Improved Intensity-Based Label-Free Quantification via Proximity-Based Intensity Normalization (PIN)

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 outperforms
them in dramatically reducing variance and in identifying 20% more
proteins with statistically significant abundance differences that
other strategies missed. Our results show the PIN enables the discovery
of statistically significant biological variation that otherwise is
falsely reported or missed.