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Samartsidis P, Seaman SR, Harrison A, Alexopoulos A, Hughes GJ, Rawlinson C, Anderson C, Charlett A, Oliver I, De Angelis D. A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes. Biostatistics 2024; 25:867-884. [PMID: 38058013 PMCID: PMC11247182 DOI: 10.1093/biostatistics/kxad030] [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: 09/08/2022] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 12/08/2023] Open
Abstract
Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19.
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Affiliation(s)
- Pantelis Samartsidis
- MRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | - Shaun R Seaman
- MRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
| | | | - Angelos Alexopoulos
- MRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
- Department of Economics, Athens University of Economics and Business, Athens, 104 34, Greece
| | | | | | | | | | | | - Daniela De Angelis
- MRC Biostatistics Unit, East Forvie Building, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
- UK Health Security Agency, London, E14 4PU, UK
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2
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Agrawal S, Buyan A, Severin J, Koido M, Alam T, Abugessaisa I, Chang HY, Dostie J, Itoh M, Kere J, Kondo N, Li Y, Makeev VJ, Mendez M, Okazaki Y, Ramilowski JA, Sigorskikh AI, Strug LJ, Yagi K, Yasuzawa K, Yip CW, Hon CC, Hoffman MM, Terao C, Kulakovskiy IV, Kasukawa T, Shin JW, Carninci P, de Hoon MJL. Annotation of nuclear lncRNAs based on chromatin interactions. PLoS One 2024; 19:e0295971. [PMID: 38709794 PMCID: PMC11073715 DOI: 10.1371/journal.pone.0295971] [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: 06/19/2023] [Accepted: 12/02/2023] [Indexed: 05/08/2024] Open
Abstract
The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.
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Affiliation(s)
- Saumya Agrawal
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Andrey Buyan
- Autosome.org, Russia
- FANTOM Consortium, Dolgoprudny, Russia
| | - Jessica Severin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Howard Y. Chang
- Center for Personal Dynamic Regulome, Stanford University, Stanford, California, United States of America
| | - Josée Dostie
- Department of Biochemistry, Rosalind and Morris Goodman Cancer Research Center, McGill University, Montréal, Québec, Canada
| | - Masayoshi Itoh
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
- Stem Cells and Metabolism Research Program, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | - Naoto Kondo
- RIKEN Center for Life Science Technologies, Yokohama, Japan
| | - Yunjing Li
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | | | - Mickaël Mendez
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yasushi Okazaki
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Jordan A. Ramilowski
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Advanced Medical Research Center, Yokohama City University, Yokohama, Japan
| | | | - Lisa J. Strug
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Ontario, Canada
- The Centre for Applied Genomics and Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ken Yagi
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kayoko Yasuzawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chi Wai Yip
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chung Chau Hon
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Michael M. Hoffman
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Chikashi Terao
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | - Takeya Kasukawa
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Jay W. Shin
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Human Technopole, Milan, Italy
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3
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Sun Z, Chung D, Neelon B, Millar-Wilson A, Ethier SP, Xiao F, Zheng Y, Wallace K, Hardiman G. A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data. Stat Med 2023; 42:5266-5284. [PMID: 37715500 DOI: 10.1002/sim.9911] [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: 03/19/2021] [Revised: 07/15/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023]
Abstract
In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi-omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene-level analysis and lack the ability to facilitate biological findings at the pathway-level. In this article, we propose Bayes-InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway-guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes-InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya-Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/).
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Affiliation(s)
- Zequn Sun
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
- The Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | | | - Stephen P Ethier
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina
| | - Feifei Xiao
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Kristin Wallace
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Gary Hardiman
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
- Faculty of Medicine, Health and Life Sciences, School of Biological Sciences and Institute for Global Food Security, Queen's University Belfast, Belfast, UK
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4
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Deans PJM, Seah C, Johnson J, Gonzalez JG, Townsley K, Cao E, Schrode N, Stahl E, O’Reilly P, Huckins LM, Brennand KJ. Non-additive effects of schizophrenia risk genes reflect convergent downstream function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.20.23287497. [PMID: 36993466 PMCID: PMC10055596 DOI: 10.1101/2023.03.20.23287497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.
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Affiliation(s)
- PJ Michael Deans
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Judit Garcia Gonzalez
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kayla Townsley
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Evan Cao
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Nadine Schrode
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Eli Stahl
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Paul O’Reilly
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kristen J. Brennand
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
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5
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Wang D, Quesnel-Vallieres M, Jewell S, Elzubeir M, Lynch K, Thomas-Tikhonenko A, Barash Y. A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers. Nat Commun 2023; 14:63. [PMID: 36599821 PMCID: PMC9813260 DOI: 10.1038/s41467-022-35369-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023] Open
Abstract
Identification of cancer sub-types is a pivotal step for developing personalized treatment. Specifically, sub-typing based on changes in RNA splicing has been motivated by several recent studies. We thus develop CHESSBOARD, an unsupervised algorithm tailored for RNA splicing data that captures "tiles" in the data, defined by a subset of unique splicing changes in a subset of patients. CHESSBOARD allows for a flexible number of tiles, accounts for uncertainty of splicing quantification, and is able to model missing values as additional signals. We first apply CHESSBOARD to synthetic data to assess its domain specific modeling advantages, followed by analysis of several leukemia datasets. We show detected tiles are reproducible in independent studies, investigate their possible regulatory drivers and probe their relation to known AML mutations. Finally, we demonstrate the potential clinical utility of CHESSBOARD by supplementing mutation based diagnostic assays with discovered splicing profiles to improve drug response correlation.
