Biomarker detection and categorization in ribonucleic acid sequencing meta‐analysis using Bayesian hierarchical models

No abstract is available for this paper.

• Publication year

  2017

• Venue

  Journal of the Royal Statistical Society, Series C: Applied Statistics

• Publication date

  2017-08-01

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1111/rssc.12199PMID 28785119PMCID PMC5543999
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Evaluation of Methods in Removing Batch Effects on RNA-seq Data

  2016cited by this paper
• Asynchronous Distributed Gibbs Sampling

  2015cited by this paper
• A comprehensive study design reveals treatment-and transcript abundance – dependent concordance between RNA-seq and microarray data

  2014cited by this paper
• A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium

  2014cited by this paper
• svaseq: removing batch effects and other unwanted noise from sequencing data

  2014cited by this paper
• The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

  2014cited by this paper
• MethylSig: a whole genome DNA methylation analysis pipeline

  2014cited by this paper
• Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq

  2014cited by this paper
• MicroRNA-384 regulates both amyloid precursor protein and β-secretase expression and is a potential biomarker for Alzheimer's disease.

  2014cited by this paper
• Transcriptome Sequencing of Gene Expression in the Brain of the HIV-1 Transgenic Rat

  2013influential reference
• Effects of Taurine Supplementation on Hepatic Markers of Inflammation and Lipid Metabolism in Mothers and Offspring in the Setting of Maternal Obesity

  2013cited by this paper
• Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

  2013cited by this paper
• Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.

  2013cited by this paper
• A comparison of methods for differential expression analysis of RNA-seq data

  2013cited by this paper
• Differential expression analysis for paired RNA-seq data

  2013influential reference
• Differential meta-analysis of RNA-seq data from multiple studies

  2013cited by this paper
• Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

  2013cited by this paper
• Aryl hydrocarbon receptor-mediated induction of the microRNA-132/212 cluster promotes interleukin-17–producing T-helper cell differentiation

  2013cited by this paper
• EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments

  2013cited by this paper
• Comprehensive literature review and statistical considerations for microarray meta-analysis

  2012cited by this paper
• Lognormal and Gamma Mixed Negative Binomial Regression

  2012cited by this paper
• Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges

  2012cited by this paper
• Differential expression analysis for sequence count data

  2011cited by this paper
• MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis

  2011cited by this paper
• An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies

  2011cited by this paper
• Differential expression analysis for sequence count data

  2010cited by this paper
• baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data

  2010cited by this paper
• From RNA-seq reads to differential expression results

  2010cited by this paper
• A Bayesian Model for Cross-Study Differential Gene Expression

  2009cited by this paper
• edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

  2009cited by this paper
• RNA-Seq: a revolutionary tool for transcriptomics

  2009cited by this paper
• Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

  2009cited by this paper
• Transcript length bias in RNA-seq data confounds systems biology

  2009cited by this paper
• Mapping and quantifying mammalian transcriptomes by RNA-Seq

  2008cited by this paper
• The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

  2008cited by this paper
• Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets

  2008cited by this paper
• Haemoglobin and albumin as markers of HIV disease progression in the highly active antiretrovial therapy era: relationships with gender *

  2007cited by this paper
• Much ado about nothing: a comparison of the performance of meta‐analytical methods with rare events

  2007cited by this paper
• RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis

  2006cited by this paper
• BMC Bioinformatics BioMed Central Methodology article

  2005cited by this paper
• Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data

  2005cited by this paper
• Detecting differential gene expression with a semiparametric hierarchical mixture method.

  2004cited by this paper
• Bayesian mixture model based clustering of replicated microarray data

  2004cited by this paper
• Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

  2003cited by this paper
• Combining multiple microarray studies and modeling interstudy variation

  2003cited by this paper
• Bayesian random effects meta‐analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales

  2002cited by this paper
• Markov Chain Sampling Methods for Dirichlet Process Mixture Models

  2000cited by this paper
• Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage

  2000cited by this paper
• Controlling the false discovery rate: a practical and powerful approach to multiple testing

  1995cited by this paper
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

  1984cited by this paper
• BAYESIAN DENSITY ESTIMATION BY MIXTURES OF NORMAL DISTRIBUTIONS

  1983cited by this paper
• Monte Carlo Sampling Methods Using Markov Chains and Their Applications

  1970cited by this paper
• Equation of State Calculations by Fast Computing Machines

  1953cited by this paper
• The American Soldier: Adjustment During Army Life.

  1949cited by this paper
• Statistical Methods for Research Workers

  1937cited by this paper
• Bmc Medical Genomics Gene Expression Analysis of Glioblastomas Identifies the Major Molecular Basis for the Prognostic Benefit of Younger Age

  year unknowncited by this paper
• Journal of the American Statistical Association Bayesian Inference for Logistic Models Using Pólya–gamma Latent Variables Bayesian Inference for Logistic Models Using P ´ Olya–gamma Latent Variables

  year unknowncited by this paper

• Integration of Gastric Cancer RNA‐Seq Datasets Along With PPI Network Suggests That Nonhub Nodes Have the Potential to Become Biomarkers

  2025cites this paper
• Meta-analysis of RNA-seq studies with an adaptive weight and truncation p-value combination test

  2024cites this paper
• Artifact of Detecting Biomarkers Associated with Sequencing Depth in RNA-Seq

  2023cites this paper
• Microarray meta-analysis reveals IL6 and p38β/MAPK11 as potential targets of hsa-miR-124 in endothelial progenitor cells: Implications for stent re-endothelization in diabetic patients

  2022cites this paper
• Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer Studies

  2021cites this paper
• Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

  2021cites this paper
• Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction

  2021cites this paper
• Integrative Data Analysis of Microarray and RNA-seq

  2018cites this paper
• Differential expression and feature selection in the analysis of multiple omics studies

  2018cites this paper
• A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data

  2017cites this paper
• Discussant paper on ‘Statistical contributions to bioinformatics: Design, modelling, structure learning and integration’

  2017cites this paper