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SOMAscan assay 变异评估。

Assessment of Variability in the SOMAscan Assay.

机构信息

Trans-NIH Center for Human Immunology, Autoimmunity, and Inflammation, National Institutes of Health, Bethesda, MD, 20892, USA.

Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.

出版信息

Sci Rep. 2017 Oct 27;7(1):14248. doi: 10.1038/s41598-017-14755-5.

Abstract

SOMAscan is an aptamer-based proteomics assay capable of measuring 1,305 human protein analytes in serum, plasma, and other biological matrices with high sensitivity and specificity. In this work, we present a comprehensive meta-analysis of performance based on multiple serum and plasma runs using the current 1.3 k assay, as well as the previous 1.1 k version. We discuss normalization procedures and examine different strategies to minimize intra- and interplate nuisance effects. We implement a meta-analysis based on calibrator samples to characterize the coefficient of variation and signal-over-background intensity of each protein analyte. By incorporating coefficient of variation estimates into a theoretical model of statistical variability, we also provide a framework to enable rigorous statistical tests of significance in intervention studies and clinical trials, as well as quality control within and across laboratories. Furthermore, we investigate the stability of healthy subject baselines and determine the set of analytes that exhibit biologically stable baselines after technical variability is factored in. This work is accompanied by an interactive web-based tool, an initiative with the potential to become the cornerstone of a regularly updated, high quality repository with data sharing, reproducibility, and reusability as ultimate goals.

摘要

SOMAscan 是一种基于适体的蛋白质组学检测方法,能够以高灵敏度和特异性检测血清、血浆和其他生物基质中的 1305 个人类蛋白分析物。在这项工作中,我们对当前的 1.3k 检测以及之前的 1.1k 版本进行了基于多个血清和血浆运行的综合荟萃分析。我们讨论了归一化程序,并研究了不同的策略来最小化板内和板间的干扰效应。我们基于校准样本实施了荟萃分析,以描述每个蛋白分析物的变异系数和信号背景强度。通过将变异系数估计值纳入统计变异性的理论模型,我们还提供了一个框架,以便在干预研究和临床试验中以及在实验室内部和之间进行严格的统计学显著性检验,以及质量控制。此外,我们还研究了健康受试者基线的稳定性,并确定了在考虑技术变异性后表现出生物学稳定基线的分析物集。这项工作伴随着一个基于网络的交互式工具,该工具具有成为定期更新、高质量的资源库的基础的潜力,该资源库的最终目标是数据共享、可重复性和可重用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db25/5660188/827733c6928c/41598_2017_14755_Fig1_HTML.jpg

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