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Affiliation(s)
- David Wang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mathieu Quesnel-Vallieres
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - San Jewell
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Moein Elzubeir
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristen Lynch
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrei Thomas-Tikhonenko
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Cancer Pathobiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yoseph Barash
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Computer and Information Sciences, School of Engineering, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Zhang W, Wendt C, Bowler R, Hersh CP, Safo SE. Robust integrative biclustering for multi-view data. Stat Methods Med Res 2022; 31:2201-2216. [PMID: 36113157 PMCID: PMC10153449 DOI: 10.1177/09622802221122427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In many biomedical research, multiple views of data (e.g. genomics, proteomics) are available, and a particular interest might be the detection of sample subgroups characterized by specific groups of variables. Biclustering methods are well-suited for this problem as they assume that specific groups of variables might be relevant only to specific groups of samples. Many biclustering methods exist for detecting row-column clusters in a view but few methods exist for data from multiple views. The few existing algorithms are heavily dependent on regularization parameters for getting row-column clusters, and they impose unnecessary burden on users thus limiting their use in practice. We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views. Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster, finds stable biclusters, and results in interpretable row-column associations. Simulations and real data analyses show that integrative sparse singular value decomposition outperforms several other single- and multi-view biclustering methods and is able to detect meaningful biclusters. iSSVD is a user-friendly, computationally efficient algorithm that will be useful in many disease subtyping applications.
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Affiliation(s)
- Weijie Zhang
- Division of Biostatistics, 5635University of Minnesota, MN, USA
| | - Christine Wendt
- Division of Pulmonary, Allergy and Critical Care, 5635University of Minnesota, MN, USA
| | - Russel Bowler
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, 551774National Jewish Health, Denver, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, 1811Harvard Medical School, USA
| | - Sandra E Safo
- Division of Biostatistics, 5635University of Minnesota, MN, USA
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Liu Z, Cai C, Ma X, Liu J, Chen L, Lui VWY, Cooper GF, Lu X. A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer. Cancers (Basel) 2022; 14:cancers14194825. [PMID: 36230748 PMCID: PMC9563147 DOI: 10.3390/cancers14194825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 02/08/2023] Open
Abstract
Head and neck squamous cell cancer (HNSCC) is an aggressive cancer resulting from heterogeneous causes. To reveal the underlying drivers and signaling mechanisms of different HNSCC tumors, we developed a novel Bayesian framework to identify drivers of individual tumors and infer the states of driver proteins in cellular signaling system in HNSCC tumors. First, we systematically identify causal relationships between somatic genome alterations (SGAs) and differentially expressed genes (DEGs) for each TCGA HNSCC tumor using the tumor-specific causal inference (TCI) model. Then, we generalize the most statistically significant driver SGAs and their regulated DEGs in TCGA HNSCC cohort. Finally, we develop machine learning models that combine genomic and transcriptomic data to infer the protein functional activation states of driver SGAs in tumors, which enable us to represent a tumor in the space of cellular signaling systems. We discovered four mechanism-oriented subtypes of HNSCC, which show distinguished patterns of activation state of HNSCC driver proteins, and importantly, this subtyping is orthogonal to previously reported transcriptomic-based molecular subtyping of HNSCC. Further, our analysis revealed driver proteins that are likely involved in oncogenic processes induced by HPV infection, even though they are not perturbed by genomic alterations in HPV+ tumors.
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Affiliation(s)
- Zhengping Liu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- School of Medicine, Tsinghua University, Beijing 100190, China
| | - Chunhui Cai
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- Correspondence:
| | - Xiaojun Ma
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
| | - Jinling Liu
- Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
- Department of Biological Sciences, Missouri University of Science and Technology, Rolla, MO 65409, USA
| | - Lujia Chen
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
| | - Vivian Wai Yan Lui
- Georgia Cancer Center, and Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Gregory F. Cooper
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
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8
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Hang Y, Burns J, Shealy BT, Pauly R, Ficklin SP, Feltus FA. Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue. BMC Genomics 2022; 23:350. [PMID: 35524179 PMCID: PMC9077899 DOI: 10.1186/s12864-022-08591-9] [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: 06/28/2021] [Accepted: 04/25/2022] [Indexed: 12/24/2022] Open
Abstract
Background Lung cancer is the leading cause of cancer death in both men and women. The most common lung cancer subtype is non-small cell lung carcinoma (NSCLC) comprising about 85% of all cases. NSCLC can be further divided into three subtypes: adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell lung carcinoma. Specific genetic mutations and epigenetic aberrations play an important role in the developmental transition to a specific tumor subtype. The elucidation of normal lung versus lung tumor gene expression patterns and regulatory targets yields biomarker systems that discriminate lung phenotypes (i.e., biomarkers) and provide a foundation for the discovery of normal and aberrant gene regulatory mechanisms. Results We built condition-specific gene co-expression networks (csGCNs) for normal lung, LUAD, and LUSC conditions. Then, we integrated normal lung tissue-specific gene regulatory networks (tsGRNs) to elucidate control-target biomarker systems for normal and cancerous lung tissue. We characterized co-expressed gene edges, possibly under common regulatory control, for relevance in lung cancer. Conclusions Our approach demonstrates the ability to elucidate csGCN:tsGRN merged biomarker systems based on gene expression correlation and regulation. The biomarker systems we describe can be used to classify and further describe lung specimens. Our approach is generalizable and can be used to discover and interpret complex gene expression patterns for any condition or species. Supplementary Information The online version contains available at 10.1186/s12864-022-08591-9.
