A scoring metric for multivariate data for reproducibility analysis using chemometric methods

Abstract

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically-obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory’s individual performance.

interlab: A Python Module for Analyzing Interlaboratory Comparison Data

“…Use of this code allows researchers to identify laboratories producing data closest to the consensus values, thereby ensuring that untargeted studies are using the most precise data available to them. The software was originally developed for analyzing nuclear magnetic resonance spectroscopic data [1,2] but can be applied to nearly any array data, including Raman or Fourier-transform infrared spectroscopy and gas or liquid chromatography. Details on the implementation of the code can be found in Ref.…”

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

“…In keeping with the goal of QC materials characterized by analytical profiles rather than by a defined composition, we have not attempted to assign an identity to any of the features in the profiles and PCA is done only on the intensity data. Ideally, we would perform consistency analysis as done in previous studies [4], but there is not enough data from the ILS for this analysis. However, for the LC-MS analysis, putative assignments were given for overlapping features that were found in all four labs.…”

“…A large part of the long development time for CRMs is the requirement for full, multi-platform validation of the materials with certified value assignment before they are released with a certificate of analysis (COA) by their producers [3]. It is much easier in the QC material paradigm to perform some smaller number of qualifying tests for a QC material and release it into the consensus-building community compared to the strict data quality and validation requirements for producing a CRM [4].…”

“…More information about the pooled samples can be found in the COAs for the SRMs [5,6,7,8]. This set of samples could provide a basis for demonstrating not only consistent analytical results on individual samples, but also inter-sample consistency for classification and pattern recognition [4]. Utilizing a ‘natural material’ which contains all the potential matrix-related complications in the various metabolomics platforms is essential for producing robust, meaningful methods of measurement and analysis that can propel the field forward.…”

A New Approach for Multivariate Data Analysis in Interlaboratory Comparisons Based on Multidimensional Scaling and Robust Confidence Ellipse

“…1 In some cases, the reported results by participants are multivariate data and univariate techniques are not suitable to analyze this kind of data. 2 For this situation, multivariate analysis techniques can be used for investigating the inherent structure of data without losing valuable information of measurement.…”