CN115545123A - Melting curve optimization method and device, electronic equipment and storage medium - Google Patents
Melting curve optimization method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a melting curve optimization method, a melting curve optimization device, electronic equipment and a storage medium, and relates to the technical field of PCR detection.
Description
Technical Field
The present invention relates to the field of PCR detection technologies, and in particular, to a method and an apparatus for optimizing a melting curve, an electronic device, and a storage medium.
Background
After the PCR (polymerase chain reaction) amplification reaction is completed, in order to examine the specificity of the amplification product, the melting curve, which is the value of the negative derivative of fluorescence intensity, is usually obtained by gradually increasing the temperature and degrading the amplification product. In the temperature rising process, when the temperature reaches the temperature of half melting, the fluorescence intensity will be rapidly reduced, so as to form a high peak point on the melting curve, the temperature corresponding to the peak point is the Tm value (i.e. melting temperature), and the number and position of the Tm value are the main points for investigation.
The currently common melting curve analysis methods include a direct search method, a hierarchical clustering method, a high-order derivative method, a curve fitting method based on Levenberg-Marquardt, a continuous wavelet transform method and the like. When the amplitude characteristics of each peak point of the melting curve are not obvious enough, the Tm value determined by the existing melting curve analysis method is easily influenced by the clutter, so that the identification of the clutter and the elimination of the clutter corresponding to the clutter are both problems to be faced.
Disclosure of Invention
The invention aims to provide a melting curve optimization method, a melting curve optimization device, an electronic device and a storage medium, so as to effectively remove the miscellaneous peaks in the melting curve.
In a first aspect, an embodiment of the present invention provides a melting curve optimization method, including:
acquiring an initial melting curve and an initial Tm value corresponding to fluorescence intensity data to be processed; wherein the initial melting curves include a first melting curve with an initial Tm value, a second melting curve without the initial Tm value and with a maximum point, and an initial negative melting curve without the initial Tm value and without the maximum point;
based on a preset rule, carrying out cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve to obtain a target negative melting curve and a candidate melting curve from which a foreign peak is to be removed; wherein the preset rule is related to a melting curve definition;
and removing the hybrid peak of the candidate melting curve based on the similarity between the candidate melting curve and the target negative melting curve to obtain an optimized target melting curve.
Further, the performing cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve based on a preset rule to obtain a target negative melting curve and a candidate melting curve from which a foreign peak is to be removed includes:
screening out a Tm value corresponding to a hetero peak from the initial Tm value of the first melting curve according to the preset rule;
carrying out clustering analysis based on the contour coefficient on the cluster set to obtain a clustering result; wherein the cluster set comprises the amplitude of the initial Tm value in the first melting curve except the Tm value corresponding to the hetero-peak and the amplitude of the maximum value point in the second melting curve;
and obtaining a target negative melting curve and a candidate melting curve of the impurity peak to be removed according to the clustering result.
Further, the screening out a Tm value corresponding to a hetero-peak from the initial Tm values of the first melting curve according to the preset rule includes:
and determining the initial Tm value of the first melting curve, which does not satisfy the occurrence of the melting peak in the fluorescence intensity decrease region, as the Tm value corresponding to the hetero-peak.
Further, the obtaining of a target negative melting curve and a candidate melting curve from which a miscellaneous peak is to be removed according to the clustering result includes:
screening out a target Tm value, a candidate Tm value and a target maximum value point from the initial Tm value and the maximum value point corresponding to the cluster set according to the clustering result; the target Tm value is an initial Tm value corresponding to a amplitude value of which the profile coefficient is greater than a set threshold value, the candidate Tm value is an initial Tm value corresponding to a amplitude value of which the profile coefficient is less than or equal to the set threshold value, and the target maximum value point is a maximum value point corresponding to an amplitude value of which the profile coefficient is greater than the set threshold value;
determining a second melting curve and the initial negative melting curve of which all maximum points are the target maximum points as a target negative melting curve;
and determining a first melting curve containing the Tm value corresponding to the hetero peak or the candidate Tm value as a candidate melting curve of the hetero peak to be removed.
