SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.
SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.
复制标题
SAND：应用于代谢物定量的 NMR 光谱自动时域建模。
DOI：
10.1021/acs.analchem.3c03078
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发表时间：
2024
影响因子：
7.4
通讯作者：
Delaglio,Frank
中科院分区：
文献类型：
--
作者：
Wu,Yue;Sanati,Omid;Uchimiya,Mario;Krishnamurthy,Krish;Wedell,Jonathan;Hoch,JeffreyC;Edison,ArthurS;Delaglio,Frank
非靶向核磁共振（NMR）代谢组学的发展使成千上万的生物样品的分析成为可能。利用这一丰富的信息源需要详细量化光谱特征。然而，由于广泛的信号重叠，开发一致和自动的工作流程一直具有挑战性。为了应对这一挑战，我们介绍了软件光谱自动NMR分解（SAND）。SAND继承了以前成功的时域建模，自动量化整个光谱，无需手动交互。SAND方法使用混合优化与马尔可夫链蒙特卡罗方法，采用子采样在时域和频域。特别是，SAND将时域数据随机划分为训练集和验证集，以帮助避免过度拟合。我们证明了SAND的准确性，它提供了0.9的相关性与地面真理的情况下，包括高度重叠的模拟数据集，两种化合物的混合物，并与不同量的四种化合物的混合物的尿样加标。我们进一步展示了一个自动化的注释，使用来自SAND分解峰的相关网络，平均而言，每个化合物的74%的峰可以在单个集群中恢复。SAND可在NMRbox中使用，NMRbox是由高级NMR网络（NAN）托管的NMR软件的云计算环境。由于SAND方法使用时域子采样（即，时域点的随机子集），它具有扩展到更高维度和非均匀采样数据的潜力。
Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.