qPCR Copy Number Determination via PDF Modeling
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Solution Overview
Problem
Current methods for determining gene copy number in biological samples, such as the comparative C t method, assume 100% efficiency in PCR processes, which can lead to inaccuracies due to variations in reaction conditions and instrumentation, necessitating the development of more robust statistical models for accurate and confident copy number assignment.
Innovation Solution
The method employs a probability density function (PDF) model to analyze ΔC t values from real-time qPCR assays, constructing frequency distributions and optimizing parameters to fit the observed data, allowing for accurate copy number determination and confidence assessment without the need for calibration samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the comparative C t method is used to determine gene copy number, then the calculation is simple and fast, but the accuracy deteriorates due to the assumption of 100% PCR efficiency which is rarely met in practice
Solution Approach 1:
The patent transforms the copy number determination from relying on the absolute assumption of 100% PCR efficiency to using relative comparisons of ΔC t values across multiple samples. By changing the parameter from absolute efficiency assumption to relative distribution analysis, the method maintains simplicity while improving accuracy through statistical robustness.
Solution Approach 2:
The patent introduces iterative refinement where copy number assignments from initial ΔC t calculations are used to identify outliers, which are then excluded to recalculate more accurate copy numbers. This feedback loop continuously improves measurement precision by eliminating data points that deviate from the established distribution patterns.
2Measurement precision
If statistical models are used to account for PCR efficiency variations and replicate variations, then the accuracy of copy number determination is improved, but the complexity of the method increases
Solution Approach 1:
The patent segments the population of samples into distinct groups based on their ΔC t values and corresponding copy number assignments. By dividing the data into discrete categories (e.g., 1 copy, 2 copies, 3 copies), the complex continuous variation in PCR efficiency is transformed into manageable discrete segments that can be analyzed independently, reducing overall method complexity while maintaining precision.
Solution Approach 2:
The patent uses multiple replicate samples for each biological sample to account for variations. By creating and analyzing copies (replicates) of the same sample, the method statistically captures and eliminates random variations, improving accuracy without requiring complex single-sample analysis methods.
3Reliability
If multiple replicate samples are analyzed to account for assay variations, then the reliability of copy number assignment is improved, but the time and resources required increase
Solution Approach 1:
The patent analyzes multiple replicate samples (excessive action) to ensure reliable copy number assignment, but then selectively excludes outlier replicates that deviate from the established distribution. This partial action approach maintains the reliability benefits of replication while reducing the time burden by not requiring all replicates to be fully processed or reported.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides reliable and confident copy number assignments by accounting for variations in PCR efficiency and instrumentation, improving the accuracy of gene copy number determination across biological samples.
Implementation Method 1
The polymerase chain reaction (PCR) represents an extensive family of chemistries that have produced numerous types of assays of impact in biological analysis
Implementation Method 2
real-time qPCR assays
Data Source
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AI summary
Methods for the determination of a copy number of a target genomic sequence; either a target gene or genomic sequence of interest, in a biological sample are described. Various methods utilize a model drawn from a probability density function (PDF) for the assignment of a copy number of a target genomic sequence in a biological sample. Additionally, the methods provide for the determination of a confidence value for a copy number assigned to a sample based on attributes of the sample data. Accordingly, the various methods for the determination of a copy number provide the end user with significant information for the evaluation of a copy number of a target genomic sequence; either a gene or genomic sequence of interest.