This work introduces a strategy for decomposing variable contributions within the data obtained fromstructured metabolomic studies. This approach was applied in the context of an in vitro human neuralmodel to investigate biochemical changes related to neuroinflammation. Neural cells were exposed tothe neuroinflammatory toxicant trimethyltin at different doses and exposure times. In the frame of anuntargeted approach, cell contents were analysed using HILIC hyphenated with HRMS. Detected featureswere annotated at level 1 by comparison against a library of standards, and the 126 identified metaboliteswere analysed using a recently proposed chemometric tool dedicated to multifactorial Omics datasets,namely, ANOVA multiblock OPLS (AMOPLS). First, the total observed variability was decomposed to high-light the contribution of each effect related to the experimental factors. Both the dose of trimethyltin andthe exposure time were found to have a statistically significant impact on the observed metabolic alter-ations. Cells that were exposed for a longer time exhibited a more mature and differentiated metabolome,whereas the dose of trimethyltin was linked to altered lipid pathways, which are known to participate inneurodegeneration. Then, these specific metabolic patterns were further characterised by analysing theindividual variable contributions to each effect. AMOPLS was highlighted as a useful tool for analysingcomplex metabolomic data. The proposed strategy allowed the separation, quantitation and characteri-sation of the specific contribution of the different factors and the relative importance of every metaboliteto each effect with respect to the total observed variability of the system.