Proximity-Based Intensity Normalization for Mass Spectrometry Variability
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Solution Overview
Problem
Current normalization methods for label-free relative quantification in HPLC-ESI-MS/MS workflows fail to adequately mitigate extraneous variability, leading to poor repeatability and reproducibility, which results in excessive false positives and false negatives in detecting differentially abundant analytes.
Innovation Solution
The introduction of the proximity-based intensity normalization (PIN) method, which computes the compositional proportionality of analyte abundances rather than relative abundance, applying a new paradigm that normalizes data locally to mitigate systemic bias and complex variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global normalization methods are used to mitigate systematic bias, then systematic bias is reduced, but data variability increases
Solution Approach 1:
The patent transitions from global normalization to local normalization by computing proximal compositional proportionality for each analyte individually. Instead of applying a single global scaling factor to all analytes, the method normalizes each analyte's abundance ratio based on its own proximal compositional data, allowing localized adaptation to systematic biases while preserving individual analyte variability patterns.
Solution Approach 2:
The patent changes the normalization parameter from global intensity scaling to proximal compositional proportionality ratios. By computing the ratio of an analyte's intensity to the sum of intensities of all analytes in proximal regions, the method transforms the normalization approach to one that inherently accounts for local compositional variations while mitigating systematic bias.
2Device complexity
If label-free relative quantification is used to simplify workflows, then workflow complexity is reduced, but repeatability and reproducibility deteriorate
Solution Approach 1:
The patent implements self-service normalization where each analyte's quantification is automatically normalized using its own proximal compositional proportionality data. The method computes normalization factors internally based on the relative abundances of all analytes in proximal regions, eliminating the need for external reference standards or manual normalization interventions while improving repeatability and reproducibility.
3Ease of operation
If relative abundance measurement is used for analyte quantification, then measurement simplicity is improved, but detection accuracy of differential abundance deteriorates
Solution Approach 1:
The patent introduces proximal compositional proportionality as an intermediary metric between raw intensity measurements and final differential abundance detection. Instead of directly comparing raw intensities or simple relative abundances, the method computes proportionality ratios that account for compositional variations in proximal regions, serving as a mediating normalization step that improves detection accuracy while maintaining operational simplicity.
Data Source
AI summary
A method of normalizing data can comprise globally normalizing at least a first and second data distribution by normalizing the proximal compositional proportionality of the abundance of the analyte using proximity-based intensity normalization. In an example, the proximity-based intensity normalization comprising using the following formula:ijx∑j=1nxijx/ijy∑j=1nyijywherein:ijx is the intensity of ion j in the first distribution x,ijy is the intensity of ion j in the second distribution y,nx is the number of surrogate ions in distribution x, andny is the number of surrogate ions in distribution y.