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

Supplementary dataset and tutorials for the "Statistical analysis in metabolic phenotyping"

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 et al2 and Dona et al. 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. et al. AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in Annals of the New York Academy of Sciences (2009). doi:10.1111/j.1749-6632.2009.05064.x

2.           Lewis, M. R. et al. Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal. Chem. 88, acs.analchem.6b01481 (2016).

3.           Birkenbihl, C. et al. ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. medRxiv (2020). doi:10.1101/2020.08.04.20168229