Mass Spectrometry Data Normalization for Cross-Cohort Comparison
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Solution Overview
Problem
Mass spectrometry data generated by different devices varies due to device characteristics, leading to challenges in comparing data across laboratories and experiments, with existing software being restricted by sample type, instrument platform, and acquisition method.
Innovation Solution
A computing device performs statistical analysis to select parameters that reduce the probability of misidentifying target molecules, using a consensus library and normalization techniques to correct for variations, enabling cross-cohort data comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mass spectrometry data is processed using existing software, then sample type and instrument platform restrictions are imposed, but data comparison across different laboratories and experiments becomes possible
Solution Approach 1:
The patent implements a universal data processing system that can handle multiple sample types, instrument platforms, and acquisition methods through a single integrated framework. The system uses standardized parameter sets and normalization techniques that work across different mass spectrometry configurations, eliminating the need for separate software for each platform while maintaining the ability to compare data across laboratories and experiments
2Measurement precision
If statistical analysis is performed to reduce misidentification probability, then identification accuracy improves, but computational time and complexity increase
Solution Approach 1:
The patent performs statistical analysis and parameter optimization before the actual data processing and comparison. By pre-determining the optimal parameter sets through statistical evaluation of target molecules, the system establishes standardized processing protocols that can be applied to new data without requiring time-consuming statistical analysis during processing, thus improving identification accuracy while controlling computational time
Solution Approach 2:
The system dynamically adjusts processing parameters based on statistical characteristics of the data and target molecules. By changing parameters such as mass accuracy thresholds, collisional cross section values, and normalization factors according to the specific experimental conditions and target molecule properties, the system achieves high identification accuracy while optimizing computational efficiency for each specific analysis
Data Source
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AI summary
Exemplary embodiments provide methods, mediums, and systems for analyzing spectrometry and/or chromatography data, and in particular to techniques to improve the reproducibility of results of spectrographic and/or chromatographic experiments. For example, some embodiments provide techniques for normalizing mass spectrometry (MS) and/or liquid chromatography (LC) data across different experimental devices, allowing data from different cohorts to be directly compared. To this end, exemplary embodiments provide a reliable, reproducible target library usable across different platforms, laboratories, and users. One embodiment leverages statistical techniques to select experimental parameters configured to reduce or minimize the chance of misidentifying a target molecule. Another embodiment leverages the law of large numbers to produce a composite product ion spectrum usable across different experiments. The composite product ion spectrum allows regression curves to be generated, where the regression curves can be used to normalize an experimental mass spectrum.