Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA.
Genome Biol. 2018 May 29;19(1):64. doi: 10.1186/s13059-018-1448-7.
Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms.
全基因组甲基化芯片是评估复杂混合物细胞组成的有力工具。我们比较了三种方法，基于使用 Illumina HumanMethylationEPIC 芯片检测到的血液衍生 DNA 甲基化特征，选择参考文库来解卷积中性粒细胞、单核细胞、B 淋巴细胞、自然杀伤细胞以及 CD4+和 CD8+T 细胞亚群。IDOL 算法确定了一个包含 450 个 CpG 的文库，当应用于由上述细胞类型构建的人工混合物的 EPIC 甲基化数据时，细胞类型的平均 R 值为 99.2。在 450 个 CpG 中，有 69%是 EPIC 特有的。该文库有可能减少不同 array 平台之间的非预期技术差异。
