Nucleic acid amplification curve analysis method, apparatus, device, and storage medium

Abstract

This application provides a method, apparatus, device, and storage medium for nucleic acid amplification curve analysis, relating to the field of molecular biology. The method includes: fitting and solving the derivatives of nucleic acid amplification data to obtain the first-order derivative curve and higher-order derivative curves of the nucleic acid amplification curve; substituting the peak points of the first-order derivative curve and higher-order derivative curves into the first-order derivative curve for calculation to obtain characteristic points on the first-order derivative curve; performing linear fitting and normalization on the characteristic points to obtain the normalized first-order derivative curve of the nucleic acid amplification curve; and determining the cycle threshold of the nucleic acid amplification curve based on the normalized first-order derivative curve. By determining the cycle threshold of the amplification curve based on the normalized first-order derivative, the problem of different fluorescence intensity values ​​caused by edge effects is avoided, and no reference dye is required, thereby greatly optimizing the experimental procedure and improving experimental efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of molecular biology, in particular to a nucleic acid amplification curve analysis method, device, equipment and storage medium. BACKGROUND

[0002] The amplification curve is a curve describing the dynamic process of PCR, with cycle number as the abscissa and real-time fluorescence signal intensity in the reaction process as the ordinate. The PCR growth curve is not a standard exponential curve, because as the cycle number increases during PCR, the DNA polymerase activity decreases, the reaction reagents are depleted, the reaction by-products pyrophosphate hinder the synthesis reaction, and other factors, so that PCR is not exponential amplification. The typical PCR growth curve is "S-shaped", which includes the baseline period, the exponential period, the linear period and the plateau period. For example, in a typical nucleic acid sample PCR curve, the Ct value is used to determine the positive and negative results.

[0003] Currently, in the nucleic acid amplification technology, two core steps of data processing are required, namely data noise filtering and Ct value confirmation. Among them, the mainstream of Ct value confirmation is to select the intersection point of the threshold line and the amplification curve confirmation method, and the mainstream method of noise filtering includes data smoothing, background subtraction and amplitude normalization. There are two schemes for amplitude normalization: direct calculation and use of reference channels, but these schemes have the defects of edge effect and increase the complexity of the experiment. SUMMARY

[0004] Therefore, the purpose of the embodiments of the present application is to provide a nucleic acid amplification curve analysis method, device, equipment and storage medium, by fitting the original nucleic acid amplification data, finding the peak points of the first, second and third derivatives, substituting the characteristic points into the first derivative to solve, and linearly fitting and normalizing the characteristic points, the cycle threshold of the amplification curve is determined, the problem of different fluorescence intensities caused by edge effect is avoided, and no reference dye is needed, thereby greatly optimizing the experimental process and improving the experimental efficiency.

[0005] In a first aspect, the embodiments of the present application provide a nucleic acid amplification curve analysis method, which comprises: fitting and derivative solving of nucleic acid amplification data to obtain a first derivative curve and a high-order derivative curve of the nucleic acid amplification curve; substituting the peak points of the first derivative curve and the high-order derivative curve into the first derivative curve to obtain characteristic points on the first derivative curve; linearly fitting and normalizing the characteristic points to obtain a normalized first derivative curve of the nucleic acid amplification curve; and determining the cycle threshold of the nucleic acid amplification curve according to the normalized first derivative curve.

[0006] In the implementation process, the peak points of the first-order and high-order derivatives of the original nucleic acid amplification data are fitted, the first-order derivative is normalized according to the peak points, and the cycle threshold of the amplification curve is determined according to the normalized first-order derivative, thereby avoiding the problem of different fluorescence intensity values caused by the edge effect, and the reference dye is not required, so that the experimental process is greatly optimized and the experimental efficiency is improved.

[0007] Optionally, the fitting and derivative solving of the nucleic acid amplification data to obtain the first-order derivative curve and the high-order derivative curve of the nucleic acid amplification curve comprises: collecting the original nucleic acid amplification data by using a sliding window to obtain nucleic acid amplification data in a fixed cycle number interval; wherein the nucleic acid amplification data comprises the fluorescence intensity of the growth process of a plurality of nucleic acid data points at the cycle number; the nucleic acid amplification data is linear least squares regression fitted to obtain a nucleic acid amplification curve; and the first-order derivative and the high-order derivative of the nucleic acid amplification curve are solved to obtain the first-order derivative curve and the high-order derivative curve.

[0008] In the implementation process, the original nucleic acid amplification data is not directly fitted by an S-shaped curve, but is fitted by a linear least squares method to obtain a nucleic acid amplification curve, thereby reducing the algorithm complexity and improving the fitting accuracy.

[0009] Optionally, the high-order derivative curve comprises a second-order derivative curve; and the first-order derivative and the high-order derivative solving of the nucleic acid amplification curve to obtain the first-order derivative curve and the high-order derivative curve comprises: solving the first-order derivative of the nucleic acid amplification curve to obtain the first-order derivative curve; determining whether the first-order derivative curve has a peak value; if it is determined that the first-order derivative curve has a peak value, performing polynomial least squares regression fitting on at least three points near the peak value of the first-order derivative curve to obtain a peak point of the first-order derivative curve; and solving the second-order derivative of the nucleic acid amplification curve to obtain a second-order derivative curve.

[0010] In the implementation process, the peak of the first-order derivative of the nucleic acid amplification curve is not directly fitted by an S-shaped curve, but is solved by a least squares polynomial curve fitting, and the first-order derivative peak point and the second-order derivative are solved by peak determination, thereby reducing the algorithm complexity, improving the accuracy of peak calculation, and facilitating rapid determination of the cycle threshold.