Further, the removing the hybrid peak of the candidate melting curve based on the similarity with the target negative melting curve to obtain an optimized target melting curve includes:
determining a target correlation coefficient and a relevant negative melting curve corresponding to the candidate melting curve; wherein the target correlation coefficient is a maximum value of correlation coefficients between fluorescence intensity curves corresponding to the candidate melting curves and fluorescence intensity curves corresponding to the target negative melting curves, and the correlation negative melting curves are target negative melting curves corresponding to the target correlation coefficients;
determining a target miscellaneous peak in the candidate melting curve according to the threshold interval to which the target correlation coefficient belongs and the related negative melting curve corresponding to the candidate melting curve;
and performing smoothing treatment on the target miscellaneous peak on the candidate melting curve by adopting a preset filtering algorithm to obtain an optimized target melting curve.
Further, the determining a target hybrid peak in the candidate melting curve according to the threshold interval to which the target correlation coefficient belongs and the relevant negative melting curve corresponding to the candidate melting curve includes:
when the target correlation coefficient belongs to a first threshold interval, determining melting peaks corresponding to all initial Tm values in the candidate melting curve as target mixed peaks;
when the target correlation coefficient belongs to a second threshold interval, performing hybrid peak investigation on the candidate Tm values in the candidate melting curve in sequence from large amplitude to small amplitude to obtain a target hybrid peak in the candidate melting curve; wherein any numerical value in the second threshold interval is smaller than any numerical value in the first threshold interval, the candidate Tm value is an initial Tm value that is not yet determined to belong to a hybrid peak, the target hybrid peak includes a melting peak corresponding to a candidate Tm value that does not meet preset melting peak requirements, and the melting peak requirements are related to the determined target Tm value set;
when the target correlation coefficient belongs to a third threshold interval, determining a target hybrid peak in the candidate melting curve based on the magnitude relation between the maximum amplitude of the candidate Tm value in the candidate melting curve and the amplitude of the corresponding position of the relevant negative melting curve; wherein any value within the third threshold interval is less than any value within the second threshold interval;
when the target correlation coefficient belongs to a fourth threshold interval, determining that a target miscellaneous peak of the candidate melting curve is empty; wherein any value within the fourth threshold interval is less than any value within the third threshold interval.
Further, the melting peak requirement includes that, in the determined target set of Tm values, there are at least a preset number of correlated Tm values located within a preset range of the candidate Tm value and greater than the amplitude of the candidate Tm value, and the candidate Tm value is greater than half of the minimum amplitude of the correlated Tm values;
the determining a target hetero-peak in the candidate melting curve based on the magnitude relation between the maximum amplitude of the candidate Tm value in the candidate melting curve and the amplitude of the corresponding position of the related negative melting curve comprises the following steps:
when the maximum amplitude of the candidate Tm values in the candidate melting curve is smaller than or equal to the amplitude of the corresponding position of the related negative melting curve, performing hybrid investigation on the candidate Tm values in the candidate melting curve according to the sequence of the amplitudes from large to small to obtain a target hybrid in the candidate melting curve;
and when the maximum amplitude of the candidate Tm values in the candidate melting curve is larger than the amplitude of the corresponding position of the related negative melting curve, determining that the target hetero-peak of the candidate melting curve is empty.
In a second aspect, an embodiment of the present invention further provides a melting curve optimization device, including:
the acquisition module is used for acquiring an initial melting curve and an initial Tm value corresponding to the fluorescence intensity data to be processed; wherein the initial melting curves comprise a first melting curve with an initial Tm value, a second melting curve without the initial Tm value and with a maximum value point, and an initial negative melting curve without the initial Tm value and without the maximum value point;
the analysis module is used for carrying out clustering analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve based on a preset rule to obtain a target negative melting curve and a candidate melting curve from which a miscellaneous peak is to be removed; wherein the preset rule is related to a melting curve definition;
and the optimization module is used for removing the hybrid peak of the candidate melting curve based on the similarity between the candidate melting curve and the target negative melting curve to obtain an optimized target melting curve.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements the melting curve optimization method of the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention further provides a storage medium, where the storage medium stores a computer program, and the computer program is executed by a processor to perform the method for optimizing a melting curve according to the first aspect.
