KR102189356B1 - Detection of abnormal signals in the data set - Google Patents

Abstract

The present invention relates to a method of detecting anomalous signals in a data set, in particular a data set for a target analyte. According to the present invention, not only can the cycle number(s) representing an abnormal signal in the data set be accurately determined using the normal score, but also, if the candidate cycle number is determined to represent an abnormal signal, the abnormal signal value in the cycle number is It is possible to provide a corrected data set that has been replaced with a normal signal value.

Description

Detection of abnormal signals in the data set

The present invention relates to the detection of abnormal signals in a data set, in particular a data set for a target analyte.

As a method for detecting a target nucleic acid sequence through nucleic acid amplification, a real-time PCR (Real time PCR) technology for measuring amplification of a target nucleic acid sequence in real-time is widely used. Real-time PCR technology uses a signal-generating means that emits a detectable fluorescent signal in proportion to the amount of the target nucleic acid sequence during a PCR reaction in order to detect a specific target nucleic acid sequence. Emission of a detectable fluorescent signal, for example, using an intercalator that emits a fluorescent signal when bound to double-stranded DNA, or using an oligonucleotide containing both a reporter molecule and a quencher molecule that inhibits its fluorescence emission. Performed.

Real-time PCR measures a fluorescence signal proportional to the amount of a target nucleic acid for each cycle, and data including a plurality of data points having a pair of coordinate values of the cycle number and the intensity (signal value) of the fluorescence signal at the cycle number Create a data set. The data set may be expressed as an amplification curve (also referred to as an amplification profile curve or growth curve) in which fluorescence intensity values are plotted against cycle numbers for convenience of data analysis. The data set representing the amplification curve can then be analyzed to determine the presence or absence of the target nucleic acid sequence in the sample. For example, the presence of a target nucleic acid sequence in the sample can be determined if there is a cycle with a fluorescence signal that exceeds a threshold applied to a data set representing an amplification curve.

For detection of the target nucleic acid sequence, it is essential to obtain an accurate and reliable data set. However, no matter how elaborately the experiment is performed, due to the change in the annealing temperature, the generation of air bubbles in the reaction tube, and the presence of foreign substances in the sample, the generated data set is an abnormal signal (error or noise). )). Examples of such abnormal signals include a rapid increase (also referred to as a jump, spike, or step) or a rapid decrease (also referred to as a dip) in the fluorescent signal. Such an abnormal signal may cause misinterpretation during the qualitative and quantitative analysis of data, thereby reducing the accuracy and reliability of the analysis.

Many attempts have been made to prevent the occurrence of abnormal signals, but the exact cause of it has not been identified. Even if the exact cause is identified, it is not easy to prevent it in advance. Therefore, before determining the presence or absence of a target nucleic acid sequence from the data set, it would be more practical to analyze the data set to determine if an abnormal signal has occurred, and correct or invalidate it if necessary.

In this regard, several analysis methods have been reported to identify abnormal signals.

U.S. Patent No. 8,560,247 discloses a technique for discriminating non-amplified data, i.e., errors such as noise and jump, the technique receiving a set of data points, calculating a linear function approximating the set of data points In the step of, analyzing the linear function to determine whether the slope of the linear function exceeds the maximum amplification slope, and when the maximum amplification slope is exceeded, the excess slope segment is converted into a non-amplified section of the curve. And confirming. However, considering that even normal signals represent amplification data exceeding the maximum amplification slope according to reaction conditions, the method has a high possibility of determining a significant number of normal signals as errors.

In addition, US Application Publication No. 2015/0186598 discloses a method of detecting a jump error by determining two consecutive cycles with different signs from the second derivative of a data set. However, the method uses a threshold that is not so strict to determine the jump error, and the application of the threshold is complicated.

Throughout this specification, various patents and publications are mentioned and citations are provided in parentheses. These patents and publications are incorporated herein by reference in their entirety to describe the present invention and the level of the technical field to which the present invention pertains in more detail.

The inventors have sought to improve the conventional method of detecting abnormal signals that may be found in the data set for the target analyte. As a result, the present inventors established a "normality-representing value" which is a parameter indicating the normality of the signal value in the cycle number for the data set, and the candidate cycle number ( S), changing the signal value in the candidate cycle number(s), and comparing the normal-display value after the change in the candidate cycle with the normal-display value before the change to detect an abnormal signal Was developed.

Accordingly, an object of the present invention is to provide a method for detecting an abnormal signal in a data set.

Another object of the present invention is to provide a computer-readable recording medium comprising instructions for implementing a method for detecting an abnormal signal in a data set.

Another object of the present invention is to provide an apparatus for detecting an abnormal signal in a data set.

Another object of the present invention is to provide a computer program stored on a computer-readable recording medium, which implements a processor for executing a method for detecting abnormal signals in a data set.

Other objects and advantages of the present invention will become more apparent by the following examples, claims and drawings.

I. Detection of abnormal signals in the data set

According to one aspect of the present invention, the present invention provides a method of detecting an abnormal signal in a data set, comprising the steps of:

(a) obtaining a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;

(b) providing a normality-representing value in each cycle number of a data set by using the signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number. Is;

(c) selecting a candidate cycle number (candidate cycle number) for an abnormal signal according to the normal-display value, wherein the normal-display value in the candidate cycle number(s) is normal-display before change. Corresponds to the value;

(d) changing a signal value in the candidate cycle number(s), and further providing a normal-indicative value in the candidate cycle number(s) by using the changed signal value, wherein the candidate cycle number The normal-display value additionally provided in (s) corresponds to the normal-display value after change;

(e) comparing the post-change normal-display value in the candidate cycle number(s) with the pre-change normal-display value; And

(f) determining that the candidate cycle number(s) represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.

The inventors have sought to improve the conventional method of detecting abnormal signals that may be found in the data set for the target analyte. As a result, the present inventors established a "normality-representing value" which is a parameter indicating the normality of the signal value in the cycle number for the data set, and the candidate cycle number ( S), changing the signal value in the candidate cycle number(s), and comparing the normal-display value after the change in the candidate cycle with the normal-display value before the change to detect an abnormal signal Was developed.

As used herein, the term “abnormal signal” refers to a signal that is not associated with an analyte (e.g., a target nucleic acid sequence), ie a sharp increase or decrease by factors other than the analyte during a signal amplification reaction. Means a signal to The term "abnormal signal" is used interchangeably with "error signal", "aberrant signal" and "noise signal". Abnormal signals herein include signals that exhibit a sharp increase (eg, jump, spike or step) or a sharp decrease (eg, dip) in the signal value in the amplification curve obtained from the signal amplification reaction. The cause of the abnormal signal may include, but is not limited to, a change in annealing temperature, generation of air bubbles in the reaction tube, and the presence of foreign substances in the sample.

The method of the present invention will be described with reference to FIG. 1 as follows:

Step (a): Acquisition of data sets (110)

In step (a), a data set for a target analyte is obtained by a signal amplification reaction ( 110 ). The data set includes a plurality of data points having a cycle number and a signal value at the cycle number.

As used herein, the term "target analyte" or "analyte" includes a variety of substances (e.g., biological substances and non-biological substances), specifically biological substances, more specifically nucleic acid molecules (such as DNA and RNA), carbohydrates, lipids, amino acids, biological compounds, hormones, antibodies, antigens, metabolites and cells. Most specifically, the target analyte is a target nucleic acid molecule. The target analyte is present in the sample.

The term “sample” as used herein refers to any material that undergoes the method of the present invention. In particular, the term “sample” refers to any material that contains or is presumed to contain a nucleic acid of interest, or any material that is the nucleic acid itself that contains or is presumed to contain a target nucleic acid sequence of interest. Most particularly, the term “sample” as used herein includes biological samples (eg, cells, tissues, and body fluids) and non-biological samples (eg, food, water, and soil). Biological samples include, but are not limited to, viruses, bacteria, tissues, cells, blood, serum, plasma, lymph, sputum, swabs, aspiration, bronchial lavage fluid, milk, urine, feces, eye fluid, saliva, Brain extract, spinal fluid (SCF), cecum, spleen and tonsil tissue extract, ascites and amniotic fluid. In addition, samples can include naturally occurring nucleic acid molecules and synthetic nucleic acid molecules isolated from biological sources.

As used herein, the term “target nucleic acid”, “target nucleic acid sequence”, or “target sequence” refers to a nucleic acid sequence of interest for analysis, detection or quantification. Target nucleic acid sequences include single-stranded as well as double-stranded sequences. The target nucleic acid sequence includes sequences initially present in the sample as well as sequences newly generated in the reaction.

Target nucleic acid sequences include any DNA (gDNA and cDNA), RNA molecules, and hybrids thereof (chimeric nucleic acids). The sequence may be in double stranded or single stranded form. When the nucleic acid as a starting material is a double-stranded, it is preferable to make the double-stranded or partially single-stranded. Known methods of separating strands include, but are not limited to, heating, alkali, formamide, urea and glycoxal treatment, enzymatic methods (eg, helicase action) and binding proteins. For example, strand separation can be achieved by heating at a temperature of 80-105°C. A general method of such treatment is disclosed in Joseph Sambrook, et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (2001).

The target nucleic acid sequence may be any naturally occurring prokaryotic nucleic acid, eukaryotic cell (e.g., protozoa and parasite, fungus, yeast, higher plants, lower animals and higher animals including mammals and humans) nucleic acids, viruses (e.g., herpes Virus, HIV, influenza virus, Epstein-Barr virus, hepatitis virus, poliovirus, etc.) nucleic acid or viroid nucleic acid. The nucleic acid molecule can also be any nucleic acid molecule that is either recombinantly produced or capable of being produced or chemically synthesized or capable of being synthesized. Thus, the target nucleic acid sequence may or may not be found in nature.

The target nucleic acid sequence should not be construed as being limited to sequences known at a given time point or sequences available from a given time point, but instead should be construed as including sequences that are available or may be known at any time present or in the future. That is, the target nucleic acid sequence may or may not be known at the time of carrying out the method of the present invention. In the case of an unknown target nucleic acid sequence, its sequence can be determined by one of the conventional sequencing methods prior to practicing the present invention.

When the target analyte is a target nucleic acid molecule, the sample can be subjected to a nucleic acid extraction process known in the art (see Sambrook, J. et al., Molecular Cloning. A Laboratory Manual , 3rd ed. Cold Spring Harbor Press. (2001)). The nucleic acid extraction process may vary depending on the type of sample. In addition, when the extracted nucleic acid is RNA, a reverse transcription process for synthesizing cDNA may be additionally performed (see Sambrook, J. et al., Molecular Cloning. A Laboratory Manual , 3rd ed. Cold). Spring Harbor Press (2001)).

According to one embodiment of the present invention, the target nucleic acid sequence includes a nucleotide variation.

The term "nucleotide variation" as used herein refers to the substitution, deletion or insertion of any single or multiple nucleotides in a DNA sequence at a specific position in a continuous DNA segment. These contiguous DNA segments contain a gene or some other part of a chromosome. These nucleotide variations can be mutant or polymorphic allele variations. For example, the nucleotide variation detected in the present invention includes single nucleotide polymorphism (SNP), mutation, deletion, insertion, substitution, and translocation. Examples of nucleotide variations include various variations in the human genome (e.g., mutations in the methylenetetrahydrofolate reductase (MTHFR) gene), mutations related to drug resistance of pathogens, and tumorigenicity-causing mutations. The term “nucleotide variation” as used herein includes all variations at a specific position in a nucleic acid sequence. That is, the term “nucleotide variation” includes the wild type and all mutant types thereof at a specific position in a nucleic acid sequence.

Data sets used herein are obtained by means of a signal generation process.

As used herein, the term “signal generation process” refers to the properties of a target analyte in a sample, ie activity, amount or presence (or absence), in particular presence (or absence), capable of generating a signal. It means an arbitrary process. The signal generation process herein includes biological and chemical reactions. Such biological reactions include PCR, genetic analysis such as real-time PCR and microarray analysis, immunological analysis and bacterial growth analysis. According to one embodiment, the process of generating a signal includes analyzing the generation, change or destruction of a chemical substance.

The signal generation process is accompanied by signal changes. Signal change can serve as an indicator qualitatively or quantitatively indicating the presence or absence of a target nucleic acid sequence.

The details of the "signal generation process" are disclosed in WO 2015/147412 filed by the present inventors, the teachings of which are incorporated herein by reference in their entirety.

According to one embodiment, the signal generation process is a signal amplification process.

According to an embodiment of the present invention, the signal generation process is a process with or without amplification of the target nucleic acid molecule.

Specifically, the signal generation process is a process accompanied by amplification of the target nucleic acid molecule. More specifically, the signal generation process is a process that accompanies the amplification of the target nucleic acid molecule and increases or decreases the signal (specifically, the signal can be increased) upon amplification of the target nucleic acid molecule.

As used herein, the term “signal generation” includes the appearance or disappearance of a signal and an increase or decrease in the signal. Specifically, the term "signal generation" means an increase in signal.

The signal generation process can be carried out according to various methods known to those skilled in the art. Examples of the method include the TaqMan ^TM probe method (U.S. Patent No. 5,210,015), molecular beacon method (Tyagi et al., Nature Biotechnology v.14 MARCH 1996), Scorpion method (Whitcombe et al., Nature Biotechnology 17:804-807 ( 1999)), Sunrise (or Amplifluor) method (Nazarenko et al., 2516-2521 Nucleic Acids Research, 25(12):2516-2521 (1997), and U.S. Patent No. 6,117,635), Lux method (U.S. Patent No. 7,537,886), CPT (Duck P, et al. Biotechniques, 9:142-148 (1990)), LNA method (US Pat. No. 6,977,295), Flexor method (Sherrill CB, et al., Journal of the American Chemical Society, 126:4550-4556 (2004)), Hybeacons ^TM (DJ French, et al., Molecular and Cellular Probes (2001) 13, 363-374 and U.S. Patent No. 7,348,141), double-labeled self-quenching Probe (Dual-labeled, self-quenched probe; U.S. Patent No. 5,876,930), hybridization probe (Bernard PS, et al., Clin Chem 2000, 46, 147-148), PTO CE (PTO cleavage and extension) method (WO 2012/096523), PCE-SH (PTO Cleavage and Extension-Dependent Signaling Oligonucleotide Hybridization) method (WO 2013/115442), PCE-NH (PTO Cleavage and Extension-Dependent Non-Hybridization) method (WO 2014/104818) and CER Method (WO 2011/037306) Can be mentioned.

TaqMan ^TM signal generation process When implemented according to the probe method, the means for generating a signal may include a primer pair, a probe having an interactive double label, and a DNA polymerase having a 5'→3' nuclease activity. When the signal generation reaction is carried out according to the PTOCE method, the signal generation means is a primer pair, PTO (Probing and Tagging Oligonucleotide), CTO (Capturing and Templating Oligonucleotide), and DNA polymerase having 5'→3' nuclease activity. Can include. PTO or CTO can be labeled with a suitable label.

According to one embodiment, the signal generation process may be performed as a process including signal amplification together with target amplification.

According to one embodiment, the signal amplification reaction as a signal generation process is performed in a manner in which the signal is amplified simultaneously with the amplification of the target nucleic acid sequence (eg, real-time PCR). Alternatively, the signal amplification reaction is carried out in such a way that the signal is amplified without amplification of the target nucleic acid molecule (e.g., CPT method (Duck P, et al., Biotechniques, 9:142-148 (1990)), Invader assay (US Patent Nos. 6,358,691 and 6,194,149).

Various methods of amplifying a target nucleic acid molecule are known, which are, but are not limited to, PCR (polymerase chain reaction), LCR (ligase chain reaction, Wiedmann M, et al., "Ligase chain reaction (LCR)-overview and applications. "PCR Methods and Applications, 3(4):S51-64(1994) reference), GLCR (gap filling LCR, WO 90/01069, see EP 0439182 and WO 93/00447), Q-beta (Q-beta replicase amplification , Cahill P, et al., Clin Chem., 37(9):1482-5(1991), see U.S. Patent 5,556,751), strand displacement amplification (SDA, GT Walker et al., Nucleic Acids Res. 20(7)) :16911696 (1992), see EP 0497272), NASBA (see nucleic acid sequence-based amplification, Compton, J. Nature 350 (6313):912 (1991)), TMA (Transcription-Mediated Amplification, Hofmann WP et al., J Clin Virol. 32(4):289-93(2005); see U.S. Patent 5,888,779) or RCA (Rolling Circle Amplification, Hutchison CA et al., Proc. Natl Acad. Sci. USA. 102:1733217336 (2005)) ).

