DNA methylation profiling offers unique insights into human development and diseases. Often the analysis of complex tissues and cell mixtures is the only feasible option to study methylation changes across large patient cohorts. Since DNA methylomes are highly cell type specific, deconvolution methods can be used to recover cell type–specific information in the form of latent methylation components (LMCs) from such ‘bulk’ samples. Reference-free deconvolution methods retrieve these components without the need for DNA methylation profiles of purified cell types. Currently no integrated and guided procedure is available for data preparation and subsequent interpretation of deconvolution results. Here, we describe a three-stage protocol for reference-free deconvolution of DNA methylation data comprising: (i) data preprocessing, confounder adjustment using independent component analysis (ICA) and feature selection using DecompPipeline, (ii) deconvolution with multiple parameters using MeDeCom, RefFreeCellMix or EDec and (iii) guided biological inference and validation of deconvolution results with the R/Shiny graphical user interface FactorViz. Our protocol simplifies the analysis and guides the initial interpretation of DNA methylation data derived from complex samples. The harmonized approach is particularly useful to dissect and evaluate cell heterogeneity in complex systems such as tumors. We apply the protocol to lung cancer methylomes from The Cancer Genome Atlas (TCGA) and show that our approach identifies the proportions of stromal cells and tumor-infiltrating immune cells, as well as associations of the detected components with clinical parameters. The protocol takes slightly >3 d to complete and requires basic R skills.

DNA 甲基化分析为人类发育和疾病提供了独特的见解。通常，复杂组织和细胞混合物的分析是研究大型患者群体甲基化变化的唯一可行选择。由于 DNA 甲基化组具有高度的细胞类型特异性，因此可以使用反卷积方法从此类“大量”样本中以潜在甲基化成分 (LMC) 的形式恢复细胞类型特异性信息。无参考反卷积方法无需纯化细胞类型的 DNA 甲基化谱即可检索这些成分。目前没有可用于数据准备和解卷积结果的后续解释的集成和指导程序。在这里，我们描述了一个三阶段协议，用于 DNA 甲基化数据的无参考反卷积，包括：（i）数据预处理，使用独立成分分析 (ICA) 调整混杂因素，使用 DecompPipeline 进行特征选择，(ii) 使用 MeDeCom、RefFreeCellMix 或 EDec 对多个参数进行反卷积，以及 (iii) 使用 R/Shiny 图形用户界面 FactorViz 引导生物推断和验证反卷积结果。我们的协议简化了分析并指导对来自复杂样本的 DNA 甲基化数据的初步解释。协调方法对于剖析和评估复杂系统（如肿瘤）中的细胞异质性特别有用。我们将该协议应用于癌症基因组图谱 (TCGA) 中的肺癌甲基化组，并表明我们的方法确定了基质细胞和肿瘤浸润免疫细胞的比例，以及检测到的成分与临床参数的关联。