According to the melting curve optimization method, the melting curve optimization device, the electronic equipment and the storage medium, on the basis of the initial melting curve obtained by the existing method, the initial melting curve is divided into the target negative melting curve and the candidate melting curve of the candidate melting curve to be removed based on the preset rule, and then the hybrid peak of the candidate melting curve is removed in a targeted manner based on the similarity between the target negative melting curve and the candidate melting curve, so that the hybrid peak in the melting curve is effectively removed, and the method, the device, the electronic equipment and the storage medium are easy to understand and realize.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a melting curve optimization method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of another melting curve optimization method according to an embodiment of the present invention;
FIG. 3 is a melting curve chart before removing impurity peaks and corresponding Tm values;
FIG. 4 is a melting curve diagram after removing the miscellaneous peaks and the corresponding Tm value positions;
fig. 5 is a schematic structural diagram of a melting curve optimization apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Based on the fact that the Tm value determined by the existing melting curve analysis method is easily affected by clutter, the melting curve optimization method, the melting curve optimization device, the electronic equipment and the storage medium provided by the embodiment of the invention can better remove the miscellaneous peaks in the melting curve by comprehensively adopting the preset rules and the similarity, obtain the smooth melting curve, are easy to understand and are easy to implement.
For the convenience of understanding the present embodiment, a detailed description will be given to a melting curve optimization method disclosed in the present embodiment.
The embodiment of the invention provides a melting curve optimization method for removing melting curve peaks based on preset rules and similarity, which can be executed by electronic equipment with data processing capacity. Referring to fig. 1, a schematic flow chart of a melting curve optimization method mainly includes the following steps S102 to S106:
step S102, acquiring an initial melting curve and an initial Tm value corresponding to fluorescence intensity data to be processed; wherein the initial melting curve includes a first melting curve having an initial Tm value, a second melting curve having no initial Tm value and having a maximum point, and an initial negative melting curve having no initial Tm value and having no maximum point.
The existing method can be adopted to obtain the initial melting curve and the initial Tm value corresponding to the fluorescence intensity data to be processed. The existing method may include a melting curve Tm value determination method based on hierarchical clustering (hierarchical clustering method), a melting curve overlapping peak separation method, a direct search method, a curve fitting method, a higher derivative method, a continuous wavelet transform method, a neural network method, and the like, which is not limited in this embodiment.
If a certain melting curve has no Tm value, the melting curve is preliminarily determined as a negative melting curve, further, if no maximum value point exists, the melting curve can be determined as the negative melting curve, if the melting curve has the maximum value point, the maximum value points can be collected, and the usable negative melting curve can be further screened out through subsequent processing. Based on this, the initial melting curves can be classified into three categories: a first melting curve where an initial Tm value exists, a second melting curve where the initial Tm value does not exist and a maximum point exists, and an initial negative melting curve where the initial Tm value does not exist and the maximum point does not exist. The negative melting curve refers to a melting curve in which the target product is not present (no Tm value). In this embodiment, it is necessary to perform a hetero-peak screening and removing on a melting peak corresponding to the initial Tm value in the first melting curve.
And step S104, performing cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve based on a preset rule to obtain a target negative melting curve and a candidate melting curve from which a miscellaneous peak is to be removed.
Wherein the predetermined rule is related to the melting curve definition.
