Wang Xiangyu, Mickiewicz Beata, Thompson Graham C, Joffe Ari R, Blackwood Jaime, Vogel Hans J, Kopciuk Karen A
Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada.
Department of Pediatrics, Cumming School of Medicine and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
Metabolites. 2022 Mar 4;12(3):227. doi: 10.3390/metabo12030227.
Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student's -tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7-86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.
使用质子核磁共振光谱进行靶向代谢物鉴定和定量的自动化程序可以克服限制代谢组学应用的时间和成本障碍。然而，它们的性能需要与经验丰富的光谱学家相当。使用先前分析的小儿脓毒症血清样本数据集，将自动化程序rDolphin和BATMAN生成的结果与通过手动分析58种已鉴定代谢物获得的结果进行比较。使用学生t检验选择代谢物，并使用多种性能指标进行评估。手动分析结果具有最高的性能指标值，尤其是灵敏度（76.9%）、受试者工作特征曲线下面积（0.90）、精确度（62.5%）以及基于神经网络的测试准确度（88.6%）。所有三种方法都具有较高的特异性值（77.7 - 86.7%）。专家光谱学家的手动分析优于两个开源自动化程序，这表明需要进一步开发以达到可接受的性能水平。