[0011] Optionally, the high-order derivative curve further comprises a third derivative curve; after the second derivative curve is obtained by performing the second derivative operation on the nucleic acid amplification curve, the first derivative operation and the high-order derivative operation on the nucleic acid amplification curve to obtain the first derivative curve and the high-order derivative curve further comprises: determining whether the second derivative curve has a peak value based on data on a smaller side of a peak value of the first derivative curve; if it is determined that the second derivative curve has a peak value, performing polynomial least square method regression fitting on at least three points near the peak value of the second derivative curve to obtain a peak point of the second derivative curve; and performing a third derivative operation on the nucleic acid amplification curve to obtain a third derivative curve; and if it is determined that the second derivative curve does not have a peak value, determining the cycle threshold of the nucleic acid amplification curve as not existing.

[0012] In the above implementation process, the peak value of the second derivative of the nucleic acid amplification curve is not directly fitted with an S-shaped curve, but is solved by polynomial curve fitting with the least square method, and the peak value is solved, and the peak value point of the second derivative and the third derivative are solved, which reduces the algorithm complexity, improves the accuracy of peak value calculation, and is beneficial to quickly determine the cycle threshold.

[0013] Optionally, after the third derivative curve is obtained by performing the third derivative operation on the nucleic acid amplification curve, the first derivative operation and the high-order derivative operation on the nucleic acid amplification curve to obtain the first derivative curve and the high-order derivative curve further comprises: determining whether the third derivative curve has a peak value based on data on a smaller side of a peak value of the second derivative curve; if it is determined that the third derivative curve has a peak value, performing polynomial least square method regression fitting on at least three points near the peak value of the third derivative curve to obtain a peak point of the third derivative curve; and if it is determined that the third derivative curve does not have a peak value, determining the cycle threshold of the nucleic acid amplification curve as less than zero.

[0014] In the above implementation process, the peak value of the third derivative is solved by polynomial curve fitting with the least square method, which reduces the algorithm complexity, improves the accuracy of peak value calculation, and is beneficial to quickly determine the range of the cycle threshold through the judgment of the peak value of the third derivative.

[0015] Optionally, the linear fitting and normalization of the feature points to obtain the normalized first derivative curve of the nucleic acid amplification curve comprises: processing a constant parameter of the first derivative curve of the nucleic acid amplification curve to obtain a normalization parameter of the first derivative curve; wherein the constant parameter comprises: a final product fluorescence amount, an initial product fluorescence amount, and an amplification efficiency of the nucleic acid amplification curve; and performing linear least square method regression fitting on the feature points and the normalization parameter to obtain the normalized first derivative curve.

[0016] In the implementation process, the normalization processing is directly performed on the first derivative of the nucleic acid amplification curve, the problem of different fluorescence intensity values caused by edge effects is avoided, the algorithm complexity is reduced, and the efficiency of the cycle threshold calculation is improved.

[0017] Optionally, the determining the cycle threshold of the nucleic acid amplification curve according to the normalized first derivative curve comprises: taking a threshold line of the normalized first derivative curve; and determining the horizontal coordinate value of the smaller intersection point of the threshold line and the normalized first derivative curve as the cycle threshold of the nucleic acid amplification curve.

[0018] In the implementation process, the normalization processing is directly performed on the first derivative of the nucleic acid amplification curve, the problem of different fluorescence intensity values caused by edge effects is avoided, the algorithm complexity is reduced, and the efficiency of the cycle threshold calculation is improved.

[0019] In a second aspect, an embodiment of the present application provides a nucleic acid amplification curve analysis device, the device comprising: a fitting and derivation module configured to perform fitting and derivation on nucleic acid amplification data to obtain a first derivative curve and a high-order derivative curve of a nucleic acid amplification curve; a characteristic point calculation module configured to calculate a characteristic point on the first derivative curve by substituting peak points of the first derivative curve and the high-order derivative curve into the first derivative curve; a normalization module configured to perform linear fitting and normalization on the characteristic point to obtain a normalized first derivative curve of the nucleic acid amplification curve; and an analysis cycle threshold module configured to determine a cycle threshold of the nucleic acid amplification curve according to the normalized first derivative curve.

[0020] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a processor and a memory, wherein the memory stores machine readable instructions executable by the processor, and when the electronic device is running, the machine readable instructions are executed by the processor to perform the steps of the method described above.

[0021] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is run by a processor, the steps of the method described above are performed.

[0022] In order to make the above objectives, characteristics and advantages of the present application more apparent and easy to understand, the following embodiments are specifically described below, and the accompanying drawings are referred to for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as limiting the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0024] Figure 1 The flow chart of the nucleic acid amplification curve analysis method provided by the embodiments of the present application;

[0025] Figure 2 The schematic diagram of the feature points of the first derivative curve provided by the embodiments of the present application;

[0026] Figure 3 The schematic diagram of the nucleic acid amplification curve and the derivative curve provided by the embodiments of the present application;

[0027] Figure 4 The curve diagram of the linear fitting function of the feature points provided by the embodiments of the present application;

[0028] Figure 5 The normalized first derivative curve diagram provided by the embodiments of the present application;

[0029] Figure 6 The functional module schematic diagram of the nucleic acid amplification curve analysis device provided by the embodiments of the present application;

[0030] Figure 7 The block schematic diagram of the electronic device of the nucleic acid amplification curve analysis device provided by the embodiments of the present application.