The rules may be determined based on a biological model generated from the melting curve. Specifically, according to the definition of the melting curve, "in the process of temperature rise, when the temperature reaches the temperature of half melting, the fluorescence intensity will rapidly decrease, so as to form a high peak point on the melting curve", and thus it can be known that, for a melting curve peak with obvious characteristics, especially a peak corresponding to the maximum amplitude of the melting curve should occur in a region where the fluorescence intensity decreases, and the amplitude is larger, whereas, the peak amplitude of the negative melting curve is smaller. The predetermined rule may be that the peak corresponding to the maximum amplitude of the melting curve should occur in the region of decreased fluorescence intensity and have a larger amplitude than the peak of the negative melting curve.
In some possible embodiments, the step S104 may be implemented by the following sub-steps:
and substep 1.1, screening out a Tm value corresponding to the hetero peak from the initial Tm values of the first melting curve according to a preset rule.
The initial Tm value of the first melting curve that does not satisfy the occurrence of a melting peak in the fluorescence intensity decreasing region may be determined as the Tm value corresponding to the hetero peak. The Tm values corresponding to the hetero-peaks can be put into the set of hetero-peaks.
Substep 1.2, carrying out clustering analysis based on the profile coefficient on the cluster set to obtain a clustering result; and the cluster set comprises the amplitude of an initial Tm value except the Tm value corresponding to the impurity removal peak in the first melting curve and the amplitude of a maximum value point in the second melting curve.
A clustering analysis based on contour coefficients (such as a Kmeans clustering analysis) may be performed on the cluster set. The contour coefficient is an evaluation mode of good and bad clustering effect, and the larger the value of the contour coefficient is, the better the clustering effect is.
And a substep 1.3, obtaining a target negative melting curve and a candidate melting curve of which the miscellaneous peak is to be removed according to the clustering result.
According to the corresponding profile coefficient of each sample (each element in the cluster set) in the clustering result, a characteristic obvious melting curve and a candidate melting curve from which a foreign peak is to be removed are screened out from the first melting curve, a negative melting curve is screened out from the second melting curve, and then the negative melting curve and the initial negative melting curve are aggregated to obtain a comprehensive negative melting curve, namely a target negative melting curve.
Substep 1.3 may be achieved by the following procedure: according to the clustering result, screening out a target Tm value, a candidate Tm value and a target maximum value point from the initial Tm value and the maximum value point corresponding to the clustering set; the target Tm value is an initial Tm value corresponding to the amplitude value of which the profile coefficient is greater than a set threshold value, the candidate Tm value is an initial Tm value corresponding to the amplitude value of which the profile coefficient is less than or equal to the set threshold value, and the target maximum value point is a maximum value point corresponding to the amplitude value of which the profile coefficient is greater than the set threshold value; determining a second melting curve and an initial negative melting curve of which all maximum points are target maximum points as target negative melting curves; and determining a first melting curve containing the Tm value or candidate Tm value corresponding to the hetero peak as a candidate melting curve of the hetero peak to be removed.
Wherein, tm values corresponding to the hetero peaks may exist in the candidate Tm values; the target Tm value can be put into a target Tm value set, the target Tm value set can be used for a subsequent similarity-based hybrid screening process, and a melting curve with only the target Tm value can be called a characteristic obvious melting curve. The set threshold may be set according to actual requirements, and is not limited herein, for example, the set threshold is 0.95.
And step S106, removing the hybrid peak of the candidate melting curve based on the similarity with the target negative melting curve to obtain an optimized target melting curve.
In some possible embodiments, the step S106 may be implemented by the following sub-steps:
substep 2.1, determining a target correlation coefficient and a related negative melting curve corresponding to the candidate melting curve; the target correlation coefficient is the maximum value of the correlation coefficient between the fluorescence intensity curve corresponding to the candidate melting curve and the fluorescence intensity curve corresponding to each target negative melting curve, and the related negative melting curve is the target negative melting curve corresponding to the target correlation coefficient.
For each candidate melting curve, a correlation coefficient between the corresponding fluorescence intensity curve and the fluorescence intensity curve corresponding to each target negative melting curve can be calculated, the maximum value in the correlation coefficients is selected as the target correlation coefficient, and the corresponding target negative melting curve is selected as the related negative melting curve.