As used herein, the term "signal" means a measurable output. As used herein, “signal value” is an expression that quantitatively represents a signal.

The size, change, etc. of the signal may serve as an indicator qualitatively or quantitatively indicating the characteristics of the target analyte (target nucleic acid sequence), particularly the presence or absence.

Examples of useful indicators include fluorescence intensity, luminescence intensity, chemiluminescence intensity, bioluminescence intensity, phosphorescence intensity, charge transfer, voltage, current, power, energy, temperature, viscosity, optical scatter, radiation intensity, reflectivity, transmittance and absorbance. Include. The most widely used indicator is fluorescence intensity. Signal change includes the occurrence or disappearance of a signal, as well as an increase or decrease in a signal.

The signal is characterized by a variety of signal characteristics obtained from signal detection, such as signal intensity (e.g., relative fluorescence unit (RFU) value or, when performing an amplification reaction, a specific cycle number, selected cycle number or RFU value at an endpoint), signal change It includes a shape (or pattern) or C _t value, or a value obtained by mathematically processing the above properties.

According to one embodiment, the term “signal” includes not only the signal itself detected at the detection temperature, but also a modified signal provided by mathematically processing the signal.

According to an embodiment of the present invention, when obtaining an amplification curve by real-time PCR, it can be used to select various signal values (or characteristics) from the amplification curve and determine the presence of a target nucleic acid sequence (eg, signal intensity, C _t- value or amplification curve data).

The signal (in particular, the signal intensity) may vary depending on its detection temperature as well as the signal generation means used.

As used herein, the term "signal generating means" refers to a means of providing a signal indicative of the properties, specifically the presence or absence of a target analyte to be analyzed.

The term "signal generating means" as used herein refers to any substance used to generate a signal indicating the presence of a target nucleic acid sequence, including, for example, oligonucleotides, labels and enzymes. Alternatively, the term “signal generating means” as used herein can be used to mean any method of using a substance for signal generation.

Various means for generating signals are known in the art. The means for generating a signal includes the label itself and the oligonucleotide having the label. The label includes a fluorescent label, a luminescent label, a chemiluminescent label, an electrochemical label, and a metal label. The label itself, for example an intercalating dye, may serve as a signal generating means. Alternatively, a single label or an interactive double label comprising a donor molecule and an acceptor molecule can be used as a means of generating a signal in a form bound to one or more oligonucleotides.

The means for generating a signal may include additional components that generate a signal, such as nucleic acid cleavage enzymes (5'-nuclease and 3'-nuclease).

The means for generating a signal comprises generating the signal in a manner dependent on the formation of a dimer; Generating a signal by using dimer formation in a manner dependent on cleavage of a mediation oligonucleotide hybridized specifically to a target analyte; And it may include generating a signal by cleavage of the detection oligonucleotide (detectoin oligonucleotide).

The term “signal amplification reaction” as used herein refers to a reaction that increases or decreases a signal generated by a signal generating means.

According to one embodiment, the signal amplification reaction refers to a reaction of increasing (amplifying) a signal generated by a signal generating means depending on the presence of a target analyte. This signal amplification reaction may or may not be accompanied by amplification of the target analyte (eg, target nucleic acid molecule). Specifically, the signal amplification reaction refers to amplification of a signal accompanied by amplification of a target analyte.

The data set obtained by the signal amplification reaction includes the cycle number.

As used herein, the term “cycle number” or “cycle” refers to a unit of change in the condition in a plurality of measurements accompanied by a change in condition. For example, changes in conditions include changes in temperature, reaction time, number of reactions, concentration, pH, and/or the number of copies of the target nucleic acid molecule sequence. Thus, cycles may include temperature, time or process cycles, unit operating cycles or regeneration cycles.

For example, when analyzing the ability of an enzyme to decompose a substrate according to the concentration of a substrate, the degree of degradation of the substrate of the enzyme is measured several times by varying the concentration of the substrate. An increase in substrate concentration may correspond to a change in conditions, and an increase unit may correspond to a cycle.

As another example, isothermal amplification allows samples to be measured several times during the reaction time under isothermal conditions, the reaction time may correspond to a change in conditions, and the unit of the reaction time may correspond to a cycle. For example, when a 5 minute interval such as 5, 10, 15 minutes, etc. is set as one reaction time, the cycle may be expressed as a 5 minute cycle, a 10 minute cycle, a 15 minute cycle, or the like. Alternatively, if the 5 minute interval is regarded as one unit, a 5 minute cycle can be expressed as 1 cycle, a 10 minute cycle is 2 cycles, a 15 minute cycle is 3 cycles, and so on.

As another example, in the case of melting analysis or hybridization analysis, a change in a signal can be measured while changing a temperature within a certain range of temperature, and a temperature may correspond to a change in a condition, and a temperature unit (for example, Measurement temperature) may correspond to the cycle. For example, if 0.5℃ interval is set as one reaction time, such as 40℃, 40.5℃, 50℃, 50.5℃, the cycle is expressed as 40℃ cycle, 40.5℃ cycle, 50℃ cycle, 50.5℃ cycle, etc. Can be. Alternatively, if the 0.5°C interval is regarded as one unit, a 40°C cycle can be expressed as 1 cycle, a 40.5°C cycle is 2 cycles, a 50°C cycle is 3 cycles, and so on.

Specifically, when repeating a series of reactions or repeating a reaction at regular time intervals, the term "cycle" or "cycle number" means one unit of the repetition.

For example, in the polymerase chain reaction (PCR), one cycle or cycle number refers to a reaction unit including denaturation of a target molecule, annealing (hybridization) between a target molecule and a primer, and primer extension. The increase in repetition of the reaction may correspond to a change in conditions, and the unit of the repetition may correspond to a cycle or a cycle number.

The data set obtained by the signal amplification reaction includes a plurality of data points, each data point having a cycle number and a signal value at the cycle number.

The term “signal value” as used herein refers to a signal value actually measured in each cycle of the signal generation process (eg, an actual value of fluorescence intensity processed by a signal amplification reaction) or a modified value thereof. The transformation may include a mathematically processed value of a measured signal value (eg, intensity). Examples of mathematically processed values of the actual measured signal values (i.e., the signal values of the raw data set) are to add a selected constant to the measured signal value, subtract the selected constant from the measured signal value, or add the selected constant to the measured signal value. A value obtained by multiplying the selected constant or dividing the measured signal value by the selected constant; Logarithmic value of the measured signal value; Alternatively, it may include a derivative of the measured signal value, but is not limited thereto. The term “signal” as used herein is intended to encompass the term “signal value” and thus the two terms may be used interchangeably.

As used herein, a signal value refers to a value obtained by absolute or relative quantification of the magnitude of the signal initially detected in the cycle number in the detector. The signal value is also referred to as a “zeroth order signal value”, “raw signal value” or “original signal value” to distinguish it from the first change value or the second change value. The unit of the signal value may vary depending on the type of signal generation reaction used. For example, when a signal value is obtained from each cycle number by a real-time PCR amplification reaction, the signal value may be expressed as RFU (Relative Fluorescence Unit).

The term "data point" as used herein means a coordinate value including a cycle number and a signal value at the cycle number. The term "data" means all the information constituting a data set. For example, each of the cycle number and signal value is data.

The signal generation process, particularly the data points obtained by the signal amplification reaction, can be expressed as coordinate values in a two-dimensional orthogonal coordinate system. In the coordinate values, the X-axis represents the cycle number, and the Y-axis represents the measured or processed signal value at the cycle number.

As used herein, the term “data set” refers to a set of data points. For example, the data set may be a set of data points obtained directly by a signal amplification reaction carried out in the presence of a signal generating means, or a set of data points modified from the original data set. The data set may be all or part of a plurality of data points obtained by a signal amplification reaction or modified data points thereof. In addition, the data set may be a set of data points including a cycle number and an nth change value in the cycle number.

Data sets can be plotted to generate amplification curves.

The term "amplification curve" as used herein means a curve obtained by a signal amplification reaction. The amplification curve includes a curve obtained when an analyte is present in a sample, or a curve (or line) obtained when an analyte is not present in a sample. According to one embodiment, the data set used in the present invention is a raw dataset that has not been subjected to mathematical processing. According to another embodiment, the data set used in the present invention is a mathematically manipulated data set, e.g., a baseline subtracted data set to remove background signals from the raw data set. Baseline subtracted data sets can be obtained by various methods known in the art (eg, US Pat. No. 8,560,247).

According to one embodiment, the method of the present invention further comprises the step of obtaining a data set by performing a signal generation reaction (eg, a signal amplification reaction) prior to step (a).

The data set as used herein comprises a data set obtained by means of generating a signal at a detection temperature. The data set herein may be any one of the data sets obtained by detection at different detection temperatures in the signal amplification reaction, or any one of the data sets obtained by detection using different signal detection means.

Data sets obtained by detection at different detection temperatures are, for example, at different detection temperatures (e.g., at least two detection temperatures in each cycle number) during a signal amplification reaction using a single signal generation means in a single reaction vessel. It means a data set obtained by detecting changes in signal values. For example, two or more data sets can be obtained from a signal amplification reaction by detection at different detection temperatures according to MuDT1 technology (WO 2015/147412) or MuDT2 technology (WO 2016/093619) developed by the inventors, and , The data set used herein may be one of the above data sets.

A data set obtained using different signal detection means means a data set obtained by detecting a change in a signal value using, for example, different detection means (eg, an optical module) in a signal amplification reaction. For example, in multiplex real-time PCR using two or more signal generating means (e.g., fluorescent labels) for detection of two or more target nucleic acid sequences, the data sets are different signal generating means using appropriate channels containing different optical modules. Can be obtained by detecting a signal from, and the data set herein may be one of the above data sets.

Step (b): Provision of a normal-indicated value in each cycle (120)

Thereafter, a normality-representing value in each cycle number of a data set is provided using the signal value.

In this step, a "normal-display value data set" is obtained which includes a cycle number and a plurality of data points having a normal-display value at the cycle number.

The normal-display value refers to a value indicating the normal degree of the signal value in the cycle number. The normal-display value is calculated for each cycle number and assigned to each cycle number.

The normal-indicated value can be expressed in a variety of ways as long as it indicates the normality of the signal.

In one embodiment, the normal-indicated value is expressed as a numerical value in which the larger normal-indicated value indicates a higher degree of normality. In this case, a smaller normal-display value indicates that the cycle number having the normal-display value is more likely to indicate an abnormal signal, whereas a larger normal-display value indicates that the cycle number having the normal-display value is a normal signal. It can indicate that it is likely to represent For example, a specific cycle number with a normal-display value of 1000 is more likely to indicate a normal signal, while another cycle number with a normal-display value of -2000 is more likely to indicate an abnormal signal. Typical examples of normal-indicated values as expressed above include normal scores, which will be described below.

In an alternative embodiment, the normal-indicated value is expressed as a numerical value in which the smaller normal-indicated value represents a higher degree of normality. In this case, a larger normal-display value indicates that the cycle number having the normal-display value has a high probability of indicating an abnormal signal, whereas a smaller normal-display value indicates that the cycle number having the normal-display value is a normal signal. It can indicate that it is likely to represent For example, a specific cycle number with a normal-display value of 1000 is more likely to indicate an abnormal signal, while another cycle number with a normal-display value of -2000 is more likely to indicate a normal signal.

The normal-indicated value at each cycle number of the data set can be provided by a variety of methods.

In one embodiment, the normal-indicated value at each cycle number is 2-5 consecutive cycle numbers including the respective cycle number, in particular the signal value at two consecutive cycle numbers including the respective cycle number Is provided by calculating the change in each cycle number from. For example, the normal-indicated value at the 10th cycle number is provided by calculating the change in the 10th cycle number from the signal value at 2-5 consecutive cycle numbers including the 10th cycle number. .

The term "change", "change value", or "value of change" as used herein in connection with the signal value at each cycle number means the amount (degree) of change in the signal value at a particular cycle number. Since "change", "change value", or "value of change" is calculated from a specific cycle number, it is referred to as "change value of signal value in cycle number" or simply "change value in cycle number" or a variant thereof. Can also be expressed. As used herein, "nth order change value" means the amount (degree) of change in the n-1 order change value at a particular cycle number. Since the nth change value is calculated from a specific cycle number, it can also be expressed as "nth change value of signal value in cycle number" or simply "nth order change value in cycle number" or a variant thereof.

Specific examples of the change value include a first order change value (e.g., a first order difference, a first order difference quotient, and a first order derivative), and a second order change value (such as a second order difference, second order Order difference and second order derivative), third order change values (eg, third order difference, third order order and third order derivative), and the like.

Thus, the normal-display value in each cycle number is calculated by calculating the first change value, the second change value, the third change value, etc. of the signal value in 2-5 consecutive cycle numbers including the cycle number. Can be provided.

In one embodiment, the normal-indicated value at each cycle number may be provided by modifying the calculated change value of the signal value at 2-5 consecutive cycle numbers including the cycle number. The transformation includes any mathematical transformation, calculation, processing or operation. Mathematical transformations, calculations, processing or operations may include addition, subtraction, multiplication or division between changes in different cycle numbers. Mathematical transformation, calculation, processing, or operation may include addition, subtraction, multiplication, or division of a change value with another factor (value).

The number of signal values used to calculate the change value includes, but is not limited to, 2, 3, 4, or 5.

When the number of signal values is 2, 3, 4, or 5, the normal-display value at each cycle number is the signal value at 2, 3, 4, or 5 consecutive cycle numbers including the cycle number. Can be provided by calculating the change value using.

A representative example of a normal-indicated value is the normality score.

While this specification describes normal scores for a clearer understanding of normal indication-values, those skilled in the art will understand that other normal-indicative values may be applied instead of the normal score.

The normal score, which is an example of a normal indication-value, is described in more detail below.

Normality Score

The normal score is determined by: (i) calculating a second order change value in each cycle number of the data set; And (ii) calculating a normal score at each cycle number of the data set using the second order change value; The calculation of the normal score is carried out by a mathematical operation indicating the magnitude of the sign change and the second order change value between the second order change values in two consecutive cycle numbers.

(i) Calculation of the second change in each cycle number

First, a second order change value is calculated at each cycle number of the data set obtained in step (a). Calculation of the second order change is performed by calculating the first change at each cycle number in the data set using two signal values at two consecutive cycle numbers, and then calculating the first order change at two consecutive cycle numbers. This is done by using the change value to calculate the second order change value at each cycle number in the data set.

In this step, "first change value data set" (including a cycle number and a plurality of data points having the first change value in the cycle number) and "second change value data set" (cycle number and the cycle Containing a plurality of data points with a second order change in number) is obtained from a raw data set (contains a plurality of data points having a cycle number and a signal value at said cycle number) or a modified data set thereof do.

The term "signal value" used to calculate the first change value and the second change value is a signal value excluding a value representing a relationship with other signal values (eg, change values such as a first change value, a second change value, etc.) It is also referred to as "zero order signal value", "raw signal value" or "original signal value".

The term “immediately adjacent cycle number” as used herein for a particular cycle number refers to the number of cycles consecutive to that particular cycle number, ie, the number of cycles immediately before (before) or immediately after (after) the particular cycle. . For example, in a typical data set where the cycle number increases by one, the cycle number immediately adjacent to the fourth cycle number is the third cycle number or the fifth cycle number.

In addition, the term "two immediately adjacent cycle numbers", "two immediately adjacent cycle numbers", or "consecutive cycle numbers" as used herein refers to two cycle numbers immediately adjacent to each other, ie, two consecutive cycle numbers. do. For example, in a typical data set where the cycle number increases by one, two immediately adjacent cycle numbers mean cycles x and x+1 or cycles x-1 and x.

