Journal article
An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates
Epigenomics, v 16(15-16), pp 1067-1080
2024
PMID: 39093129
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample t-test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.
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Details
- Title
- An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates
- Creators
- Alexander Alsup - University of Kansas Medical CenterEmily Nissen - University of Kansas Medical CenterLucas A. Salas - Dartmouth Coll, Geisel Sch Med, Dept Epidemiol, Lebanon, NH 03756 USAAnnette M. Molinaro - University of California, San FranciscoAlexander Reiner - Fred Hutchinson Canc Ctr, Div Publ Hlth Sci, Seattle, WA 98109 USASimin Liu - Brown UniversityTracy E. Madsen - Brown UniversityLongjian Liu - Drexel University, Urban Health CollaborativePaul L. Auer - Medical College of WisconsinBrock C. Christensen - Dartmouth CollegeJohn K. Wiencke - University of California, San FranciscoKarl T. Kelsey - Brown UniversityDevin C. Koestler - University of Kansas Medical Center
- Publication Details
- Epigenomics, v 16(15-16), pp 1067-1080
- Publisher
- Taylor & Francis
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:001283399400001
- Scopus ID
- 2-s2.0-85200248704
- Other Identifier
- 991022025738304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Genetics & Heredity