and linked these subnetworks/clusters to CVD risk. Results Lipid metabolites with more double bonds clustered within one subnetwork, whereas lipid metabolites with fewer double bonds clustered within other subnetworks. We identified 10 lipid clusters that were divergently associated with CVD risk. The hazard ratios [HRs, 95% confidence interval (CI)] of CVD per a 1-standard deviation (SD) increment in cluster score were 1.39 (1.17-1.66) for the hydroxylated phosphatidylcholine (HPC) cluster and 1.24 (1.11-1.37) for a cluster that included diglycerides and a monoglyceride with stearic acyl chain. Every 1-SD increase in the score of cluster that included highly unsaturated phospholipids and cholesterol esters was associated with an HR for CVD of 0.81 (95% CI, 0.67-0.98). Despite a suggestion that MedDiet modified the association between a subnetwork that included most lipids with a high degree of unsaturation and CVD, changes in lipid subnetworks/clusters during the first-year follow-up were not significantly different between intervention groups. Conclusions The degree of unsaturation was a major determinant of the architecture of lipid metabolic network. Lipid clusters that strongly predicted CVD risk, such as the HPC cluster, warrant further functional investigations.
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Affiliation(s)
- Dong D Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yan Zheng
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Estefanía Toledo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IDISNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IDISNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IDISNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | | | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Ramón Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Internal Medicine, Institut d'Investigacions Biomediques August Pi Sunyer (IDI- BAPS), University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Lipid Clinic, Department of Endocrinology and Nutrition, Institut d'Investigacions Biomediques August Pi Sunyer (IDI- BAPS), University of Barcelona, Barcelona, Spain
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Family Medicine, Primary Care Division of Sevilla, San Pablo Health Center, Sevilla, Spain
| | - Montserrat Fito
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Aros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Lluis Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Department of Clinical Sciences, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Clary B Clish
- Broad Institute and MIT, Harvard University, Cambridge, MA, USA
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain.,Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Liming Liang
- Department of Epidemiology.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain.,IDISNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA, USA
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6
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Yuan L, Huang DS. A Network-guided Association Mapping Approach from DNA Methylation to Disease. Sci Rep 2019; 9:5601. [PMID: 30944378 PMCID: PMC6447594 DOI: 10.1038/s41598-019-42010-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 03/12/2019] [Indexed: 01/11/2023] Open
Abstract
Aberrant DNA methylation may contribute to development of cancer. However, understanding the associations between DNA methylation and cancer remains a challenge because of the complex mechanisms involved in the associations and insufficient sample sizes. The unprecedented wealth of DNA methylation, gene expression and disease status data give us a new opportunity to design machine learning methods to investigate the underlying associated mechanisms. In this paper, we propose a network-guided association mapping approach from DNA methylation to disease (NAMDD). Compared with existing methods, NAMDD finds methylation-disease path associations by integrating analysis of multiple data combined with a stability selection strategy, thereby mining more information in the datasets and improving the quality of resultant methylation sites. The experimental results on both synthetic and real ovarian cancer data show that NAMDD substantially outperforms former disease-related methylation site research methods (including NsRRR and PCLOGIT) under false positive control. Furthermore, we applied NAMDD to ovarian cancer data, identified significant path associations and provided hypothetical biological path associations to explain our findings.
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Affiliation(s)
- Lin Yuan
- Institute of Machine Learning and Systems Biology, College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P.R. China
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P.R. China.
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7
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Fang J, Zhang JG, Deng HW, Wang YP. Joint Detection of Associations between DNA Methylation and Gene Expression from Multiple Cancers. IEEE J Biomed Health Inform 2017; 22:1960-1969. [PMID: 29990049 DOI: 10.1109/jbhi.2017.2784621] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
DNA methylation plays an important role in the development of various cancers mainly through the regulation on gene expression. Hence, the study on the relation between DNA methylation and gene expression is of particular interest to understand cancers. Recently, an increasing number of datasets are available from multiple cancers, which makes it possible to study both the similarity and difference of genomic alterations across multiple tumor types. However, most of the existing pan-cancer analysis methods perform simple aggregations, which may overlook the heterogeneity of the interactions. In this paper, we propose a novel method to jointly detect complex associations between DNA methylation and gene expression levels from multiple cancers. The main idea is to apply joint sparse canonical correlation analysis to detect a small set of methylated sites, which are associated with another set of genes either shared across cancers or specific to a particular group (group-specific) of cancers. These methylated sites and genes form a complex module with strong multivariate correlations. We further introduced a joint sparse precision matrix estimation method to identify driver methylation-gene pairs in the module. These pairs are characterized by significant partial correlations, which may imply high functional impacts and contribute to complementary information to the main step. We apply our method to The Cancer Genome Atlas(TCGA) datasets with 1166 samples from four cancers. The results reveal significant shared and groupspecific interactions between DNA methylation and gene expression levels. To promote reproducible research, the Matlab code is available at https://sites.google.com/site/jianfang86/jointTCGA.
