The Impact of Data Treatment Strategies on the Analysis of Bacterial Metabolomics Data

This study investigates the impact of various data treatment strategies on the analysis of bacterial metabolomics data obtained through GC-MS. Metabolomics data was generated from different sets of Staphylococcus aureus and Pseudomonas aeruginosa samples, including clinical isolates and type strains, cultured in different media conditions. The raw data was preprocessed using GC-MS data preprocessing and metabolite annotation software, followed by manual curation. We focused on evaluating the effects of missing value imputation, data transformation, data centering, and data normalization on the dataset. Descriptive statistics and data distribution are used to assess the impact of different data treatment steps. Univariate analysis using t-tests and Mann-Whitney U tests, and multivariate analysis employing OPLS-DA, and OPLS-EP were used to assess biological variation of compared groups. The results highlight the importance of careful data quality check throughout the data treatment process to ensure accurate and reliable interpretation of metabolomics data.