Uncertainty quantification of reference-based cellular deconvolution algorithms.

IF 2.9 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Epigenetics Pub Date : 2023-12-01 Epub Date: 2022-12-20 DOI:10.1080/15592294.2022.2137659
Dorothea Seiler Vellame, Gemma Shireby, Ailsa MacCalman, Emma L Dempster, Joe Burrage, Tyler Gorrie-Stone, Leonard S Schalkwyk, Jonathan Mill, Eilis Hannon
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引用次数: 0

Abstract

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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基于参考的细胞反卷积算法的不确定性量化。
迄今为止,大多数表观遗传学流行病学研究都从大块组织(如全血)中生成了全基因组图谱,但这些图谱很容易受到细胞组成变化的混淆。细胞组成的近似值可以使用去卷积算法从大块组织轮廓数学地导出;然而,对于真实细胞比例未知的数据集,没有方法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准确性指标。CETYGO评分捕捉了样本的DNA甲基化图谱与其预期图谱之间的偏差,给出了估计的细胞比例和细胞类型参考图谱。我们证明,当应用于重建的全血图谱时,CETYGO评分一致地区分了不准确和不完整的去卷积。通过将我们的新指标应用于>6300个经验全血图谱,我们发现估计准确的细胞组成受到技术和生物变化的影响。特别是,我们发现,当使用全血通用参考面板时,对女性、新生儿、老年人和吸烟者的估计不太准确。我们的研究结果强调了评估细胞去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指标的DNA甲基化研究。为了便于将我们的方法纳入现有的管道,我们将其作为R包免费提供(https://github.com/ds420/CETYGO)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epigenetics
Epigenetics 生物-生化与分子生物学
CiteScore
6.80
自引率
2.70%
发文量
82
审稿时长
3-8 weeks
期刊介绍: Epigenetics publishes peer-reviewed original research and review articles that provide an unprecedented forum where epigenetic mechanisms and their role in diverse biological processes can be revealed, shared, and discussed. Epigenetics research studies heritable changes in gene expression caused by mechanisms others than the modification of the DNA sequence. Epigenetics therefore plays critical roles in a variety of biological systems, diseases, and disciplines. Topics of interest include (but are not limited to): DNA methylation Nucleosome positioning and modification Gene silencing Imprinting Nuclear reprogramming Chromatin remodeling Non-coding RNA Non-histone chromosomal elements Dosage compensation Nuclear organization Epigenetic therapy and diagnostics Nutrition and environmental epigenetics Cancer epigenetics Neuroepigenetics
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