The term "change", "change value", or "value of change" as used herein in relation to the signal value at each cycle number refers to the amount (or degree) of change in the signal value at a particular cycle number. it means. Since "change", "change value", or "value of change" is calculated from a specific cycle number, "change value of signal value at cycle number" or simply "change value in cycle number" or its It can also be expressed as a transformation. As used herein, "nth order change value" means the amount (or degree) of change in the n-1 order change value at a particular cycle number. Since the nth change value is calculated from a specific cycle number, it can also be expressed as "nth change value of signal value in cycle number" or simply "nth order change value in cycle number" or a variant thereof. In particular, the zero-order change means the raw signal value. According to the above definition, the first change value means the amount (or degree) of the change of the zero order change value (i.e., the signal value) in a specific cycle number, and the second change value is the first change in a specific cycle number. It means the amount (or degree) of change in value (ie, the amount (or degree) of change in the signal value). As will be described below, it will be understood that the first change value and the second change value in a specific cycle number include those calculated in cycle numbers other than the specific cycle number in a broad sense. The term "change value" may be used interchangeably with the term "rate of change".

In this step, the second order change value at each cycle number is obtained using the signal value at each cycle number. Specifically, the second change value is calculated by calculating the first change value at each cycle number in the data set using two signal values at two consecutive (immediately adjacent) cycle numbers, and then two consecutive (immediately adjacent) cycle numbers. ) Obtained by calculating the second order change in each cycle number in the data set using the two first order change values in the cycle number.

The calculation of the second change value may be performed in the same or different manner as the calculation of the first change value.

The change value may be any one selected from the group consisting of a difference, a difference quotient, and a derivative, and thus the nth change value (e.g., the first change value and the second change value) is n It may be a difference difference, an nth order or an nth order differential.

The change value, in particular the difference (difference value), the difference (difference value) and the derivative (differential value), can be calculated or obtained by various methods known in the art.

For example, the difference as used herein can be obtained by calculating the difference between the signal values at two immediately adjacent cycle numbers. The difference as used herein can be obtained by dividing the difference by the interval between two immediately adjacent cycle numbers. Differentiation as used herein can be obtained by applying the least squares method to the signal values at 2, 3, 4 or more data points, or by determining the tangent (slope) at each cycle number in the amplification curve.

The symbols y(x), D(x), D'(x) and D"(x) used when calculating the secondary change value will have the following meanings:

The symbol y(x) means the signal value at the xth cycle number; D'(x) means the first change value in the xth cycle number; D"(x) means the second-order change value in the x-th cycle number. In the above definition, x is an integer greater than or equal to 1, meaning a specific cycle number in the data set. For example, the obtained data set is 45 When consisting of four cycle numbers, each cycle number is distinguished using an indication such as 1st cycle number (cycle number 1), 2nd cycle number (cycle number 2)...45th cycle number (cycle number 45). Can be.

The second change value used herein may be any one selected from the group consisting of a second difference, a second order, and a second derivative.

The second difference is a calculation of the first difference (including the cycle number and a plurality of data points having the first difference in the cycle number) at each cycle number using the values of the two signals at two immediately adjacent cycle numbers. Then, by calculating the second difference (including the cycle number and a plurality of data points having the second difference in the cycle number) in each cycle number using the two first difference in two adjacent cycle numbers. I can.

Differences as used herein can be calculated in a variety of ways known in the art.

In one embodiment, the second order difference (or second order difference) is calculated by a forward difference method or a backward difference method.

For example, after calculating a first difference from two signal values by a forward difference method or a backward difference method, a second difference may be calculated from the two first differences by a forward difference method or a backward difference method. The forward difference method can be applied to the calculation of both the first and second difference, the backward difference method can be applied to the calculation of both the first and second difference, or the forward difference method can be applied to the calculation of the first difference. And, the backward difference method can be applied to the calculation of the second difference, or the backward difference method can be applied to the calculation of the first difference and the forward difference method can be applied to the calculation of the second difference.

According to the forward difference method, the second difference may be calculated by sequentially using the following equations I and II , or by using the following equation III alone.

Equation I

D'(x) = y(x+1)-y(x)

Equation II

D"(x) = D'(x+1)-D'(x)

Equation III

D"(x) = y(x+2)-2 * y(x+1) + y(x)

In the above formula, y(x), y(x+1) and y(x+2) represent signal values at the xth cycle number, x+1th cycle number, and x+2th cycle number, respectively; D'(x) represents the first difference in the xth cycle number; D"(x) represents the second difference in the x-th cycle number.

According to the backward difference method, the second difference may be calculated by sequentially using the following Equations IV and V , or using the following Equation VI alone.

Equation IV

D'(x) = y(x)-y(x-1)

Equation V

D"(x) = D'(x)-D'(x-1)

Equation VI

D"(x) = y(x)-2 * y(x-1) + y(x-2)

In the above formula, y(x), y(x-1) and y(x-2) represent signal values at the xth cycle number, x-1th cycle number, and x-2th cycle number, respectively; D'(x) represents the first difference in the xth cycle number; D"(x) represents the second difference in the x-th cycle number.

For example, in the case of the forward difference method, if y(1), y(2), y(3), y(4) and y(5) are 1, 8, 30, 100, and 500, respectively, D' (1), D'(2), D'(3) and D'(4) are respectively 7 (=8-1), 22 (=30-8), 70 (=100-30) and 400 (= 500-100), while D"(1), D"(2) and D"(3) are respectively 15 (=22-7), 48 (=70-22) and 330 (=400-70) , In the case of the backward difference method, if y(1), y(2), y(3), y(4) and y(5) are 1, 8, 30, 100 and 500, respectively, D'(2) , D'(3), D'(4) and D'(5) are respectively 7 (=8-1), 22 (=30-8), 70 (=100-30) and 400 (=500-100 ), and D"(3), D"(4) and D"(5) will be 15 (=22-7), 48 (=70-22) and 330 (=400-70), respectively.

D'(x) can be calculated by another equation, e.g. D'(x) = y(x)-y(x+1) or D'(x) = y(x-1)-y(x) And D"(x) is different from the equation D"(x) = D'(x)-D'(x+1), D"(x) = y(x)-2 * y(x+1) ) + y(x+2), D"(x) = D'(x-1)-D'(x), or D"(x) = y(x-2)-2 * y(x-1 Note that it can be calculated by) + y(x).

In the forward difference method, D"(x) is not calculated in the last cycle and in the cycle immediately preceding the last cycle. For example, if the signal amplification reaction is carried out up to 45 cycle numbers, D"(45) and D"( 44) cannot be calculated because there is no value of the signal at the 46th cycle number or the 47th cycle number.

Similarly, in the backward difference method, D"(x) is not calculated in the first cycle and in the cycle immediately after the first cycle. For example, if the signal amplification reaction is carried out up to 45 cycle numbers, D"(1) and D "(2) cannot be calculated because there is no signal value at -1st cycle number or 0th cycle number.

As such, functions such as y(x), D(x), D"(x) and D"(x) are not calculated for cycle numbers that do not exist or cannot be defined.

It is noted that the results obtained by the forward difference method and the backward difference method have a certain interconversion potential with respect to the cycle number.

Specifically, D'(x) obtained by the forward difference method is the same as D'(x+1) obtained by the backward difference method. For example, the first difference in the first cycle number obtained by the forward difference method is the same as the second difference in the second cycle number obtained by the backward difference method. Therefore, in view of this interconversion possibility, a person skilled in the art can easily convert the result of the forward difference method into the result of the backward difference method, or vice versa. For example, if the first difference is obtained by the forward difference method, the first difference calculated at the x+1th cycle number can be regarded as the first difference at the xth cycle number to convert to the result of the backward difference method. I can.

In addition, D"(x) obtained by the forward difference method is the same as D"(x+2) obtained by the backward difference method. For example, the second difference in the first cycle number obtained by the forward difference method is the same as the second difference in the third cycle number obtained by the backward difference method.

Therefore, in view of this interconversion possibility, a person skilled in the art can easily convert the result of the forward difference method into the result of the backward difference method, or vice versa. For example, if a second difference is obtained by the forward difference method, the second difference calculated at the x+2 cycle number can be regarded as the second difference at the x cycle number to convert to the result of the backward difference method. I can.

The calculation of the second order difference by the backward difference method is illustrated in the embodiment of the present application. However, those skilled in the art can use the forward difference method instead of the backward difference method and change only the cycle number to obtain the same result as the backward difference method. Accordingly, it will be understood that the first difference and the second difference in a particular cycle herein may include those calculated in other cycles depending on the calculation method used.

(ii) Calculation of the normal score at each cycle number

Next, the second-order change is used to calculate a normal score at each cycle number in the data set.

Here, a "normal score data set" is obtained comprising a cycle number and a plurality of data points having a normal score at the cycle number.

The calculation of the normal score is carried out by a mathematical operation indicating the magnitude of the sign change and the second order change value between the second order change values in two consecutive cycle numbers.

As used herein, the term "normal score" (abbreviated as'NS') refers to a sign change between two immediately adjacent cycle numbers and a second order change in two immediately adjacent cycle numbers. Means a number representing both the size of. The normal score may be a value representing the extent of normality of signals obtained by a signal amplification reaction. A small normal score indicates that the cycle number with the normal score is likely to show an abnormal signal. For example, a specific cycle number with a normal score of 1000 is more likely to indicate a normal signal, while another cycle number with a normal score of -2000 is more likely to indicate an abnormal signal.

The normal score is used to select a candidate cycle number from among all the cycle numbers in the data set, as well as the normal score before the change of the signal value (also referred to as the "normal score before change") and the normal score after the change of the signal value (the "normal score after change". It is used to determine abnormal signals by comparing (also referred to as ").

As used herein, the term “normal score at cycle number” refers to a normal score calculated using a second order change in a specific cycle number and a second change in a cycle number immediately adjacent to that specific cycle number, It is assigned (assigned) to a specific cycle number. It can be described in various expressions, such as a normal score obtained from the signal value at the cycle number, a normal score obtained from the secondary change value of the signal value at the cycle number, the normal score calculated at the cycle number, or a variant thereof. It should be understood that the normal score at a particular cycle number can include those calculated at other cycle numbers in a broad sense.

Immediately adjacent cycle numbers used to calculate the normal score at the xth cycle number include cycles immediately before or immediately after the xth cycle. For example, in a typical data set where the cycle number increases by one, the cycle number immediately adjacent to the xth cycle number is cycle number x+1 or cycle number x-1.

The normal score is obtained by a mathematical operation indicating the magnitude of the sign change and the second order change value between the second order change values in two immediately adjacent cycle numbers. As described above, the second change value may be, for example, a second difference, a second order, or a second derivative.

According to one embodiment of the present invention, the normal score at the x-th cycle number is a mathematical operation representing the magnitude of the sign change and the second-order change value between the x-th cycle number and the second-order change value at the x+1 cycle number. Is calculated by

According to another embodiment of the present invention, the normal score at the x-th cycle number is a mathematical expression representing the magnitude of the sign change and the second-order change value between the x-th cycle number and the second-order change value at the x-th cycle number. It is calculated by operation.

The sign change as used herein is that the second change value at the xth cycle number has a positive sign (positive value, +) and the second change value at the x+1 or x-1th cycle number is a negative sign. It represents a situation with (negative value, -). A mathematical operation that represents the sign change between the second change values is that the second change at the xth cycle number has a positive sign and the second change at the x+1 or x-1 cycle number is a negative sign. If it has, it gives a negative normal score, and both the second-order change value at the xth cycle number and the second order change value at the x+1 or x-1th cycle number are the same sign (e.g., all positive values or all It is an arbitrary operation that gives a positive normal score if it has a negative value).

As used herein, the magnitude of the secondary change value represents a combination of the magnitude of the secondary change value at the xth cycle number and the magnitude of the secondary change value at the x+1 or x-1th cycle number.

Examples of mathematical operations that represent the sign change between the second change values and the magnitude of the second change value include mathematical amplification of the second change value (e.g., multiplication), the mathematical ratio of the second change value (e.g., division), etc. do.

According to one embodiment, the normal score is calculated by multiplying the second order change in two immediately adjacent cycle numbers. The multiplication of the second-order change value effectively represents a sign change between the second-order change values and the magnitude of the second change value.

More specifically, the normal score can be calculated by the following Equation VII or VIII :

Equation VII

NS(x) = D"(x) * D"(x+1)

In the above formula, NS(x) represents the normal score at the xth cycle number, D"(x) represents the secondary change value at the xth cycle number, and D"(x+1) represents the x+1th cycle number. Represents the secondary change value in the cycle number, and x is an integer of 1 or more.

Equation VIII

NS(x) = D"(x) * D"(x-1)

In the above formula, NS(x) represents the normal score at the xth cycle number, D"(x) represents the secondary change value at the xth cycle number, and D"(x-1) is the x-1th Represents the secondary change value in the cycle number, and x is an integer of 2 or more.

In the above equations VII and VIII , D"(x-1), D"(x) and D"(x+1) represent the second difference in cycle numbers x-1, x and x+1, respectively.

For example, when D"(1), D"(2), D"(3) and D"(4) are -100, 50, 350, -400, respectively, in NS(1), Equation VII For NS(2) and NS(3) are -5000 (=-100*50), 17500 (=50*350) and -14000 (=350*-400) respectively; For Equation VIII , NS(2), NS(3) and NS(4) would be 5000 (=-100*50), 17500 (=50*350) and -14000 (=350*-400).

Note that the normal score at the last cycle number cannot be calculated when Equation VII is used, whereas the normal score at the first cycle number cannot be calculated when Equation VIII is used.

In addition, it is noted that the normal score obtained by equation ( VII) and the normal score obtained by equation ( VIII) have a specific interconversion potential for cycle number.

Specifically, NS(x) obtained by Equation VII is the same as NS(x+1) obtained by Equation VIII . For example, the normal score at the fourth cycle number obtained by Equation VII is the same as the normal score at the fifth cycle number obtained by Equation VIII .

Therefore, in view of this interconversion possibility, those skilled in the art can easily convert the normal score obtained by Equation VII to the normal score obtained by Equation VIII , and vice versa.

The calculation of the normal score by Equation VII is illustrated in the examples of the present application. However, those skilled in the art use Equation VIII instead of Equation VII to calculate the normal score at each cycle number, and then adjust the cycle number, that is, subtract 1 from the calculated cycle number, which is the same as Equation VII. You will get results. Accordingly, it should be understood that the normal score at a particular cycle number may include the calculated normal score at other cycle numbers depending on the method of its calculation.

Meanwhile, the calculation of the normal score using Equation VII or VIII may be variously combined with the calculation of the second-order change value using the forward difference method or the backward difference method.

For example, the calculation of the second order difference using the forward difference method can be combined with the calculation of the normal score using Equation VII ; The calculation of the second order difference using the forward difference method can be combined with the calculation of the normal score using Equation VIII ; The calculation of the second order difference using the backward difference method can be combined with the calculation of the normal score using Equation VII ; The calculation of the second order difference using the backward difference method can be combined with the calculation of the normal score using Equation VIII .

However, in various combinations of any one of the forward difference method or the backward difference method and any one of Equation VII or Equation VIII , the cycle number having the same normal score will vary with a certain regularity, thereby generating an abnormal signal. It should be noted that the cycle number indicated will also vary with a certain regularity.

For example, if the combination of the forward difference method and Equation VII produces a normal score of -63 at the second cycle number, the combination of the forward difference method and Equation VIII will produce the same normal score at the third cycle number, The combination of the backward difference method and Equation VII will produce the same normal score at the fourth cycle number, and the combination of the backward difference method and Equation VIII will produce the same normal score at the fifth cycle number. Therefore, the cycle number indicating the abnormal signal may vary depending on the method of calculating the normal score.

The present inventors confirmed that the cycle number theoretically determined to represent an abnormal signal by the combination of the backward difference method and Equation VII represents an abnormal signal in the visual inspection of the actual data set. This proves that the method of the present invention using the combination of the backward difference method and Equation VII is very accurate and effective in determining the cycle number representing an abnormal signal. However, considering the above-described rule of changing the cycle number, a person skilled in the art may use other combinations. For example, when a combination of the forward difference method and Equation VII is used, the cycle number indicating an abnormal signal can be adjusted by adding the cycle number 2 to the result. Accordingly, it will be understood by those skilled in the art that these various combinations are within the scope of the present invention.

As described above, according to the combination of the forward difference method or the backward difference method and Equation VII or Equation VIII , the first difference, the second difference, and the normal score corresponding to each cycle number may vary with a certain regularity. Accordingly, it is to be understood that herein the first difference, second difference and normal score at a particular cycle number may include those calculated at different cycle numbers.

