The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies
Open accessAbstract
BACKGROUND: Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking. RESULTS: Here we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH (Epigenetic Dissection of Intra-Sample Heterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking. CONCLUSIONS: Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.
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Cites
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Cited by
Salas, L. A., Koestler, D. C., Butler, R. A., Hansen, H. M., Wiencke, J. K., Kelsey, K. T., & Christensen, B. C. (2018) · Genome Biology · doi:10.1186/s13059-018-1448-7 · Cited by 416
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Evaluation of Cell Type Annotation R Packages on Single-Cell RNA-Seq Data
Huang, Q., Liu, Y., Du, Y., & Garmire, L. X. (2021) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.07.004 · Cited by 149
Bridging biological cfDNA features and machine learning approaches
Moser, T., Kühberger, S., Lazzeri, I., Vlachos, G., & Heitzer, E. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.01.004 · Cited by 133
Metadata
- Authors
- Teschendorff, A. E., Breeze, C. E., Zheng, S. C., & Beck, S.
- Venue
- BMC Bioinformatics
- Published
- 2017-02-13
- Volume
- 18
- Issue
- 1
- Pages
- 105-105
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2950086703
- Cited by
- 544
- References
- 45
- FWCI
- 9.41
- Type
- journal article
- Language
- en
- Open access
- gold
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