ArticleNAR genomics and bioinformatics2025
ComBat-met: adjusting batch effects in DNA methylation data.
Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of DNA methylation aging signatures in 17 human tissues.Nature aging · 2026Pooled it
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence Drives Advances in Multi-Omics Analysis and Precision Medicine for Sepsis.Biomedicines · 2026Review
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- From population science to the clinic? Limits of epigenetic clocks as personal biomarkers.Epigenomics · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- iComBat: An incremental framework for batch effect correction in DNA methylation array data.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integration of genomics data is routinely hindered by unwanted technical variations known as batch effects. Despite wide availability, existing batch correction methods often fall short in capturing the unique characteristics of DNA methylation data. We present ComBat-met, a beta regression framework to adjust batch effects in DNA methylation studies. Our method fits beta regression models to the data, calculates batch-free distributions, and maps the quantiles of the estimated distributions to their batch-free counterparts. Compared to traditional methods, ComBat-met followed by differential methylation analysis shows improved statistical power without compromising false positive rates based on simulated data. Additionally, we demonstrate the ability of ComBat-met to remove cross-batch variations and recover biological signals using data from The Cancer Genome Atlas.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.