This repository contains processed data outputs from a metabolic phenotyping study of urinary biomarkers for dementia and Alzheimer's disease.
Baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed 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 al1 and Dona et al2. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone et al3 and the ANMERGE repository4, which can be accessed via the Sage BioNetworks portal. Information about the metabolic profiling experiments can be found in the study's MetaboLights entry - MTBLS719.
This repository contains supporting materials and worked tutorials/examples for the R package peakPantheR5 and the Python package nPYc-Toolbox6. The functionality of these tools is exemplified using the three LC-MS profiling assays described in Lewis et al1: reversed-phase chromatography with positive ionisation (RPOS), reversed-phase chromatography with negative ionisation (RNEG), and hydrophilic interaction liquid chromatography (HILIC) with positive ionisation (HPOS).
This repository has the following structure:
- \Datasets
- \peakPantheR Final LC-MS datasets, ready for statistical analysis
- \peakPantheR
- \LC-MS Annotations
- \peakPantheR Urine RPOS/RNEG/HPOS peakPantheR processing outputs
- \ROI files Lists of compound annotations for the RPOS/RNEG/HPOS assays
- \R scripts R scripts with examples of the peakPantheR workflow. Note Running these scripts requires the mzML files, which can be downloaded from MTBLS719
- \LC-MS Annotations
- \nPYc-Toolbox scripts Jupyter Notebooks with the nPYc-Toolbox commands and parameters used to generate the files in the "Datasets" folder from the peakPantheR outputs.
Each of the directories contains a readme file with information about the accompanying files.
1 Lewis, M. R., Pearce, J. T. M., Spagou, K., Green, M., Dona, A. C., Yuen, A. H. Y., … Nicholson, J. K. (2016). Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping. Analytical Chemistry, 88(18), 9004–9013. https://doi.org/10.1021/acs.analchem.6b01481
2 Dona AC, Jiménez B, Schäfer H, et al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Analytical Chemistry. 2014; 86(19):9887-94. https://doi.org/10.1021/ac5025039
3 Lovestone, S., Francis, P., Kloszewska, I., Mecocci, P., Simmons, A., Soininen, H., … Ward, M. (2009). AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. In Annals of the New York Academy of Sciences. https://doi.org/10.1111/j.1749-6632.2009.05064.x
4 Birkenbihl C, Westwood S, Shi L, Nevado-Holgado A, Westman E, Lovestone S; AddNeuroMed Consortium, Hofmann-Apitius M. ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset. J Alzheimers Dis. 2021;79(1):423-431. https://doi.org/10.3233/JAD-200948.
5 Arnaud M Wolfer, Gonçalo D S Correia, Caroline J Sands, Stephane Camuzeaux, Ada H Y Yuen, Elena Chekmeneva, Zoltán Takáts, Jake T M Pearce, Matthew R Lewis, peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets, Bioinformatics, 2021;, btab433, https://doi.org/10.1093/bioinformatics/btab433
6 Caroline J Sands, Arnaud M Wolfer, Gonçalo D S Correia, Noureddin Sadawi, Arfan Ahmed, Beatriz Jiménez, Matthew R Lewis, Robert C Glen, Jeremy K Nicholson, Jake T M Pearce, The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets, Bioinformatics, Volume 35, Issue 24, 15 December 2019, Pages 5359–5360, https://doi.org/10.1093/bioinformatics/btz566