Optimization of metabolomic data processing using NOREVA

Zhaorong Li, Jing Tang, Jianbo Fu, Ying Zhang, Yunxia Wang, Hongning Zhang, Jin Liu, Qingxia Yang, Huaicheng Sun, Wenqi Qiu, Yinghui Ma, Mingyue Zheng, Feng Zhu

Published: 2021-12-23 DOI: 10.1038/s41596-021-00636-9

NOREVA

metabolomic data processing

optimization

performance criteria

R package

AI 解读

Extraction of nuclei from archived postmortem tissues for single-nucleus sequencing applications

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Longitudinal interrogation of sympathetic neural circuits and hemodynamics in preclinical models

Experimental modulation of physiological force application on leg joint neurons in intactDrosophila melanogaster

Combining surface-accessible Ag and Au colloidal nanomaterials with SERS for in situ analysis of molecule–metal interactions in complex solution environments

Generation of mouse–human chimeric embryos

Labeling and tracking of immune cells in ex vivo human skin

Large-scale preparation of fluorescence multiplex host cell reactivation (FM-HCR) reporters

Mutate-and-chemical-shift-fingerprint (MCSF) to characterize excited states in RNA using NMR spectroscopy

Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing

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A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While var
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References
ACKNOWLEDGMENTS

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Optimization of metabolomic data processing using NOREVA

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