Multidimensional Standard Curve for Real-Time PCR Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for absolute quantification of nucleic acids in real-time PCR are limited by their reliance on single features, which restricts the potential for improved accuracy and robustness, and fail to fully utilize the information contained in multidimensional amplification data.
Innovation Solution
A multidimensional standard curve approach that combines multiple linear features to enhance quantification, enable outlier detection, and provide insights into amplification kinetics, using techniques such as principal component analysis and Mahalanobis distance for data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple linear features are combined in multidimensional space, then quantification accuracy and robustness are improved, but data analysis complexity increases
Solution Approach 1:
The patent transitions from unidimensional analysis (single feature) to multidimensional analysis by extracting multiple linear features (Ct, Cy, F0, Fmax, Fc) and representing them in N-dimensional space. This dimensional expansion allows simultaneous consideration of multiple amplification curve characteristics, improving quantification accuracy while maintaining analytical rigor through systematic mathematical frameworks.
Solution Approach 2:
The patent merges multiple linear features into a unified multidimensional representation where each sample is characterized by a vector of features rather than a single value. This combining approach integrates information from different aspects of amplification kinetics, enabling more robust outlier detection and quantification through collective feature analysis.
2Adaptability or versatility
If multiple linear features are extracted and analyzed, then the degrees of freedom in data analysis increase, but computational requirements increase
Solution Approach 1:
By representing samples in N-dimensional space defined by multiple linear features, the patent increases the degrees of freedom available for analysis. This multidimensional framework enables flexible exploration of data relationships, pattern recognition, and statistical analysis while providing a systematic approach to handling increased computational complexity through established mathematical methods.
3Reliability
If a multidimensional approach is used for outlier detection, then detection robustness is improved, but method complexity increases
Solution Approach 1:
The patent combines multiple linear features into a multidimensional feature space where outlier detection can simultaneously evaluate deviations across all feature dimensions. This merging approach provides more comprehensive and robust outlier identification by considering the collective behavior of multiple features rather than relying on single-feature thresholds, enhancing reliability through multidimensional consensus.
Data Source
AI summary
This disclosure relates to methods, systems, computer programs and computer-readable media for the multidimensional analysis of real-time amplification data. A framework is presented that shows that the benefits of standard curves extend beyond absolute quantification when observed in a multidimensional environment. Relating to the field of Machine Learning, the disclosed method combines multiple extracted features (e.g. linear features) in order to analyse real-time amplification data using a multidimensional view. The method involves two new concepts: the multidimensional standard curve and its ‘home’, the feature space. Together they expand the capabilities of standard curves, allowing for simultaneous absolute quantification, outlier detection and providing insights into amplification kinetics. The new methodology thus enables enhanced quantification of nucleic acids, single-channel multiplexing, outlier detection, characteristic patterns in the multidimensional space related to amplification kinetics and increased robustness for sample identification and quantification.