Centroid Processing for LC/MS Biological Feature Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mass spectrometry data compression into centroid data disrupts the contiguous nature of biological features in LC/MS images, hindering the detection of biological features of interest due to non-contiguous representation of mass spectral peaks.
Innovation Solution
A system and method that utilize a centroid rasterizer to determine integration ranges and mass/charge uncertainties, calculating bin widths to create LC/MS images from centroid data, and an image processor to merge and segment these images for peak reassembly, effectively transforming centroid data into a contiguous format suitable for image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mass spectrometry data is compressed into centroid data, then data processing efficiency is improved, but the contiguous nature of biological features in LC/MS images is disrupted
Solution Approach 1:
The patent introduces an intermediary process that transforms centroid data back into a contiguous image format. The system uses centroid data as input but applies image processing techniques (rasterization, interpolation) to reconstruct the continuous spatial representation, thereby mediating between the compressed centroid format and the required contiguous image format for biological feature detection
Solution Approach 2:
The patent changes the parameter representation of the data by transforming centroid coordinates and intensity values into a continuous image matrix. This involves converting discrete centroid points into pixel values across a spatial grid, effectively changing the data structure from sparse point-cloud format to dense matrix format, thereby restoring contiguity
2Stability of the object's composition
If raw analog mass spectrometry data is used, then the contiguous nature of biological features is preserved, but the massive amount of data hinders computational analysis
Solution Approach 1:
The patent segments the computational process into distinct stages: first compressing raw analog data into centroid data for efficient storage and transmission, then selectively reconstructing contiguous images only for regions of interest or specific analytical purposes. This segmentation allows the system to handle large datasets by processing only necessary portions in contiguous format rather than maintaining entire datasets in high-resolution contiguous form
Solution Approach 2:
The patent implements a dynamic data representation system that can switch between centroid format and contiguous image format based on analytical needs. The system maintains data in compressed centroid form by default but dynamically reconstructs contiguous representations when biological feature detection is required, optimizing the balance between data size and feature detectability
Data Source
AI summary
Hardware and software components are configured to image-process centroid data as if they were profile data to discover biological features. Proper mass/charge bin size are determined to raster centroid data into LC/MS images. To correct for the decrease in precision, the LC/MS images are re-evaluated in view of the original centroid data. Peak detection from the binned image is refined by re-considering the corresponding centroid data through cluster analysis. Additionally, some peaks that were merged through processing may be resolved into individual peaks by identifying more than one significant cluster masked by a peak from the binned image.