[0031] Icon: 210-fitting derivative module; 220-computing feature point module; 230-normalizing module; 240-analyzing cycle threshold module; 300-electronic device; 311-memory; 312-storage controller; 313-processor; 314-peripheral interface; 315-input output unit; 316-display unit. DETAILED DESCRIPTION

[0032] The technical solutions of the embodiments of the present application will be described clearly and completely in the embodiments of the present application combined with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of the present application.

[0033] It should be noted that like reference numerals and characters refer to like elements throughout the following description and the claims attached hereto. The terms "including", "comprising" or any other variations thereof are intended to cover a non-exclusive inclusion such that processes, methods, articles, or apparatuses that comprise a list of elements are not required to comprise only those elements but can include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses. Without additional restrictions, an element preceded by "comprises a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. The terms "first", "second", or the like do not denote any absolute significance, but are used to distinguish one element from another.

[0034] Before introducing the present application, several background concepts involved are briefly described.

[0035] Real-time PCR: The amount of specific product is determined by continuously monitoring the change of fluorescence signal intensity during the exponential amplification of PCR. The whole PCR process is monitored in real time by adding fluorescence groups in the PCR reaction system, and the starting template is quantitatively analyzed by using appropriate data analysis methods. The sample preparation process will cause differences between samples due to many factors, and data normalization is a process to eliminate these differences.

[0036] Cycle threshold (Ct value): Generally falls in the exponential phase of the PCR growth curve, and is expressed as the cycle number when the detected fluorescence signal intensity reaches the set threshold in the threshold intersection method, and is expressed as the maximum point of the change rate of the amplification speed of the PCR reaction in the second derivative method. The greater the starting copy number of the template in the sample to be tested, the smaller the Ct value, so the Ct value can be used to correlate the starting copy number of the template. In clinical practice, the Ct value of the sample is used to determine the positive and negative.

[0037] Four stages of amplification curve: 1. Baseline period: the horizontal part of the amplification curve, the fluorescence signal of amplification is covered by the fluorescence background signal, and the change of product generation cannot be judged. 2. Exponential period: the exponential amplification period of PCR, the amplification curve peaks, the product amount of each cycle is in exponential relationship with the initial template amount, and there is a linear relationship between the logarithmic value of PCR product amount and the initial template amount. 3. Linear period: the late stage of PCR reaction, due to the influence of factors such as consumption of PCR reaction system components and product inhibition, the reaction efficiency is reduced, and the product amount is no longer in exponential relationship with the initial template amount. 4. Plateau period: PCR reaction stops, and PCR product no longer increases with the increase of cycle number. Due to the complex factors affecting PCR amplification, the time of each reaction entering the plateau period and the height of the plateau period are different.

[0038] The present application inventors noticed that the existing normalization processing when analyzing the cycle threshold of PCR amplification curve generally has two kinds: one is to convert the original fluorescence data, remove the background to obtain the normalized signal; two, use reference channel, divide the signals of two channels, and then remove the background to obtain the normalized signal. However, in the above two methods, the first method has edge effect, if the fluorescence signal intensity of different wells is different, the normalized data will have proportional deviation; the second method overcomes this point, but needs to use reference dye, increases the complexity of the test, and also uses an additional channel, which also increases the complexity of the instrument. Therefore, the present application provides a nucleic acid amplification curve analysis method as follows:

[0039] Please refer to Figure 1 , Figure 1 A flowchart of a nucleic acid amplification curve analysis method provided by the present application embodiment, the method comprises: step 100, step 120, step 140 and step 160.

[0040] Step 100: fitting and derivative solving of nucleic acid amplification data, obtaining first derivative curve and high-order derivative curve of nucleic acid amplification curve;

[0041] Step 120: substituting the peak points of the first derivative curve and the high-order derivative curve into the first derivative curve to calculate the characteristic points on the first derivative curve;

[0042] Step 140: linear fitting and normalization of the characteristic points to obtain the normalized first derivative curve of the nucleic acid amplification curve;

[0043] Step 160: determining the cycle threshold of the nucleic acid amplification curve according to the normalized first derivative curve.

[0044] Exemplarily, a nucleic acid amplification (growth) curve shows when the amount of nucleic acid data increases over time, which is generated by a polymerization chain reaction (PCR) and can be represented by a two-dimensional curve graph, with the cycle number defined as the x-axis and the cumulative growth indicator defined as the y-axis. Among them, the cumulative growth indicator can be the fluorescence intensity value generated by the fluorescent marker, and other physical quantity indicators can be used according to the specific marker and / or detection scheme used, such as: light emission intensity, bioluminescence intensity, phosphorescence intensity, charge transfer, voltage, current, power, energy, temperature, viscosity, light scattering, radiation intensity, reflectivity, transmittance and absorbance. The definition of cycle number can be: time, processing cycle, unit operation cycle and reproductive cycle.

