ReviewBriefings in bioinformatics2016
Statistical methods for detecting differentially methylated regions based on MethylCap-seq data.
Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 15 citations in OpenAlex.
- Estimating energy expenditure of sperm whales living in social units.Conservation physiology · 2026Article
- Epigenetic profiles of tissue informative CpGs inform ALS disease status and progression.Genome medicine · 2025Article
- Nonparametric two-sample tests of high dimensional mean vectors via random integration.Journal of the American Statistical Association · 2024Article
- Differential RNA methylation using multivariate statistical methods.Briefings in bioinformatics · 2022Article
- BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Standardized Regression Coefficients and Newly Proposed Estimators forPsychometrika · 2020Article
- Review
- DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts.Nucleic acids research · 2018Article
- Statistical challenges in analyzing methylation and long-range chromosomal interaction data.Statistics in biosciences · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from 'curse of dimensionality' and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation studies are conducted to highlight the performance of these tests under different settings. Based on our observation, we make recommendations on the optimal test to use. We illustrate the superiority of mean vector tests in detecting cancer-related canonical gene pathways, which are significantly enriched for acute myeloid leukemia and ovarian cancer.
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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.