ArticleGenome biology2019
CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response.medRxiv : the preprint server for health sciences · 2025Article
- Single-cell DNA methylome and 3D genome atlas of human subcutaneous adipose tissue.Nature genetics · 2025Article
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- Genome-Wide DNA Methylation Identifies Potential Disease-Specific Biomarkers and Pathophysiologic Mechanisms in Irritable Bowel Syndrome, Inflammatory Bowel Disease, and Celiac Disease.Neurogastroenterology and motility · 2025Article
- Single-cell DNA methylome and 3D genome atlas of the human subcutaneous adipose tissue.bioRxiv : the preprint server for biology · 2024Article
- Epigenetic patient stratification via contrastive machine learning refines hallmark biomarkers in minoritized children with asthma.Research square · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz.Nature protocols · 2020Article
- BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching.iScience · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED .
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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.