Consensus Library for Sample Analysis to Filter Background Signals
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Solution Overview
Problem
Conventional methods for analyzing sample components in complex biological matrices, such as mass spectrometry, struggle to distinguish drug-related materials from background signals, often missing 'unexpected metabolites' due to inefficient binary filtration approaches.
Innovation Solution
Generating a consensus library that captures native components of a sample matrix based on characteristics like mass-to-charge ratio, collision cross section, and product ions, allowing for the identification of known and unidentified components, and using this library to filter out native components from analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary filtration methods are used to remove components from control samples, then some background signals are filtered out, but a large number of unknown data points still remain and false positives increase
Solution Approach 1:
The patent performs preliminary actions by generating a comprehensive consensus library of all components present in the sample matrix before analyzing the actual sample. This pre-characterization of the background matrix allows for more effective filtering during sample analysis, as the system already knows what to expect from background signals.
Solution Approach 2:
The patent creates a consensus library that serves as a reference copy of the sample matrix components. This library is generated by aggregating data from multiple control samples and serves as a template for comparing against actual sample data, enabling efficient identification of non-native components.
2Reliability
If comprehensive analysis of all sample components is performed, then all potential drug-related materials are detected, but time and resources are wasted on known background components
Solution Approach 1:
The patent extracts and removes known background components from the analysis by comparing sample data against the consensus library. This extraction process isolates only the non-native components that are not present in the control samples, eliminating the need to manually examine known background signals.
Solution Approach 2:
The consensus library acts as an intermediary between the raw mass spectrometry data and the final interpretation. It mediates the comparison process by providing a reference framework that automatically identifies which components are native to the matrix and which are of interest, streamlining the analysis workflow.
3Reliability
If multiple control samples are analyzed to build a consensus library, then the library becomes more comprehensive and reliable, but the initial setup time and computational resources increase
Solution Approach 1:
The patent merges data from multiple control samples by aggregating their mass spectrometry profiles to generate a single consensus library. This combining process consolidates redundant information about background components while maintaining comprehensive coverage, reducing the overall data volume that needs to be processed during sample analysis.
Data Source
AI summary
Techniques and apparatus for generating consensus libraries for sample matrices (Flow A) and using the consensus libraries to determine unknown-unidentified components of a sample (Flow B) are described. For example, in an embodiment, an apparatus may include at least one memory, and processing circuitry (220) coupled to said at least one memory, wherein said processing circuitry is adapted to receive a plurality of sample matrix data sets (210a-210n) for a sample matrix generated via mass analysis of said sample matrix, and to generate a consensus library (220) for the sample matrix based on said plurality of sample matrix data sets, the consensus library comprising a plurality of known-unidentified components for the sample matrix.