Molecular Time Series Data Rotation for PCR Phase Detection
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Solution Overview
Problem
Current molecular data analysis techniques struggle to robustly detect and identify characteristic phases in PCR amplification curves due to variations and anomalies introduced by factors like sample handling, reagent imbalances, and instrument calibration.
Innovation Solution
The method involves rotating and interpolating molecular time series data to better align data points with potential characteristic phases, allowing for detection without relying on specific patterns or mathematical fitting, thus enhancing robustness to signal variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical modelling techniques are used to analyze molecular time series data, then information can be derived from the data, but the models fail when encountering variations and anomalies not explicitly conceptualized in the model
Solution Approach 1:
The patent applies preliminary action by rotating the molecular time series data before analysis. The data undergoes multiple rotations (e.g., 0°, 45°, 90°, 135°) in advance, and characteristic phases are detected at each rotation angle. This preliminary rotation prepares the data to accommodate various orientations of characteristic phases, ensuring reliable detection regardless of signal variations or anomalies in the original orientation.
2Adaptability or versatility
If machine learning techniques are used to generalize the data problem, then broader applicability is achieved, but the method becomes a black-box with limited explainability
Solution Approach 1:
The patent creates multiple copies of the molecular time series data, each rotated at different angles (0°, 45°, 90°, 135°). Each copy is then independently analyzed to detect characteristic phases. This approach provides generalization capability across different data orientations while maintaining full explainability, as the detection process is transparent and based on geometric transformation rather than black-box machine learning.
3Ease of operation
If data points are analyzed in their original orientation, then analysis is straightforward, but detection of characteristic phases is less accurate when data varies from expected patterns
Solution Approach 1:
The patent introduces a rotational dimension to the analysis by transforming the molecular time series data through multiple rotation angles. Instead of analyzing data in a single fixed orientation, the data is rotated in the Euclidean plane, creating multiple dimensional perspectives. This allows characteristic phases to be detected regardless of their orientation, significantly improving measurement precision while maintaining operational simplicity through automated rotation and analysis.
Data Source
AI summary
A computer controlled method and device for interpreting molecular time series data comprises acquiring a plurality of discrete measurements of data in time on the presence of molecular fragments to generate a molecular time series data and interpolating the plurality of discrete measurements of data in time of the molecular time series data to obtain an interpolated molecular time series data comprising a spacing of an equal Euclidean distance between the interpolated discrete measurements of data in time. The method further includes rotating the interpolated molecular time series data comprising the interpolated plurality of discrete measurements of data in time in discrete steps, calculating a distribution of the interpolated discrete measurements of data in time of the interpolated molecular time series data and detecting a characteristic phase at occurrence of a subset of the interpolated discrete measurements of data in time wherein the subset are distributed substantially in one dimension.