Uncertainty quantification of reference-based cellular deconvolution algorithms

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample’s DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).迄今为止，绝大多数表观遗传流行病学（epigenetic epidemiology）研究均从混合组织（bulk tissue，例如全血）中获取全基因组范围内的图谱，但这类图谱极易因细胞组成的变异而产生混杂偏倚。可通过解卷积算法（deconvolution algorithm）从混合组织图谱中数学推导得到细胞组成的替代指标；但目前尚无方法可在真实细胞比例未知的数据集下，评估这类估算结果的有效性。本研究针对推导得到的细胞异质性变量，构建、验证并表征了一种样本级准确率指标。CETYGO评分（CETYGO score）可衡量样本的DNA甲基化（DNA methylation）图谱与基于估算细胞比例及细胞类型参考图谱得到的预期图谱之间的偏差。我们证实，将CETYGO评分应用于重构的全血图谱时，可稳定区分不准确且不完整的解卷积结果。将这一新型指标应用于超过6300份实际全血图谱后，我们发现准确估算细胞组成的过程同时受到技术变异与生物学变异的影响。具体而言，我们发现当使用通用的全血参考面板（reference panel）时，针对女性、新生儿、老年人群及吸烟者的细胞组成估算结果准确性较低。本研究结果凸显了评估细胞解卷积准确性的指标的实用价值，并阐明了该指标可如何优化依赖于细胞异质性统计替代指标的DNA甲基化研究。为便于将本研究方法整合至现有分析流程中，我们已将其作为R包（R package）免费公开，链接为：https://github.com/ds420/CETYGO。