The calculation of the normal score by the combination of the backward difference method and Equation VII can also be confirmed in the examples herein.

Step (c): Selection of candidate cycle number(s) for abnormal signals by normal-indicated value (130)

In this step, a candidate cycle(s) for an abnormal signal is selected based on the normal-display value, and the normal-display value in the candidate cycle(s) corresponds to the normal-display value before change. ( 130 ). In particular, when the normal-indicated value is a normal score, the candidate cycle(s) for a non-static signal are selected by the normal score.

The term "candidate cycle number" as used herein refers to a cycle number that is likely to exhibit an abnormal signal or is predicted to exhibit an abnormal signal.

The present inventors have found that a normal-indicative value, such as a normal score presented by the present inventors, is highly correlated with an abnormal signal, that is, a normal-indicated value such as a normal score can be used as an indicator for an abnormal signal. According to the findings of the present inventors, when the normal-indicated value is a normal score and the normal score is calculated by Equation VII or VIII , the smaller the normal score calculated in a specific cycle number, the more likely that the cycle number represents an abnormal signal. high. Thus, a normal-indicative value, such as a normal score, is used to find a candidate cycle number for an abnormal signal.

The expression "selecting the candidate cycle number(s) for an abnormal signal by the normal score" means to select the candidate cycle number(s) based on its normal-display value, ie the normal-display value is the candidate cycle It means that it serves as a criterion for choosing a number.

The selection of the candidate cycle number can be carried out based on the normal-indicated value, in particular the size (large and small) of the normal score. For example, a normal score “70” is considered to be larger in size than a normal score “-80”.

The selection of candidate cycle numbers can be accomplished by various methods as follows:

In one embodiment, if the larger normal-indicative value indicates a higher degree of normality in the cycle number, the candidate cycle number selected is a cycle number having a normal-indicative value less than the threshold.

Selection of the candidate cycle number can be carried out by applying a threshold value to the normal-display value in all cycle numbers, and selecting a cycle number(s) having a normal-display value less than the threshold value.

The number of selected candidate cycle number(s) may be zero or more. For example, if there is no cycle number with a normal-display value less than the threshold, the number of candidate cycle numbers to be selected is zero. If there is only one cycle number with a normal-display value less than the threshold, the number of candidate cycle numbers to be selected is 1. If there are a plurality of cycle numbers with normal-indicated values less than the threshold, the number of candidate cycle numbers to be selected is greater than 1.

In view of the fact that a cycle number having a small normal-indicative value is likely to indicate an abnormal signal, a threshold used to select a candidate cycle number may have a signal value less than zero. The threshold can be determined empirically or experimentally. Thresholds are, for example, -100, -200, -300, -400, -500, -600, -700, -800, -900, -1000, -2000, -3000, -4000 or less (all in between Value) of RFU (relative fluorescence unit).

In another embodiment, when a larger normal-indicative value indicates a higher degree of normality in the cycle number, the candidate cycle number selected is a cycle number with a smallest normal-indicative value less than the threshold.

The selection of the candidate cycle number can be carried out by applying a threshold value to the normal-indicated value in all cycle numbers, and selecting the cycle number(s) having a normal-indicated value that is smaller than the threshold and is the minimum.

The number of selected candidate cycle number(s) may be 0 or 1. For example, if there is no cycle number with a normal-display value less than the threshold, the number of candidate cycle numbers to be selected is zero. If there is more than one cycle number with a normal-indicated value less than the threshold, the number of candidate cycle numbers to be selected is 1.

In view of the fact that a cycle number having a small normal-indicative value is likely to indicate an abnormal signal, a threshold used to select a candidate cycle number may have a signal value less than zero. The threshold can be determined empirically or experimentally. Thresholds are, for example, -100, -200, -300, -400, -500, -600, -700, -800, -900, -1000, -2000, -3000, -4000 or less (all in between Value) of RFU (relative fluorescence unit).

In another embodiment, if the larger normal-indicative value indicates a higher degree of normality in the cycle number, the selected candidate cycle number is a cycle number with a negative sign and a minimum normal-indicative value.

The selection of the candidate cycle number is carried out without using a threshold value, i.e., by selecting a cycle number having a negative sign and a minimum normal-display value as the candidate cycle number.

The number of selected candidate cycle number(s) may be 0 or 1. For example, if there is no cycle number with a negative sign, the number of candidate cycle numbers to be selected is zero. If there is more than one cycle number having a negative sign, the number of candidate cycle numbers to be selected is 1.

In any of the embodiments as described above, satisfying the defined criterion (e.g., having a normal-display value less than a threshold, a normal-display value less than the threshold, or a negative sign and a minimum If there is no cycle number (with a normal-display value), no candidate cycle is selected in the data set. This indicates that the data set analyzed by the present invention does not contain cycle numbers indicating abnormal signals. In this case, an indication that the data set obtained in step (a) does not contain an abnormal signal, that is, an indication that the data set consists of a normal signal may be displayed.

In an alternative embodiment, if the smaller normal-indicative value indicates a higher degree of normality of the signal value in the cycle number, the candidate cycle is (i) a cycle with a normal-indicative value greater than a threshold selected from values greater than zero. number; (ii) a cycle number having a normal-indicated value that is greater than or equal to a threshold selected from values greater than zero; Or (iii) a cycle number with a positive sign and a maximum normal-indicated value.

As described above, when selecting a candidate cycle number, the normal-display value in the candidate cycle number is adopted as the normal-display value before change in the candidate cycle number.

As used herein, the term "pre-change normal-indicative value" (e.g., pre-change normal score) is a normal-indicative value corresponding to the selected candidate cycle number from among the normal-indicated values or normal scores provided in step (b) , Normal score). The pre-modified normal-indicated value (eg, normal score) is calculated at a specific cycle number in step (b), which is assigned to a specific cycle number. That is, the normal-display value before change (e.g., normal score) is by using the signal value before change by the signal change method in step (d) (i.e., using the unchanged signal value or the signal value before change). ) Calculated at a specific cycle number

The term "normal-indicated value before change" (eg, normal score before change) is distinguished from the "normal-indicated value after change" (eg, normal score after change) described below.

The pre-change normal-indicative value (eg, pre-change normal score) in the candidate cycle number is not newly calculated or generated in this step, but is provided or obtained simultaneously or automatically upon selection of the candidate cycle number.

Specifically, the normal-indicated value (e.g., normal score) in all cycle numbers is provided in step (b), and the normal-indicated value (e.g., normal score) in the candidate cycle number is before change in the candidate cycle number. It is assigned or assigned a normal-indicated value (eg, normal score before change). Thus, the normal display value (eg, normal score) in the candidate cycle number corresponds to the normal display value before change (eg, normal score before change).

The pre-change normal-display value (eg, pre-change normal score) in the candidate cycle number is compared with the “post-change normal-display value” (eg post change normal score) at the candidate cycle number, and the candidate cycle number Is used to determine whether or not represents an abnormal signal.

Step (d): Modification of the signal value in the candidate cycle number and provision of the addition of a normal-indicated value (140)

In this step, the signal value in the candidate cycle number(s) is changed, and a normal-display value in the candidate cycle number(s) is additionally provided by using the changed signal value, and the candidate cycle number ( S) additionally provided in the normal-display value corresponds to the normal-display value after the change ( 140 ).

The modifiers "pre-alteration" and "post-alteration" are two elements, obtained before changing the signal value at the candidate cycle number and after changing the signal value at the candidate cycle number. It is used herein to distinguish between another obtained. The modifier "before" may be used interchangeably with "unchanged", and the modifier "after" may be used interchangeably with "changed".

In this regard, the term “signal value before change” refers to the signal value at a specific cycle number that has not yet changed (in particular, the candidate cycle number), while the term “signal value after change” refers to the signal at a specific cycle number that has changed. Refers to the value. The term "cycle number before change" refers to a cycle number having a signal value before change, while the term "cycle number after change" refers to a cycle number having a signal value after change. The terms “first difference before change”, “second difference before change” and “normal-display value before change” (eg “normal score before change”) are the first difference and second difference calculated from the cycle number before change, respectively. While referring to the difference and normal-indicated values (e.g., normal score), the terms "first difference after change", "second difference after change" and "normal-display value after change" (e.g., "normal score after change ") refers to the 1st difference, 2nd difference and normal-indicated value (eg, normal score) calculated from the cycle number after the change, respectively. Further, the term "pre-change data set" refers to a data set consisting of pre-change signal values, while the term "post-change data set" refers to a data set comprising one or more post-change signal values.

In this step, the signal value in the candidate cycle number may be changed in various ways, including a signal correction method known in the art.

The signal value in the candidate cycle number can be changed by the signal change method (method) described herein.

The term "signal changing method (method)" as used herein means a means or rule for changing a signal value in a candidate cycle number to another suitable signal value. The signal change method (method) is, for example, the direction of change to the signal value in the candidate cycle (eg, whether the signal value in the candidate cycle number increases or decreases) and the degree of change (eg, the signal in the candidate cycle number). It may be a means of determining how much the value increases or decreases. Alternatively, the signal change method (method) is a means for determining the post-change signal value (greater or less than the pre-change signal value) by which the pre-change signal value in the candidate cycle is changed.

The signal change method according to the present invention will be described in detail as follows.

In one implementation, the signal modification scheme includes changing the signal value in the candidate cycle number to decrease its absolute value. That is, the signal value in the candidate cycle number is changed by the signal change method so that its absolute value decreases.

According to one embodiment, this change does not include a change in which the sign of the signal value changes.

For example, assuming that the signal value in the candidate cycle number is "+10", this change in the signal value is, for example, +9, +8, +7, +6, +5, +4, Includes changes to +3, +2, +1, etc., but changes to -1, -2, -3, -4, -5, -6, -7, -8, -9, etc. Does not include Similarly, assuming that the signal value in the candidate cycle number is "-10", this change in the signal value is, for example, -9, -8, -7, -6, -5, -4,- Includes changes to 3, -2, -1, etc., but changes to +1, +2, +3, +4, +5, +6, +7, +8, +9, etc. do not include.

According to one embodiment, the absolute value of the signal value in the candidate cycle number is reduced to 50% or less, 40% or less, 30% or less, 20% or less, 10% or less, or 5% or less of the previous one.

In a specific embodiment, the signal change method is to change the signal value in the candidate cycle number so that its absolute value is greater than or equal to a relatively small absolute value among signal values in two cycle numbers immediately adjacent to the candidate cycle number. Includes that. That is, the signal value in the candidate cycle number is changed so that its absolute value is greater than or has a relatively small absolute value among signal values in two cycle numbers immediately adjacent to the candidate cycle number.

For example, assuming that the signal value in the candidate cycle number is "11" and the two signal values in the cycle number immediately adjacent to the candidate cycle number are "6" and "8", the signal in the candidate cycle number The value "11" can be changed to an arbitrary signal value less than "11" and greater than "6". Assuming that the signal value in the candidate cycle number is "-11" and the two signal values in the cycle number immediately adjacent to the candidate cycle number are "-6" and "-8", the signal value in the candidate cycle number "-11" can be changed to any signal value greater than "-11" and less than "-6".

In one embodiment, the signal change method is a signal value at the candidate cycle number by a signal change method such that its absolute value is less than a relatively large absolute value among signal values at two cycle numbers immediately adjacent to the candidate cycle number. Includes changing.

In a further specific embodiment, the signal change method is to change the signal value in the candidate cycle number such that its absolute value approaches a relatively small absolute value among signal values in two cycle numbers immediately adjacent to the candidate cycle number. Includes that. That is, the signal value in the candidate cycle number is changed by the signal change method so that its absolute value approaches a relatively small absolute value among the signal values in two cycle numbers immediately adjacent to the candidate cycle number.

For example, assuming that the signal value in the candidate cycle number is "11" and the two signal values in the cycle number immediately adjacent to the candidate cycle number are "6" and "8", the signal in the candidate cycle number The value "11" can be changed to a value close to "6", for example 6.1, 6.2, etc.

In a further specific implementation, the signal modification scheme includes changing the signal value in the candidate cycle number such that its absolute value approximates the signal value in the cycle number immediately preceding the candidate cycle.

In one embodiment, the signal value at the candidate cycle number is (i) a relatively small absolute value of the signal values at two cycle numbers immediately adjacent to the candidate cycle number or (ii) at the cycle number immediately preceding the candidate cycle number. When changed in consideration of a reference signal value such as a signal value, the signal value after the change is within 150%-50%, within 140%-60%, within 130%-70%, 120%-80% of the reference signal value. Within, within 110%-90% or within 105%-95%. In one embodiment, the signal value after the change is 150% or less, 140% or less, 130% or less, 120% or less, 110% or less, or 105% or less of the reference signal value. In one embodiment, the signal value after the change is 50% or more, 60% or more, 70% or more, 80% or more, 90% or more, or 95% or more of the reference signal value.

In one embodiment, the signal change method provides a post-change signal value equal to or greater than a reference signal value in a data set representing a signal increase curve, or a post-change signal value equal to or less than a reference signal value in a data set representing a signal decrease curve.

In one implementation, the change by the signal change method does not include a change in which the sign of the signal value changes.

In another embodiment, the signal change method includes changing the signal value in the candidate cycle number such that the first change value in the candidate cycle number decreases. That is, the signal value in the candidate cycle number is changed by a signal change method that reduces the first change value in the candidate cycle number.

In another embodiment, the signal change method is, when the second change value in the candidate cycle number is negative, decreases the signal value in the candidate cycle number, or the second change value in the candidate cycle number is positive. If so, it includes increasing the signal value in the candidate cycle number.

The sign of the second change value in the candidate cycle number is associated with an increase or decrease in the signal value in the candidate cycle number.

A positive second change value indicates that the first change value in the candidate cycle number is greater than the first change value in the cycle number immediately preceding the candidate cycle, which means that the signal value in the candidate cycle number is immediately after the candidate cycle number. It implies that it is relatively larger than the signal value at the previous cycle number. Conversely, a negative second change value in the candidate cycle number indicates that the first change value in the candidate cycle number is smaller than the first change value in the cycle number immediately preceding the candidate cycle. It implies that the signal value of is relatively smaller than the signal value in the cycle number immediately preceding the candidate cycle number. Accordingly, the direction of change (increase/decrease) of the signal value in the candidate cycle number can be determined based on the sign (positive sign or negative sign) of the second difference in the candidate cycle number. The second order change value in the candidate cycle number may be a second order difference, a second order order, or a second order derivative of the signal value, in particular a second order difference of the signal value as described above.

In an embodiment using the sign of the second difference in the candidate cycle number, the signal change method, when the second change value in the candidate cycle number is negative, decreases the signal value in the candidate cycle number, or When the second-order change value in the cycle number is positive, increasing the signal value in the candidate cycle number.

In a specific implementation using the sign of the second difference in the candidate cycle number, when the second change value in the candidate cycle number is positive, the signal change method in step (d) is the signal value in the candidate cycle number It includes reducing the signal value in the candidate cycle number so as to be more than a relatively small signal value among signal values in two cycle numbers immediately adjacent to the cycle number.

In a specific embodiment using the sign of the second difference in the candidate cycle number, when the second change value in the candidate cycle number is negative, the signal change method of step (d) is the signal value in the candidate cycle number It includes increasing the signal value at the candidate cycle number so that it is less than or equal to a relatively large signal value among signal values at two cycle numbers immediately adjacent to the candidate cycle number.

Specifically, when the secondary change value in the candidate cycle number is positive, the signal value in the candidate cycle number is smaller than the signal value in the candidate cycle number, and a relatively small cycle or more of the two cycles immediately adjacent to the candidate cycle number. Can be changed by value. For example, among three consecutive signal values "6", "11" and "8", a cycle number having a signal value of "11" as a candidate cycle number is selected, and the secondary change value in the candidate cycle number is positive. In this case, the signal value "11" in the candidate cycle number may be changed to an arbitrary signal value smaller than "11" and greater than "6". Examples of a signal value (a value after change) of which the signal value in the candidate cycle number is changed may include 10, 9, 8, 7 or 6 (including all values between these numbers). The value after the change may be arbitrarily selected within the above range.

