ReviewHuman genetics2011
Statistical approaches for the analysis of DNA methylation microarray data.
Review in Human genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- Epigenetic Changes Induced by High Glucose in Human Pancreatic Beta Cells.Journal of diabetes research · 2023Article
- Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data.Biomedicines · 2021Review
- Human methylome variation across Infinium 450K data on the Gene Expression Omnibus.NAR genomics and bioinformatics · 2021Article
- Article
- Identification of novel epigenetic abnormalities as sputum biomarkers for lung cancer risk among smokers and COPD patients.Lung cancer (Amsterdam, Netherlands) · 2020Article
- pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).BMC bioinformatics · 2019Article
- BPRMeth: a flexible Bioconductor package for modelling methylation profiles.Bioinformatics (Oxford, England) · 2018Article
- The Effect of Citalopram on Genome-Wide DNA Methylation of Human Cells.International journal of genomics · 2018Article
- Analysis of the interplay between methylation and expression reveals its potential role in cancer aetiology.Functional & integrative genomics · 2017Article
- Exploratory analysis of ERCC2 DNA methylation in survival among pediatric medulloblastoma patients.Cancer epidemiology · 2016Article
- Estimating DNA methylation levels by joint modeling of multiple methylation profiles from microarray data.Biometrics · 2016Article
- Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data.International journal of oncology · 2016Article
- Establishing an analytic pipeline for genome-wide DNA methylation.Clinical epigenetics · 2016Review
- Article
- Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods.Microarrays (Basel, Switzerland) · 2015Article
- Epigenetic Repression of CCDC37 and MAP1B Links Chronic Obstructive Pulmonary Disease to Lung Cancer.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2015Article
- Integrative analysis of DNA methylation and gene expression data identifies EPAS1 as a key regulator of COPD.PLoS genetics · 2015Article
- A comprehensive overview of Infinium HumanMethylation450 data processing.Briefings in bioinformatics · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.
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What OpenQuestion holds
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.