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Affiliation(s)
- Yuqing Hang
- Department of Genetics & Biochemistry, Clemson University, Clemson, 29634, USA
| | - Josh Burns
- Department of Horticulture, Washington State University, Pullman, 99164, USA
| | - Benjamin T Shealy
- Department of Electrical and Computer Engineering, Clemson University, Clemson, 29634, USA
| | - Rini Pauly
- Biomedical Data Science and Informatics Program, Clemson University, Clemson, 29634, USA
| | - Stephen P Ficklin
- Department of Horticulture, Washington State University, Pullman, 99164, USA
| | - Frank A Feltus
- Department of Genetics & Biochemistry, Clemson University, Clemson, 29634, USA. .,Biomedical Data Science and Informatics Program, Clemson University, Clemson, 29634, USA. .,Center for Human Genetics, Clemson University, Clemson, 29634, USA. .,Biosystems Research Complex, 302C, 105 Collings St, Clemson, SC, 29634, USA.
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9
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Du Q, Campbell MT, Yu H, Liu K, Walia H, Zhang Q, Zhang C. Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants. Methods Mol Biol 2022; 2539:261-268. [PMID: 35895209 DOI: 10.1007/978-1-0716-2537-8_20] [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] [Indexed: 06/15/2023]
Abstract
Environmental factors, including different stresses, can have an impact on the expression of genes and subsequently the phenotype and development of plants. Since a large number of genes are involved in response to the perturbation of the environment, identifying groups of co-expressed genes is meaningful. The gene co-expression network models can be used for the exploration, interpretation, and identification of genes responding to environmental changes. Once a gene co-expression network is constructed, one can determine gene modules and the association of gene modules to the phenotypic response. To link modules to phenotype, one approach is to find the correlated eigengenes of given modules or to integrate all eigengenes in regularized linear model. This manuscript describes the method from construction of co-expression network, module discovery, association between modules and phenotypic data, and finally to annotation/visualization.
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Affiliation(s)
- Qian Du
- School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, USA
| | - Malachy T Campbell
- Department of Agronomy and Horticulture, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, USA
| | - Huihui Yu
- School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, USA
| | - Kan Liu
- School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, USA
| | - Harkamal Walia
- Department of Agronomy and Horticulture, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, College of Engineering and Physical Sciences (CEPS), University of New Hampshire, Durham, NH, USA
| | - Chi Zhang
- School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, USA.
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10
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Nicholls K, Wallace C. Comparison of sparse biclustering algorithms for gene expression datasets. Brief Bioinform 2021; 22:6265183. [PMID: 33951731 PMCID: PMC8574648 DOI: 10.1093/bib/bbab140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 03/08/2021] [Accepted: 03/25/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. RESULTS We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithms generally struggled on simulated datasets with a large number of genes or implanted biclusters. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and did not require any post-processing, but were considerably slower than other algorithm classes. We found that non-negative matrix factorisation algorithms performed poorly, but could be re-purposed for biclustering through a sparsity-inducing post-processing procedure we introduce; one such algorithm was one of the most highly ranked on real datasets. In a multi-tissue knockout mouse RNA-seq dataset, the algorithms rarely returned clusters containing samples from multiple different tissues, whilst such clusters were identified in a human dataset of more closely related cell types (sorted blood cell subsets). This highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. AVAILABILITY Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4581206.
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Affiliation(s)
- Kath Nicholls
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, CB2 0AW, UK
| | - Chris Wallace
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, CB2 0AW, UK.,MRC Biostatistics Unit, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK
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11
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12
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Wang W, Stephens M. Empirical Bayes Matrix Factorization. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2021; 22:120. [PMID: 37920532 PMCID: PMC10621241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Matrix factorization methods, which include Factor analysis (FA) and Principal Components Analysis (PCA), are widely used for inferring and summarizing structure in multivariate data. Many such methods use a penalty or prior distribution to achieve sparse representations ("Sparse FA/PCA"), and a key question is how much sparsity to induce. Here we introduce a general Empirical Bayes approach to matrix factorization (EBMF), whose key feature is that it estimates the appropriate amount of sparsity by estimating prior distributions from the observed data. The approach is very flexible: it allows for a wide range of different prior families and allows that each component of the matrix factorization may exhibit a different amount of sparsity. The key to this flexibility is the use of a variational approximation, which we show effectively reduces fitting the EBMF model to solving a simpler problem, the so-called "normal means" problem. We demonstrate the benefits of EBMF with sparse priors through both numerical comparisons with competing methods and through analysis of data from the GTEx (Genotype Tissue Expression) project on genetic associations across 44 human tissues. In numerical comparisons EBMF often provides more accurate inferences than other methods. In the GTEx data, EBMF identifies interpretable structure that agrees with known relationships among human tissues. Software implementing our approach is available at https://github.com/stephenslab/flashr.
