Abstract
SummaryAs large-scale metabolic phenotyping studies become increasingly common, the need for systemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysis has become increasingly important, both within a study, and to allow meaningful inter-study comparisons. The nPYc-Toolbox provides software for the import, pre-processing, QC and visualization of metabolic phenotyping datasets, either interactively, or in automated pipelines.Availability and implementationThe nPYc-Toolbox is implemented in Python, and is freely available from the Python package index https://pypi.org/project/nPYc/, source is available at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation can be found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials at https://github.com/phenomecentre/nPYc-toolbox-tutorials.
Highlights
Metabolic phenotyping offers a powerful window into geneenvironment interactions (Nicholson et al, 2012)
quality control (QC) in profiling studies has typically been conducted on an ad-hoc basis in individual studies, there is an increasing push towards the systematization and automation of pre-processing procedures (Giacomoni et al, 2015; van Rijswijk et al, 2017)
Dataset objects are initialized from raw (Bruker Nuclear Magnetic Resonance spectroscopy (NMR)) or featureextracted data [outputs of software such as XCMS (Tautenhahn et al, 2008), Progenesis QITM, TargetLynxTM, &c], and associated with study design parameters or metadata read directly from the raw data or from CSV files
Summary
Metabolic phenotyping offers a powerful window into geneenvironment interactions (Nicholson et al, 2012). Inter-study comparison in the field is complicated by the diversity of analytical platforms used to generate data, and the lack of standard quality criteria. Standards are emerging around the most common platforms: Nuclear Magnetic Resonance spectroscopy (NMR), and hyphenated-Mass Spectrometry (MS), and procedures for the acquisition of profiles from human biofluid samples in particular are well established (Dona et al, 2014; Lewis et al, 2016). QC in profiling studies has typically been conducted on an ad-hoc basis in individual studies, there is an increasing push towards the systematization and automation of pre-processing procedures (Giacomoni et al, 2015; van Rijswijk et al, 2017). The toolbox presented here provides software for preprocessing, QC and visualization of metabolic profiling datasets, embodying the MRC-NIHR National Phenome Centre (NPC) practices and focusing on the interpretability of the output to both data generators and analysts (Fig. 1)
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