Cell-type deconvolution from DNA methylation: a review of recent applications

• Published in: Human molecular genetics Vol. 26; no. R2; pp. R216 - R224
• Main Authors: Titus, Alexander J, Gallimore, Rachel M, Salas, Lucas A, Christensen, Brock C
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.10.2017
• Subjects: Algorithms; Animals; Computational Biology - methods; Computer Simulation; DNA Methylation - genetics; DNA Methylation - physiology; Genome-Wide Association Study; Humans; Invited Reviews; Sequence Analysis, DNA - methods
• ISSN: 0964-6906; 1460-2083
• DOI: 10.1093/hmg/ddx275

Recent advances in cell-type deconvolution approaches are adding to our understanding of the biology underlying disease development and progression. DNA methylation (DNAm) can be used as a biomarker of cell types, and through deconvolution approaches, to infer underlying cell type proportions. Cell-type deconvolution algorithms have two main categories: reference-based and reference-free. Reference-based algorithms are supervised methods that determine the underlying composition of cell types within a sample by leveraging differentially methylated regions (DMRs) specific to cell type, identified from DNAm measures of purified cell populations. Reference-free algorithms are unsupervised methods for use when cell-type specific DMRs are not available, allowing scientists to estimate putative cellular proportions or control for potential confounding from cell type. Reference-based deconvolution is typically applied to blood samples and has potentiated our understanding of the relation between immune profiles and disease by allowing estimation of immune cell proportions from archival DNA. Bioinformatic analyses using DNAm to infer immune cell proportions, part of a new field known as Immunomethylomics, provides a new direction for consideration in epigenome wide association studies (EWAS).