Method and device for analyzing target analyte in sample
Inventors
Kim, Young Wook • PARK, Young Yong • KO, Sung Moon • Lee, Young Jo • LEE, Han Bit
Assignees
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Publication Number
US-12018318-B2
Publication Date
2024-06-25
Expiration Date
Abstract
The present invention relates to a method and device for determining the presence or absence of a target analyte in a sample. The present invention may analyze the target analyte without false results, especially false positive results by using a fitting accuracy of a nonlinear function to a data set as a direct indicator for target analyte analysis.
Core Innovation
The invention provides a method for analyzing a target analyte in a sample based on real-time signal analysis from a signal-generating reaction. A data set for the target analyte is obtained, where the data set includes cycle numbers and signal values from a signal-generating means, and the data set is amended before further analysis.
The amended data set is used to generate a sigmoid function, and a fitting accuracy of the sigmoid function to the amended data set is determined. The fitting accuracy is R2 (R-square value) of the sigmoid function to the amended data set, and presence or absence of the target analyte is then determined by comparing the R2 with a pre-determined threshold value.
The invention further supports refinements to improve discrimination, including generating additional discriminators for improved negative filtering and supporting quadratic-function-based baselining using a first quadratic to guide a second quadratic fit in a baseline fitting region. It also supports integration with multiplex detection using different detection temperatures, including reference-based signal extraction for a second target nucleic acid sequence.
Claims Coverage
The independent claims define a computer-implemented method for determining presence or absence of a target analyte using sigmoid fitting accuracy (R2) compared to a pre-determined threshold, after amending a dataset obtained from a signal-generating reaction. Across the independent claims, the main inventive features comprise dataset acquisition including cycle numbers and signal values, data amendment, sigmoid generation, R2 fitting-accuracy determination, and a threshold-based presence/absence decision.
Sigmoid fit accuracy (R2) threshold decision
Determining a fitting accuracy of the sigmoid function to the amended data set, wherein the fitting accuracy is R2 (R-square value) of the sigmoid function to the amended data set, and determining the presence or absence of the target analyte in the sample by comparing the R2 with a pre-determined threshold value.
Amending the cycle-number/signal-value dataset prior to sigmoid modeling
Obtaining a data set for the target analyte from a signal-generating reaction using a signal-generating means, wherein the data set comprises a plurality of data points including cycle numbers and signal values, and amending the data set.
Generating a sigmoid function for the amended data set
Generating a sigmoid function to the amended data set and determining how the sigmoid function fits the amended data set using an R2 fitting accuracy.
Computer readable storage medium implementing the sigmoid-R2 decision
A computer readable storage medium containing instructions to configure a processor to perform a method comprising obtaining a data set for the target analyte including cycle numbers and signal values, amending the data set, generating a sigmoid function to the amended data set, determining an R2 fitting accuracy of the sigmoid function to the amended data set, and determining presence or absence by comparing the R2 to a pre-determined threshold value.
Overall, the claim coverage centers on deciding presence versus absence of a target analyte by sigmoid modeling of an amended dataset and using sigmoid fitting accuracy (R2) compared to a pre-determined threshold value; dependent claim refinements specify additional discriminators, quadratic-function-based baselining, and multiplex-related signal extraction contexts.
Stated Advantages
Reduced false positives/false negatives versus C(t)-threshold comparative methods.
Improved inter-/intra-instrument variation after SBN normalization.
Documented Applications
Detection of a target analyte in a sample, including target nucleic acid molecules, by determining presence or absence using amended cycle-number/signal-value data, sigmoid fitting, and an R2 threshold decision.
Multiplex detection integration with MuDT technology using different detection temperatures, including reference-based extraction of a second target signal for a second target nucleic acid sequence.
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