Alternatively, if the second change value in the candidate cycle number is negative, the signal value in the candidate cycle number is greater than the signal value in the candidate cycle number and is relatively relatively among the two cycles immediately adjacent to the candidate cycle number. It can be changed to another value that is less than the larger one. For example, a cycle number having a signal value of "3" as a candidate cycle number is selected from among three consecutive signal values "6", "3" and "8", and the second change value in the candidate cycle number is When negative, the signal value "3" in the candidate cycle number may be changed to an arbitrary signal value greater than "3" and less than "8". An example of a signal value (a value after change) of which the signal value in the candidate cycle number is changed may include 4, 5, 6, 7 or 8 (including all values between these numbers). The value after the change may be arbitrarily selected within the above range.

In a further embodiment, when a normal score is used as an example of a normal-indicative value, the signal value at the candidate cycle number is: (i) the first order change value before change in the candidate cycle number and the candidate cycle number. Obtaining a first change value after change in the candidate cycle number using the normal score before change of, and (ii) using the first change value after change in the candidate cycle number It is changed to the post-change signal value obtained by the step of obtaining the post-change signal value. According to an embodiment, the absolute value of the first change value after change in the candidate cycle number is smaller than the absolute value of the first change value before change in the candidate cycle number. When a normal score is used as an example of a normal-indicative value, the first order change value before change in the candidate cycle number and the normal score before change in the candidate cycle number provide a smaller absolute value of the first order change value after this change. It can be used to set up an expression. In one implementation, a smaller absolute value of the first change value after the change may provide a post-change signal value that is close to the signal value at the cycle number immediately preceding the candidate cycle number.

According to the above embodiment, the first change value before the change can be calculated using two signal values in two consecutive cycle numbers. The first change value before change may be the first difference before change. The first difference before change may be calculated by a forward difference method as in Equation I or a backward difference method as in Equation IV .

According to certain embodiments, when a normal score is used as an example of a normal-indicative value, the normal score before change in the candidate cycle number can be calculated by Equation VII or VIII .

The post-change signal value in the candidate cycle number may be calculated by summing a pre-change signal value in a cycle number immediately adjacent to the candidate cycle number and a first-order change value after the change in the candidate cycle number.

This embodiment can be further implemented using the following equations IX and X.

The signal value (pre-change signal value) in the candidate cycle number may be changed to a signal value (post-change signal value) determined by Equations IX and X.

Equation IX

In the above equation, _after D' _change (c) represents the first change value after change in the candidate cycle number, D' _{before change} (c) represents the first change value before change in the candidate cycle number, and before NS _change (c) represents the normal score before change in the candidate cycle number; k is a number of 1 or more; c represents the candidate cycle number.

Equation X

_{After changing} y (c) = _{Before changing} y (c-1) + _{After changing} D'(c)

In the above formula, _after y _change (c) represents the post-change signal value in the candidate cycle number, y _{before change} (c-1) represents the pre-change signal value in (candidate cycle number-1), and D' _{change After} (c) shows the first change value after change in the candidate cycle number calculated by Equation IX .

The first change value after change in the candidate cycle number ( _after D' _change (c)) is calculated by (c) = _after y _change (c)-after y _change (c-1) _{after changing the} equation D'. _after, y can _change (c) may be represented by the equation y _{after the change} (c) = y _{change after} (c-1) + D _{'after the change} (c). In view of the fact that the signal value at the (c-1) th cycle number does not _change, (c-1) _after y _change may be considered the same as (c-1) _before y _change . Therefore, (c) _after y _change can be calculated by the sum of (c-1) _before y _change and (c) _after D' _change as shown in Equation X.

In Equation IX , k may be approximately 1, particularly a number of 1.

An example of a signal change method using Equations IX and X will be described below.

When changing the signal value at the c-th cycle number (as a candidate cycle) using Equations IX and X , the first difference before change in the candidate cycle number (D' _{before change} (c)) and the normal score before change By applying ((c) _before NS _change ) to Equation IX , the first difference after change in the candidate cycle number ( _{after change} D'(c)) is obtained.

For example, a specific data set whose candidate cycle number (eg, 10th cycle number) has a signal value of "8916.60" (RFU), a first difference before change of "128.81" and a normal score before change of "-20235.34". Assuming that, the first difference after the change in the candidate cycle can be calculated by Equation IX as follows:

Subsequently, the first difference after the change in the candidate cycle number ( _after D' _change (c)) and the signal value at the cycle number immediately before the candidate cycle ( _before y _change (c-1)) are applied to Equation X. Thus, a signal value after change in the candidate cycle number ( _after y _change (c)) is obtained.

For example, assuming that the signal value at the ninth cycle number is 8797.79, the signal value after the change in the candidate cycle number can be calculated by Equation X as follows: y _{after change} (10) = 8787.79 + 0.90 = 8788.69.

As a result, the signal value "8916.60" in the candidate cycle number may be changed to another signal value "8788.69" according to Equations IX and X.

A signal change method according to this embodiment of the present invention is illustrated in FIG. 4. As shown in FIG. 4, the signal value in the candidate cycle number ( _before y _change (10)) is replaced with the _post-change signal value in the candidate cycle number ( _after y _change (10)).

As described above, when Equations IX and X are used, the signal value after change in the candidate cycle can be automatically provided, and accordingly, the original signal value in the candidate cycle is another signal value, that is, the signal value after change. Can be replaced automatically.

After changing the signal value in the candidate cycle number as described above, a normal-indicative value in the candidate cycle number is additionally provided by using the changed signal value (the signal value after the change). In particular, when the normal score is used as an example of the normal-indicative value, the normal score in the candidate cycle number is additionally provided using the changed signal value (the signal value after the change).

The normal-indicated value or normal score additionally provided in the candidate cycle number using the changed signal value is referred to as "normal-indicated value after change" or "normal score after change" in the candidate cycle number. Thus, the normal-indicated value or normal score additionally provided in the candidate cycle number(s) corresponds to the normal-indicated value after change or the normal score after change.

As used herein, the term "normal-indicated value after change" or "normal score after change" is the normal calculated at a specific cycle number using the signal value changed in step (d) (ie, using the signal value after change). -Refers to the indicated value or normal score.

The post-change normal-display value or post-change normal score may be calculated in the same manner as the calculation of the pre-change normal-display value or the pre-change normal score in step (b).

For example, as an example of a normal-display value, the post-change normal score recalculates the second order change value in the candidate cycle number using the post change signal value in the candidate cycle number, and uses the second order change value. It can be further provided by recalculating the normal score at the candidate cycle number.

The post-change normal-indicative value in the candidate cycle number is used to determine whether the candidate cycle number represents an abnormal signal by comparing it with the "pre-change normal-indicative value" in the candidate cycle number.

Step (e): Comparison of the normal-display value after change and the normal-display value after change (150)

In this step, the normal-display value after the change in the candidate cycle number(s) is compared with the normal-display value before the change ( 150 ). In particular, when a normal score is used as an example of a normal-indicated value, the normal score after change in the candidate cycle number(s) is compared with the normal score before change.

The terms "compare", "compare" or "compare" in relation to two normal-display values refer to the process of determining which of the two normal-display values is greater. The comparison between two normal-marked values shall be made taking into account the sign of the normal-marked value, i.e. the positive or negative sign. For example, a normal-display value "10" is considered to be greater than a normal-display value "-80".

The post-modified normal-displayed values used for comparison are those additionally provided in step (d), whereas the post-modified normal-displayed values are provided in step (b) and adopted in step (c).

Step (f): Determination of cycle number indicating abnormal signal (160)

In this step, it is determined whether the candidate cycle number(s) represents an abnormal signal based on the comparison result in step (e) ( 160 ).

Specifically, if the normal-display value after change in the candidate cycle number indicates a higher degree of normality than the normal-display value before change, it is determined that the candidate cycle number represents an abnormal signal.

In one embodiment, in which the larger normal-indicated value indicates a higher degree of normality in the cycle number, if the normal-indicated value after the change in the candidate cycle number is greater than the normal-indicated value before the change, the candidate cycle number is an abnormal signal. Is determined to represent. Typical examples of normal-indicated values with these characteristics include the normal scores described herein.

In this case, a small normal-indicated value at a specific cycle number is highly correlated with an abnormal signal at that cycle number. In other words, the smaller the normal-display value in a specific cycle number is, the higher the probability that the cycle number indicates an abnormal signal. Therefore, if the size of the normal-display value in the candidate cycle number becomes larger due to the change of the signal value according to the method of the present invention, it can be seen that the abnormal signal in the candidate cycle number has been changed to a normal signal. The number can be determined as a cycle number indicating an abnormal signal. Conversely, if the size of the normal-display value in the candidate cycle number is smaller due to the change of the signal value according to the method of the present invention, it can be seen that the normal signal in the candidate cycle number has been changed to an abnormal signal. The candidate cycle number can be determined as a cycle number representing a normal signal.

In an alternative embodiment where the smaller normal-indicated value indicates a higher degree of normality in the cycle number, if the normal-indicated value after the change in the candidate cycle number is less than the normal-indicated value before the change, the candidate cycle number is It is determined to indicate an abnormal signal.

In this case, a small normal-indicated value at a specific cycle number is highly correlated with a normal signal at that cycle number. In other words, the smaller the normal-display value in a specific cycle number is, the less likely the cycle number indicates an abnormal signal. Therefore, if the size of the normal-display value in the candidate cycle number is smaller due to the change of the signal value according to the method of the present invention, it can be considered that the abnormal signal in the candidate cycle number has been changed to a normal signal. The cycle number can be determined as the cycle number indicating an abnormal signal. Conversely, if the size of the normal-display value in the candidate cycle number is larger due to the change of the signal value according to the method of the present invention, it can be seen that the normal signal in the candidate cycle number has been changed to an abnormal signal. The cycle number can be determined as the cycle number representing a normal signal.

As described above, the present invention can distinguish between a cycle number indicating an abnormal signal and a cycle number indicating a normal signal by comparing the normal-display value before change and the normal-display value after change in the cycle number.

The method of section I described above can be applied to multiple data sets obtained by multiple PCR. For example, if multiple data sets are obtained by multiplex PCR, the method in section I can be applied to each data set to determine the cycle number representing an abnormal signal in each data set as well as calibrate each data set. can do. Specifically, in the case of obtaining multiple data sets by detection at different detection temperatures from a single amplification reaction according to the MuDT1 technology developed by the inventors (see WO 2015/147412), the method in Section I To determine the cycle number that represents an abnormal signal in each data set, as well as to correct each data set.

In addition to determining the cycle number representing the abnormal signal, the method of the present invention can be applied to correct the abnormal signal in the data set, ie to provide a corrected data set.

In one embodiment, when it is determined that the candidate cycle number represents an abnormal signal according to the method of the present invention, a corrected data set may be provided by changing a signal value in the candidate cycle number to an appropriate signal value.

Regarding the provision of the corrected data set, if the normal-display value after change in the candidate cycle number indicates a higher degree of normality than the normal-display value before change, the data set after change will be provided as a corrected data set. On the other hand, if the normal-indicated value after change in the candidate cycle number exhibits a lower degree of normality than the normal-indicated value before change, the change in the signal value in the candidate cycle number is negated and instead of the post-change data set. Pre-modification data sets can be provided.

As used herein, the term "change" refers to simply replacing or replacing a specific value with another value, whereas the term "correction" refers to replacing or replacing an abnormal value with a normal value. In other words, unlike change, correction means that the characteristic of a particular value is improved.

Therefore, the method of the present invention provides a corrected data set (data set after modification) when the candidate cycle number is determined to represent an abnormal signal, whereas when the candidate cycle number is determined to represent a normal signal, uncorrected data Provides a set (pre-change data set).

Alternatively, the method of the present invention may be designed to provide either a post-change data set or a pre-change data set, or to provide a cycle number indicating an abnormal signal.

In one embodiment, when a corrected data set (data set after change) is provided, the corrected data set not only changes the signal value in the candidate cycle number to a suitable signal value, but also a candidate cycle number representing an abnormal signal. It can be provided by further changing the signal value at all subsequent cycle numbers. In this case, the change of the signal value in all cycle numbers after the cycle number indicating the abnormal signal is the same as the difference in the cycle number indicating the abnormal signal in the difference between the signal value after change and the signal value before change in each cycle number. Can be carried out to do so.

In one embodiment, the present invention is designed to display only cycle numbers indicating abnormal signals without indicating the data set after the change.

In yet another embodiment, the method of the present invention is designed to display only the data set after the change without indicating the cycle number indicating an abnormal signal.

In another embodiment, the method of the present invention is designed to indicate a cycle number indicating an abnormal signal with a pre-change data set, or indicate a cycle number indicating an abnormal signal with an indication such as'retest' or'invalid'.

The design of this method of the present invention can be easily adjusted by those skilled in the art, which should also be construed as falling within the scope of the present invention.

As described above, the candidate cycle number selected in step (c) of the method of the present invention may be a plurality of candidate cycle numbers.

In this case, the candidate cycle numbers are applied sequentially or simultaneously in steps (d)-(f).

Specifically, when a plurality of candidate cycle numbers, such as a first candidate cycle number and a second candidate cycle number, are selected in step (c), a signal value in the candidate cycle number is individually changed by a signal change method, and each After the change in the candidate cycle number, the normal-display value after the change is compared with the normal-display value before the change, and then it is determined whether each candidate cycle number indicates an abnormal signal.

According to an embodiment, when a plurality of candidate cycle numbers are selected and a signal value in one candidate cycle is changed and a normal-display value is calculated after the change in one candidate cycle, the signal in another candidate cycle The value is not the signal value after change, but the signal value before change. Alternatively, a signal value after change in another candidate cycle may be used instead of a signal value before change in another candidate cycle.

According to an embodiment, the detection of an abnormal signal in the data set may be performed several times by changing a method of selecting a candidate cycle number.

II. Detection of abnormal signals in data sets by repetition method

The method of the present invention can be implemented repeatedly to detect additional abnormal signals. Repetition of steps (a)-(f) not only makes it possible to detect additional anomalous signals, but it also makes it possible to provide a more accurately corrected data set.

In another aspect of the present invention, the present invention provides a method of detecting an abnormal signal in a data set in an iterative manner, further comprising repeating the following steps: (g) a modification after performing step (f) above. Providing a post-data set, wherein the post-modification data set is obtained by changing a signal value at cycle number(s) indicating an abnormal signal; (h) performing steps (a)-(f) using the post-modified data set instead of the data set of step (a).

This method may also be referred to as a “repeated mode”, “continuous mode”, or “loop mode” in that steps (a)-(f) are repeated to detect additional abnormal signals.

According to the repetition scheme, an additional abnormal signal can be detected as follows:

Step (g): Provision of data set after change

First, perform step (f) and then provide the data set after modification.

The data set after the change is obtained by changing the signal value at cycle number(s) determined to represent an abnormal signal.

In the step (f), if the post-change normal-display value in the candidate cycle number exhibits a higher degree of normality than the pre-change normal-display value, the candidate cycle number is determined to indicate an abnormal signal. In this case, the signal value in the candidate cycle number may be changed to provide a data set after the change. Conversely, if the normal-display value after the change in the candidate cycle number shows a lower normality degree than the normal-display value before the change, the data set after the change is not provided and the repetition mode is no longer performed.

The post-modification data set can be obtained by changing the value of the signal at the cycle number representing an abnormal signal in the same manner as in step (d).

The iteration of steps (a)-(f) is performed using the newly provided post-modification data set instead of the data set of step (a).

When performing steps (a)-(f), the candidate cycle number can be selected in the same way or in a different way. For example, in the first implementation of steps (a)-(f), a cycle number with a normal-displayed value less than a threshold may be selected as the candidate cycle number, and the second in steps (a)-(f) In an implementation, a cycle number with a normal-indicated value that is smaller than the threshold and is the minimum may be the cycle selected as the candidate cycle number.

In one embodiment, the post-modification data set is obtained by further changing the signal value at all cycle numbers after the cycle number indicating an abnormal signal.

The phrase "all cycle numbers after the cycle number indicating an abnormal signal" refers to the cycle area from the cycle number immediately after the cycle indicating the abnormal signal to the last cycle number. For example, in the case of a data set consisting of a total of 45 cycle numbers, all cycle numbers after the 25th cycle number indicate an area from the 26th cycle number to the 45th cycle number.

