Mass Spectrometer Bayesian Quantitation
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Solution Overview
Problem
Conventional mass spectrometry methods face challenges in accurately quantifying analyte compounds in biological samples due to incomplete and noisy measurements, leading to uncertain data and rejection of potentially valid results.
Innovation Solution
A Bayesian approach is employed to probabilistically determine the relative intensity and concentration of analytes in multiple samples, using prior probability distribution functions and Markov Chain Monte Carlo methods to account for noise and uncertainty, allowing for the identification and quantification of analytes even in noisy and incomplete data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mass spectrometry methods are used to measure analyte compounds, then measurement speed and throughput are maintained, but measurement precision and reliability deteriorate due to incomplete and noisy measurements
Solution Approach 1:
The patent transforms the measurement data from raw intensity values to probability distribution functions, changing the parameter representation from deterministic to probabilistic. This allows the system to handle incomplete and noisy measurements by representing them as distributions rather than fixed values, thereby improving quantitation accuracy without sacrificing data completeness
Solution Approach 2:
The patent introduces Markov Chain Monte Carlo sampling as an intermediary computational method that bridges the gap between incomplete measurements and reliable quantitation. The sampling process generates representative data points from probability distributions, acting as a mediator that recovers information from noisy measurements while maintaining statistical rigor
2Measurement precision
If conventional methods reject outliers and incomplete data, then measurement precision is maintained for accepted data, but loss of information increases due to rejection of potentially valid results
Solution Approach 1:
The patent converts the previously harmful effect of noisy and incomplete measurements into a beneficial feature by representing them as probability distributions. Rather than rejecting data points that deviate from expected patterns, the system incorporates them as distributions with appropriate uncertainty, transforming data quality issues into quantifiable probabilistic information that improves overall analysis
Solution Approach 2:
The patent performs preliminary probabilistic modeling of measurement data before final quantitation, establishing probability distribution functions and confidence intervals in advance. This preliminary action allows the system to account for potential outliers and incomplete data before they become problems, enabling more robust statistical analysis and reducing the need for data rejection
Data Source
AI summary
A mass spectrometer and method of mass spectrometry are disclosed wherein two separate samples are mass analysed and then the relative intensity, concentration or expression level of one or more components, molecules or analytes in a first sample is quantitated relative to the intensity, concentration or expression level of one or more components, molecules or analytes in a second sample. The relative quantitation is performed probabilistically without the need to resort to using internal calibrants.