Abstract
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.
Highlights
Another 10 datasets were generated by the random sampling of half of the pretreated experimental dataset for 10 times, which were applied for the evaluation of robustness of the Results Integration (ReIn) strategy
The metabolites annotated from two experimental datasets were collectively considered for assessing identification precision of the ReIn strategy, the classification models constructed based on experimental datasets were integrated for evaluating ReIn’s classification capacity, and the robustness of the ReIn strategy was collectively determined by the average overlap values between two experiments
The majority of the overlap values of direct merge strategy (DiMe) were larger than 0.3, while that of the other strategies were lower than 0.3. These findings indicated that the DiMe strategy performed better than others in the robustness of the identified markers
Summary
Liquid chromatography-mass spectrometry has been widely applied in pharmaceutical and clinical metabolomics to comprehensively reveal metabolic alteration in given biological system (Paglia and Astarita, 2017; Fu et al, 2018; Tang et al, 2018; Yang et al, 2019), identify biomarkers and therapeutic targets for a variety of complex diseases (Zhu et al, 2009; Yang et al, 2016; Hu et al, 2017; Li et al, 2017c, 2019) and illuminate mechanism of action of drugs or drug candidates (Chen et al, 2017; Li X. et al, 2018; Li X.X. et al, 2018; Xue et al, 2018b; Zhang et al, 2018). Because of the extended period of clinical data collection and huge size of analyzed samples, the long-term and large-scale metabolomic profiling is frequently encountered in current medical study to identify physiological perturbation in various living systems (Zhao et al, 2016; Zheng et al, 2018), analyze time-dependency of metabolic alteration (He et al, 2015; Han et al, 2018) and evaluate therapy and patient stratification in personalized medicine (Li et al, 2017a; Wang et al, 2017a). Since the bias of current metabolic explorations is reported to come frequently from the inadequacy of studied samples (Zhang et al, 2006; Subramanian, 2016), there is an urgent need to maximally enlarge the sample size and in turn enhance the statistical power of a given metabolomics study (Button et al, 2013)
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