In one embodiment, the change in the signal value in all cycle numbers after the cycle number indicating the abnormal signal is a difference in the cycle number indicating the abnormal signal in the difference between the signal value after the change and the signal value before the change in each cycle number. It is carried out in the same way as.

For example, assuming that the difference between the pre-change signal value and the post-change signal value in the cycle number indicating the abnormal signal is "10", the signal value in each cycle number after the cycle number indicating the abnormal signal is " It can be as low as 10" difference.

In another embodiment, the change of the signal value in all cycle numbers after the cycle number indicating an abnormal signal is implemented such that the first difference in each cycle number does not change. For example, when the signal value at the 10th cycle number is changed, the signal value after the change at the 11th cycle number is 10 so that the 1st difference after the change at the 11th cycle number is the same as the 1st difference before the change. It is calculated by summing the signal value after the change at the 1st cycle number and the 1st difference before the change at the 11th cycle number. In the same way, the signal value at the 12th cycle number can be calculated by adding the signal value after the change at the 11th cycle number and the first difference before the change at the 12th cycle number.

In particular, when there are a plurality of cycle numbers indicating an abnormal signal, the change of the signal value at one of the cycle numbers and the signal value after the cycle number is a signal value at another cycle number performed previously. And changing the signal value after the cycle number.

For example, if there are 10th and 27th cycle numbers indicating abnormal signals, all signal values of the 10th cycle and thereafter may be changed, and then all signal values of the 27th and afterwards may be changed. In this case, the change of the signal value at the 10th cycle number may also affect the signal value at all cycle numbers (including the 27th cycle number) after the 10th cycle number. For example, when the signal value at the 10th cycle number is changed from "300" to "100", the signal value at the 27th cycle number is also lowered by a difference of "200". Therefore, the change in the 27th cycle number thereafter should be performed in consideration of the change in the 10th cycle number and the signal value thereafter. For example, when the signal value in the 27th cycle and thereafter is changed, the signal value may be additionally changed from the value affected by the change in the 10th cycle number.

Step (h): Implementation of steps (a)-(f)

Next, steps (a)-(f) are repeated using the post-modified data set instead of the data set of step (a).

Repetition of steps (a)-(f) is a step of providing a normal-indicative value at each cycle number of the data set after the change and selecting a candidate cycle number(s) for an abnormal signal by the normal-indicated value. , The normal-display value in the candidate cycle number(s) corresponds to the normal-display value before change; Changing the signal value in the candidate cycle number(s) and further providing a normal-indicative value in the candidate cycle number(s) using the changed signal value, wherein the candidate cycle number(s) The supplied normal-display value corresponds to the normal-display value after the change; If the post-change normal-display value in the candidate cycle number exhibits a higher degree of normality than the pre-change normal-display value, it is carried out by determining that the candidate cycle number(s) represent an abnormal signal.

When the normal-indicated value is a normal score, steps (a)-(f) include: calculating a second order change value in each cycle number of the data set after the change; Calculating a normal score at each cycle number of the data set after the change using the second change value; Selecting a candidate cycle number(s) for an abnormal signal based on the normal score, wherein the normal score calculated from the candidate cycle number(s) corresponds to a normal score before change; Changing a signal value in the candidate cycle number(s) and recalculating a normal score in the candidate cycle number(s) using the changed signal value, recalculated from the candidate cycle number(s) The normal score corresponds to the normal score after the change; Comparing the post-change normal score with the pre-change normal score in the candidate cycle number; And determining that the candidate cycle number(s) represent an abnormal signal if the normal-display value after the change in the candidate cycle number exhibits a higher degree of normality than the normal-display value before the change. .

The repetition of steps (a)-(f) is the same as described in section I , except that the new post-modification data set is used instead of the pre-modification data set to select a new candidate cycle number.

The repetition of steps (a)-(f) can be terminated if there is no cycle number determined to indicate an abnormal signal. For example, when one candidate cycle number is selected and it is determined that the selected candidate cycle number does not represent an abnormal signal, or a plurality of candidate cycle numbers are selected and all of the selected candidate cycle numbers do not represent an abnormal signal. If determined, the repetition of steps (a)-(f) may end.

When the iteration is terminated, all cycle numbers determined during the iteration are finally determined as cycle numbers indicating abnormal signals in the original data set.

In the repetition of steps (a)-(f), the normal-indicated value in all cycle numbers can be calculated in advance together with the normal-indicated value after change in the candidate cycle number. That is, if the normal-indicated value after change in the candidate cycle number is calculated in the first run of step (d), the normal-indicated value in the other cycle number calculated in the second run of step (d) is the candidate It can be predetermined in the first run of step (d) together with the post-change signal values in all cycle numbers after the cycle number.

According to the iteration scheme, each data set used for selection of the candidate cycle number during the iteration of steps (a)-(f) can be distinguished using different terms. For example, if steps (a)-(f) are performed twice, each data set used in each iteration will be "pre-change data set", "first change post data set", "second post-change data It can be called "set", etc.

&Quot;first change data set" refers to a data set in which the signal value at the cycle number determined to represent an abnormal signal in the first implementation of steps (a)-(f) has been replaced with a signal value after its appropriate change; "Secondary post-modification data set" is a data set in which the signal value at another cycle number determined to represent an abnormal signal in the second implementation of steps (a)-(f) is further replaced by its appropriate post-change signal value. Refers to.

The method of section II described above can be applied to multiple data sets obtained by multiple PCR. For example, if multiple data sets are obtained by multiplex PCR, the method of section II can not only determine the cycle number representing an abnormal signal in each data set, but also correct each data set. Specifically, when multiple data sets are obtained by detection at different detection temperatures from a single amplification reaction according to the MuDT1 technology developed by the inventors (see WO 2015/147412), the method in Section II To determine the cycle number that represents an abnormal signal in each data set, as well as to correct each data set.

In addition to determining the cycle number representing an abnormal signal, the method of section II can be applied to correct for an abnormal signal in a data set, ie to provide a corrected data set.

When it is determined that the candidate cycle number represents an abnormal signal according to the method of the present invention, a data set in which the signal value in the candidate cycle number is changed may be provided as a corrected data set (eg, an amplification curve).

When the iteration is over, all cycle numbers determined during the iteration are finally determined as cycle numbers representing abnormal signals in the original data set.

When the repetition is terminated, a data set in which a signal value at a cycle number determined to indicate an abnormal signal before the end is changed may be finally provided as a corrected data set.

The calibrated data set can be used for further analysis. For example, the data set can be used to determine the presence/absence of a target analyte.

The methods of Sections I and II can be applied to a specific area, such as a region of interest in the data set, not all cycle numbers in the data set.

In one embodiment, the method of the present invention can be applied only to cycle numbers within the baseline region within the data set.

In another embodiment, the method of the present invention can be applied only to cycle numbers within the exponential region within the data set.

In another embodiment, the method of the present invention can be applied only to cycle numbers within the congestion area within the data set.

In another embodiment, the method of the present invention can be applied only to cycle numbers selected by the user. The cycle number to be analyzed can be arbitrarily selected. For example, the cycle number to be analyzed is from the 3rd cycle number to the last cycle number, the 5th cycle number to the last cycle number, or the 7th cycle number to the last cycle number. Alternatively, the cycle number to be analyzed is the mid-range cycle number.

The methods of Sections I and II described above can be used in combination with other methods known in the art, such as those disclosed in US Pat. No. 8,560,247 and US Patent Application Publication No. 2015/0186598. For example, after comparing the results of any of the methods described above with the results of any conventional method known in the art, the commonly determined cycle number can ultimately be determined as the cycle number indicative of an abnormal signal.

The normal-indicated value may include the normal score described above as well as a value obtained by multiplying the two first order changes in the two cycle numbers.

In one implementation, a known method of correcting an abnormal signal in the cycle number can be applied in step (d) to change the signal value in the selected cycle number.

III. Detection of abnormal signals in the data set (no selection of candidate cycles)

Detection of an abnormal signal is performed without selecting a candidate cycle.

In another aspect of the invention, the invention provides a method for detecting anomalous signals in a data set, comprising the steps of:

(a) obtaining a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;

(b) performing the following steps (b-1) to (b-4) for all cycle numbers in the data set;

(b-1) providing a normality-representing value in a cycle number by using a signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number;

(b-2) changing the signal value in the cycle number and additionally providing a normal-display value, wherein the normal-display value additionally provided in the cycle number corresponds to the normal-display value after the change;

(b-3) comparing the normal-display value after the change in the cycle number with the normal-display value before the change; And

(b-4) determining that the cycle number represents an abnormal signal if the normal-display value after change in the candidate cycle number is greater than the normal-display value before change.

Unlike the methods of Sections I and II, the method of Section III is characterized by sequentially analyzing all cycle numbers instead of analyzing the selected candidate cycle numbers.

The method of section III is not limited to the sequence of analysis of cycle numbers, as long as all cycle numbers are analyzed at least once. That is, all cycle numbers in the data set can be applied to the method of section III , for example sequentially, randomly, or in a regular manner.

In one embodiment, the data set obtained in step (a) may be a data set excluding a cycle region in which an abnormal signal is unlikely to appear. For example, the data set in step (a) is the 1st cycle number to the 10th cycle number, the 1st cycle number to the 9th cycle number, the 1st cycle number to the 8th cycle number, the 1st cycle number to the 7th Cycle number, 1st cycle number to 6th cycle number, 1st cycle number to 5th cycle number, 1st cycle number to 4th cycle number, 1st cycle number to 3rd cycle number, 1st cycle number to 2nd It may be a data set excluding a cycle number or a cycle area of the first cycle number.

Since the method of section III is similar to the method of sections I and II , the common content between them is omitted to avoid undue complexity of the present specification due to repeated description.

IV. Recording media for signal extraction, computer programs and devices

Since the recording medium, apparatus, and computer program of the present invention described below are for implementing the method of the present invention in a computer, descriptions of the contents common therebetween are omitted in order to avoid excessive complexity of the present specification due to repeated description.

In yet another aspect of the present invention, the present invention provides a computer-readable recording medium comprising instructions for implementing a method for detecting an abnormal signal in a data set comprising the following steps:

(a) receiving a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;

(b) providing a normality-representing value in each cycle number of a data set by using the signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number. Is;

(c) selecting a candidate cycle number (candidate cycle number) for an abnormal signal according to the normal-display value, wherein the normal-display value in the candidate cycle number(s) is normal-display before change. Corresponds to the value;

(d) changing a signal value in the candidate cycle number(s), and further providing a normal-indicative value in the candidate cycle number(s) by using the changed signal value, wherein the candidate cycle number The normal-display value additionally provided in (s) corresponds to the normal-display value after change;

(e) comparing the post-change normal-display value in the candidate cycle number(s) with the pre-change normal-display value; And

(f) determining that the candidate cycle number(s) represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.

According to another aspect of the present invention, the present invention provides a computer-readable recording medium comprising instructions for implementing a method for detecting an abnormal signal in a data set comprising the following steps:

(a) obtaining a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;

(b) performing the following steps (b-1) to (b-4) on all cycle numbers in the data set;

(b-1) providing a normality-representing value in a cycle number by using a signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number;

(b-2) changing the signal value in the cycle number and additionally providing a normal-display value, wherein the normal-display value additionally provided in the cycle number corresponds to the normal-display value after the change;

(b-3) comparing the normal-display value after the change in the cycle number with the normal-display value before the change; And

(b-4) determining that the cycle number represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.

According to another aspect of the present invention, the present invention provides a computer program stored on a computer-readable recording medium, which implements a processor for executing a method for detecting an abnormal signal in a data set comprising the following steps:

(a) receiving a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;

(b) providing a normality-representing value in each cycle number of a data set by using the signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number. Is;

(c) selecting a candidate cycle number (candidate cycle number) for an abnormal signal according to the normal-display value, wherein the normal-display value in the candidate cycle number(s) is normal-display before change. Corresponds to the value;

(d) changing a signal value in the candidate cycle number(s), and further providing a normal-indicative value in the candidate cycle number(s) by using the changed signal value, wherein the candidate cycle number The normal-display value additionally provided in (s) corresponds to the normal-display value after change;

(e) comparing the post-change normal-display value in the candidate cycle number(s) with the pre-change normal-display value; And

(f) determining that the candidate cycle number(s) represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.

According to another aspect of the present invention, the present invention provides a computer program stored on a computer-readable recording medium, which implements a processor for executing a method for detecting an abnormal signal in a data set comprising the following steps:

(a) obtaining a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;

(b) performing the following steps (b-1) to (b-4) on all cycle numbers in the data set;

(b-1) providing a normality-representing value in a cycle number by using a signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number;

(b-2) changing the signal value in the cycle number and additionally providing a normal-display value, wherein the normal-display value additionally provided in the cycle number corresponds to the normal-display value after the change;

(b-3) comparing the normal-display value after the change in the cycle number with the normal-display value before the change; And

(b-4) determining that the cycle number represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.

Program instructions, when executed by the processor, cause the processor to execute the method of the present invention described above. Program instructions for implementing the method of the present invention may include the following instructions: (i) instructions to calculate a second order change value at each cycle number in the data set; (ii) instructions to calculate the normal-indicated value at each cycle number using the second change value; (iii) an indication to select the candidate cycle number(s) for the abnormal signal by the normal-indicative value; (iv) an instruction to change the signal value in the candidate cycle number(s), and to recalculate the normal-indicative value from the candidate cycle number(s) using the changed signal value; (v) an instruction to compare the normal-indicated value after the change in the candidate cycle number(s) with the normal-indicated value before the change; And (vi) an indication to determine whether the candidate cycle number(s) indicate an abnormal signal.

The method of the present invention is executed in a processor, which may be a processor in a data acquisition device such as a stand alone computer, a network attached computer, or a real-time PCR device.

Types of computer-readable recording media include CD-R, CD-ROM, DVD, flash memory, floppy disk, hard drive, portable HDD, USB, magnetic tape, MINIDISC, nonvolatile memory card, EEPROM, optical disk, optical storage medium. , RAM, ROM, system memory, and a variety of recording media including a web server.

The signal value from the signal generation process can be received through several mechanisms. For example, the signal value may be collected by a processor in the PCR data collection device. The signal value may be provided to the processor in real time as the signal value is collected, or the signal value may be stored in a memory unit or buffer and provided to the processor after completion of the experiment. Similarly, the signal value is a separate system such as a desktop computer system by a network connection (e.g., LAN, VPN, Internet and intranet) or a direct connection (e.g., USB or other direct wired connection or wireless connection) with the collection device. Or a portable medium such as CD, DVD, floppy disk, and portable HDD. Similarly, the signal value may be provided to the server system through a network connection (eg, LAN, VPN, Internet, intranet, and wireless communication network) to a client such as a notebook or desktop computer system.

Instructions for implementing a processor implementing the present invention may be included in the logic system. The instructions are downloadable and memory modules (e.g., hard drives or other memory such as local or attached RAM or ROM), although they can be provided on any software recording medium such as portable HDD, USB, floppy disk, CD and DVD. ) Can be stored. Computer code for implementing the present invention can be executed in various coding languages such as C, C++, Java, Visual Basic, VBScript, JavaScript, Perl and XML. In addition, various languages and protocols can be used for external and internal storage and delivery of signals and commands according to the present invention.

In a further aspect of the invention, the invention provides an apparatus for detecting anomalous signals in a data set comprising (a) a computer processor, and (b) a computer readable recording medium as described above coupled to the computer processor. to provide.

According to one embodiment, the device further comprises a reaction vessel capable of accommodating a sample and a signal generating means, a temperature control means for controlling the temperature of the reaction vessel and/or a detector for detecting a signal at the cycle number.

The computer processor can be built so that one processor performs all of the above-described performances. Alternatively, the processor unit can be built so that multiple processors execute each performance.

According to one embodiment, the processor may be implemented by installing software in a conventional device (eg, a real-time PCR device) for detection of a target nucleic acid sequence.

The signal value can be received as an amplification curve in various ways. For example, the signal value may be received and collected by a processor in a data collector of a real-time PCR device. Upon collection of signal values, they may be provided to the processor in a real-time manner, or may be stored in a memory unit or buffer and then provided to the processor after experimentation.