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Affiliation(s)
- Wei Wang
- Department of Statistics, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Statistics and Department of Human Genetics, University of Chicago, Chicago, IL, USA
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13
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Lu J, Dumitrascu B, McDowell IC, Jo B, Barrera A, Hong LK, Leichter SM, Reddy TE, Engelhardt BE. Causal network inference from gene transcriptional time-series response to glucocorticoids. PLoS Comput Biol 2021; 17:e1008223. [PMID: 33513136 PMCID: PMC7875426 DOI: 10.1371/journal.pcbi.1008223] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 02/10/2021] [Accepted: 08/07/2020] [Indexed: 11/19/2022] Open
Abstract
Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.
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Affiliation(s)
- Jonathan Lu
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Ian C. McDowell
- Element Genomics, A UCB Company, Durham, North Carolina, United States of America
| | - Brian Jo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Alejandro Barrera
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Linda K. Hong
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Sarah M. Leichter
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Timothy E. Reddy
- Department of Genome Sciences, Duke University, Durham, North Carolina, United States of America
| | - Barbara E. Engelhardt
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, United States of America
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Savino A, Provero P, Poli V. Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression. Int J Mol Sci 2020; 21:E9461. [PMID: 33322692 PMCID: PMC7764314 DOI: 10.3390/ijms21249461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/02/2020] [Accepted: 12/09/2020] [Indexed: 02/02/2023] Open
Abstract
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
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Affiliation(s)
- Aurora Savino
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
| | - Paolo Provero
- Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Ázeglio 52, 10126 Turin, Italy;
- Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
| | - Valeria Poli
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
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15
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Chowdhury HA, Bhattacharyya DK, Kalita JK. (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1154-1173. [PMID: 30668502 DOI: 10.1109/tcbb.2019.2893170] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.
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16
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Xie J, Ma A, Zhang Y, Liu B, Cao S, Wang C, Xu J, Zhang C, Ma Q. QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data. Bioinformatics 2020; 36:1143-1149. [PMID: 31503285 PMCID: PMC8215922 DOI: 10.1093/bioinformatics/btz692] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 08/05/2019] [Accepted: 09/05/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed. RESULTS We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq. AVAILABILITY AND IMPLEMENTATION The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Juan Xie
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Yu Zhang
- Colleges of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Sha Cao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA
| | - Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Jennifer Xu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chi Zhang
- Department of Medical & Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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17
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Zhang P, Southey BR, Rodriguez-Zas SL. Co-expression networks uncover regulation of splicing and transcription markers of disease. PROCEEDINGS OF THE ... ANNUAL INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2020; 70:119-128. [PMID: 35047432 DOI: 10.29007/rl4h] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Gene co-expression networks based on gene expression data are usually used to capture biologically significant patterns, enabling the discovery of biomarkers and interpretation of regulatory relationships. However, the coordination of numerous splicing changes within and across genes can exert a substantial impact on the function of these genes. This is particularly impactful in studies of the properties of the nervous system, which can be masked in the networks that only assess the correlation between gene expression levels. A bioinformatics approach was developed to uncover the role of alternative splicing and associated transcriptional networks using RNA-seq profiles. Data from 40 samples, including control and two treatments associated with sensitivity to stimuli across two central nervous system regions that can present differential splicing, were explored. The gene expression and relative isoform levels were integrated into a transcriptome-wide matrix, and then Graphical Lasso was applied to capture the interactions between genes and isoforms. Next, functional enrichment analysis enabled the discovery of pathways dysregulated at the isoform or gene levels and the interpretation of these interactions within a central nervous region. In addition, a Bayesian biclustering strategy was used to reconstruct treatment-specific networks from gene expression profile, allowing the identification of hub molecules and visualization of highly connected modules of isoforms and genes in specific conditions. Our bioinformatics approach can offer comparable insights into the discovery of biomarkers and therapeutic targets for a wide range of diseases and conditions.
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Affiliation(s)
- Pan Zhang
- Illinois Informatics Institute, University of Illinois at Urbana-Champaign, Urbana, IL, the U.S.,Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, the U.S
| | - Bruce R Southey
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, the U.S
| | - Sandra L Rodriguez-Zas
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, the U.S.,Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, the U.S.,Carle Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, the U.S
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18
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Cowman T, Coşkun M, Grama A, Koyutürk M. Integrated querying and version control of context-specific biological networks. Database (Oxford) 2020; 2020:baaa018. [PMID: 32294194 PMCID: PMC7158887 DOI: 10.1093/database/baaa018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 01/13/2020] [Accepted: 02/21/2020] [Indexed: 01/26/2023]
Abstract
MOTIVATION Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. RESULTS We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. CONCLUSION Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. AVAILABILITY AND IMPLEMENTATION VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion. CONTACT tyler.cowman@case.edu.
