Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN).

ABSTRACT: Researchers are increasingly turning to label-free MS1 intensity-based quantification strategies within HPLC-ESI-MS/MS workflows to reveal biological variation at the molecule level. Unfortunately, HPLC-ESI-MS/MS workflows using these strategies produce results with poor repeatability and reproducibility, primarily due to systematic bias and complex variability. While current global normalization strategies can mitigate systematic bias, they fail when faced with complex variability stemming from transient stochastic events during HPLC-ESI-MS/MS analysis. To address these problems, we developed a novel local normalization method, proximity-based intensity normalization (PIN), based on the analysis of compositional data. We evaluated PIN against common normalization strategies. PIN outperfor

SUBMITTER: Van Riper SK 

PROVIDER: S-EPMC3993879 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature