This work introduces a strategy for decomposing variable contributions within the data obtained from structured metabolomic studies. This approach was applied in the context of an in vitro human neural model to investigate biochemical changes related to neuroinflammation. Neural cells were exposed to the neuroinflammatory toxicant trimethyltin at different doses and exposure times. In the frame of an untargeted approach, cell contents were analysed using HILIC hyphenated with HRMS. Detected features were annotated at level 1 by comparison against a library of standards, and the 126 identified metabolites were 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 highlight the contribution of each effect related to the experimental factors. Both the dose of trimethyltin and the exposure time were found to have a statistically significant impact on the observed metabolic alterations. 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 in neurodegeneration. Then, these specific metabolic patterns were further characterised by analysing the individual variable contributions to each effect. AMOPLS was highlighted as a useful tool for analysing complex metabolomic data. The proposed strategy allowed the separation, quantitation and characterisation of the specific contribution of the different factors and the relative importance of every metabolite to each effect with respect to the total observed variability of the system.