Mass Spectrometry Data Analysis Using Partial Covariance Mapping
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Solution Overview
Problem
Current mass spectrometry techniques face challenges in accurately and reliably determining the structure of complex biomolecules due to low success rates in interpreting tandem mass spectra, caused by limitations in data interpretation, sequence-dependent fragmentation, and poor signal-to-noise ratios, leading to many wasted data sets and incorrect identifications.
Innovation Solution
The method involves obtaining a data set of spectra, dividing them into bins, determining control parameters for synchronized fluctuations, and using partial covariance mapping to correct intensity correlations, which enhances the identification of true correlations between fragment ions and their parent ions, thereby improving the accuracy of structural assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mass spectrometry data interpretation methods are used (database matching or de novo algorithms), then structural information can be obtained, but the success rate of interpretation is low (50% or more of spectra are wasted)
Solution Approach 1:
The patent transforms the traditional one-dimensional mass spectrum into a two-dimensional covariance map. This dimensional transformation allows visualization of correlations between fragment ions and their parent ions, enabling identification of true spectral correlations that were previously invisible in conventional 1D spectra, thereby increasing interpretation success rate
Solution Approach 2:
The patent introduces covariance analysis as an intermediary step between raw mass spectral data and structural interpretation. By calculating covariance between different m/z values across multiple spectra, the method creates an intermediate representation that highlights true correlations while filtering out random noise, improving reliability of structural assignments
2Measurement precision
If high mass resolution is used to identify atoms from accurate masses, then the number of prospective hits is reduced, but false positive/negative results increase due to lack of experimental evidence for fragment origins
Solution Approach 1:
The patent implements a feedback mechanism where the covariance map provides experimental evidence that feeds back into the identification process. By showing statistical correlations between fragment ions and parent ions across multiple spectra, the method validates or refutes putative fragment assignments, reducing false positives and negatives despite high mass accuracy
Solution Approach 2:
The patent replaces the purely mass-based identification mechanism with a statistical correlation mechanism. Instead of relying solely on accurate mass matching, the method uses covariance analysis to establish probabilistic relationships between ions, providing experimental evidence for fragment origins that mechanical mass measurement alone cannot provide
3Ease of operation
If threshold-based peak selection is used in spectrum interpretation algorithms, then processing is simplified, but low-intensity peaks bearing crucial structural information are missed
Solution Approach 1:
The patent adds a statistical dimension to peak selection by computing covariance values for all spectral peaks regardless of intensity. This transforms the selection criterion from intensity-based (1D) to covariance-based (2D), allowing low-intensity peaks with strong statistical correlations to be identified and utilized for structural determination
4Loss of information
If sequence-dependent fragmentation patterns are used for peptide identification, then structural information is obtained, but interpretation becomes complex and less reliable due to unexpected variations
Solution Approach 1:
The patent enables the data itself to serve the interpretation process by using covariance patterns to automatically reveal true fragment-parent relationships. The statistical correlations in the covariance map self-organize the spectral information, making sequence-dependent fragmentation patterns more interpretable without requiring complex manual analysis
Data Source
AI summary
A method of analysing a structure of a composition of matter in a sample includes obtaining a data set comprising a plurality of spectra from the composition, from a first method of analysis, dividing each of the spectra into a plurality of bins, determining a control parameter or parameters indicative of synchronised fluctuations in signal intensity across some or all channels, resulting in universal correlation between said bins, and determining a partial covariance of different bins across the plurality of spectra using the control parameter to correct the correlation of intensity fluctuations between said bins.