[0045] In biology, when a population grows in limited resources, its growth conforms to a sigmoid function (S-shaped function), and its growth curve changes over time in an S-shaped shape. In PCR amplification, the situation in the test tube is similar, and nucleic acids are amplified in limited resources, so its S-shaped curve can also be expressed by a sigmoid function, which can be expressed by a mathematical expression as follows:

[0046]

[0047] Among them, N is the fluorescence amount of the final product, N0 is the fluorescence amount of the initial product, k is the amplification efficiency, and x is the cycle number. The basic amplification curve can have a linear part, followed by a growth (amplification) part, and finally an asymptotic part. In particular, the growth part can be an S-shaped curve in addition to the sigmoid function mentioned above, such as a Logistic function, and can also have an exponential, a high-order polynomial, or other types of logical functions or logical curves that model growth. The general PCR amplification curve also has a straight line segment, which is related to the evaporation and condensation of the liquid, so the nucleic acid amplification curve of the present application can also have a linear function, which can be expressed by a mathematical expression as follows:

[0048] f2(x)=ax+b

[0049] Among them, a and b are corresponding constant parameters related to the specific marker and / or detection scheme actually used, so the nucleic acid amplification curve of the present application can finally be expressed by a mathematical expression as follows: f(x)=f1(x)+f2(x).

[0050] First, the nucleic acid amplification data is fitted, and then the first order and high order derivative of the fitted nucleic acid amplification curve is solved, where the high order derivative can be second order, third order, or fourth order, fifth order or higher order; second, the peak points of the first order and high order derivative are solved according to the obtained derivative, and then the abscissa of the peak points is substituted into the first order derivative curve to obtain the characteristic points on the first order derivative curve, and the abscissa and ordinate of the first order derivative curve at this time are established as the coordinate values of the characteristic points; third, linear fitting and first order derivative normalization are performed on these characteristic points, which is very different from the existing normalization of original fluorescence data. Finally, according to the normalized first order derivative curve, the threshold line method can be used to calculate and determine the cycle threshold Ct, so as to achieve the purpose of final analysis and determination.

[0051] By solving the peak points of the first order and high order derivative of the fitted original nucleic acid amplification data, and then normalizing the first order derivative according to these peak points, and determining the cycle threshold of the amplification curve according to the normalized first order derivative, the problem of different fluorescence intensity values caused by edge effect is avoided, and reference dye is not required, thereby greatly optimizing the experimental process and improving the experimental efficiency.

[0052] In one embodiment, step 100 can include step 101, step 102 and step 103.

[0053] Step 101: acquiring original nucleic acid amplification data by using a sliding window to obtain nucleic acid amplification data in a fixed cycle number interval; wherein the nucleic acid amplification data includes the fluorescence intensity of the growth process of a plurality of nucleic acid data points at the cycle number;

[0054] Step 102: performing linear least squares regression fitting on the nucleic acid amplification data to obtain a nucleic acid amplification curve;

[0055] Step 103: performing first order derivative solving and high order derivative solving on the nucleic acid amplification curve to obtain a first order derivative curve and a high order derivative curve.

[0056] For example, for four gradient template amplification reactions, the fluorescence signal intensity during the extension of each cycle is taken for each reaction, and the maximum value is used as the raw data, for a total of 40 values. A sliding window is fixed at 5 data points. Linear least squares regression is performed on the raw nucleic acid amplification data from the first cycle to the 40th cycle to obtain the nucleic acid amplification curve f(x) = f1(x) + f2(x). The slope of all fitted line segments is then calculated as the value of f′(x), thus determining the first derivative curve f′(x). Further solving for higher derivatives on the first derivative curve yields the corresponding higher derivative curves. Instead of directly fitting an S-shaped curve to the raw nucleic acid amplification data, linear least squares is used to fit the nucleic acid amplification curve, reducing algorithm complexity and improving fitting accuracy.

[0057] In one embodiment, the higher-order derivative curve may include: a second-order derivative curve; step 100 may also include: steps 104, 105, 106 and 107.

[0058] Step 104: Solve for the first derivative of the nucleic acid amplification curve to obtain the first derivative curve;

[0059] Step 105: Determine if the first derivative curve has a peak.

[0060] Step 106: If it is determined that the first derivative curve has a peak, then perform polynomial least squares regression fitting on at least three points near the peak of the first derivative curve to obtain the peak point of the first derivative curve.

[0061] Step 107: Solve for the second derivative of the nucleic acid amplification curve to obtain the second derivative curve.

[0062] For example, least squares polynomial curve fitting can be based on m given points, without requiring the curve to pass through these points exactly, but rather approximating the curve y = φ(x) of the curve y = f(x), thereby finding an approximate solution. m is at least equal to 3. When m is 3, least squares quadratic polynomial curve fitting can be performed. When m takes values ​​of 4, 5...9, least squares cubic polynomial curve fitting can be performed.

[0063] For the nucleic acid amplification curve fitted in step 100, calculate the slope of all fitted line segments as the value of f′(x), thereby determining the first derivative curve f′(x); determine whether f′(x) has a peak by the peak value; if the first derivative curve has a peak, then perform polynomial least squares regression fitting on at least three points near the peak of the first derivative curve f′(x) to obtain the peak point of the first derivative curve. Optionally: for the nucleic acid amplification curve fitted by a series of fluorescence intensities corresponding to cycle numbers 1-40 in the original amplification data: 19899, ​​20383, 21056, 21530, 22024, 22300, 22560, 22695, 22860, 22949, 23151, etc., perform cubic polynomial least squares regression fitting on 9 points around the peak of the first derivative to obtain the horizontal and vertical coordinates of the peak point: x1 and f′(x1). Specifically, since the peak point coordinates are directly obtained from the first derivative curve, there is no need to substitute them into the first derivative to find characteristic points; they can be directly regarded as first derivative characteristic points. For example... Figure 2 As shown, the horizontal axis represents the cycle number, and the vertical axis can represent the fluorescence intensity of multiple nucleic acid data points at the cycle number during the growth process. Figure 2 The first derivative characteristic point is located at the peak of the first derivative curve, and its value can be calculated as (28.48, 16625.88).

