ArticleFrontiers in genetics2014
Stratified randomization controls better for batch effects in 450K methylation analysis: a cautionary tale.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses 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.
The trial behind it
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
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Meta-analysis examining fetal sex-specific placental DNA methylation intensities and estimated cell composition post IVF.Molecular human reproduction · 2025Pooled it
- Multivariate meta-analysis reveals global transcriptomic signatures underlying distinct human naive-like pluripotent states.PloS one · 2021Pooled it
- From case-control averages to validated subgroups: Interpreting inflammatory and neuroaxonal-injury biomarkers in schizophrenia and major depression.Brain, behavior, & immunity - health · 2026Review
- Reproductive and metabolic hormone associations in adult Samoan males with and without obesity.Evolution, medicine, and public health · 2026Article
- Integrated fibre-specific methylome and proteome profiling of human skeletal muscle across males and females with fibre-type deconvolution.Skeletal muscle · 2025Article
- Accounting for differences between Infinium MethylationEPIC v2 and v1 in DNA methylation-based tools.Life science alliance · 2025Article
- Accounting for differences between Infinium MethylationEPIC v2 and v1 in DNA methylation-based tools.bioRxiv : the preprint server for biology · 2025Article
- APPLICATION OF QUANTILE DISCRETIZATION AND BAYESIAN NETWORK ANALYSIS TO PUBLICLY AVAILABLE CYSTIC FIBROSIS DATA SETS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Cross-study analyses of microbial abundance using generalized common factor methods.BMC bioinformatics · 2023Article
- DNA methylation markers for kidney function and progression of diabetic kidney disease.Nature communications · 2023Article
- Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs.Translational psychiatry · 2023Article
- Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects.Journal of mass spectrometry and advances in the clinical lab · 2023Review
- Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments.BMC bioinformatics · 2023Article
- An overview of DNA methylation-derived trait score methods and applications.Genome biology · 2023Review
- Normalization of Large-Scale Transcriptome Data Using Heuristic Methods.Bioinformatics and biology insights · 2023Article
- Stable methylation loci are associated with systolic blood pressure in a Croatian island population.Epigenomics · 2022Article
- ImmuMethy, a database of DNA methylation plasticity at a single cytosine resolution in human blood and immune cells.Database : the journal of biological databases and curation · 2022Article
- Epigenome-wide association studies: current knowledge, strategies and recommendations.Clinical epigenetics · 2021Review
- Social epigenomics: are we at an impasse?Epigenomics · 2021Review
- Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundBatch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples.
methodsTwo pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were, respectively, employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.
resultsChip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After "removing" batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.
conclusionEven the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects.
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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.