ArticlePloS one2016
BEclear: Batch Effect Detection and Adjustment in DNA Methylation Data.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The aging epigenome: integrative analyses reveal intersection with Alzheimer's disease.GeroScience · 2026Pooled it
- An Epigenetic Clock for Accurate Age Prediction in Atlantic Cod Populations for Improved Fisheries Management.Molecular ecology resources · 2026Article
- OmicsQ: a user-friendly platform for interactive quantitative omics data analysis.Bioinformatics (Oxford, England) · 2026Article
- DNA Methylation-Based Classification of Kidney Neoplasms.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- High performance data integration for large-scale analyses of incomplete Omic profiles using Batch-Effect Reduction Trees (BERT).Nature communications · 2025Article
- RareNet: a deep learning model for rare cancer diagnosis.Scientific reports · 2025Article
- ComBat-met: adjusting batch effects in DNA methylation data.NAR genomics and bioinformatics · 2025Article
- DNA methylation signature of a lifestyle-based resilience index for cognitive health.Alzheimer's research & therapy · 2025Article
- Blood DNA methylation signature for incident dementia: Evidence from longitudinal cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Associations between inflammation and striatal dopamine D2-receptor availability in aging.Journal of neuroinflammation · 2025Article
- Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.Cell genomics · 2023Article
- Whole-genome methylation analysis of aging human tissues identifies age-related changes in developmental and neurological pathways.Aging cell · 2023Article
- Local data commons: the sleeping beauty in the community of data commons.BMC bioinformatics · 2022Article
- Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Review
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.Nature protocols · 2020Article
- Accelerating research on biological aging and mental health: Current challenges and future directions.Psychoneuroendocrinology · 2019Review
- The Cord Blood Insulin and Mitochondrial DNA Content Related Methylome.Frontiers in genetics · 2019Article
- Meffil: efficient normalization and analysis of very large DNA methylation datasets.Bioinformatics (Oxford, England) · 2018Article
- An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Batch effects describe non-natural variations of, for example, large-scale genomic data sets. If not corrected by suitable numerical algorithms, batch effects may seriously affect the analysis of these datasets. The novel array platform independent software tool BEclear enables researchers to identify those portions of the data that deviate statistically significant from the remaining data and to replace these portions by typical values reconstructed from neighboring data entries based on latent factor models. In contrast to other comparable methods that often use some sort of global normalization of the data, BEclear avoids changing the apparently unaffected parts of the data. We tested the performance of this approach on DNA methylation data for various tumor data sets taken from The Cancer Genome Atlas and compared the results to those obtained with the existing algorithms ComBat, Surrogate Variable Analysis, RUVm and Functional normalization. BEclear constantly performed at par with or better than these methods. BEclear is available as an R package at the Bioconductor project http://bioconductor.org/packages/release/bioc/html/BEclear.html.
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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.