10.1021/pr400866r.s001 Susan K. Van Riper Susan K. Van Riper Ebbing P. de Jong Ebbing P. de Jong LeeAnn Higgins LeeAnn Higgins John V. Carlis John V. Carlis Timothy J. Griffin Timothy J. Griffin Improved Intensity-Based Label-Free Quantification via Proximity-Based Intensity Normalization (PIN) American Chemical Society 2015 bias strategy HPLC variability workflow variation normalization strategies analysis MS 2015-12-17 00:57:12 Journal contribution https://acs.figshare.com/articles/journal_contribution/Improved_Intensity_Based_Label_Free_Quantification_via_Proximity_Based_Intensity_Normalization_PIN_/2029770 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.