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Affiliation(s)
- Tyler Cowman
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Mustafa Coşkun
- Department of Computer Engineering, Abdullah Gül University, Kayseri 38080, Turkey
| | - Ananth Grama
- Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA
| | - Mehmet Koyutürk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
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19
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Kundu N, Ma X, Brox R, Fan X, Kochel T, Reader J, Tschammer N, Fulton A. The Chemokine Receptor CXCR3 Isoform B Drives Breast Cancer Stem Cells. BREAST CANCER-BASIC AND CLINICAL RESEARCH 2019; 13:1178223419873628. [PMID: 31619923 PMCID: PMC6777055 DOI: 10.1177/1178223419873628] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 07/25/2019] [Indexed: 12/04/2022]
Abstract
We are seeking to identify molecular targets that are relevant to breast cancer
cells with stem-like properties. There is growing evidence that cancer stem
cells (CSCs) are supported by inflammatory mediators expressed in the tumor
microenvironment. The chemokine receptor CXCR3 binds the interferon-γ-inducible,
ELR-negative CXC chemokines CXCL9, CXCL10, and CXCL11 and malignant cells have
co-opted this receptor to promote tumor cell migration and invasion. There are 2
major isoforms of CXCR3: CXCR3A and CXCR3B. The latter is generated from
alternative splicing and results in a protein with a longer N-terminal domain.
CXCR3 isoform A is generally considered to play a major role in tumor
metastasis. When the entire tumor cell population is examined, CXCR3 isoform B
is usually detected at much lower levels than CXCR3A and for this, and other
reasons, was not considered to drive tumor progression. We have shown that
CXCR3B is significantly upregulated in the subpopulation of breast CSCs in
comparison with the bulk tumor cell population in 3 independent breast cancer
cell lines (MDA-MB-231, SUM159, and T47D). Modulation of CXCR3B levels by knock
in strategies increases CSC populations identified by aldehyde dehydrogenase
activity or CD44+CD24− phenotype as well as
tumorsphere-forming capacity. The reverse is seen when CXCR3B is gene-silenced.
CXCL11 and CXCL10 directly induce CSC. We also report that novel CXCR3
allosteric modulators BD064 and BD103 prevent the induction of CSCs. BD103
inhibited experimental metastasis. This protective effect is associated with the
reversal of CXCR3 ligand-mediated activation of STAT3, ERK1/2, CREB, and NOTCH1
pathways. We propose that CXCR3B, expressed on CSC, should be explored further
as a novel therapeutic target.
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Affiliation(s)
- Namita Kundu
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA.,Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xinrong Ma
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA
| | - Regine Brox
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich-Alexander-Universität Erlangen-Nurnberg, Erlangen, Germany
| | - Xiaoxuan Fan
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA
| | - Tyler Kochel
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA
| | - Jocelyn Reader
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA.,Department of Obstetrics and Gynecology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nuska Tschammer
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Emil Fischer Center, Friedrich-Alexander-Universität Erlangen-Nurnberg, Erlangen, Germany
| | - Amy Fulton
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA.,Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA
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Li J, Lai Y, Zhang C, Zhang Q. TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework. J Appl Stat 2019; 47:1064-1083. [PMID: 35706920 PMCID: PMC9041782 DOI: 10.1080/02664763.2019.1667311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 09/09/2019] [Indexed: 10/26/2022]
Abstract
Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. Although the correlation-based gene networks are computationally affordable, they have limitations after being applied to gene expression time-course data. We proposed Temporal Gene Coexpression Network Analysis (TGCnA) framework for the transcriptomic time-course data. The mathematical nature of TGCnA is the joint modeling of multiple covariance matrices across time points using a 'low-rank plus sparse' framework, in which the network similarity across time points is explicitly modeled in the low-rank component. We demonstrated the advantage of TGCnA in covariance matrix estimation and gene module discovery using both simulation data and real transcriptomic data. The code is available at https://github.com/QiZhangStat/TGCnA.
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Affiliation(s)
- Jinyu Li
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yutong Lai
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Qi Zhang
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, USA
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21
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Wang J, Hossain MS, Lyu Z, Schmutz J, Stacey G, Xu D, Joshi T. SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data. PLANT DIRECT 2019; 3:e00167. [PMID: 31549018 PMCID: PMC6747016 DOI: 10.1002/pld3.167] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 08/12/2019] [Accepted: 08/20/2019] [Indexed: 05/04/2023]
Abstract
The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA-Seq data generated in the project serve as a valuable resource for discovering tissue-specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context-specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context-specific networks at both transcriptome and interactome levels. BicMix and Cross-Conditions Cluster Detection algorithms were applied to detect modules based on co-expression relationships across all the tissues. Soybean context-specific interactomes were predicted by combining soybean tissue gene expression and protein-protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen-fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium-legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at http://soykb.org/SoyCSN. SoyCSN provides a useful web-based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.
