Improved batch correction in untargeted MS-based metabolomics

Abstract

IntroductionBatch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. ObjectivesThis paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.MethodsBatch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana.ResultsThe three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered.ConclusionThe use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.

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

“…The likely explanation is overfitting, a phenomenon in which fitting of random noise in the QC sample data results in introduction of additional noise into the corrected data. This phenomenon has been observed previously by others in this and similar contexts [9, 42] and is visually illustrated in Figure 2B, in which the CUBSPL curve perfectly fits the profile of the QC sample intensity, in spite of the fact that much of the sample-to-sample variation is likely due to noise. A similar effect is likely to occur for LOESS curve fitting when the smoothing parameter, α, is set too low.…”

Pre-analytic evaluation of volumetric absorptive microsampling and integration in a mass spectrometry-based metabolomics workflow

“…Intrabatch variability, usually due to drift in instrument performance during the analytical run, was corrected in an approach similar to Wehrens et al [24]. In detail, we applied robust linear regression [25] to estimate the injection orderdependent signal drift for each feature based on the intensities measured in all samples (since the sample order in the run was randomized) and subsequently adjusted the intensities based on the fitted model.…”

Metabolomics: A Primer

“…When carrying out analyses in the absence of these standards, we therefore recommend making comparisons using material from a similar origin (compare serum A to serum B or tumor A to tumor B) and as similiar an amount of material as possible. Alternatively, pooled quality control samples can be used to reduce variation due to batch effects[66] . When applying these principles, most MRM and HRMS methods have yielded a linear range of quantitation for 3 to 4 orders of magnitude.…”