Abstract
Purpose: Data stability is a critical factor in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) single-cell analysis. However, various factors, such as sample processing, instrument condition, and data acquisition, can introduce uncertainties into ToF-SIMS data. Correcting this data is vital, yet current methods mainly focus on total ion intensity normalization or using consistent substrates. No specific correction method exists for ToF-SIMS single-cell metabolomics. Methods: This study utilizes the Normalized Support Vector Regression (Norm-SVR), commonly used methods for correcting large-scale metabolomics data, for the correction of ToF-SIMS single-cell metabolomic analysis and assesses its performance in comparison to traditional total ion intensity normalization. Results and Conclusions: The results suggest that Norm-SVR effectively diminishes batch effects and reduces variability, thereby underscoring the method's efficacy and practicality. This approach is expected to improve data quality assurance in extensive ToF-SIMS analytical datasets.