NEQAM

ABSTRACT

NEQAM project aims at the development, assessment and application of chemometric strategies for the detection and elimination of intra- and inter-batch effects in untarget high resolution liquid chromatography-mass spectrometry (LC-MS).

Batch effect elimination is among the top priorities in LC-MS based metabolomics. It decreases the power to detect biological responses and hinders data interpretation and joint analysis of data acquired in different batches, laboratories, instruments, or analytical platforms, thereby limiting its potential. It is especially critical in human studies where, due to very high inter-subject variability, the number of samples to obtain a given statistical power is higher than that required in in vitro studies or those involving controlled animal models.

An accurate elimination of batch effects in metabolomics will increase the reproducibility and repeatability of the analysis of larger cohorts and, simultaneously, it will enable the use of a higher number of metabolomics features after data clean-up, a simultaneous increase in the precision of multivariate metabolic models and it will facilitate the joint analysis of multi-source data and calibration transfer. In summary, these improvements will have a synergistic effect on the statistical power of metabolomic studies and on their reproducibility and repeatability.

In this project we will continue with the development of batch effect elimination strategies based on the use of quality control (QC) samples and support vector regression (SVR) for the fitting of functions describing changes in the instrument response over the batch. Then, to provide an unbiased evaluation of their applicability they will be thoroughly assessed indifferent scenarios: i) single batch analysis involving a large number of samples; ii) across several batches and LC-MS platforms; and iii) in multi-source metabolomic analysis. This evaluation of the chemometric methods will be carried out during the development of three novel metabolomic applications from different areas of research (bladder cancer, sample quality assessment, nanotoxicology), that will potentially benefit from the application of novel batch effect elimination methods.