[0064] Using a fixed sliding window of 5 data points, linear least squares regression was applied to f′(x) from the first cycle to the 40th cycle. The slope of all fitted line segments was calculated as the value of f″(x), thus determining the second derivative curve f″(x). Instead of directly fitting an sigma curve to find the peak value of the first derivative of the nucleic acid amplification curve, a least squares polynomial curve fitting was used. The peak value was determined by identifying the peak point of the first derivative and the second derivative, reducing algorithm complexity, improving the accuracy of peak value calculation, and facilitating the rapid determination of the cycle threshold.

[0065] In one embodiment, the higher-order derivative curve may further include: a third-order derivative curve; after step 107, step 100 may further include: steps 108, 109 and 110.

[0066] Step 108: Based on the data from the side with the smaller peak value of the first derivative curve, determine whether the second derivative curve has a peak.

[0067] Step 109: If it is determined that the second derivative curve has a peak, then perform polynomial least squares regression fitting on at least three points near the peak of the second derivative curve to obtain the peak point of the second derivative curve; and perform third derivative solution on the nucleic acid amplification curve to obtain the third derivative curve.

[0068] Step 110: If it is determined that the second derivative curve does not have a peak, then the cycle threshold of the nucleic acid amplification curve is determined to be non-existent.

[0069] For example, for the second derivative curve obtained in step 107, peak value judgment is performed on the data before x1 (the side with smaller data values) to determine whether f″(x) has a maximum peak. If the second derivative curve has a maximum peak, polynomial least squares regression fitting is performed on at least three points near the peak of the second derivative curve f″(x) to obtain the peak point of the second derivative curve. Optionally, cubic polynomial least squares regression fitting is performed on 9 points near the peak of f″(x) to obtain the abscissa x2 of the peak point. This abscissa x2 is then substituted into the first derivative curve f′(x) for calculation to obtain the ordinate value f′(x2) of the corresponding feature point on the first derivative curve. Figure 2 The second derivative feature point shown is located on the left side of the first derivative curve (the side with the smaller data value), on the slope where the change is fastest, with values ​​of (26.15, 9740.58). Simultaneously, with a fixed sliding window of 5 data points, linear least squares regression is performed on f″(x) from the first cycle to the 40th cycle. The slope of all fitted line segments is calculated as the value of f″′(x), thus determining the third derivative curve f″′(x).

[0070] If neither the second derivative curve nor the first derivative curve has a peak, then the nucleic acid amplification curve can be considered to have undergone no amplification, and there is no cycle threshold Ct value. If the second derivative curve has no peak, but the first derivative curve has a peak, then the nucleic acid amplification curve can be considered to have undergone rapid amplification, but due to the high initial concentration, there is also no cycle threshold Ct value. The peak value of the second derivative of the nucleic acid amplification curve is not directly fitted with an sigmoid curve, but rather determined using least squares polynomial curve fitting. Furthermore, the peak value is used to determine the peak point of the second derivative and the third derivative, reducing algorithm complexity, improving the accuracy of peak value calculation, and facilitating the rapid determination of the cycle threshold.

[0071] In one embodiment, after step 110, step 100 may further include steps 111, 112, and 113.

[0072] Step 111: Based on the data from the side with the smaller value of the peak value of the second derivative curve, determine whether the third derivative curve has a peak value;

[0073] Step 112: If it is determined that the third derivative curve has a peak, then perform polynomial least squares regression fitting on at least three points near the peak of the third derivative curve to obtain the peak point of the third derivative curve.

[0074] Step 113: If it is determined that the third derivative curve does not have a peak, then the cycle threshold of the nucleic acid amplification curve is set to be less than zero.

[0075] For example, for the third derivative curve obtained in step 109, peak value judgment is performed on the data before x2 (the side with smaller data values) to determine whether f″′(x) has a maximum peak. If the third derivative curve has a maximum peak, polynomial least squares regression fitting is performed on at least three points near the peak of the third derivative curve f″′(x) to obtain the peak point of the third derivative curve. Optionally, cubic polynomial least squares regression fitting is performed on 9 points near the peak of f″′(x) to obtain the abscissa x3 of the peak point. This abscissa x3 is then substituted into the first derivative curve f′(x) for calculation to obtain the ordinate value f′(x3) of the corresponding feature point on the first derivative curve. Figure 2 The third derivative characteristic point shown is located on the left side of the first derivative curve, where the slope changes slightly slower than that at the second derivative characteristic point, and its value can be (23.68, 2571.75).

[0076] like Figure 3 As shown, Figure 3 The diagram shows the approximate shapes of the nucleic acid amplification curve, the first derivative curve, the second derivative curve, and the third derivative curve. It also shows the peak point of the first derivative (characteristic point of the first derivative) A, the peak point of the second derivative B, the corresponding first derivative value of the second derivative peak point (characteristic point of the second derivative) C, the peak point of the third derivative D, and the corresponding first derivative value of the third derivative peak point (characteristic point of the third derivative) E. If the third derivative curve does not have a maximum peak, but has both the first and second derivatives, i.e. Figure 3 The nucleic acid amplification data segment before point D (to the left of point D (the side with the smaller data value)) is not present. It is easy to see that the initial concentration of the nucleic acid amplification curve is high, and the cycle threshold can be determined to be less than zero.

[0077] By employing the least squares polynomial curve fitting method to solve for the peak point of the third derivative, the algorithm complexity is reduced and the accuracy of peak calculation is improved. Furthermore, the determination of the peak value of the third derivative is helpful in quickly determining the range of the loop threshold.