Similarly, the signal value can be via a network connection (e.g. LAN, VPN, Intranet and Internet) or a direct connection (e.g. USB and wired or wireless direct connection), or a portable medium such as CD, DVD, floppy disk and portable HDD. Through, it can be provided from a real-time PCR device to a computer system such as a desktop computer system. Alternatively, the signal value may be provided to the server system through a network connection (eg, LAN, VPN, intranet, Internet and wireless communication network) connected to a client such as a notebook or desktop computer system.

As described above, the method of the present invention may be implemented by an application (ie, a program) installed by a supplier or directly installed by a user into the computer system, and recorded on a computer-readable recording medium.

The computer program implementing the method of the present invention can implement all functions for detecting abnormal signals. The computer program may be a program including program instructions stored in a computer-readable recording medium implementing a processor for executing the method of the present invention.

Computer programs can be coded using suitable computer languages such as C, C++, JAVA, Visual basic, VBScript, JavaScript, Perl, XML and machine languages. The program may include a function code for the mathematical function described above and a control code for implementing the processor in sequence by the processor of the computer system.

The code may further include a memory reference code to which additional information or a medium necessary for implementing the functions described above by the processor is referred to at a location (address) of an internal or external memory of the computer system.

If the computer system requires communication with another remote computer or server in order to implement the function of the processor, the code can be used by the processor using a communication module (e.g., wired and/or wireless communication module). It may further include a communication related code for coding how to communicate with another computer or server or what information or medium is transmitted.

Functional programs and codes (code segments) for implementing the present invention can be easily deduced or modified by a programmer in the art in consideration of a system environment of a computer that reads a recording medium and executes the program.

The recording medium networked to the computer system can be distributed, and the computer readable code can be stored and executed in a distributed manner. In this case, at least one computer among the plurality of distributed computers may implement some of the functions, and may implement a part of the function and transmit the implementation result to at least one computer that may transmit the implementation result to the at least one computer.

In order to implement the present invention, a recording medium on which an application (i.e., a program) is recorded is a recording medium (e.g., a hard disk) included in an application store or an application provider server, the application provider server itself, the program and the recording medium thereof. Includes other computers.

Computer systems capable of reading recording media include general PCs such as desktop or notebook computers, mobile terminals such as smart phones, tablet PCs, personal digital assistants (PDAs) and mobile communication terminals, as well as all computing executable devices. I can.

The features and advantages of the present invention are summarized as follows:

(a) The method of the present invention can accurately determine a cycle number representing an abnormal signal in a data set or, further, a plurality of abnormal signals using the normal-indicated value. As such, the method of the present invention provides information about correction or invalidation of the data set by indicating whether there are cycles indicating abnormal signals in the data set and which cycle numbers indicate abnormal signals.

(b) The method of the present invention can provide a corrected data set in which the abnormal signal in the cycle number is replaced with a normal signal value when it is determined that the candidate cycle number represents an abnormal signal. The calibrated data set may be used to provide qualitative and/or quantitative information on a target analyte as a new data set for a target analyte (especially, a target nucleic acid sequence).

(c) The method of the present invention can achieve both the determination of the cycle representing the abnormal signal and the correction of the abnormal signal in a simple and time efficient manner.

(d) In addition, the use of the data set corrected by the method of the present invention can significantly reduce false positive or false negative results for the target analyte.

1 is a flowchart illustrating a process of determining a cycle indicating an abnormal signal in a data set according to an exemplary embodiment of the present invention.
2 shows an exemplary data set (pre-change data set) used in the analysis of Example 1.
3 is a graph showing the normal score (NS) calculated at each cycle number of the data set of FIG. 2.
4 shows an example of a signal change method of the present invention for changing a signal value in a candidate cycle for the data set of FIG. 2. According to the signal change method, the signal value at the 10th cycle number ( _before y _change (10)) as the candidate cycle number is replaced with the signal value _{after change} ( _after y _change (10)), and then after the 10th cycle number The signal values at all cycle numbers are also replaced by the signal values after each suitable change.
Fig. 5 is a data set before change (corresponding to the data set in Fig. 2; dotted line); And a signal value at a candidate cycle number (10th cycle number) in the data set before change and all cycle numbers after the candidate cycle number (11th cycle number to 45th cycle number) according to an embodiment of the present invention. Represents the first post-modification data set (solid line) in which the signal value has been replaced by the signal value after each appropriate change.
6 is a graph showing a normal score calculated at each cycle number of a data set (indicated by a solid line in FIG. 5) after the first change.
7 is a data set (dotted line) after the first change; And a signal value at another candidate cycle number (11th cycle number) in the data set after the first change and all cycle numbers after the candidate cycle number (12th cycle number to 45th cycle) according to an embodiment of the present invention. Number) represents the second post-modification data set (solid line) in which the signal value in each appropriate change has been replaced by the signal value.
8 is a graph showing a normal score calculated at each cycle number of a data set (indicated by a solid line in FIG. 7) after a second change.
9 is a data set (dotted line) after the second change; And a signal value at another candidate cycle number (23th cycle number) in the data set after the second change according to an embodiment of the present invention and all cycle numbers after the candidate cycle number (from the 24th cycle number to the 45th cycle number). Number) represents the third post-modification data set (solid line) in which the signal value in each appropriate change has been replaced by the signal value.
10 is a graph showing a normal score calculated at each cycle number of a data set (indicated by a solid line in FIG. 9) after the third change.
Fig. 11 is a pre-change data set (dotted line) of Fig. 2; And a corrected data set (data set after secondary modification; solid line) finally provided according to an embodiment of the method of the present invention.

Hereinafter, the present invention will be described in more detail through examples. These examples are only for describing the present invention in more detail, and that the scope of the present invention is not limited by these examples according to the gist of the present invention, to those of ordinary skill in the art. It will be self-evident.

Example

Example 1: detection of abnormal signals

According to an embodiment of the present invention, it was confirmed whether an abnormal signal can be detected in a data set obtained from a real-time PCR reaction.

<1-1> Real-time PCR Obtaining data sets by reaction

Among the data sets obtained by real-time PCR reactions for analytes, a data set suspected of having an abnormal signal by visual inspection was selected to apply an embodiment of the present invention. The real-time PCR reaction was carried out in 45 amplification cycles using a TaqMan probe as a signal-generating means in CFX96™ Real-Time PCR Detection System (Bio-Rad Laboratories).

The selected data set (amplification curve) and the signal value (RFU) in each cycle number are shown in FIGS. 2 and 1, respectively.

X One 2 3 4 5 6 7 8 9 10 RFU 8776.39 8762.88 8768.55 8768.85 8778.93 8789.20 8801.55 8790.66 8787.79 8916.60 X 11 12 13 14 15 16 17 18 19 20 RFU 8891.73 8897.98 8895.84 8897.33 8910.11 8923.05 8950.22 8992.13 9066.27 9175.60 X 21 22 23 24 25 26 27 28 29 30 RFU 9316.99 9485.84 9666.92 9827.49 9961.62 10053.62 10124.35 10162.62 10192.80 10209.73 X 31 32 33 34 35 36 37 38 39 40 RFU 10230.79 10253.57 10265.73 10278.29 10284.25 10295.40 10294.58 10288.54 10286.77 10283.82 X 41 42 43 44 45 - - - - - RFU 10286.75 10288.49 10295.78 10289.51 10300.51 - - - - -

<1-2> Calculation of the second difference in each cycle number

For this data set, the first order difference in each cycle number was calculated by subtraction of the signal values in two immediately adjacent cycle numbers. Then, the second order difference in each cycle number was calculated by subtraction of the first difference in two immediately adjacent cycle numbers. The first difference and the second difference were calculated by a backward difference method.

Specifically, the second difference in each cycle number was calculated using the following Equations IV and V sequentially, or using the following Equation VI alone.

Equation IV

D'(x) = y(x)-y(x-1)

Equation V

D"(x) = D'(x)-D'(x-1)

Equation VI

D"(x) = y(x)-2 * y(x-1) + y(x-2)

In the above formula, D'(x) represents the first difference at the xth cycle number, D"(x) represents the second difference at the xth cycle number, and y(x) represents the first difference at the xth cycle number. Is a signal value, y(x-1) is a signal value at the x-1th cycle number, and y(x-2) is a signal value at the x-2th cycle number.

<1-3> Calculation of normal score (NS) at each cycle number

Thereafter, a normality score (NS) in each cycle number was calculated by multiplying the second difference in two immediately adjacent cycle numbers according to Equation VII below. The normal score was used as an example of a normal-indicated value.

Equation VII

NS(x) = D"(x) * D"(x+1)

In the above formula, NS(x) represents the normal score at the xth cycle number, D"(x) represents the second difference at the xth cycle, and D"(x+1) represents the x+1th cycle number Represents the second difference in

The NS at each of the calculated cycle numbers is schematically shown in FIG. 3 and Table 2.

X One 2 3 4 5 6 7 8 9 10 NS - - -102.90 -52.50 1.75 0.37 -48.65 -186.50 1056.35 -20235.34 X 11 12 13 14 15 16 17 18 19 20 NS -4782.73 -261.09 -30.48 41.00 1.87 2.36 209.66 475.09 1134.40 1128.29 X 21 22 23 24 25 26 27 28 29 30 NS 880.30 335.69 -250.71 542.08 1113.66 896.35 690.30 262.68 107.27 -54.72 X 31 32 33 34 35 36 37 38 39 40 NS 7.12 -18.31 -4.19 -2.60 -34.23 -62.05 62.44 -22.27 -5.04 -6.95 X 41 42 43 44 45 - - - - - NS -7.00 -6.60 -75.19 -234.17 - - - - - -

<1-4> Selection of candidate cycles and provision of NS before change

Based on the NS calculated in Example <1-3> (see Fig. 3 and Table 2), the cycle number with the minimum NS was selected as the candidate cycle number. As a result, the 10th cycle number (NS = -20235.34; signal value: 8916.60) was selected as the candidate cycle number.

In addition, NS "-20235.34" in the candidate cycle number was adopted as the pre-change NS, and this was provided for comparison in Example <1-6>.

<1-5> Change of signal value in candidate cycle number and provision of NS after change

The candidate cycle number, that is, the signal value at the 10th cycle number, was changed according to a signal value change method set in advance using the following equations IX and X.

Specifically, the first difference before change in the candidate cycle number (the first difference calculated using the signal value before change) and the NS before change in the candidate cycle number are applied to Equation IX below, and 1 after change The difference was obtained.

Equation IX

In the above equation, _after D' _change (c) represents the first change value after change in the candidate cycle number, D' _{before change} (c) represents the first change value before change in the candidate cycle number, and before NS _change (c) represents the normal score before change in the candidate cycle number; k is a number of 1 or more; c represents the candidate cycle number.

In this example, the first difference was used as one of the first change values.

In this embodiment, the candidate cycle number was the 10th cycle number, the first difference _{before change} (D' _{before change} (10)) in the candidate cycle number was 128.81, and NS before change in the candidate cycle number (NS (NS)) (10)) _{before the change} was -20235.34.

The first difference after change in the candidate cycle number by Equation IX (in the equation, K=1) using D' _{before change} (10) and NS _{before change} (10) as follows (after D' _change (10)) ) Was calculated:

Thereafter, the signal value before the change in the cycle number immediately preceding the candidate cycle number and the first difference after the change in the candidate cycle number were applied to Equation X below to obtain a signal value after change in the candidate cycle number.

Equation X

_{After changing} y (c) = _{Before changing} y (c-1) + _{After changing} D'(c)

In the above formula, _after y _change (c) represents the post-change signal value in the candidate cycle number, y _{before change} (c-1) represents the pre-change signal value in (candidate cycle number-1), and D' _{change After} (c) shows the first change value after change in the candidate cycle number, calculated by Equation IX .

In this example, the first difference was used as one of the first change values.

The post-change signal value at the tenth cycle number can be calculated as follows: _after y _change (10) = y _{before change} (9) + D' _{after change} (10).

In this embodiment, the signal value _{before change} ( _before y _change (9)) at the ninth cycle number was 8797.79, and the first difference ( _after D'change) after the change in the tenth cycle number calculated by Equation IX (10)) was 0.90, so _after y _change, (10) = 8797.79 + 0.90 = 8788.69.

Based on the calculation result, the signal value at the 10th cycle number ( _before y _change (10)) "8916.60" was changed to the signal value _{after change} ( _after y _change (10)) "8788.69".

Then, the NS in the candidate cycle number was recalculated using the changed signal value. The recalculated NS at the candidate cycle number was "-97.10". NS "-97.10" in the candidate cycle number was adopted as NS after the change, and this was provided for comparison in Example <1-6>.

<1-6> Determination of abnormal signals

According to an embodiment of the present invention, the post-change NS in the candidate cycle number and the pre-change NS are compared, and if the post-change NS in the candidate cycle number is greater than the pre-change NS, the candidate cycle number indicates an abnormal signal. To decide.

In this example, NS "-97.10" after change in the candidate cycle number (10th cycle number) was greater than NS "-20235" before change.

Therefore, it was determined that the 10th cycle number is a cycle number indicating an abnormal signal.

Example 2: of the present invention In implementation Detection of additional abnormal signals according to (1)

It was investigated whether additional abnormal signals could be detected using the data set in which the abnormal signals were changed to normal signals. To this end, a data set after the first change in which the signal value in the cycle number indicating the abnormal signal is changed is prepared, and this is replaced with the data set of Example <1-1> and Examples <1-1> to <1-6 Applied to >.

<2-1> 1st after Provision of data set

In order to further confirm the cycle number indicating an abnormal signal, a data set after modification was used instead of the data set of Example <1-1>.

After this change, a data set was obtained as follows:

First, the signal value after changing the signal value ( _before y _change (10): "8916.60") at the 10th cycle number determined to represent an abnormal signal in Example <1-6> (after y _change (10): "8788.69") (see Fig. 4).

Then, the signal values in all cycle numbers after the candidate cycle number were further changed (see FIG. 4). In this case, the signal value in the cycle number after the candidate cycle number is changed so that the difference between the signal value before change and the signal value after change in each cycle number is the same as the difference in the candidate cycle number. Specifically, the first difference before the change at the 11th cycle number (D' _{before change} (11)) and the post change signal value at the 10th cycle number ( _after y _change (10)) are added to the 11th cycle number. After the change, the signal value was calculated. In this way, the signal values from the 12th cycle number to the last cycle number were changed.

The resulting data set was named "data set after the first change", and it is shown in Table 3.

X One 2 3 4 5 6 7 8 9 10 RFU 8776.39 8762.88 8768.55 8768.85 8778.93 8789.20 8801.55 8790.66 8787.79 8788.69 X 11 12 13 14 15 16 17 18 19 20 RFU 8763.82 8770.07 8767.93 8769.42 8782.20 8795.15 8822.31 8864.22 8938.36 9047.70 X 21 22 23 24 25 26 27 28 29 30 RFU 9189.09 9357.94 9539.01 9699.58 9833.72 9925.71 9996.44 10034.72 10064.90 10081.82 X 31 32 33 34 35 36 37 38 39 40 RFU 10102.88 10125.66 10137.83 10150.39 10156.35 10167.49 10166.67 10160.64 10158.87 10155.91 X 41 42 43 44 45 - - - - - RFU 10158.85 10160.59 10167.87 10161.60 10172.61 - - - - -

The data set after the primary change (solid line) and the data set before the change of the signal value (pre-change data set in Example <1-1>; dotted line) are shown in FIG. 5.

<2-2> Calculation of the second difference in each cycle number

For the data set after the first change, the second difference in each cycle number was calculated as in Example <1-2>.

<2-3> Calculation of normal score (NS) at each cycle number

As in Example <1-3>, the NS at each cycle number was calculated by multiplying the second difference in the two immediately adjacent cycle numbers.

The NS at each recalculated cycle number is shown in Figure 6 and Table 4.

X One 2 3 4 5 6 7 8 9 10 NS - - -102.90 -52.50 1.75 0.37 -48.65 -186.50 30.23 -97.10 X 11 12 13 14 15 16 17 18 19 20 NS -802.05 -261.09 -30.48 41.00 1.87 2.36 209.66 475.09 1134.40 1128.29 X 21 22 23 24 25 26 27 28 29 30 NS 880.30 335.69 -250.71 542.08 1113.66 896.35 690.30 262.68 107.27 -54.72 X 31 32 33 34 35 36 37 38 39 40 NS 7.12 -18.31 -4.19 -2.60 -34.23 -62.05 62.44 -22.27 -5.04 -6.95 X 41 42 43 44 45 - - - - - NS -7.00 -6.60 -75.19 -234.17 - - - - - -

<2-4> Selection of candidate cycle number and Before change NS offers

Based on the recalculated NS in Example <2-3> (see Fig. 6 and Table 4), the cycle number with the minimum NS was selected as the candidate cycle number. As a result, the 11th cycle number (NS = -802.05; signal value = 8763.82) was selected as the candidate cycle number.

