Mass Spectrometry Data Compression via GPU Parallel Processing
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Solution Overview
Problem
Current data compression techniques for mass spectrometry data, particularly in LC/MS and LC/IMS/MS systems, face challenges in efficiently reducing the large volume of raw data while maintaining analytical results, as they often require significant computation and are not suitable for real-time processing due to the complexity of handling high-dimensional data.
Innovation Solution
The Data Sweep algorithm employs parallel processing using a GPU to compress data by summing intensities within geometric shapes centered on data points, determining partial and combined sums, and deciding which data points to keep or remove based on threshold values, effectively reducing the data size while preserving essential information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression techniques are used for mass spectrometry data, then data size is reduced, but processing time increases and real-time processing capability is lost
Solution Approach 1:
The patent replaces conventional CPU-based sequential processing with GPU-based parallel processing. The GPU architecture enables simultaneous execution of multiple compression operations across different data points, transforming the mechanical processing system into a highly parallel computational system that achieves real-time compression without sacrificing processing speed
Solution Approach 2:
The patent segments the mass spectrometry data into discrete data points across multiple dimensions (m/z ratio, retention time, ion mobility). Each data point is processed independently through the compression algorithm, allowing parallel computation. This segmentation enables the GPU to process numerous data points simultaneously, reducing overall processing time while maintaining compression effectiveness
2Quantity of substance
If complex compression algorithms are applied to high-dimensional mass spectrometry data, then compression effectiveness improves, but computational complexity increases
Solution Approach 1:
The patent changes the computational parameters by implementing a simplified compression algorithm that operates on intensity values rather than complex spectral patterns. The algorithm uses straightforward comparison operations (current intensity vs. previous intensity) and basic mathematical operations (summing, averaging), transforming a potentially complex computational problem into a series of simple parameter-based decisions that are easily parallelizable
Solution Approach 2:
The patent employs a compression approach that creates simplified representations of the original data, accepting some information loss in exchange for computational efficiency. The algorithm discards redundant data points and uses approximations rather than exact calculations, similar to using disposable or temporary computational structures that are quickly created and discarded, reducing overall computational complexity
3Quantity of substance
If data points are removed to reduce file size, then storage requirements decrease, but analytical accuracy may be compromised
Solution Approach 1:
The patent implements a feedback mechanism where the compression algorithm continuously monitors the intensity values and decides whether to retain or remove data points based on comparison with adjacent points. The algorithm uses local context (previous and next data points) to make retention decisions, ensuring that significant spectral features are preserved while removing only redundant information. This feedback-based approach maintains analytical accuracy by adapting retention decisions to the actual data characteristics
Solution Approach 2:
The patent applies different retention decisions to different data points based on their local characteristics. Rather than uniformly compressing all data, the algorithm evaluates each data point's importance in its local context (comparing intensity values locally) and makes targeted retention decisions. This local quality approach ensures that regions with significant spectral features maintain high fidelity while less important regions are compressed more aggressively
Data Source
AI summary
Techniques are described for performing data compression of a first data set including data points in a plurality of dimensions. First processing is performed to determine a second set of final sums. For each data point in the first data set, a corresponding final sum of the second set is determined that is a sum of intensities associated with a first portion of data points, wherein the first portion of data points for which the intensities are summed for said each data point are included within a geometric shape centered on said each data point. Second processing is performed to determine a final set of final combined keep values. Each of the final combined keep values corresponds to a different one of the data points in the first data set and denotes whether to keep or otherwise remove the different one of the data points from the first data set.