Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026

Same author

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.

Statistics in biosciences·2026

Same author

Effect of exercise training on clinical and physiological variables in adults with myotonic dystrophy type 1: a systematic review and meta-analysis.

Journal of neurology·2026

Same author

Splicing of HPV16 E6 promotes aggressive invasion in oropharyngeal cancer via endocytosis of E-cadherin.

bioRxiv : the preprint server for biology·2025

Same author

Bayesian Pairwise Comparison of High-Dimensional Images.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2025

Same author

Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.

Statistics in biosciences·2025

Search research articles

Related Experiment Video

Updated: May 25, 2026

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data.

Chiyu Gu¹, Veerabhadran Baladandayuthapani², Subharup Guha³

¹Formerly at the University of Missouri. Currently employed at Bayer Crop Science, 700 Chesterfield Pkwy W, Chesterfield, MO 63017.

Bayesian Analysis

|May 23, 2025

Summary

This study introduces BayesDiff, a new Bayesian method for analyzing DNA methylation differences in cancer. It effectively identifies disease genomic signatures across patient groups, improving upon existing techniques.

Keywords:

2R2CF First order models Mixture models Sticky Pitman-Yor process Two-restaurant two-cuisine franchise

More Related Videos

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Related Experiment Videos

Last Updated: May 25, 2026

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

Area of Science:

Genomics
Biostatistics
Cancer Research

Background:

DNA methylation datasets in cancer studies involve numerous cytosine-phosphate-guanine (CpG) sites with complex correlations.
Identifying disease genomic signatures across different patient groups is a key challenge in cancer research.

Purpose of the Study:

To propose BayesDiff, a novel nonparametric Bayesian approach for differential DNA methylation analysis.
To develop a method that can flexibly utilize information from all CpG sites and accommodate serial dependence.

Main Methods:

Proposed BayesDiff, a nonparametric Bayesian method utilizing a novel class of first-order mixture models (Sticky Pitman-Yor process or 2R2CF).
Employed simulation studies to compare BayesDiff's effectiveness against existing statistical techniques.
Applied the methodology to a gastrointestinal (GI) cancer dataset with serial correlation.

Main Results:

BayesDiff demonstrated effectiveness in identifying differential genomic signatures across patient groups.
The method adaptively accommodates serial dependence common in DNA methylation data.
Analysis of a GI cancer dataset supported known aspects of DNA methylation and gene association.

Conclusions:

BayesDiff offers a robust approach for differential DNA methylation analysis in cancer studies.
The methodology enhances the identification of disease genomic signatures, particularly in datasets with complex correlation structures.
Findings complement existing knowledge on DNA methylation and gene association in upper GI cancers.