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Jiang L, Greenlaw K, Ciampi A, Canty AJ, Gross J, Turecki G, Greenwood CMT. A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states. Genet Epidemiol 2022; 46:446-462. [PMID: 35753057 DOI: 10.1002/gepi.22489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 04/20/2022] [Accepted: 05/04/2022] [Indexed: 11/09/2022]
Abstract
5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.
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Affiliation(s)
- Lai Jiang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada
| | - Keelin Greenlaw
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
| | - Angelo J Canty
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
| | - Jeffrey Gross
- Department of Psychiatry, Douglas Institute, McGill University, Montréal, Québec, Canada
| | - Gustavo Turecki
- Department of Psychiatry, Douglas Institute, McGill University, Montréal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada.,Department of Human Genetics, McGill University, Montréal, Québec, Canada
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Krleža D, Vrdoljak B, Brčić M. Statistical hierarchical clustering algorithm for outlier detection in evolving data streams. Mach Learn 2020. [DOI: 10.1007/s10994-020-05905-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Spurek P, Byrski K, Tabor J. Online updating of active function cross-entropy clustering. Pattern Anal Appl 2019. [DOI: 10.1007/s10044-018-0701-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion. BIG DATA AND COGNITIVE COMPUTING 2018. [DOI: 10.3390/bdcc2040032] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data growth in today’s world is exponential, many applications generate huge amount of data streams at very high speed such as smart grids, sensor networks, video surveillance, financial systems, medical science data, web click streams, network data, etc. In the case of traditional data mining, the data set is generally static in nature and available many times for processing and analysis. However, data stream mining has to satisfy constraints related to real-time response, bounded and limited memory, single-pass, and concept-drift detection. The main problem is identifying the hidden pattern and knowledge for understanding the context for identifying trends from continuous data streams. In this paper, various data stream methods and algorithms are reviewed and evaluated on standard synthetic data streams and real-life data streams. Density-micro clustering and density-grid-based clustering algorithms are discussed and comparative analysis in terms of various internal and external clustering evaluation methods is performed. It was observed that a single algorithm cannot satisfy all the performance measures. The performance of these data stream clustering algorithms is domain-specific and requires many parameters for density and noise thresholds.
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Mansalis S, Ntoutsi E, Pelekis N, Theodoridis Y. An evaluation of data stream clustering algorithms. Stat Anal Data Min 2018. [DOI: 10.1002/sam.11380] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Stratos Mansalis
- Department of Informatics; University of Piraeus; Piraeus Greece
| | - Eirini Ntoutsi
- L3S Research Center; Leibniz Universität Hannover; Hannover Germany
| | - Nikos Pelekis
- Department of Statistics and Insurance Science; University of Piraeus; Piraeus Greece
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Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection. Symmetry (Basel) 2018. [DOI: 10.3390/sym10040113] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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