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8
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Chai H, Shi X, Zhang Q, Zhao Q, Huang Y, Ma S. Analysis of cancer gene expression data with an assisted robust marker identification approach. Genet Epidemiol 2017; 41:779-789. [PMID: 28913902 DOI: 10.1002/gepi.22066] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 04/10/2017] [Accepted: 07/10/2017] [Indexed: 12/22/2022]
Abstract
Gene expression (GE) studies have been playing a critical role in cancer research. Despite tremendous effort, the analysis results are still often unsatisfactory, because of the weak signals and high data dimensionality. Analysis is often further challenged by the long-tailed distributions of the outcome variables. In recent multidimensional studies, data have been collected on GEs as well as their regulators (e.g., copy number alterations (CNAs), methylation, and microRNAs), which can provide additional information on the associations between GEs and cancer outcomes. In this study, we develop an ARMI (assisted robust marker identification) approach for analyzing cancer studies with measurements on GEs as well as regulators. The proposed approach borrows information from regulators and can be more effective than analyzing GE data alone. A robust objective function is adopted to accommodate long-tailed distributions. Marker identification is effectively realized using penalization. The proposed approach has an intuitive formulation and is computationally much affordable. Simulation shows its satisfactory performance under a variety of settings. TCGA (The Cancer Genome Atlas) data on melanoma and lung cancer are analyzed, which leads to biologically plausible marker identification and superior prediction.
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Affiliation(s)
- Hao Chai
- Department of Biostatistics, Yale University, New Haven, Connecticut, United States of America
| | - Xingjie Shi
- Department of Statistics, Nanjing University of Finance and Economics, Nanjing Shi, Jiangsu Sheng, China
| | - Qingzhao Zhang
- School of Economics, Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen Shi, Fujian Sheng, China
| | - Qing Zhao
- Merck Research Laboratories, Rahway, New Jersey, United States of America
| | - Yuan Huang
- Department of Biostatistics, Yale University, New Haven, Connecticut, United States of America
| | - Shuangge Ma
- Department of Biostatistics, Yale University, New Haven, Connecticut, United States of America
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9
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Fang J, Lin D, Schulz SC, Xu Z, Calhoun VD, Wang YP. Joint sparse canonical correlation analysis for detecting differential imaging genetics modules. Bioinformatics 2016; 32:3480-3488. [PMID: 27466625 PMCID: PMC5181564 DOI: 10.1093/bioinformatics/btw485] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 06/17/2016] [Accepted: 07/12/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Imaging genetics combines brain imaging and genetic information to identify the relationships between genetic variants and brain activities. When the data samples belong to different classes (e.g. disease status), the relationships may exhibit class-specific patterns that can be used to facilitate the understanding of a disease. Conventional approaches often perform separate analysis on each class and report the differences, but ignore important shared patterns. RESULTS In this paper, we develop a multivariate method to analyze the differential dependency across multiple classes. We propose a joint sparse canonical correlation analysis method, which uses a generalized fused lasso penalty to jointly estimate multiple pairs of canonical vectors with both shared and class-specific patterns. Using a data fusion approach, the method is able to detect differentially correlated modules effectively and efficiently. The results from simulation studies demonstrate its higher accuracy in discovering both common and differential canonical correlations compared to conventional sparse CCA. Using a schizophrenia dataset with 92 cases and 116 controls including a single nucleotide polymorphism (SNP) array and functional magnetic resonance imaging data, the proposed method reveals a set of distinct SNP-voxel interaction modules for the schizophrenia patients, which are verified to be both statistically and biologically significant. AVAILABILITY AND IMPLEMENTATION The Matlab code is available at https://sites.google.com/site/jianfang86/JSCCA CONTACT: wyp@tulane.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Fang
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, ShaanXi 710049, China
| | - Dongdong Lin
- The Mind Research Network, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
| | - S Charles Schulz
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, USA
| | - Zongben Xu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, ShaanXi 710049, China
| | - Vince D Calhoun
- The Mind Research Network, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
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10
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Culibrk L, Croft CA, Tebbutt SJ. Systems Biology Approaches for Host-Fungal Interactions: An Expanding Multi-Omics Frontier. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 20:127-38. [PMID: 26885725 PMCID: PMC4799697 DOI: 10.1089/omi.2015.0185] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic "two systems" biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host-fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale.
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Affiliation(s)
- Luka Culibrk
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Carys A. Croft
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Scott J. Tebbutt
- Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, British Columbia, Canada
- Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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11
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Li Y, Pearl SA, Jackson SA. Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis. TRENDS IN PLANT SCIENCE 2015; 20:664-675. [PMID: 26440435 DOI: 10.1016/j.tplants.2015.06.013] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2015] [Revised: 06/28/2015] [Accepted: 06/30/2015] [Indexed: 05/25/2023]
Abstract
Even though vast amounts of genome-wide gene expression data have become available in plants, it remains a challenge to effectively mine this information for the discovery of genes and gene networks, for instance those that control agronomically important traits. These networks reflect potential interactions among genes and, therefore, can lead to a systematic understanding of the molecular mechanisms underlying targeted biological processes. We discuss methods to analyze gene networks using gene expression data, specifically focusing on four common statistical approaches used to reconstruct networks: correlation, feature selection in supervised learning, probabilistic graphical model, and meta-prediction. In addition, we discuss the effective use of these methods for acquiring an in-depth understanding of biological systems in plants.
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Affiliation(s)
- Yupeng Li
- Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, GA 30602; Institute of Plant Breeding, Genetics and Genomics, University of Georgia, 111 Riverbend Road, Athens, GA 30602; Department of Statistics, University of Georgia, 101 Cedar Street, Athens, GA 30602
| | - Stephanie A Pearl
- Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, GA 30602
| | - Scott A Jackson
- Center for Applied Genetic Technologies, University of Georgia, 111 Riverbend Road, Athens, GA 30602; Institute of Plant Breeding, Genetics and Genomics, University of Georgia, 111 Riverbend Road, Athens, GA 30602.
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