Feature Clustering in Fluidic Sample Analysis
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Solution Overview
Problem
Conventional data analysis systems are inconvenient for users when evaluating measurement data from instruments like liquid chromatography and mass spectrometry, as they often require preprocessing and peak detection, leading to data loss and alignment issues.
Innovation Solution
A data analysis system that clusters features from multiple data sets using a non-recursive algorithm, ordering features by a measurement parameter and grouping them if their differences are below a threshold, while also displaying the spread of features to indicate clustering reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional data analysis systems perform preprocessing and peak detection, then data can be processed, but data loss occurs and alignment issues arise
Solution Approach 1:
The patent extracts only the essential features (retention time, intensity) from chromatographic data without performing traditional peak detection or preprocessing. By taking out only the necessary information directly from the raw data, the system avoids data loss while maintaining processing capability.
Solution Approach 2:
The system performs preliminary alignment of multiple chromatograms using correlation optimized warping before feature extraction. This preliminary action ensures proper alignment of features across different measurements, preventing alignment issues that would otherwise require complex post-processing.
2Ease of operation
If features from multiple data sets are clustered together, then evaluation is simplified, but false clustering may occur
Solution Approach 1:
The system provides visual feedback through graphical display of feature clusters with uncertainty indicators. Users can see the clustering results and assess the reliability visually, allowing them to verify or adjust clustering assignments. This feedback mechanism prevents false clustering by making unreliable clusters visible.
Solution Approach 2:
The clustering approach dynamically groups features based on their similarity in retention time and intensity across multiple measurements. The system adapts the clustering to the actual data characteristics rather than applying fixed rules, improving both ease of evaluation and reliability by responding to the specific patterns in the measurement data.
3Loss of information
If the entire chromatographic data matrix is used without preprocessing, then data loss is avoided, but alignment issues due to experimental condition variations arise
Solution Approach 1:
The patent applies correlation optimized warping as a preliminary alignment step to the entire chromatographic data matrix before feature extraction. This preliminary action corrects for small drifts caused by experimental condition variations while preserving all the original data, thus avoiding both data loss and alignment issues.
Solution Approach 2:
The system uses a universal alignment approach (correlation optimized warping) that can be applied to the entire chromatographic data matrix regardless of specific experimental variations. This multi-functional method handles different types of drift and variation while maintaining data integrity, making it applicable to diverse chromatographic conditions.
Data Source
AI summary
A device for analyzing measurement data having a plurality of data sets, each data set being assigned to a respective one of a plurality of measurements, each data set having multiple features being indicative of different fractions of a fluidic sample, the device comprising a cluster determining unit configured for determining feature clusters by clustering features from different data sets presumably relating to the same fraction, a spread determining unit configured for determining for at least a part of the feature clusters a spread of the features within a respective feature cluster, and a display unit configured for displaying at least the part of the feature clusters together with a graphical indication of the corresponding spread.