[0078] In one embodiment, step 140 may include steps 141 and 142.

[0079] Step 141: Process the constant parameters of the first derivative curve of the nucleic acid amplification curve to obtain the normalized parameters of the first derivative curve; among which, the constant parameters include: the fluorescence intensity of the final product, the fluorescence intensity of the initial product, and the amplification efficiency of the nucleic acid amplification curve.

[0080] Step 142: Perform linear least squares regression fitting on the feature points and normalized parameters to obtain the normalized first derivative curve.

[0081] For example, the normalization parameters can be a series of specific constants obtained by normalizing the first derivative curves of the nucleic acid amplification curves when the higher derivatives reach their maximum values. Optionally, for the sigmoid function, a special case can be discussed: when the fluorescence intensity N of the final product of the nucleic acid amplification curve is 1, the fluorescence intensity N0 of the initial product is equal to 1 / 2, and the amplification efficiency is 1, the sigmoid function can be expressed as:

[0082]

[0083] Taking the first, second, and third derivatives of f3(x) respectively, we can obtain:

[0084]

[0085]

[0086]

[0087] We can find the maximum value of e for the first, second, and third derivatives respectively. -x Find the value of f3′(x) at that point, and when f3′(x) reaches its maximum value, e -x =1, When f3″(x) reaches its maximum value When f3″(′x) ​​reaches its maximum value It is not difficult to find that when the first, second, and third derivatives of f1(x) reach their maximum values, the value of f1′(x) is: when f1′(x) reaches its maximum value, When f1″(x) reaches its maximum value When f1″(′x) ​​reaches its maximum value

[0088] For the amplification curve f(x) = f1(x) + f2(x); when the first, second, and third derivatives of f(x) reach their maximum values, the value of f1′(x) is: when f′(x) reaches its maximum value, When f″(x) reaches its maximum value, When f″′(x) reaches its maximum value

[0089] Therefore, for different amplification curves, the specific constant value of their first derivative is only related to N and a. The first derivative of all curves can be normalized. Therefore, when the higher derivatives are up to the third order, the normalization parameters can be 1 / 4, 1 / 6, and 1 / 12, respectively.

[0090] like Figure 4 The graph shown depicts the linear fitting function curve. The horizontal axis represents the eigenvalues ​​of f'(x) corresponding to the peak points of each derivative, and the vertical axis represents the normalization parameter. The graph shows the function curve fitted based on the eigenpoint points and the coefficient of determination R. 2 The coefficient of determination R 2 =0.9999, indicating a very good fit; the actual feature points match the feature points calculated by the sigmoid function, making normalization using this algorithm feasible. The specific process can be: based on normalization parameters such as 1 / 4, 1 / 6, 1 / 12, etc., and... Figure 3 The ordinate values ​​of the first derivative characteristic point A, the second derivative characteristic point C, and the third derivative characteristic point E obtained from the previous step are used for linear fitting, i.e., for the array (matrix). Linear least squares regression fitting was performed to obtain the normalized first derivative curve g′(x)=kf′(x)+b, where k=1 / N and b=-a / N. By directly normalizing the first derivative of the nucleic acid amplification curve, the problem of different fluorescence intensity values ​​caused by edge effects was avoided, the algorithm complexity was reduced, and the efficiency of loop threshold calculation was improved.

[0091] In one embodiment, step 160 may include steps 161 and 162.

[0092] Step 161: Take the threshold line for the normalized first derivative curve;

[0093] Step 162: Determine the abscissa value of the smaller intersection point of the threshold line and the normalized first derivative curve as the cycle threshold of the nucleic acid amplification curve.

[0094] For example, such as Figure 5 As shown, the horizontal axis represents the cycle number, and the vertical axis can represent the fluorescence intensity of multiple nucleic acid data points during the growth process at the cycle number. A threshold line g′(x) = n is taken for all normalized first derivatives, where n can take values ​​ranging from... The value of Ct is obtained by finding the smaller intersection point (Ct, g′(Ct)) between Ct and g′(x) using interpolation. Figure 4 As shown, when n is 0.1, the straight line intersects the normalized first derivative curve at two points. The value with the smaller x-coordinate can be determined as the final Ct value. Specifically, when g′(x) takes... When Ct is the peak of the second derivative of its amplification curve; when g′(x) takes At that time, Ct represents the highest peak of the third derivative of its amplification curve. By directly normalizing the first derivative of the nucleic acid amplification curve and determining the loop threshold based on the normalized first derivative curve, the problem of different fluorescence intensity values ​​caused by edge effects is avoided, the algorithm complexity is reduced, and the efficiency of loop threshold calculation is improved.

[0095] Please see Figure 6 , Figure 6 This is a schematic diagram of a nucleic acid amplification curve analysis device provided in an embodiment of this application. The device includes: a fitting derivative module 210, a feature point calculation module 220, a normalization module 230, and an analysis cycle threshold module 240.

[0096] The fitting and differentiation module 210 is used to fit and solve the derivatives of nucleic acid amplification data to obtain the first-order derivative curve and higher-order derivative curve of nucleic acid amplification curve;

[0097] The feature point calculation module 220 is used to substitute the peak points of the first derivative curve and the higher derivative curves into the first derivative curve for calculation to obtain the feature points on the first derivative curve.

[0098] Normalization module 230 is used to perform linear fitting and normalization on feature points to obtain the normalized first derivative curve of nucleic acid amplification curve.