The correlation coefficient may be a pearson correlation coefficient, which is expressed as follows:
wherein,X n for the fluorescence intensity curve corresponding to the candidate melting curve to be examined,Y n the fluorescence intensity curve is composed of fluorescence intensity data for the fluorescence intensity curve corresponding to the target negative melting curve to be compared.
And a substep 2.2, determining a target miscellaneous peak in the candidate melting curve according to the threshold interval to which the target correlation coefficient belongs and the related negative melting curve corresponding to the candidate melting curve.
All or part of the hetero peaks on the candidate melting curve can be determined according to a given threshold interval of the correlation coefficient and the comparison result of the candidate melting curve and the corresponding peak value of the correlation negative melting curve (the peak value of the melting peak, namely the amplitude of the Tm value).
Alternatively, substep 2.2 may be implemented by four cases:
(1) When the target correlation coefficient belongs to the first threshold interval, the candidate melting curve is very close to the related negative melting curve, so that melting peaks corresponding to all initial Tm values in the candidate melting curve are determined as target hybrid peaks.
The first threshold interval may be set according to practical situations, and is, for example, [0.9999,1].
(2) When the target correlation coefficient belongs to the second threshold interval, the candidate melting curve is similar to the related negative melting curve, and therefore, the candidate Tm values in the candidate melting curve are subjected to hybrid peak investigation sequentially according to the sequence from large amplitude to small amplitude, and the target hybrid peak in the candidate melting curve is obtained.
Wherein any value in the second threshold interval is smaller than any value in the first threshold interval, and the second threshold interval can be set according to the situation, for example, 0.999,0.9999; the candidate Tm value is an initial Tm value for which it has not been determined whether or not it belongs to a hetero-peak; the target hybrid peak comprises a melting peak corresponding to a candidate Tm value which does not meet the requirement of a preset melting peak, and can also comprise a hybrid peak determined in the previous process; melting peak requirements relate to the set of target Tm values that have been determined. In the investigation process, if the melting peak corresponding to the current candidate Tm value is already judged to be a mixed peak, determining that all the melting peaks corresponding to the remaining candidate Tm values with smaller amplitude are mixed peaks, and exiting the loop.
Alternatively, considering that whether a melting peak corresponding to a candidate Tm value is a hetero-peak or not, which cannot be determined by the aforementioned clustering process, indicates that the candidate Tm value has a smaller amplitude than a target Tm value near a corresponding position in the characteristic significant melting curve, and if the candidate Tm value is actually the target Tm value, it indicates that it is required to satisfy a requirement of a larger amplitude, based on which the melting peak requirement may include that, in the determined set of target Tm values, there are at least a preset number of related Tm values that are located within a preset range of the candidate Tm value and are larger than half of the minimum amplitude of the related Tm values. The preset number and the preset range can be set according to actual requirements, for example, the preset number is 2, and the preset range is plus or minus 1 ℃ of the candidate Tm value.
(3) When the target correlation coefficient belongs to the third threshold interval, it is indicated that the candidate melting curve is not similar to the related negative melting curve, and therefore, the target hybrid peak in the candidate melting curve is determined based on the magnitude relation between the maximum amplitude of the candidate Tm value in the candidate melting curve and the amplitude of the corresponding position of the related negative melting curve.
Wherein any value within the third threshold interval is less than any value within the second threshold interval; the third threshold interval may be set according to circumstances, for example, 0.95,0.999). In specific implementation, when the maximum amplitude of the candidate Tm value in the candidate melting curve is less than or equal to the amplitude of the corresponding position of the relevant negative melting curve, it indicates that there may still be a mixed peak in the candidate melting curve, and further investigation may be performed according to the foregoing situation (2), that is, the candidate Tm values in the candidate melting curve are subjected to mixed peak investigation in sequence according to the order of the amplitudes from large to small, so as to obtain a target mixed peak in the candidate melting curve; when the maximum amplitude of the candidate Tm values in the candidate melting curve is greater than the amplitude of the corresponding position of the related negative melting curve, it is indicated that no hybrid peak exists in the candidate melting curve, and it can be determined that the target hybrid peak of the candidate melting curve is empty.
