Statistical analysis in metabolic phenotyping

Benjamin J. Blaise, Gonçalo D. S. Correia, Gordon A. Haggart, Izabella Surowiec, Caroline Sands, Matthew R. Lewis, Jake T. M. Pearce, Johan Trygg, Jeremy K. Nicholson, Elaine Holmes, Timothy M. D. Ebbels

Published: 2021-07-27 DOI: 10.1038/s41596-021-00579-1

Metabolic phenotyping

Statistical analysis

Analytical technologies

Biomarker selection

Data normalization

AI 解读

Author Correction: Full-length sequencing of circular DNA viruses and extrachromosomal circular DNA using CIDER-Seq

Generation of heart-forming organoids from human pluripotent stem cells

Design and fabrication of wearable electronic textiles using twisted fiber-based threads

Activation of mechanoluminescent nanotransducers by focused ultrasound enables light delivery to deep-seated tissue in vivo

Chromatin accessibility profiling by ATAC-seq

Deoxyfluorination of phenols for chemoselective18F-labeling of peptides

Author Correction: cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Human primordial germ cell-like cells specified from resetting precursors develop in human hindgut organoids

An inexpensive semi-automated sample processing pipeline for cell-free RNA extraction

Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening

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Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data
Introduction
Materials
- Equipment
Procedure
Troubleshooting
Timing
Anticipated results
References
ACKNOWLEDGMENTS

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Statistical analysis in metabolic phenotyping

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