[0099] The analysis cycle threshold module 240 is used to determine the cycle threshold of the nucleic acid amplification curve based on the normalized first derivative curve.

[0100] Optionally, the fitting derivative module 210 can be used for:

[0101] Raw nucleic acid amplification data was acquired using a sliding window to obtain nucleic acid amplification data within a fixed cycle number range; the nucleic acid amplification data included the fluorescence intensity of multiple nucleic acid data points during the growth process at each cycle number.

[0102] Linear least squares regression fitting was performed on the nucleic acid amplification data to obtain the nucleic acid amplification curve;

[0103] The first and higher derivatives of the nucleic acid amplification curves are obtained by solving for them.

[0104] Optionally, the fitting derivative module 210 can be used for:

[0105] The first derivative curve is obtained by solving the first derivative of the nucleic acid amplification curve;

[0106] Determine whether the first derivative curve has a peak.

[0107] If it is determined that the first derivative curve has a peak, then polynomial least squares regression fitting is performed on at least three points near the peak of the first derivative curve to obtain the peak point of the first derivative curve.

[0108] The second derivative curve is obtained by solving the second derivative of the nucleic acid amplification curve.

[0109] Optionally, the fitting derivative module 210 can be used for:

[0110] Based on the data from the side with the smaller peak value of the first derivative curve, determine whether the second derivative curve has a peak.

[0111] If it is determined that the second derivative curve has a peak, then polynomial least squares regression fitting is performed on at least three points near the peak of the second derivative curve to obtain the peak point of the second derivative curve; and the third derivative is solved on the nucleic acid amplification curve to obtain the third derivative curve.

[0112] If it is determined that the second derivative curve does not have a peak, then the cycle threshold of the nucleic acid amplification curve is determined to be non-existent.

[0113] Optionally, the fitting derivative module 210 can be used for:

[0114] Based on the data from the smaller side of the peak value of the second derivative curve, determine whether the third derivative curve has a peak value;

[0115] If it is determined that the third derivative curve has a peak, then polynomial least squares regression fitting is performed on at least three points near the peak of the third derivative curve to obtain the peak point of the third derivative curve.

[0116] If it is determined that the third derivative curve does not have a peak, then the cycle threshold of the nucleic acid amplification curve is set to be less than zero.

[0117] Optionally, the normalization module 230 can be used for:

[0118] The constant parameters of the first derivative curve of the nucleic acid amplification curve are processed to obtain the normalized parameters of the first derivative curve; among which, the constant parameters include: the fluorescence intensity of the final product, the fluorescence intensity of the initial product, and the amplification efficiency of the nucleic acid amplification curve.

[0119] Linear least squares regression fitting is performed on the feature points and normalized parameters to obtain the normalized first derivative curve.

[0120] Optionally, the analysis loop threshold module 240 can be used for:

[0121] Take a threshold line for the normalized first derivative curve;

[0122] The abscissa value of the smaller intersection point of the threshold line and the normalized first derivative curve is determined as the cycle threshold of the nucleic acid amplification curve.

[0123] Please see Figure 7 , Figure 7 This is a block diagram of an electronic device. The electronic device 300 may include a memory 311, a memory controller 312, a processor 313, a peripheral interface 314, an input / output unit 315, and a display unit 316. Those skilled in the art will understand that... Figure 7 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device 300. For example, the electronic device 300 may also include components that are more... Figure 7 The more or fewer components shown, or having the same Figure 7 The different configurations shown.

[0124] The aforementioned memory 311, memory controller 312, processor 313, peripheral interface 314, input / output unit 315, and display unit 316 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The aforementioned processor 313 is used to execute executable modules stored in the memory.

[0125] The memory 311 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 311 stores programs, and the processor 313 executes these programs upon receiving execution instructions. The methods executed by the electronic device 300, as defined in any embodiment of this application, can be applied to or implemented by the processor 313.

[0126] The aforementioned processor 313 may be an integrated circuit chip with signal processing capabilities. The processor 313 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.

[0127] The peripheral interface 314 described above couples various input / output devices to the processor 313 and the memory 311. In some embodiments, the peripheral interface 314, the processor 313, and the memory controller 312 can be implemented in a single chip. In other instances, they can be implemented by separate chips.

[0128] The input / output unit 315 described above is used to provide user input data. The input / output unit 315 may be, but is not limited to, a mouse and keyboard.

[0129] The aforementioned display unit 316 provides an interactive interface (e.g., a user interface) for the user to reference between the electronic device 300 and the user. In this embodiment, the display unit 316 may be a liquid crystal display (LCD) or a touch screen display. The LCD or touch screen display can show the process of the processor executing the program.

[0130] The electronic device 300 in this embodiment can be used to perform the various steps in the various methods provided in the embodiments of this application.

[0131] Furthermore, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps described in the above method embodiments.

[0132] The computer program product of the above-described method provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the steps in the above-described method embodiments. For details, please refer to the above-described method embodiments, which will not be repeated here.

