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

被引:19
作者
Cui, Xuejiao [1 ,2 ,3 ]
Yang, Qingxia [1 ,2 ,3 ]
Li, Bo [2 ,3 ]
Tang, Jing [1 ,2 ,3 ]
Zhang, Xiaoyu [1 ,2 ,3 ]
Li, Shuang [1 ,2 ,3 ]
Li, Fengcheng [1 ]
Hu, Jie [4 ]
Lou, Yan [5 ]
Qiu, Yunqing [5 ]
Xue, Weiwei [2 ,3 ]
Zhu, Feng [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Coll Pharmaceut Sci, Hangzhou, Zhejiang, Peoples R China
[2] Chongqing Univ, Sch Pharmaceut Sci, Chongqing, Peoples R China
[3] Chongqing Univ, Collaborat Innovat Ctr Brain Sci, Chongqing, Peoples R China
[4] Zhejiang Univ, Sch Int Studies, Hangzhou, Zhejiang, Peoples R China
[5] Zhejiang Univ, Affiliated Hosp 1, Zhejiang Prov Key Lab Drug Clin Res & Evaluat, Hangzhou, Zhejiang, Peoples R China
来源
FRONTIERS IN PHARMACOLOGY | 2019年 / 10卷
基金
中国国家自然科学基金;
关键词
direct data merging; classification capacity; robustness; false discovery rate; long-term and large-scale metabolomics; HEPATOCELLULAR-CARCINOMA; NORMALIZATION METHODS; ENRICHED RESOURCE; SPECTROMETRY DATA; SAMPLE-SIZE; RNA-SEQ; METABOLOMICS; BIOMARKERS; IDENTIFICATION; INTEGRATION;
D O I
10.3389/fphar.2019.00127
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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 (ReIn) 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 (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe 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 DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.
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页数:14
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