Data analysis of an LC-MS dataset from a human urine biofluid cohort study

Supplementary dataset and tutorials for the "<strong>Statistical analysis in metabolic phenotyping"</strong> This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed¹ and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and ¹H NMR, using the methods described by Lewis <em>et al</em>² and Dona <em>et al</em>. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE³ repository, which can be accessed via the Sage BioNetworks portal (https://doi.org/10.7303/syn22252881). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: https://www.ebi.ac.uk/metabolights/MTBLS719. 1. Lovestone, S. <em>et al.</em> AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in <em>Annals of the New York Academy of Sciences</em> (2009). doi:10.1111/j.1749-6632.2009.05064.x 2. Lewis, M. R. <em>et al.</em> Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. <em>Anal. Chem.</em> <strong>88</strong>, acs.analchem.6b01481 (2016). 3. Birkenbihl, C. <em>et al.</em> ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. <em>medRxiv</em> (2020). doi:10.1101/2020.08.04.20168229