(4) And when the target correlation coefficient belongs to the fourth threshold interval, the candidate melting curve is not similar to the related negative melting curve, so that the target mixed peak of the candidate melting curve is determined to be empty.
Wherein any value in the fourth threshold interval is smaller than any value in the third threshold interval, and the fourth threshold interval may be set according to circumstances, for example, smaller than 0.95.
For ease of understanding, the target correlation coefficient of a candidate melting curve is denoted as r, and one possible threshold setting method is as follows (it should be noted that different threshold settings may be performed for different initial melting curve calculation methods, experimental conditions, and data cases):
1) When r is greater than or equal to 0.9999, the candidate melting curve is a negative melting curve, and melting peaks (i.e., all the miscellaneous peaks) corresponding to all the initial Tm values on the candidate melting curve should be removed.
2) When r is more than or equal to 0.999 and less than 0.9999, melting peaks corresponding to each initial Tm value in the candidate melting curve need to be respectively examined according to the magnitude sequence of the amplitudes. For example, a melting peak a is compared with a peak value of a characteristic distinct melting curve in the vicinity of the Tm value (plus or minus 1 ℃), and if there are 2 or more melting peak sets larger than the peak value other than the melting peak a and the melting peak a is larger in amplitude than half of the minimum peak value of the melting peak set, the melting peak is not a hetero peak, otherwise, the melting peak is a hetero peak. Similarly, the melting peaks corresponding to all other initial Tm values of the candidate melting curve are examined. In the process of investigation, if the melting peak is judged to be a mixed peak, the remaining melting peaks with smaller amplitude are all mixed peaks, and the cycle is exited.
3) When r is more than or equal to 0.95 and less than 0.999, if the maximum peak value of the candidate melting curve is less than or equal to the amplitude value of the corresponding position of the relevant negative melting curve, the step 2) is carried out for further investigation; and if the maximum peak value is larger than the amplitude value of the corresponding position of the related negative melting curve, the cycle is exited, and the melting peaks corresponding to all the initial Tm values are not hetero-peaks.
4) When r is less than 0.95, all melting peaks corresponding to the initial Tm values in the candidate melting curve are not hetero-peaks.
And a substep 2.3, performing target hybrid peak smoothing processing on the candidate melting curve by adopting a preset filtering algorithm to obtain an optimized target melting curve.
Finding out a specific melting curve with a reduced Tm value number compared with the initial melting curve from the candidate melting curves, namely finding out the specific melting curve with a hetero-peak; smoothing the specific melting curve by adopting a filtering algorithm until the miscellaneous peak is no longer obvious. The filtering algorithm can adopt any filtering algorithm, such as a classical Savitzky-Golay smoothing method, a wavelet filtering method, a classical butterworth low-pass filtering method and the like. Taking the Savitzky-Golay smoothing method as an example, a specific melting curve is smoothed for multiple times under the condition that the size of a data window is set to be 5 sampling points (note: the window size is odd according to the Savitzky-Golay fitting principle) and the polynomial order is 1 time until a miscellaneous peak is no longer the maximum value position.
According to the melting curve optimization method provided by the embodiment of the invention, on the basis of the initial melting curve obtained by the existing method, the initial melting curve is divided into the target negative melting curve and the candidate melting curve of the hybrid peak to be removed based on the preset rule, and then the hybrid peak of the candidate melting curve is specifically removed based on the similarity between the target negative melting curve and the candidate melting curve, so that the hybrid peak in the melting curve is effectively removed, and the method is easy to understand and realize.
For ease of understanding, see fig. 2 for a schematic flow chart of another melting curve optimization method, an exemplary flow chart of the melting curve optimization method is as follows:
1. acquiring data: an initial melting curve is obtained by a conventional method, and an initial negative melting curve (i.e., an initial melting curve having no initial Tm value and no maximum point) is obtained by an extremum method.
