Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Meta-analysis of DNA methylation aging signatures in 17 human tissues.

Nature aging·2026
Same author

Variations in Innate Immune Cell Subtypes Correlate with Epigenetic Clocks, Inflammaging and Health Outcomes.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

An improved reference library and method for accurate cell-type deconvolution of bulk-tissue miRNA data.

Nature communications·2025
Same author

Epigenetic clocks and inflammaging: pitfalls caused by ignoring cell-type heterogeneity.

GeroScience·2025
Same author

Epigenetic ageing clocks: statistical methods and emerging computational challenges.

Nature reviews. Genetics·2025
Same author

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma.

Communications biology·2024
Same journal

Ciliary flow and morphology shape mass transport at the surface and within gastrovascular cavities of black corals.

Communications biology·2026
Same journal

Virus-mediated prokaryotic community adaptation dynamics under thermal stress in municipal organic solid waste microbiomes.

Communications biology·2026
Same journal

Multi-omics insights into the woolly trait of Saussurea medusa and the plant's coordinated regulation of flavonoid biosynthesis.

Communications biology·2026
Same journal

Loss contexts enhance dorsolateral prefrontal interpersonal neural synchrony during successful human deceptive recommendations.

Communications biology·2026
Same journal

Neuro-regulator role of H<sub>2</sub>S in astrocyte activation and its effects on neurological damage and behavior of VPA-exposed rats.

Communications biology·2026
Same journal

Temporal orchestration of transcriptional and epigenomic programming underlying maternal embryonic diapause in a cricket model.

Communications biology·2026
See all related articles

Related Experiment Video

Updated: Feb 20, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

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

Published on: May 6, 2022

5.2K

Guidelines on optimizing DNA methylation reference panels for cell-type deconvolution.

Xiaolong Guo1, Andrew E Teschendorff2

  • 1Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China. guoxiaolong2022@sinh.ac.cn.

Communications Biology
|February 18, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing DNA methylation reference panels is key for accurate cell-type deconvolution in epigenome-wide association studies. An effect-size optimization approach outperforms machine learning, especially when using hypomethylated markers for adult blood panels.

More Related Videos

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing
10:44

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Published on: May 5, 2023

2.0K
Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

26.5K

Related Experiment Videos

Last Updated: Feb 20, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

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

Published on: May 6, 2022

5.2K
In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing
10:44

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

Published on: May 5, 2023

2.0K
Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

26.5K

Area of Science:

  • Epigenetics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate cell-type deconvolution is essential for interpreting Epigenome-Wide Association Studies (EWAS).
  • Estimating cell-type fractions typically relies on DNA methylation (DNAm) reference panels derived from sorted or single-cell data.
  • Two primary methods exist for constructing these reference panels: machine learning and effect size/cell-type specificity optimization.

Purpose of the Study:

  • To compare the performance of machine learning versus effect size optimization for building DNA methylation reference panels.
  • To identify optimal strategies for constructing DNA methylation reference panels for improved cell-type deconvolution.
  • To provide guidelines for future DNA methylation reference panel development.

Main Methods:

  • Demonstrated the superiority of an effect size and cell-type specificity optimization approach over standard machine learning for reference panel construction.
  • Evaluated panel performance on independent datasets, highlighting overfitting issues with machine learning due to small sorted sample sizes.
  • Compared DNA methylation reference panels built from hypomethylated versus hypermethylated markers for adult blood cell types.

Main Results:

  • The effect size optimization method demonstrated superior performance and reduced overfitting compared to machine learning approaches.
  • DNA methylation reference panels built using cell-type specific hypomethylated markers resulted in improved estimation of cell-type fractions in adult blood.
  • Standard machine learning models tended to overfit and underperform on independent data when trained on limited sorted samples.

Conclusions:

  • The effect size optimization approach is preferable for building DNA methylation reference panels due to its robustness against overfitting.
  • Utilizing hypomethylated markers in adult blood panels enhances the accuracy of cell-type fraction estimation.
  • These findings offer critical guidelines for developing more accurate DNA methylation reference panels and include an optimized panel-building function in the EpiDISH R-package.