ArticleFrontiers in bioinformatics2021
The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation Analysis.
Article in Frontiers in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.Genome biology · 2025Article
- Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response.medRxiv : the preprint server for health sciences · 2025Article
- Epigenetic patient stratification via contrastive machine learning refines hallmark biomarkers in minoritized children with asthma.Research square · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
Calling differential methylation at a cell-type level from tissue-level bulk data is a fundamental challenge in genomics that has recently received more attention. These studies most often aim at identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention has been given to the fact that this directionality assumption may not hold and can consequently affect statistical power and control for false positives. We demonstrate that misspecification of the model directionality can lead to a drastic decrease in performance and increase in risk of spurious findings in cell-type-specific differential methylation analysis, and we discuss the need to carefully consider model directionality before choosing a statistical method for analysis.
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