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Empirical Bayes Model Comparisons for Differential Methylation Analysis

Mingxiang Teng, Yadong Wang, Seongho Kim, Lang Li, Changyu Shen, Guohua Wang, Yunlong Liu, Tim H. M. Huang, Kenneth P. Nephew, Curt Balch
2012 Comparative and Functional Genomics  
Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis.  ...  We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene  ...  Our group was one of the first to use the empirical Bayes model for the analysis of differential methylation microarray data, by developing a log-normal empirical Bayes model for microarray analysis of  ... 
doi:10.1155/2012/376706 pmid:22956892 pmcid:PMC3432337 fatcat:ibwgaw362bcb3n675sud6zjgui

An evaluation of statistical methods for DNA methylation microarray data analysis

Dongmei Li, Zidian Xie, Marc Le Pape, Timothy Dye
2015 BMC Bioinformatics  
Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and  ...  Conclusions: For DNA methylation studies with small sample size, the bump hunting method and the empirical Bayes method are recommended when DNA methylation levels across CpG loci are independent, while  ...  We would like to thank the Center for Integrated Research Computing at the University of Rochester for providing high performance computing resources.  ... 
doi:10.1186/s12859-015-0641-x pmid:26156501 pmcid:PMC4497424 fatcat:l466gu6libf2vbtnte5i67mrca

Assessing Differential Expression in Two-Color Microarrays: A Resampling-Based Empirical Bayes Approach

Dongmei Li, Marc A. Le Pape, Nisha I. Parikh, Will X. Chen, Timothy D. Dye, Holger Fröhlich
2013 PLoS ONE  
Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci.  ...  The Resamplingbased empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes  ...  Acknowledgments Many thanks to the reviewers for their thorough review and insightful comments which helped us improved this manuscript. Author Contributions  ... 
doi:10.1371/journal.pone.0080099 pmid:24312198 pmcid:PMC3842292 fatcat:3fjl62p3jfgp5ppx7m2awufrlu

Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach [article]

Daniel W Kennedy, Nicole M White, Miles C Benton, Rodney A Lea, Kerrie Mengersen
2019 bioRxiv   pre-print
We present a Bayesian model-based approach for inferring cell-type specific differential methylation (Bayes-CDM) from heterogeneous blood samples.  ...  Results: Cell-type specific differential methylation loci selected using Bayes-CDM contained most (> 84%) of the ground-truth loci for all cell-types.  ...  Acknowledgements The authors wish to acknowledge Professor David Balding and Professor Terry Speed for their advice regarding modelling constrained data.  ... 
doi:10.1101/682070 fatcat:xizn7fquijeefcpun5i2x4rttm

LuxUS: Detecting differential DNA methylation using generalized linear mixed model with spatial correlation structure: Supplementary file [article]

Viivi Halla-aho, Harri Lähdesmäki
2019 bioRxiv   pre-print
We make use of the correlation between neighboring cytosines through a generalized linear mixed model to facilitate analysis of differential methylation where the experimental design can be taken into  ...  It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level.  ...  Hypothesis testing for the differential methylation analysis is done using Bayes factors.  ... 
doi:10.1101/536722 fatcat:2a6tz3jai5h3rnjph4n2fiiz2q

LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation

Viivi Halla-aho, Harri Lähdesmäki, Inanc Birol
2020 Bioinformatics  
Results We have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation.  ...  LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry.  ...  As we use the Savage-Dickey Bayes factor estimate, it may be advisable to empirically calibrate Bayes factor cutoffs for significance e.g. using some known differentially methylated loci.  ... 
doi:10.1093/bioinformatics/btaa539 pmid:32484876 pmcid:PMC7750928 fatcat:pts4pu7xn5a6vpun4ilptdc77m

Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses

Dunbar, Xu, Ryu, Ghosh, Shi, George
2019 Genes  
Most of these methods are being developed for detecting differential methylation rates between cases and controls.  ...  A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region.  ...  Comparison of Bayesian Method with Scan Statistic Method for Two Groups First, we tested for differential methylation under binary response, by dividing the samples into two groups based on CD38 level  ... 
doi:10.3390/genes10090721 pmid:31533352 pmcid:PMC6770971 fatcat:xjomdgcoindsxbrs3pfy3g2qhe

A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data

Henan Wang, Chong He, Garima Kushwaha, Dong Xu, Jing Qiu
2016 BMC Bioinformatics  
In order to provide accurate identification of methylation loci, especially for low coverage data, we propose a full Bayesian partition model to detect differentially methylated loci under two conditions  ...  Since hypo-methylation and hyper-methylation have distinct biological implication, it is desirable to differentiate these two types of differential methylation.  ...  (3) an empirical Bayes Wald test [10] .  ... 
doi:10.1186/s12859-015-0850-3 pmid:26818685 pmcid:PMC4895387 fatcat:fkofkq3p35cazc6aft7amduy5q

m6Acorr: an online tool for the correction and comparison of m6A methylation profiles

