Evaluation of batch effect elimination using quality control replicates in LC-MS metabolite profiling.

Systematic variation of the instrument's response both within- and between-batches is frequently observed in untarget LC-MS metabolomics involving the analysis of a large number of samples. The so-called batch effect decreases the statistical power and has a negative impact on repeatability and reproducibility of the results. As there is no standard way of assessing or correcting LC-MS batch effects and there is no single method providing optimal results in all situations, the selection of the optimal approach is not trivial. This work explores the effectiveness of a set of tools for batch effect assessment. Qualitative tools include the monitoring of spiked internal standards, principal component analysis and hierarchical cluster analysis. Quantitative tools comprise the distribution of RSDQC values, the median Pearson correlation coefficient in QCs, the ratio of random features in QCs using the runs test, as well as multivariate tools such as the δ-statistic, Silhouette plots, Principal Variance Component Analysis and the expected technical variation in the prediction. Results show that qualitative and quantitative approaches are complementary and that by limiting the analysis to QCs the power to detect and evaluate both within and between batch effects is increased. Besides, the graphical integration of outputs from multiple quantitative tools facilitates the evaluation of batch effects and it is proposed as a straightforward way for comparing and tailoring batch effect elimination approaches.