AUTHOR=Cui Xuejiao , Yang Qingxia , Li Bo , Tang Jing , Zhang Xiaoyu , Li Shuang , Li Fengcheng , Hu Jie , Lou Yan , Qiu Yunqing , Xue Weiwei , Zhu Feng 

TITLE=Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

JOURNAL=Frontiers in Pharmacology

VOLUME=10

YEAR=2019

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2019.00127

DOI=10.3389/fphar.2019.00127

ISSN=1663-9812

ABSTRACT=<p>Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (<italic>ReIn</italic>) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (<italic>DiMe</italic>) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the <italic>ReIn</italic>, the <italic>DiMe</italic> has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether <italic>DiMe</italic> can enhance the performance of metabolic profiling or not. Herein, the performance of <italic>DiMe</italic> on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria <italic>(classification capacity, robustness and false discovery rate). As</italic> a result, integration/merging-based strategies (<italic>ReIn</italic> and <italic>DiMe</italic>) were found to perform better under all criteria than those strategies based on single experiment. Moreover, <italic>DiMe</italic> was discovered to outperform <italic>ReIn</italic> in <italic>classification capacity</italic> and <italic>robustness</italic>, while the <italic>ReIn</italic> showed superior capacity in controlling <italic>false discovery rate</italic>. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.</p>