The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets

Abstract

Summary As 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 implementation The 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.

SMolESY: an efficient and quantitative alternative to on-instrument macromolecular 1H-NMR signal suppression

“…SMolESY was then applied on a third dataset consisting of publicly available 1D-NOESY spectra from normal human urine samples 22 (see paragraph Plasmaurine spectra employed for the present study in the Experimental section). Urine's complex SM composition in the virtual absence of macromolecules was used to assess SMolESY's preservation of SM signal information.…”

“…Its application only requires high resolution 1 H-NMR data (>65k data points) input which is the established norm within modern high quality metabolomics and analytical studies. 22,33…”

Plasma lipid and liporotein biomarkers in LBC1936: Do they predict general cognitive ability and brain structure?

“…Targeted integration of signals arising from known lipid species in the UPLC-MS datasets was performed using the peakPantheR R package 31 and an in-house database of empirical retention time and theoretical m/z values for annotated lipids. The nPYc-Toolbox 32 was then used to correct ion intensities for sample analysis-order intensity drift using a LOWESS smoother estimated using the intensities measured across repeated injections of the SR. Following drift correction, the SR samples and their serial dilution series were used to calculate feature-wise coefficients of variation (CV) and Pearson correlation coefficients with the dilution factor, respectively.…”

“…Data was prepared for statistical analysis with the nPYc-Toolbox 32 , by re-calibrating the chemical shift scale using the α-glucose signal at δ 5.233 and re-interpolating all spectra to a common chemical shift axis.…”

A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows

“…Preprocessing LC–MS-based untargeted metabolomics data involves as well the removal of unwanted features (retention time, m/z pairs) to retain only those analytically robust enough for data analysis and interpretation [ 15 , 16 ]. To this end, several tools are available, such as SECIM-TOOLS [ 17 ] and nPYc-Toolbox [ 18 ]. Previous works have discussed strategies for data cleaning based on QC practices [ 2 , 19 ].…”