PCR Cross-Talk Coefficient Determination Using Weighted Signal Regions
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Solution Overview
Problem
Conventional methods for determining cross-talk coefficients in PCR detection systems rely exclusively on the plateau region, which contains less than 10% of the data and is generated during an unstable chemistry state, leading to noisy signals and incorrect assumptions that result in errors, especially when the plateau is not flat or has significant noise.
Innovation Solution
The method determines cross-talk coefficients by minimizing the sum of squares between the basis signal and the cross-talk signal across the entire signal acquisition range, using a linear subtractive model that analyzes all data points, including the baseline, growth, and plateau regions, to provide a more robust correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If cross-talk coefficients are determined using only the plateau region, then the calculation is simple, but the measurement precision deteriorates due to noisy signals and limited data
Solution Approach 1:
The patent segments the PCR curve into multiple regions (baseline, growth, plateau) and applies different weighting factors to each region. The baseline and growth regions are given higher weights because they contain more reliable signal information, while the plateau region is given lower weight due to its noisy and unstable nature. This segmented approach allows the system to use more data points without being dominated by the noisy plateau region.
Solution Approach 2:
The patent changes the parameter of data selection from exclusively using the plateau region to using a weighted combination of multiple regions (baseline, growth, and plateau). By introducing weighting parameters that reflect the reliability of each region, the system transforms the calculation from a simple average to a weighted optimization that improves precision while maintaining computational feasibility.
2Quantity of substance
If cross-talk coefficients are determined using the entire acquisition range with equal weighting, then more data is used, but the measurement precision deteriorates due to inclusion of noisy baseline and growth regions
Solution Approach 1:
The patent introduces weighting parameters that change the effective contribution of different data regions. Instead of treating all data points equally, the system applies higher weights to the baseline and growth regions (which have lower noise) and lower weights to the plateau region (which has higher noise). This parameter adjustment allows the system to utilize more data points while maintaining or improving measurement precision.
Solution Approach 2:
The patent applies different quality assessments to different parts of the data curve. The baseline and growth regions are considered to have higher signal quality and reliability, while the plateau region is considered to have lower quality due to noise and chemical instability. By applying local quality-based weighting, the system optimizes the contribution of each region to the overall cross-talk coefficient calculation.
3Ease of operation
If conventional ratio method is used to calculate cross-talk coefficients, then the calculation is straightforward, but the reliability deteriorates when plateau is not flat or has significant noise
Solution Approach 1:
The patent replaces the simple mechanical ratio calculation method with an optimization-based approach. Instead of directly computing the ratio of average plateau values, the system formulates the cross-talk coefficient determination as an optimization problem that minimizes a weighted sum of squared differences across multiple regions. This substitution of calculation methodology improves reliability by accounting for variations in signal quality across different regions.
Solution Approach 2:
The patent introduces a feedback mechanism through iterative optimization. The system calculates initial cross-talk coefficients, evaluates their performance across different regions with appropriate weighting, and refines the coefficients to minimize the weighted error. This feedback loop allows the system to adapt to the actual signal characteristics and improve reliability even when the plateau is not flat or has significant noise.
Data Source
AI summary
Systems and methods for determining cross-talk coefficients in curves, such as sigmoid-type or growth curves, and PCR curves and nucleic acid melting curves in particular, as well as for applying the cross-talk coefficients to produce cross-talk corrected data sets using a linear subtractive model. Cross-talk signal coefficients are determined using cross-talk data acquired across the entire signal acquisition range. Analyzing across all of the signal curve data provides for a more robust cross-talk correction across the entire data acquisition range. A linear subtractive model is used to correct data sets having cross-talk components.