Mass Spectra Normalization via Exclusion Lists and P-Norm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current normalization methods in mass spectrometry imaging, such as TIC and vector norm, often introduce artifacts in mass images due to high-intensity signals or inhomogeneous distributions, leading to inaccurate comparison and interpretation of biomarker or drug spatial distribution in tissue sections.
Innovation Solution
The method involves creating an exclusion list to exclude high-intensity mass ranges, using noise level or median-based normalization factors, and applying p-norm normalization without relying on peak areas or maximum intensities, thereby minimizing artifacts and ensuring accurate representation of compound distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional normalization methods (TIC or vector norm) are used, then the normalization process is simple and fast, but artifacts are introduced in mass images due to high-intensity signals or inhomogeneous distributions
Solution Approach 1:
The patent segments the mass spectrum into different mass ranges and applies different normalization strategies to each segment. High-intensity mass ranges are excluded from normalization calculations, while low-intensity ranges are normalized using robust factors. This segmentation approach eliminates artifacts caused by dominant signals while maintaining simplicity in the overall process.
Solution Approach 2:
The patent extracts and excludes high-intensity mass ranges from the normalization calculation. By identifying and removing these problematic signals from the normalization factor computation, the method prevents them from distorting the normalized mass images, thereby improving reliability without requiring complex iterative procedures.
2Measurement precision
If normalization factors are based on peak areas or maximum intensities, then the normalization is computationally efficient, but artifacts are introduced due to inhomogeneous signal distributions
Solution Approach 1:
The patent applies local quality by using noise level or median-based normalization factors that are representative of the local signal characteristics rather than being dominated by global high-intensity peaks. This local approach ensures that normalization reflects the true spatial distribution of compounds in each region, improving measurement precision while maintaining computational efficiency through direct calculation.
3Adaptability or versatility
If no normalization is applied, then the original signal intensities are preserved, but comparison between different tissue types and imaging datasets becomes unreliable
Solution Approach 1:
The patent creates a normalized copy of the mass spectra that preserves the relative spatial distribution information while removing intensity variations between different tissue types and datasets. This normalized representation enables reliable comparison across diverse samples without losing the essential spatial distribution patterns of compounds.
Data Source
Figure 1A~2
Figure 3A~3F
Figure 4
AI summary
The invention provides methods for normalizing mass spectra acquired by imaging mass spectrometry (IMS), in particular MALDI imaging of tissue sections. Mass spectra of the IMS data set are each normalized by one of: the p-norm of the mass spectrum transformed by applying an exclusion list, the p-norm of the mass spectrum transformed by square rooting the intensity values, the median of the mass spectrum, and the median absolute deviation of the noise level of the mass spectrum.