Mass Spectrometry Binning Pipeline for Image-Based Signal Analysis
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Solution Overview
Problem
Conventional mass spectrometry data extraction techniques are inefficient and unreliable, particularly when handling large scales of samples, and struggle with minor errors in mass-to-charge ratios, noise prevalence, and false positives.
Innovation Solution
An image-based processing approach is implemented, where raw data from a mass spectrometer is processed and transformed into a format suitable for analysis by a machine learning model. This approach involves binning data in both retention time and mass-to-charge ratio axes, determining optimal bin values to balance data reduction and signal retention, and generating image-based representations of the data to enhance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data extraction techniques are used for mass spectrometry data analysis, then the processing method is simple, but the accuracy and reliability are insufficient particularly for large scale samples
Solution Approach 1:
The patent segments mass spectrometry data into discrete bins along the mass-to-charge ratio axis and retention time axis, creating a grid structure that divides continuous spectral data into manageable discrete units. This segmentation enables systematic processing and improves analysis reliability by reducing data complexity while maintaining information integrity.
Solution Approach 2:
The patent transforms one-dimensional mass spectrometry data (intensity vs. mass-to-charge ratio) into two-dimensional binned data by adding the retention time dimension. This dimensional transformation allows for more comprehensive data organization and analysis, improving reliability through enhanced data structure while managing complexity through systematic binning.
2Measurement precision
If data binning is applied to reduce noise and improve signal retention, then the accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent divides the mass-to-charge ratio range into discrete bins with defined boundaries, allowing precise localization of spectral features. This segmentation approach improves measurement precision by confining measurements to specific intervals while maintaining manageable data processing through the structured binning framework.
Solution Approach 2:
The patent changes the parameter representation by transforming continuous mass-to-charge ratio values into discrete bin indices. This parameter transformation improves precision by reducing measurement variability while simplifying data processing through discrete categorization, though it introduces binning complexity.
3Productivity
If conventional extraction techniques are used, then the processing is faster, but noise prevalence and false positives increase
Solution Approach 1:
By segmenting data into bins, the patent enables parallel processing of discrete units while improving signal-to-noise ratio through localized analysis. Each bin can be processed independently, maintaining productivity through efficient computation while improving reliability through reduced noise interference in segmented data structures.
Solution Approach 2:
The patent performs preliminary binning and data organization before detailed analysis, pre-processing the data into a structured format that facilitates faster subsequent processing. This preliminary action improves productivity by preparing data in advance while enhancing reliability through early noise reduction and signal consolidation during the binning phase.
Data Source
AI summary
Systems and methods are provided for obtaining raw mass spectrometry data from samples, determining signals present across the samples, determining a bin value to apply to the filtered mass spectrometry data, and after determining the bin value, generating an image-based representation of the raw mass spectrometry data, wherein the image-based representation indicates frequencies of peak intensities in each bin.