In addition, NS "-802.05" in the candidate cycle number was adopted as the pre-change NS, and this was provided for comparison in Example <2-6>.

<2-5> Change of signal value in candidate cycle number and after NS offers

The candidate cycle number, that is, the signal value at the 11th cycle number, was changed according to a preset signal change method as in Example <1-5>.

As a result, the signal value after the change in the 11th cycle number was calculated as follows: _after y _change (11) = 8788.69 + 0.85 = 8789.54.

Based on the calculation result, the signal value ( _before y _change (11)) “8763.82” in the 11th cycle was _{changed to} “8789.54” after the change signal value ( _after y _change (11)).

Then, the NS in the candidate cycle number was recalculated using the changed signal value. The NS recalculated from the candidate cycle number was "-0.27". NS "-0.27" in the candidate cycle number was adopted as NS after change, and this was provided for comparison in Example <2-6>.

<2-6> Determination of abnormal signals

According to an embodiment of the present invention, the NS after the change in the candidate cycle number was compared with the NS before the change in the candidate cycle number.

In this embodiment, the NS "-0.27" after the change in the candidate cycle number (11th cycle number) was greater than the NS "-802.05" before the change.

Therefore, the 11th cycle number was determined as a cycle number indicating an abnormal signal.

Example 3: of the present invention In implementation Detection of additional abnormal signals according to (II)

It was investigated whether additional abnormal signals could be detected using the data set in which the abnormal signals were changed to normal signals. To this end, a data set after the secondary change in which the signal value in the cycle number indicating the abnormal signal is changed is prepared, and this is replaced with the data set in Example <1-1>. Applied to >.

<3-1> 2nd after Provision of data set

In order to further confirm the cycle number indicating an abnormal signal, the data set after the second modification was used instead of the data set of Example <1-1>.

Data sets were obtained as follows after the second modification:

First, for the data set after the primary change, the signal value after changing the signal value at the 11th cycle number ( _before y _change (11)) “8763.82”) determined to indicate an abnormal signal in Example <2-6> (Y _{change after} (11)) was changed to "8789.54").

Then, the signal values in all cycle numbers after the candidate cycle number were further changed. In this case, the signal value in the cycle number after the candidate cycle number is changed so that the difference between the signal value before change and the signal value after change in each cycle number is the same as the difference in the candidate cycle number.

The resulting data set was named "data set after the second change", and it is shown in Table 5.

X One 2 3 4 5 6 7 8 9 10 RFU 8776.39 8762.88 8768.55 8768.85 8778.93 8789.20 8801.55 8790.66 8787.79 8788.69 X 11 12 13 14 15 16 17 18 19 20 RFU 8789.54 8795.79 8793.65 8795.14 8807.92 8820.87 8848.04 8889.94 8964.08 9073.42 X 21 22 23 24 25 26 27 28 29 30 RFU 9214.81 9383.66 9564.73 9725.30 9859.44 9951.43 10022.16 10060.44 10090.62 10107.54 X 31 32 33 34 35 36 37 38 39 40 RFU 10128.60 10151.38 10163.55 10176.11 10182.07 10193.21 10192.39 10186.36 10184.59 10181.64 X 41 42 43 44 45 - - - - - RFU 10184.57 10186.31 10193.59 10187.32 10198.33 - - - - -

The data set after the second change (solid line) and the first change data set (dotted line) are shown in FIG. 7.

<3-2> Calculation of the second difference in each cycle number

For the data set after the second change, the second difference in each cycle number was calculated as in Example <1-2>.

<3-3> Calculation of normal score (NS) at each cycle number

As in Example <1-3>, the NS at each cycle number was calculated by multiplying the second difference in the two immediately adjacent cycle numbers.

The NS at each recalculated cycle number is shown in Figure 8 and Table 6.

X One 2 3 4 5 6 7 8 9 10 NS - - -102.90 -52.50 1.75 0.37 -48.65 -186.50 30.23 -0.19 X 11 12 13 14 15 16 17 18 19 20 NS -0.27 -45.32 -30.48 41.00 1.87 2.36 209.66 475.09 1134.40 1128.29 X 21 22 23 24 25 26 27 28 29 30 NS 880.30 335.69 -250.71 542.08 1113.66 896.35 690.30 262.68 107.27 -54.72 X 31 32 33 34 35 36 37 38 39 40 NS 7.12 -18.31 -4.19 -2.60 -34.23 -62.05 62.44 -22.27 -5.04 -6.95 X 41 42 43 44 45 - - - - - NS -7.00 -6.60 -75.19 -234.17 - - - - - -

<3-4> Selection of candidate cycle number and provision of NS before change

Based on the recalculated NS in Example <3-3> (see Fig. 8 and Table 6), the cycle number with the minimum NS was selected as the candidate cycle number. As a result, the 23rd cycle number (NS = -250.71; signal value = 9564.74) was selected as the candidate cycle number.

In addition, NS "-250.71" in the candidate cycle number was adopted as the pre-change NS, and this was provided for comparison in Example <3-6>.

<3-5> Change of signal value in candidate cycle number and provision of NS after change

The candidate cycle number, that is, the signal value at the 23rd cycle number, was changed according to a preset signal change method as in Example <1-5>.

As a result, the signal value after the change in the 23rd cycle number was calculated as follows: _after y _change (23) = 9383.66 + 10.6 = 9394.42

Based on the calculation result, the signal value of "9564.74" in the 23rd cycle ( _before y _change (11)) was changed to "9394.42" _{after the change} ( _{after change} y (11)).

Then, the NS in the candidate cycle number was recalculated using the changed signal value. The NS recalculated from the candidate cycle number was "-23682.71". NS "-23682.71" in the candidate cycle number was adopted as NS after the change, and this was provided for comparison in Example <3-6>.

<3-6> Determination of abnormal signals

According to an embodiment of the present invention, the NS after the change in the candidate cycle number was compared with the NS before the change in the candidate cycle number.

In this embodiment, the NS "-23682.71" after the change in the candidate cycle number (the 23rd cycle number) was smaller than the NS "-250.71" before the change.

Therefore, the 23rd cycle number was determined as a cycle number indicating a normal signal.

According to an embodiment of the present invention, if the post-change NS in the candidate cycle for each data set is smaller than the pre-change NS, the method of the present invention may be terminated. In this example, since the post-change NS in the candidate cycle number of Example 3 is smaller than the pre-change NS, the cycle number indicating an abnormal signal was no longer determined.

According to Examples 1 to 3, the 10th cycle number and the 11th cycle number were finally determined as cycle numbers indicating abnormal signals.

In addition, a data set in which the signal values at the 10th and 11th cycle numbers were replaced with respective appropriate post-change signal values (data set after the second change) was finally provided as a corrected data set. The finally provided corrected data set is shown in FIG. 11. In the above figure, the dotted line represents the data set before correction (the data set before the change), and the solid line represents the data set finally corrected (the data set after the second change).

As shown in Fig. 11, it was found that the cycle number indicating an abnormal signal can be accurately corrected by the method of the present invention. Thus, using the method of the present invention, it is possible not only to determine the cycle number representing the abnormal signal in the data set, but also to correct the data set containing the abnormal signal(s).

As described above, specific parts of the present invention have been described in detail, and it is clear that these specific techniques are merely preferred embodiments, and the scope of the present invention is not limited thereto for those of ordinary skill in the art. Therefore, it will be said that the practical scope of the present invention is defined by the appended claims and their equivalents.

Claims (22)

A method for detecting an abnormal signal in a data set, comprising the following steps:
(a) obtaining a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;
(b) providing a normality-representing value in each cycle number of a data set by using the signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number. Is;
(c) selecting a candidate cycle number (candidate cycle number) for an abnormal signal according to the normal-display value, wherein the normal-display value in the candidate cycle number(s) is normal-display before change. Corresponds to the value;
(d) changing a signal value in the candidate cycle number(s), and further providing a normal-indicative value in the candidate cycle number(s) by using the changed signal value, wherein the candidate cycle number The normal-display value additionally provided in (s) corresponds to the normal-display value after change;
(e) comparing the post-change normal-display value in the candidate cycle number(s) with the pre-change normal-display value; And
(f) determining that the candidate cycle number(s) represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.
The method of claim 1, wherein the normal-indicated value at each cycle number is provided by calculating a change value in each cycle number from a signal value at 2-5 consecutive cycle numbers including each cycle number. The method characterized in that.
The method of claim 2, wherein the normal-indicated value comprises the steps of: (i) calculating a second order change value in each cycle number of the data set; And (ii) a normality score obtained by calculating a normal score at each cycle number of the data set using the second change value, wherein the calculation of the normal score is performed at two consecutive cycle numbers. A method, characterized in that it is carried out by a mathematical operation representing the magnitude of the sign change and the magnitude of the second change value between the second order change values of.
The method of claim 3, wherein the calculation of the second order change value is performed by calculating the first change value at each cycle number of the data set using two signal values at two consecutive cycle numbers, and then two consecutive cycles. A method characterized in that it is carried out by calculating a second order change value in each cycle number of the data set using two first order change values in the number.
The method of claim 3, wherein the second order change value is any one selected from the group consisting of a second order difference, a second order difference, and a second order derivative.
6. The method according to claim 5, wherein the second difference or second order difference is obtained by a forward difference method or a backward difference method.
4. The method of claim 3, wherein the calculation of the normal score is carried out by multiplying the value of a second order change in two consecutive cycle numbers.
The method of claim 7, wherein the calculation of the normal score is carried out by the following Equation VII or VIII :
Equation VII
NS(x) = D"(x) * D"(x+1)
In the above formula, NS(x) represents the normal score at the xth cycle number, D"(x) represents the secondary change value at the xth cycle number, and D"(x+1) represents the x+1th cycle number. Represents the secondary change value in the cycle number, and x is an integer of 1 or more.
Equation VIII
NS(x) = D"(x) * D"(x-1)
In the above formula, NS(x) represents the normal score at the xth cycle number, D"(x) represents the secondary change value at the xth cycle number, and D"(x-1) is the x-1th Represents the secondary change value in the cycle number, and x is an integer of 2 or more.
The method according to claim 1, wherein when the larger normal-indicative value indicates a higher degree of normality in the cycle number, the candidate cycle number comprises: (i) a cycle number having a normal-indicative value less than a threshold selected from values less than zero; (ii) a cycle number having a normal-indicated value that is less than or equal to a threshold selected from values less than zero; Or (iii) a cycle number with a negative sign and a minimum normal-indicated value; If the smaller normal-indicative value indicates a higher degree of normality of the signal value in the cycle number, the candidate cycle is: (i) a cycle number having a normal-indicative value greater than a threshold selected from values greater than zero; (ii) a cycle number having a normal-indicated value that is greater than or equal to a threshold selected from values greater than zero; Or (iii) a cycle number with a positive sign and a maximum normal-indicated value.
The method of claim 1, wherein the signal value in the candidate cycle number is changed to decrease its absolute value.
The method of claim 10, wherein the signal value in the candidate cycle number is changed so that its absolute value is greater than or equal to a relatively small absolute value among signal values in two cycle numbers immediately adjacent to the candidate cycle number. How to.
The method of claim 3, wherein the signal value in the candidate cycle number is: (i) in the candidate cycle number using a pre-change primary change value in the candidate cycle number and a pre-change normal score in the candidate cycle number. Obtaining a first change value after the change of; And (ii) using the first change value after the change in the candidate cycle number to obtain a post change signal value in the candidate cycle number.
The method of claim 3, wherein the signal value in the candidate cycle number is changed to a signal value after change determined by Equations IX and X:
Equation IX

In the above equation, _after D' _change (c) represents the first change value after change in the candidate cycle number, D' _{before change} (c) represents the first change value before change in the candidate cycle number, and before NS _change (c) represents the normal score before change in the candidate cycle number; k is a number of 1 or more; c represents the candidate cycle number.
Equation X
_{After changing} y (c) = _{Before changing} y (c-1) + _{After changing} D'(c)
In the above formula, _after y _change (c) represents the post-change signal value in the candidate cycle number, y _{before change} (c-1) represents the pre-change signal value in (candidate cycle number-1), and D' _{change After} (c) shows the first change value after change in the candidate cycle number calculated by Equation IX .
The method of claim 1, wherein when a plurality of candidate cycle numbers are selected in the step (c), the candidate cycle numbers are applied sequentially or simultaneously to the steps (d)-(f).
The method of claim 1, further comprising repeating the following steps:
(g) providing a post-change data set after performing step (f), wherein the post-change data set is obtained by changing the signal value at cycle number(s) indicating an abnormal signal; (h) performing steps (a)-(f) using the post-modified data set instead of the data set of step (a).
16. The method of claim 15, wherein the post-modification data set is obtained by further changing the signal value at all cycle numbers after the cycle number indicating an abnormal signal.
The method of claim 16, wherein the change of the signal value in all cycle numbers after the cycle number indicating the abnormal signal is a difference between the signal value after the change and the signal value before the change in each cycle number at a cycle number indicating an abnormal signal. Method, characterized in that carried out to be the same as the difference of.
The method of claim 15, wherein the repetition of steps (a)-(f) is terminated if there is no cycle number determined to indicate an abnormal signal.
A method of detecting an abnormal signal in a data set, comprising the following steps:
(a) obtaining a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;
(b) performing the following steps (b-1) to (b-4) for all cycle numbers in the data set;
(b-1) providing a normality-representing value in a cycle number by using a signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number;
(b-2) changing the signal value in the cycle number and additionally providing a normal-display value, wherein the normal-display value additionally provided in the cycle number corresponds to the normal-display value after the change;
(b-3) comparing the normal-display value after the change in the cycle number with the normal-display value before the change; And
(b-4) determining that the cycle number represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.
A computer-readable recording medium containing instructions for implementing a method for detecting an abnormal signal in a data set comprising the steps of:
(a) receiving a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;
(b) providing a normality-representing value in each cycle number of a data set by using the signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number. Is;
(c) selecting a candidate cycle number (candidate cycle number) for an abnormal signal according to the normal-display value, wherein the normal-display value in the candidate cycle number(s) is normal-display before change. Corresponds to the value;
(d) changing a signal value in the candidate cycle number(s), and further providing a normal-indicative value in the candidate cycle number(s) by using the changed signal value, wherein the candidate cycle number The normal-display value additionally provided in (s) corresponds to the normal-display value after change;
(e) comparing the post-change normal-display value in the candidate cycle number(s) with the pre-change normal-display value; And
(f) determining that the candidate cycle number(s) represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.
21. An apparatus for detecting abnormal signals in a data set, comprising: (a) a computer processor, and (b) the computer readable recording medium of claim 20 coupled to the computer processor.
A computer program stored on a computer-readable recording medium, which implements a processor for executing a method for detecting an abnormal signal in a data set comprising the steps of:
(a) receiving a data set for a target analyte by a signal amplification reaction, the data set comprising a cycle number and a plurality of data points having a signal value at the cycle number;
(b) providing a normality-representing value in each cycle number of a data set by using the signal value, wherein the normality-representing value is a value indicating a normal degree of the signal value in the cycle number. Is;
(c) selecting a candidate cycle number (candidate cycle number) for an abnormal signal according to the normal-display value, wherein the normal-display value in the candidate cycle number(s) is normal-display before change. Corresponds to the value;
(d) changing a signal value in the candidate cycle number(s), and further providing a normal-indicative value in the candidate cycle number(s) by using the changed signal value, wherein the candidate cycle number The normal-display value additionally provided in (s) corresponds to the normal-display value after change;
(e) comparing the post-change normal-display value in the candidate cycle number(s) with the pre-change normal-display value; And
(f) determining that the candidate cycle number(s) represents an abnormal signal if the normal-display value after the change in the candidate cycle number indicates a higher normality than the normal-display value before the change.

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