Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac449.
We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.
我们对在从大量甲基化数据估计细胞类型比例方面发挥重要作用的计算去卷积方法进行了系统评估。所提出的框架 methylDeConv（可作为 R 包使用）集成了几种用于甲基化谱（Illumina HumanMethylation450 和 MethylationEPIC 阵列）的去卷积方法，并提供了不同的细胞类型特异性 CpG 选择，以构建包含主要免疫细胞亚群、上皮细胞和无细胞 DNA 的扩展参考库。我们通过模拟和基准数据集比较了不同去卷积算法的性能，并进一步研究了估计的细胞类型比例与乳腺癌和黑色素瘤甲基化病例研究中癌症治疗的关联。我们的结果表明，基于扩展参考库的去卷积对于获得非血液组织中细胞比例的准确估计至关重要。
