A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples

Abstract

The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography–mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. “Background correction” methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.

Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy

“…Quality control (QC, pooled plasma) samples were subjected to the same preparation protocol and injected every 10 and 5 samples for GC-MS and LC-MS analysis, respectively. Each metabolite's signals were normalized with a QC-based correction method using the smooth-spline algorithm (43)(44)(45). Information on all the measured metabolites, including retention time, m/z, and ion transitions, is summarized in Supplemental Tables 2 and 3.…”

Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data

“…The first three methods, quadratic fit (QUAD), cubic splines fit (CUBSPL), and LOESS, use QC samples to correct for within- and between-batch drift. QC-sample-based drift correction is performed by adjusting raw peak area according to the equation [36, 37]:…”

LC–MS based global metabolite profiling: the necessity of high data quality

“…Thus, in order to conclude a biologically relevant hypothesis, the biological difference between two or more sample groups needs to be larger than the analytical variance within and between each batch, hence reproducibility is of utmost importance (Van Der Kloet et al 2009;Zelena et al 2009). Worth noting here is the progress made concerning signal correction based on mathematical models which has the potential to correct for batch-to-batch variations as well as instrumental drift (Draisma et al 2010;Dunn et al 2011;Hendriks et al 2011;Kamleh et al 2012;Kuligowski et al 2015;Rusilowicz et al 2016;Van Der Kloet et al 2009;Wehrens et al 2016).…”