Jianwei Li, Yan Huang, Qinghua Cui, Yuan Zhou
2020 BMC Bioinformatics  
The analysis and comparison of RNA m6A methylation profiles have become increasingly important for understanding the post-transcriptional regulations of gene expression.  ...  And m6Acorr, an effective pipeline for correcting m6A profiles, was presented on the basis of quantile normalization and empirical Bayes batch regression method. m6Acorr could efficiently correct laboratory  ...  Acknowledgements We appreciate the researchers who shared their m 6 A methylation profiles and analysis work. Availability and requirements Project name: m6Acorr.  ... 
doi:10.1186/s12859-020-3380-6 pmid:31996134 pmcid:PMC6988237 fatcat:k4uecyt4jnap3purooxntrkbpe

Gene set enrichment analysis for genome-wide DNA methylation data

Jovana Maksimovic, Alicia Oshlack, Belinda Phipson
2021 Genome Biology  
GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis.  ...  Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions.  ...  Acknowledgements We would like to acknowledge Peter Langfelder for providing detailed code and simulations which helped us to discover and correct for multi-gene bias in our testing framework.  ... 
doi:10.1186/s13059-021-02388-x pmid:34103055 pmcid:PMC8186068 fatcat:n4nj2hwnobeizjgory4musa7de

Gene set enrichment analysis for genome-wide DNA methylation data [article]

Jovana Maksimovic, Alicia Oshlack, Belinda Phipson
2020 bioRxiv   pre-print
GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis.  ...  Methylation array analysis has primarily focused on preprocessing, normalisation and identification of differentially methylated CpGs and regions.  ...  Acknowledgements We would like to acknowledge Peter Langfelder for providing detailed code and simulations which helped us to discover and correct for multi-gene bias in our testing framework.  ... 
doi:10.1101/2020.08.24.265702 fatcat:ffujpnffgfbq7eyg27jnzzkg7y

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Viktorian Miok, Saskia M Wilting, Mark A van de Wiel, Annelieke Jaspers, Paula I van Noort, Ruud H Brakenhoff, Peter JF Snijders, Renske DM Steenbergen, Wessel N van Wieringen
2014 BMC Bioinformatics  
Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation.  ...  Available methods for temporal differential expression analysis are not designed for integrative genomic studies.  ...  Hyperparameters are estimated for each analysis separately, but only the variance of the random time effect is shrunken via the empirical Bayes procedure.  ... 
doi:10.1186/1471-2105-15-327 pmid:25278371 pmcid:PMC4288633 fatcat:3xdmvvgttbdezamzkmaoor66y4

Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

Hao Wu, Tianlei Xu, Hao Feng, Li Chen, Ben Li, Bing Yao, Zhaohui Qin, Peng Jin, Karen N. Conneely
2015 Nucleic Acids Research  
We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation.  ...  Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs).  ...  Dispersions at all CpG sites are estimated using an empirical Bayes (EB) method developed in (11) for data with three replicates, or DSSsingle for data with a single replicate.  ... 
doi:10.1093/nar/gkv715 pmid:26184873 pmcid:PMC4666378 fatcat:avrc5azuarhirbjf5chsknon3i

MOABS: model based analysis of bisulfite sequencing data

Deqiang Sun, Yuanxin Xi, Benjamin Rodriguez, Hyun Park, Pan Tong, Mira Meong, Margaret A Goodell, Wei Li
2014 Genome Biology  
Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation.  ...  MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours.  ...  Acknowledgements We are grateful to Wei Xie for sharing the mouse methylome data, and Grant A. Challen for critical reading of this manuscript.  ... 
doi:10.1186/gb-2014-15-2-r38 pmid:24565500 pmcid:PMC4054608 fatcat:tjh66bif5va3hkztawe72srdsu

A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

Changyu Shen, Yiwen Huang, Yunlong Liu, Guohua Wang, Yuming Zhao, Zhiping Wang, Mingxiang Teng, Yadong Wang, David A Flockhart, Todd C Skaar, Pearlly Yan, Kenneth P Nephew (+2 others)
2011 BMC Systems Biology  
Results: We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by  ...  The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper-or hypo-methylation of promoter CpG islands and repressive histone methylations.  ...  Modulated empirical bayes model: DBGA, I-DBGA, and NGA mechanism determination based on ChIP-chip peak, TF motif scan and differential gene expression data Based on FDRs calculated from empirical Bayes  ... 
doi:10.1186/1752-0509-5-67 pmid:21554733 pmcid:PMC3117732 fatcat:6s6o3uwrtra3xgsk6fgdv6m4ka
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