Titus Alexander J, Gallimore Rachel M, Salas Lucas A, Christensen Brock C
Program in Quantitative Biomedical Sciences.
Department of Epidemiology.
Hum Mol Genet. 2017 Oct 1;26(R2):R216-R224. 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).
细胞类型反卷积方法的最新进展加深了我们对疾病发生和发展背后生物学机制的理解。DNA甲基化（DNAm）可用作细胞类型的生物标志物，并通过反卷积方法来推断潜在的细胞类型比例。细胞类型反卷积算法主要有两类：基于参考的和无参考的。基于参考的算法是监督方法，通过利用从纯化细胞群体的DNAm测量中识别出的细胞类型特异性差异甲基化区域（DMR）来确定样本中细胞类型的潜在组成。无参考算法是在没有细胞类型特异性DMR时使用的无监督方法，使科学家能够估计假定的细胞比例或控制细胞类型潜在的混杂因素。基于参考的反卷积通常应用于血液样本，通过允许从存档DNA估计免疫细胞比例，增强了我们对免疫谱与疾病之间关系的理解。使用DNAm推断免疫细胞比例的生物信息学分析是一个名为免疫甲基组学的新领域的一部分，为表观基因组全关联研究（EWAS）提供了一个新的思考方向。
