Sun Tongyue, Yuan Jinqi, Zhu Yacheng, Li Jingqi, Yang Shen, Zhou Junpeng, Ge Xinzhou, Qu Susu, Li Wei, Li Jingyi Jessica, Li Yumei
School of Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou, 215123, China.
Department of Statistics, Oregon State University, Corvallis, OR, 97331, USA.
Genome Biol. 2024 Dec 19;25(1):318. doi: 10.1186/s13059-024-03456-8.
Plasma cell-free DNA (cfDNA) is derived from cellular death in various tissues. Investigating the tissue origin of cfDNA through cell type deconvolution, we can detect changes in tissue homeostasis that occur during disease progression or in response to treatment. Consequently, cfDNA has emerged as a valuable noninvasive biomarker for disease detection and treatment monitoring. Although there are many methylation-based methods for cfDNA cell type deconvolution, a comprehensive and systematic evaluation of these methods has yet to be conducted.
In this study, we benchmark five methods: MethAtlas, cfNOMe toolkit, CelFiE, CelFEER, and UXM. Utilizing deep whole-genome bisulfite sequencing data from 35 human cell types, we generate in silico cfDNA samples with ground truth cell type proportions to assess the deconvolution performance of the five methods under multiple scenarios. Our findings indicate that multiple factors, including reference marker selection, sequencing depth, and reference atlas completeness, jointly influence the deconvolution performance. Notably, an incomplete reference with missing markers or cell types leads to suboptimal results. We observe performance differences among methods under varying conditions, underscoring the importance of tailoring cfDNA deconvolution analyses. To increase the clinical relevance of our findings, we further evaluate each method's performance in potential clinical applications using real-world datasets.
Based on the benchmark results, we propose general guidelines to choose the suitable methods based on sequencing depth of the cfDNA data and completeness of the reference atlas to maximize the performance of methylation-based cfDNA cell type deconvolution.
血浆游离DNA（cfDNA）源自各种组织中的细胞死亡。通过细胞类型反卷积研究cfDNA的组织来源，我们可以检测疾病进展过程中或对治疗反应时发生的组织稳态变化。因此，cfDNA已成为疾病检测和治疗监测的一种有价值的非侵入性生物标志物。尽管有许多基于甲基化的方法用于cfDNA细胞类型反卷积，但尚未对这些方法进行全面系统的评估。
在本研究中，我们对五种方法进行了基准测试：MethAtlas、cfNOMe工具包、CelFiE、CelFEER和UXM。利用来自35种人类细胞类型的深度全基因组亚硫酸氢盐测序数据，我们生成了具有真实细胞类型比例的虚拟cfDNA样本，以评估这五种方法在多种情况下的反卷积性能。我们的研究结果表明，包括参考标记选择、测序深度和参考图谱完整性在内的多个因素共同影响反卷积性能。值得注意的是，缺少标记或细胞类型的不完整参考会导致次优结果。我们观察到不同条件下各方法的性能差异，强调了定制cfDNA反卷积分析的重要性。为了提高我们研究结果的临床相关性，我们使用真实世界数据集进一步评估了每种方法在潜在临床应用中的性能。
基于基准测试结果，我们提出了一般指南，根据cfDNA数据的测序深度和参考图谱的完整性选择合适的方法，以最大限度地提高基于甲基化的cfDNA细胞类型反卷积的性能。
