Uncertainty quantification of reference-based cellular deconvolution algorithms.
Uncertainty quantification of reference-based cellular deconvolution algorithms.
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DOI：
10.1080/15592294.2022.2137659
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发表时间：
2023-12
期刊：
影响因子：
3.7
通讯作者：
Hannon, Eilis
中科院分区：
文献类型：
--
作者：
Vellame, Dorothea Seiler;Shireby, Gemma;MacCalman, Ailsa;Dempster, Emma L.;Burrage, Joe;Gorrie-Stone, Tyler;Schalkwyk, Leonard S.;Mill, Jonathan;Hannon, Eilis
关键词：
迄今为止，大多数表观遗传流行病学研究已经从大量组织（例如全血）中生成了全基因组图谱，但是这些图谱很容易受到细胞组成变化的混淆。细胞组成的代理可以使用反卷积算法从大量组织轮廓中以数学方式导出；然而，对于真实细胞比例未知的数据集，没有方法可以评估这些估计的有效性。在这项研究中，我们描述、验证和表征了衍生细胞异质性变量的样本水平精度指标。 CETYGO 评分捕获了样本 DNA 甲基化谱与给定估计细胞比例和细胞类型参考谱的预期谱之间的偏差。我们证明，当应用于重建的全血谱时，CETYGO 评分始终能够区分不准确和不完整的反卷积。通过将我们的新指标应用于超过 6,300 个经验全血谱，我们发现估计准确的细胞成分会受到技术和生物学变化的影响。特别是，我们表明，当使用全血的通用参考组时，对女性、新生儿、老年人和吸烟者产生的估计值不太准确。我们的结果强调了评估细胞反卷积准确性的指标的实用性，并描述了它如何增强依赖于细胞异质性统计代理的 DNA 甲基化研究。为了便于将我们的方法整合到现有的流程中，我们将其作为 R 包免费提供（https://github.com/ds420/CETYGO）。
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).
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影响因子：
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作者：
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