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Affiliation(s)
- Juexin Wang
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriSt. LouisMOUSA
- Christopher S. Bond Life Sciences CenterUniversity of MissouriSt. LouisMOUSA
| | - Md Shakhawat Hossain
- Christopher S. Bond Life Sciences CenterUniversity of MissouriSt. LouisMOUSA
- Divisions of Plant Science and BiochemistryUniversity of MissouriSt. LouisMOUSA
| | - Zhen Lyu
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriSt. LouisMOUSA
| | - Jeremy Schmutz
- HudsonAlpha Institute for BiotechnologyHuntsvilleALUSA
- DOE Joint Genome InstituteWalnut CreekCAUSA
| | - Gary Stacey
- Christopher S. Bond Life Sciences CenterUniversity of MissouriSt. LouisMOUSA
- Divisions of Plant Science and BiochemistryUniversity of MissouriSt. LouisMOUSA
| | - Dong Xu
- Department of Electrical Engineering and Computer ScienceUniversity of MissouriSt. LouisMOUSA
- Christopher S. Bond Life Sciences CenterUniversity of MissouriSt. LouisMOUSA
- Informatics InstituteUniversity of MissouriSt. LouisMOUSA
| | - Trupti Joshi
- Christopher S. Bond Life Sciences CenterUniversity of MissouriSt. LouisMOUSA
- Informatics InstituteUniversity of MissouriSt. LouisMOUSA
- Department of Health Management and Informatics and Office of ResearchSchool of MedicineUniversity of MissouriSt. LouisMOUSA
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22
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Su L, Liu G, Wang J, Xu D. A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions. Methods 2019; 166:22-30. [PMID: 31121299 PMCID: PMC6708461 DOI: 10.1016/j.ymeth.2019.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/14/2019] [Accepted: 05/13/2019] [Indexed: 12/12/2022] Open
Abstract
Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.
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Affiliation(s)
- Lingtao Su
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Guixia Liu
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Juexin Wang
- Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
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23
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Maind A, Raut S. COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification. J Biosci 2019; 44:48. [PMID: 31180061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Biclustering is an increasingly used data mining technique for searching groups of co-expressed genes across the subset of experimental conditions from the gene-expression data. The group of co-expressed genes is present in the form of various patterns called a bicluster. A bicluster provides significant insights related to the functionality of genes and plays an important role in various clinical applications such as drug discovery, biomarker discovery, gene network analysis, gene identification, disease diagnosis, pathway analysis etc. This paper presents a novel unsupervised approach 'COmprehensive Search for Column-Coherent Evolution Biclusters (COSCEB)' for a comprehensive search of biologically significant column-coherent evolution biclusters. The concept of column subspace extraction from each gene pair and Longest Common Contiguous Subsequence (LCCS) is employed to identify significant biclusters. The experiments have been performed on both synthetic as well as real datasets. The performance of COSCEB is evaluated with the help of key issues. The issues are comprehensive search, Deep OPSM bicluster, bicluster types, bicluster accuracy, bicluster size, noise, overlapping, output nature, computational complexity and biologically significant biclusters. The performance of COSCEB is compared with six all-time famous biclustering algorithms SAMBA, OPSM, xMotif, Bimax, Deep OPSM- and UniBic. The result shows that the proposed approach performs effectively on most of the issues and extracts all possible biologically significant column-coherent evolution biclusters which are far more than other biclustering algorithms. Along with the proposed approach, we have also presented the case study which shows the application of significant biclusters for hub gene identification.
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Affiliation(s)
- Ankush Maind
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra 440 010, India
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24
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COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification. J Biosci 2019. [DOI: 10.1007/s12038-019-9862-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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25
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Wang M, Fischer J, Song YS. THREE-WAY CLUSTERING OF MULTI-TISSUE MULTI-INDIVIDUAL GENE EXPRESSION DATA USING SEMI-NONNEGATIVE TENSOR DECOMPOSITION. Ann Appl Stat 2019; 13:1103-1127. [PMID: 33381253 PMCID: PMC7771883 DOI: 10.1214/18-aoas1228] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The advent of high-throughput sequencing technologies has led to an increasing availability of large multi-tissue data sets which contain gene expression measurements across different tissues and individuals. In this setting, variation in expression levels arises due to contributions specific to genes, tissues, individuals, and interactions thereof. Classical clustering methods are ill-suited to explore these three-way interactions and struggle to fully extract the insights into transcriptome complexity contained in the data. We propose a new statistical method, called MultiCluster, based on semi-nonnegative tensor decomposition which permits the investigation of transcriptome variation across individuals and tissues simultaneously. We further develop a tensor projection procedure which detects covariate-related genes with high power, demonstrating the advantage of tensor-based methods in incorporating information across similar tissues. Through simulation and application to the GTEx RNA-seq data from 53 human tissues, we show that MultiCluster identifies three-way interactions with high accuracy and robustness.
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Affiliation(s)
- Miaoyan Wang
- University of Wisconsin, Madison and University of California, Berkeley
| | - Jonathan Fischer
- University of Wisconsin, Madison and University of California, Berkeley
| | - Yun S Song
- University of Wisconsin, Madison and University of California, Berkeley
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26
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Singh A, Bhanot G, Khiabanian H. TuBA: Tunable biclustering algorithm reveals clinically relevant tumor transcriptional profiles in breast cancer. Gigascience 2019; 8:giz064. [PMID: 31216036 PMCID: PMC6582332 DOI: 10.1093/gigascience/giz064] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 04/17/2019] [Accepted: 05/06/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Traditional clustering approaches for gene expression data are not well adapted to address the complexity and heterogeneity of tumors, where small sets of genes may be aberrantly co-expressed in specific subsets of tumors. Biclustering algorithms that perform local clustering on subsets of genes and conditions help address this problem. We propose a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes' expression profiles to identify similarly altered signatures. RESULTS TuBA's predictions are consistent in 3,940 breast invasive carcinoma samples from 3 independent sources, using different technologies for measuring gene expression (RNA sequencing and Microarray). More than 60% of biclusters identified independently in each dataset had significant agreement in their gene sets, as well as similar clinical implications. Approximately 50% of biclusters were enriched in the estrogen receptor-negative/HER2-negative (or basal-like) subtype, while >50% were associated with transcriptionally active copy number changes. Biclusters representing gene co-expression patterns in stromal tissue were also identified in tumor specimens. CONCLUSIONS TuBA offers a simple biclustering method that can identify biologically relevant gene co-expression signatures not captured by traditional unsupervised clustering approaches. It complements biclustering approaches that are designed to identify constant or coherent submatrices in gene expression datasets, and outperforms them in identifying a multitude of altered transcriptional profiles that are associated with observed genomic heterogeneity of diseased states in breast cancer, both within and across tumor subtypes, a promising step in understanding disease heterogeneity, and a necessary first step in individualized therapy.