[0133] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. The functional modules in the embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0134] It should be noted that if the function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0136] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for analyzing nucleic acid amplification curves, characterized in that, The method includes: By fitting and solving the derivatives of the nucleic acid amplification data, the first-order derivative curves and higher-order derivative curves of the nucleic acid amplification curves are obtained; The peak points of the first derivative curve and the higher derivative curves are substituted into the first derivative curve for calculation to obtain the feature points on the first derivative curve; the feature points are linearly fitted and normalized to obtain the normalized first derivative curve of the nucleic acid amplification curve. The cycle threshold of the nucleic acid amplification curve is determined based on the normalized first derivative curve. The step of substituting the peak points of the first-order derivative curve and the higher-order derivative curve into the first-order derivative curve for calculation to obtain the feature points on the first-order derivative curve includes: substituting the abscissa of the peak point of the higher-order derivative curve into the first-order derivative curve to obtain the corresponding ordinate value as the feature point. The step of linearly fitting and normalizing the feature points to obtain the normalized first derivative curve of the nucleic acid amplification curve includes: processing the constant parameters of the first derivative curve of the nucleic acid amplification curve to obtain the normalized parameters of the first derivative curve; wherein, the constant parameters include: the fluorescence intensity of the final product, the fluorescence intensity of the initial product, and the amplification efficiency of the nucleic acid amplification curve; and performing linear least squares regression fitting on the feature points and the normalized parameters to obtain the normalized first derivative curve.

2. The method according to claim 1, characterized in that, The process of fitting and solving the derivatives of nucleic acid amplification data to obtain the first-order derivative curve and higher-order derivative curves of the nucleic acid amplification curve includes: Raw nucleic acid amplification data is acquired using a sliding window to obtain nucleic acid amplification data within a fixed cycle number range; wherein, the nucleic acid amplification data includes: fluorescence intensity of multiple nucleic acid data points during the growth process at the cycle number; Linear least squares regression fitting was performed on the nucleic acid amplification data to obtain the nucleic acid amplification curve; The first derivative and higher derivative curves are obtained by solving the first derivative and higher derivative curves of the nucleic acid amplification curve.

3. The method according to claim 2, characterized in that, in, The higher-order derivative curve includes: a second-order derivative curve; the process of solving for the first-order derivative and higher-order derivatives of the nucleic acid amplification curve to obtain the first-order derivative curve and the higher-order derivative curve includes: The first derivative curve is obtained by solving the first derivative of the nucleic acid amplification curve. Determine whether the first derivative curve has a peak value; If it is determined that the first derivative curve has a peak, then polynomial least squares regression fitting is performed on at least three points near the peak of the first derivative curve to obtain the peak point of the first derivative curve. The second derivative curve is obtained by solving the second derivative of the nucleic acid amplification curve.

4. The method according to claim 3, characterized in that, in, The higher-order derivative curve further includes: a third-order derivative curve; after solving for the second derivative of the nucleic acid amplification curve to obtain the second-order derivative curve, the next step of solving for the first-order derivative and higher-order derivatives of the nucleic acid amplification curve to obtain the first-order derivative curve and the higher-order derivative curve further includes: Based on the data from the side with the smaller peak value of the first derivative curve, determine whether the second derivative curve has a peak value; If it is determined that the second derivative curve has a peak, then polynomial least squares regression fitting is performed on at least three points near the peak of the second derivative curve to obtain the peak point of the second derivative curve; and the third derivative is performed on the nucleic acid amplification curve to obtain the third derivative curve. If it is determined that the second derivative curve does not have a peak, then the cycle threshold of the nucleic acid amplification curve is determined to be non-existent.

5. The method according to claim 4, characterized in that, After obtaining the third derivative curve by solving the third derivative of the nucleic acid amplification curve, the next step of obtaining the first derivative curve and the higher derivative curve by solving the first derivative curve and the higher derivative curve by solving the first derivative curve and the higher derivative curve, further includes: Based on the data from the smaller side of the peak value of the second derivative curve, determine whether the third derivative curve has a peak value; If it is determined that the third derivative curve has a peak, then polynomial least squares regression fitting is performed on at least three points near the peak of the third derivative curve to obtain the peak point of the third derivative curve. If it is determined that the third derivative curve does not have a peak, then the cycle threshold of the nucleic acid amplification curve is set to be less than zero.

6. The method according to claim 1, characterized in that, Determining the cycle threshold of the nucleic acid amplification curve based on the normalized first derivative curve includes: A threshold line is taken for the normalized first derivative curve; The abscissa value of the smaller intersection point of the threshold line and the normalized first derivative curve is determined as the cycle threshold of the nucleic acid amplification curve.

7. A nucleic acid amplification curve analysis device, characterized in that, The device includes: The fitting and derivative-finding module is used to fit nucleic acid amplification data and solve for derivatives to obtain the first-order derivative curve and higher-order derivative curve of nucleic acid amplification curve; The feature point calculation module is used to substitute the peak points of the first derivative curve and the higher derivative curves into the first derivative curve for calculation to obtain the feature points on the first derivative curve. The normalization module is used to perform linear fitting and normalization on the feature points to obtain the normalized first derivative curve of the nucleic acid amplification curve. The cycle threshold analysis module is used to determine the cycle threshold of the nucleic acid amplification curve based on the normalized first derivative curve. The feature point calculation module is specifically used to: substitute the abscissa of the peak point of the higher-order derivative curve into the first-order derivative curve to obtain the corresponding ordinate value as the feature point. The normalization module is specifically used to: process the constant parameters of the first derivative curve of the nucleic acid amplification curve to obtain the normalized parameters of the first derivative curve; wherein, the constant parameters include: the fluorescence intensity of the final product, the fluorescence intensity of the initial product, and the amplification efficiency of the nucleic acid amplification curve; and perform linear least squares regression fitting on the feature points and the normalized parameters to obtain the normalized first derivative curve.

8. An electronic device, characterized in that, include: The processor and memory, wherein the memory stores machine-readable instructions executable by the processor, wherein when the electronic device is running, the machine-readable instructions are executed by the processor to perform the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 6.

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