Robust normalization is a prerequisite for reliable metabonomic analysis especially when intervention treatments cause drastic metabolomic changes or when spot urinary samples are employed without knowing the drinking...Robust normalization is a prerequisite for reliable metabonomic analysis especially when intervention treatments cause drastic metabolomic changes or when spot urinary samples are employed without knowing the drinking water quantity.With the simulated and real datasets,here,we report a probabilistic quotient normalization method based on the mode-of-quotients(mPQN)which is suitable for metabonomic analysis of both NMR and LC-MS data with little and/or drastic metabolite changes.When applied to metabonomic analysis of both animal plasma samples and human urinary samples,this newly proposed method has clearly shown better robustness than all classical normalization methods especially when drastic changes of some metabolites occur.展开更多
基金the National Key R&D Program of China(No.2017YFC0906800)the National Natural Science Foundation of China(Nos.81590953,31821002 and 21405020)。
文摘Robust normalization is a prerequisite for reliable metabonomic analysis especially when intervention treatments cause drastic metabolomic changes or when spot urinary samples are employed without knowing the drinking water quantity.With the simulated and real datasets,here,we report a probabilistic quotient normalization method based on the mode-of-quotients(mPQN)which is suitable for metabonomic analysis of both NMR and LC-MS data with little and/or drastic metabolite changes.When applied to metabonomic analysis of both animal plasma samples and human urinary samples,this newly proposed method has clearly shown better robustness than all classical normalization methods especially when drastic changes of some metabolites occur.
基金Supposed by the Project of National Science Foundation for Distinguished Young Scholars of China under Grant No.60225008(国家杰出青年科学基金)the Key Project of National Natural Science Foundation of China under Grant Nos.603320lO,60575007(国家自然科学基金重点项目)+2 种基金the Project for Young Scientists' Foundation of National Natural Science of China under Grant No.60303022(国家自然科学基金青年科学基金)the Project of Natural Science Foundation of Beijing of China under Grant No.4052026(北京市自然科学基金)the Beijing Municipal Education Commission Foundation of China under Grant No.KM200610005011(北京市教委基金)