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Affiliation(s)
- Amartya Singh
- Department of Physics and Astronomy, Rutgers University, 136 Frelinghuysen Rd, Piscataway, NJ 08854
- Center for Systems and Computational Biology, Rutgers Cancer Institute, Rutgers University, 195 Little Albany St, New Brunswick, NJ 08903
| | - Gyan Bhanot
- Department of Physics and Astronomy, Rutgers University, 136 Frelinghuysen Rd, Piscataway, NJ 08854
- Center for Systems and Computational Biology, Rutgers Cancer Institute, Rutgers University, 195 Little Albany St, New Brunswick, NJ 08903
- Department of Molecular Biology and Biochemistry, Rutgers University, 604 Allison Rd, Piscataway, NJ 08854
| | - Hossein Khiabanian
- Department of Physics and Astronomy, Rutgers University, 136 Frelinghuysen Rd, Piscataway, NJ 08854
- Center for Systems and Computational Biology, Rutgers Cancer Institute, Rutgers University, 195 Little Albany St, New Brunswick, NJ 08903
- Department of Molecular Biology and Biochemistry, Rutgers University, 604 Allison Rd, Piscataway, NJ 08854
- Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, One Robert Wood Johnson Place, New Brunswick, NJ, 08903
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27
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Parsana P, Ruberman C, Jaffe AE, Schatz MC, Battle A, Leek JT. Addressing confounding artifacts in reconstruction of gene co-expression networks. Genome Biol 2019; 20:94. [PMID: 31097038 PMCID: PMC6521369 DOI: 10.1186/s13059-019-1700-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 04/24/2019] [Indexed: 11/12/2022] Open
Abstract
Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We demonstrate theoretically, in simulation, and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using data from the GTEx project in multiple tissues, we show that this approach reduces false discoveries beyond correcting only for known confounders.
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Affiliation(s)
- Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Claire Ruberman
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Andrew E Jaffe
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Jeffrey T Leek
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
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28
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Lea A, Subramaniam M, Ko A, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Mononen N, Raitakari OT, Ala-Korpela M, Pajukanta P, Zaitlen N, Ayroles JF. Genetic and environmental perturbations lead to regulatory decoherence. eLife 2019; 8:e40538. [PMID: 30834892 PMCID: PMC6400502 DOI: 10.7554/elife.40538] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 02/14/2019] [Indexed: 01/24/2023] Open
Abstract
Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Amanda Lea
- Department of Ecology and EvolutionPrinceton UniversityPrincetonUnited States
- Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonUnited States
| | - Meena Subramaniam
- Department of Medicine, Lung Biology CenterUniversity of California, San FranciscoSan FranciscoUnited States
| | - Arthur Ko
- Department of Medicine, David Geffen School of Medicine at UCLAUniversity of California, Los AngelesLos AngelesUnited States
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Emma Raitoharju
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Mika Kähönen
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Department of Clinical PhysiologyTampere University, Tampere University HospitalTampereFinland
| | - Ilkka Seppälä
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Nina Mononen
- Finnish Cardiovascular Research Center, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular MedicineUniversity of TurkuTurkuFinland
- Department of Clinical Physiology and Nuclear MedicineTurku University HospitalTurkuFinland
| | - Mika Ala-Korpela
- Systems Epidemiology, Baker Heart and Diabetes InstituteMelbourneAustralia
- Computational Medicine, Faculty of Medicine, Biocenter OuluUniversity of OuluOuluFinland
- NMR Metabolomics Laboratory, School of PharmacyUniversity of Eastern FinlandKuopioFinland
- Population Health Science, Bristol Medical SchoolUniversity of BristolBristolUnited Kingdom
- Medical Research Council Integrative Epidemiology UnitUniversity of BristolBristolUnited Kingdom
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health SciencesThe Alfred Hospital, Monash UniversityMelbourneAustralia
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLAUniversity of California, Los AngelesLos AngelesUnited States
| | - Noah Zaitlen
- Department of Medicine, Lung Biology CenterUniversity of California, San FranciscoSan FranciscoUnited States
| | - Julien F Ayroles
- Department of Ecology and EvolutionPrinceton UniversityPrincetonUnited States
- Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonUnited States
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29
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Rangan AV, McGrouther CC, Kelsoe J, Schork N, Stahl E, Zhu Q, Krishnan A, Yao V, Troyanskaya O, Bilaloglu S, Raghavan P, Bergen S, Jureus A, Landen M. A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data. PLoS Comput Biol 2018; 14:e1006105. [PMID: 29758032 PMCID: PMC5997363 DOI: 10.1371/journal.pcbi.1006105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 06/12/2018] [Accepted: 03/23/2018] [Indexed: 11/18/2022] Open
Abstract
A common goal in data-analysis is to sift through a large data-matrix and detect any significant submatrices (i.e., biclusters) that have a low numerical rank. We present a simple algorithm for tackling this biclustering problem. Our algorithm accumulates information about 2-by-2 submatrices (i.e., 'loops') within the data-matrix, and focuses on rows and columns of the data-matrix that participate in an abundance of low-rank loops. We demonstrate, through analysis and numerical-experiments, that this loop-counting method performs well in a variety of scenarios, outperforming simple spectral methods in many situations of interest. Another important feature of our method is that it can easily be modified to account for aspects of experimental design which commonly arise in practice. For example, our algorithm can be modified to correct for controls, categorical- and continuous-covariates, as well as sparsity within the data. We demonstrate these practical features with two examples; the first drawn from gene-expression analysis and the second drawn from a much larger genome-wide-association-study (GWAS).
