ArticleBMC bioinformatics2020
Methylation data imputation performances under different representations and missingness patterns.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- SOPA and SIMPA: normalized single-sample integrated multiomics pathway analysis of tumor heterogeneity in solid cancers.Briefings in bioinformatics · 2026Article
- Epigenome-wide mediation analysis identified Cytosine-phosphate-Guanine sites linking environmental factors with diabetes indicators.Epigenomics · 2026Article
- Methylation-based alcohol consumption scores as prognostic biomarkers in colorectal cancer: Insights from a population-based cohort.International journal of cancer · 2025Article
- From population science to the clinic? Limits of epigenetic clocks as personal biomarkers.Epigenomics · 2025Article
- Article
- Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia.Nature communications · 2025Article
- DNAm age differences between infinium methylationEPICv1 vs EPICv2 in buffy coat, PBMC, and saliva samples.Communications biology · 2025Article
- Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions.Science advances · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- Insights to aging prediction with AI based epigenetic clocks.Epigenomics · 2025Review
- Epigenome-wide mediation analysis of the relationship between psychosocial stress and cardiometabolic risk factors in the Health and Retirement Study (HRS).Clinical epigenetics · 2024Article
- Examining the Effects of Environment, Geography, and Elevation on Patterns of DNA Methylation Across Populations of Two Widespread Bumble Bee Species.Genome biology and evolution · 2024Article
- Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors.NPJ precision oncology · 2024Article
- Blood-based multivariate methylation risk score for cognitive impairment and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Fast matrix completion in epigenetic methylation studies with informative covariates.Biostatistics (Oxford, England) · 2024Article
- Technical and biological sources of unreliability of Infinium probes on Illumina methylation microarrays.Clinical epigenetics · 2024Article
- methyLImp2: faster missing value estimation for DNA methylation data.Bioinformatics (Oxford, England) · 2024Article
- Where are we in the implementation of tissue-specific epigenetic clocks?Frontiers in bioinformatics · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data. Several general and specific imputation methods are suitable for DNA methylation data. However, there are no detailed studies of their performances under different missing data mechanisms -(completely) at random or not- and different representations of DNA methylation levels (β and M-value).
resultsWe make an extensive analysis of the imputation performances of seven imputation methods on simulated missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) methylation data. We further consider imputation performances on the popular β- and M-value representations of methylation levels. Overall, β-values enable better imputation performances than M-values. Imputation accuracy is lower for mid-range β-values, while it is generally more accurate for values at the extremes of the β-value range. The MAR values distribution is on the average more dense in the mid-range in comparison to the expected β-value distribution. As a consequence, MAR values are on average harder to impute.
conclusionsThe results of the analysis provide guidelines for the most suitable imputation approaches for DNA methylation data under different representations of DNA methylation levels and different missing data mechanisms.
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