S-Shaped Function Fitting for Real-Time PCR Ct Detection
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Solution Overview
Problem
Existing methods for detecting target nucleic acid molecules in samples using real-time PCR data sets are inefficient and require extensive data collection for threshold determination, often leading to inaccurate quantification and detection.
Innovation Solution
A novel method utilizing an S-shaped function, such as sigmoid, logistic, or Gompertz functions, to approximate a selected portion of the slope data set from a real-time amplification curve to determine the threshold cycle (Ct) value for accurate detection of nucleic acid molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional threshold-based methods are used for detecting target nucleic acid molecules, then detection can be performed, but extensive data collection is required to determine suitable threshold values, reducing efficiency
Solution Approach 1:
The patent applies preliminary action by pre-defining the threshold as a fixed proportion (0.2-0.5 times the maximum slope value) rather than requiring extensive data collection and analysis to determine threshold values. This allows the detection method to proceed efficiently with a predetermined threshold criterion that has been optimized in advance through theoretical analysis of the S-shaped amplification curve characteristics.
Solution Approach 2:
The patent changes the parameter approach from collecting extensive empirical data to determine thresholds to using a mathematically defined parameter relationship (threshold = k × maximum slope value, where k is 0.2-0.5). This parameter transformation enables efficient detection by replacing data-intensive threshold determination with a simple calculation based on the amplification curve's intrinsic maximum slope point.
2Measurement precision
If multiple data sets are collected to determine threshold values, then more accurate threshold determination is possible, but the process becomes more complex and time-consuming
Solution Approach 1:
The patent extracts only the essential feature needed for threshold determination - the maximum slope value from the amplification curve - and uses it to define the threshold through a simple proportional relationship. This extraction approach eliminates the need to collect and analyze multiple complete data sets, reducing complexity while maintaining the ability to determine accurate threshold values based on the critical inflection point of the S-shaped curve.
Solution Approach 2:
The patent transforms the complex multi-dataset threshold determination process into a simple parameter calculation (threshold = k × maximum slope) by changing from an empirical data-collection approach to a mathematical parameter relationship. This parameter change simplifies the overall process while preserving detection accuracy.
3Reliability
If conventional amplification curve analysis is used, then the complete amplification process can be monitored, but the baseline phase and plateau phase introduce noise that reduces measurement precision
Solution Approach 1:
The patent segments the amplification curve analysis by focusing specifically on the exponential phase where the maximum slope occurs, rather than attempting to analyze the entire curve including baseline and plateau phases. This segmentation isolates the most informative region of the amplification process, eliminating noise from other phases while maintaining reliable detection through the characteristic S-shaped curve morphology.
Solution Approach 2:
The patent applies preliminary action by using the S-shaped function fit to predict and establish the threshold before analyzing individual data points. This preliminary threshold establishment based on the fitted curve's maximum slope point allows for precise measurement by setting the detection criterion in advance, avoiding the need to navigate through noisy baseline and plateau regions during analysis.
Data Source
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AI summary
The present invention relates to the detection of a target analyte in a sample using an S-shaped function for a slope data set. The method of the present invention comprises obtaining a first data set representing a growth curve by an amplification reaction for the target analyte; wherein said first data set includes a plurality of data points, each having a cycle number and a signal value at the cycle number; calculating a slope value at each cycle number for the first data set to obtain a second data set; wherein said second data set includes a plurality of data points, each having a cycle number and a slope value at the cycle number; calculating an S-shaped function that approximates a selected portion of the second data set; and using the S-shaped function to detect the target analyte in a sample.