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Affiliation(s)
- Aaditya V. Rangan
- Mathematics, New York University, New York, New York, United States of America
- Center for Computational Biology, Flatiron Institute, New York, New York, United States of America
- * E-mail:
| | | | - John Kelsoe
- Psychiatry, University of California, San Diego, California, United States of America
| | - Nicholas Schork
- Human Biology, J. Craig Venters Institute, La Jolla, California, United States of America
| | - Eli Stahl
- Genetics and Genomic Sciences, Mount Sinai Medical School, New York, New York, United States of America
| | - Qian Zhu
- Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Arjun Krishnan
- Computational Mathematics Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Vicky Yao
- Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Olga Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, New York, United States of America
- Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Seda Bilaloglu
- Department of Rehabilitation Medicine, New York University Medical School, New York, New York, United States of America
| | - Preeti Raghavan
- Department of Rehabilitation Medicine, New York University Medical School, New York, New York, United States of America
| | - Sarah Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anders Jureus
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Physiology and Biophysics, University of Gothenburg, Gothenburg, Sweden
| | - Mikael Landen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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30
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Aguiar D, Cheng LF, Dumitrascu B, Mordelet F, Pai AA, Engelhardt BE. Bayesian nonparametric discovery of isoforms and individual specific quantification. Nat Commun 2018; 9:1681. [PMID: 29703885 PMCID: PMC5923247 DOI: 10.1038/s41467-018-03402-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 02/11/2018] [Indexed: 12/18/2022] Open
Abstract
Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop biisq, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. biisq does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. biisq shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios. Alternative splicing leads to transcript isoform diversity. Here, Aguiar et al. develop biisq, a Bayesian nonparametric approach to discover and quantify isoforms from RNA-seq data.
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Affiliation(s)
- Derek Aguiar
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
| | - Li-Fang Cheng
- Department of Electrical Engineering, Princeton University, Princeton, NJ, 08540, USA
| | - Bianca Dumitrascu
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, 08544, USA
| | - Fantine Mordelet
- Institute for Genome Sciences and Policy, Duke University, Durham, NC, 27708, USA
| | - Athma A Pai
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, 01605, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA. .,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, 08540, USA.
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31
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Zhang S. Comparisons of gene coexpression network modules in breast cancer and ovarian cancer. BMC SYSTEMS BIOLOGY 2018; 12:8. [PMID: 29671401 PMCID: PMC5907153 DOI: 10.1186/s12918-018-0530-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Breast cancer and ovarian cancer are hormone driven and are known to have some predisposition genes in common such as the two well known cancer genes BRCA1 and BRCA2. The objective of this study is to compare the coexpression network modules of both cancers, so as to infer the potential cancer-related modules. Methods We applied the eigen-decomposition to the matrix that integrates the gene coexpression networks of both breast cancer and ovarian cancer. With hierarchical clustering of the related eigenvectors, we obtained the network modules of both cancers simultaneously. Enrichment analysis on Gene Ontology (GO), KEGG pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) in the identified modules was performed. Results We identified 43 modules that are enriched by at least one of the four types of enrichments. 31, 25, and 18 modules are enriched by GO terms, KEGG pathways, and DO terms, respectively. The structure of 29 modules in both cancers is significantly different with p-values less than 0.05, of which 25 modules have larger densities in ovarian cancer. One module was found to be significantly enriched by the terms related to breast cancer from GO, KEGG and DO enrichment. One module was found to be significantly enriched by ovarian cancer related terms. Conclusion Breast cancer and ovarian cancer share some common properties on the module level. Integration of both cancers helps identifying the potential cancer associated modules. Electronic supplementary material The online version of this article (10.1186/s12918-018-0530-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shuqin Zhang
- Center for Computational Systems Biology, Shanghai Key Laboratory for Contemporary Applied Mathematics, School of Mathematical Sciences, Fudan University, No.220 Handan Road, Shanghai, 200433, China.
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32
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Saha A, Kim Y, Gewirtz ADH, Jo B, Gao C, McDowell IC, Engelhardt BE, Battle A. Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res 2017; 27:1843-1858. [PMID: 29021288 PMCID: PMC5668942 DOI: 10.1101/gr.216721.116] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 08/22/2017] [Indexed: 11/24/2022]
Abstract
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
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