2. And (4) classification: and determining a classification rule (namely a preset rule) and carrying out cluster analysis.
3. And (3) determining a mixed peak: and calculating a correlation coefficient, and determining a Tm value corresponding to the hetero-peak.
4. Smoothing: and finding out a melting curve with the reduced Tm value, and performing self-adaptive smoothing.
On the basis of obtaining an initial melting curve based on the existing method, the initial melting curve is divided into a characteristic obvious melting curve and a negative melting curve based on a preset rule, then a foreign peak is determined based on the similarity of the characteristic obvious melting curve and the negative melting curve, and on the basis, the corresponding melting curve is subjected to self-adaptive smoothing to remove the foreign peak in a targeted manner. The method better removes the miscellaneous peak in the melting curve by comprehensively adopting the preset rules and the similarity, obtains a smooth melting curve, and is easy to understand and realize.
In order to test the effectiveness of the melting curve optimization method, a fluorescence quantitative PCR detection system is used for carrying out PCR amplification-melting experiments on a plurality of reagents, fluorescence intensity data collected by a melting section are analyzed by a melting curve Tm value determination method based on hierarchical clustering, and an initial melting curve is obtained. Taking one of the data as an example, FIG. 3 is a graph showing the melting curve of the whole reaction plate before removing the impurity peaks and the corresponding Tm values (note: small solid circles show the Tm values), and there are still more visible impurity peaks; FIG. 4 is a melting curve diagram after removing the miscellaneous peaks and the corresponding Tm value positions, and it can be seen from FIG. 4 that the effect is obvious, the miscellaneous peaks are eliminated, and the melting peaks are clear and distinguishable.
Corresponding to the melting curve optimization method, the embodiment of the invention also provides a melting curve optimization device. Referring to fig. 5, a schematic diagram of a melting curve optimizing device is shown, which comprises:
an obtaining module 501, configured to obtain an initial melting curve and an initial Tm value corresponding to fluorescence intensity data to be processed; wherein the initial melting curve comprises a first melting curve with an initial Tm value, a second melting curve without the initial Tm value and with a maximum value point, and an initial negative melting curve without the initial Tm value and with no maximum value point;
an analysis module 502, configured to perform cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve based on a preset rule, to obtain a target negative melting curve and a candidate melting curve from which a foreign peak is to be removed; wherein the preset rule is related to the definition of the melting curve;
and an optimizing module 503, configured to perform hybrid peak removal on the candidate melting curve based on similarity between the candidate melting curve and the target negative melting curve, so as to obtain an optimized target melting curve.
The melting curve optimization device provided by the embodiment of the invention divides the initial melting curve into the target negative melting curve and the candidate melting curve of the hybrid peak to be removed based on the preset rule on the basis of the initial melting curve obtained by the existing method, and then specifically removes the hybrid peak of the candidate melting curve based on the similarity between the target negative melting curve and the candidate melting curve, so that the hybrid peak in the melting curve is effectively removed, and the device is easy to understand and realize.
Optionally, the analysis module 502 is specifically configured to: screening out a Tm value corresponding to a hetero-peak from the initial Tm value of the first melting curve according to a preset rule; carrying out clustering analysis based on the contour coefficient on the cluster set to obtain a clustering result; the cluster set comprises the amplitude of an initial Tm value except the Tm value corresponding to an impurity removal peak in a first melting curve and the amplitude of a maximum value point in a second melting curve; and obtaining a target negative melting curve and a candidate melting curve of the impurity peak to be removed according to the clustering result.
Optionally, the analyzing module 502 is further configured to: the initial Tm value of the first melting curve, which does not satisfy the occurrence of a melting peak in a region of decreased fluorescence intensity, is determined as the Tm value corresponding to the hetero peak.
Optionally, the analysis module 502 is further configured to: according to the clustering result, screening out a target Tm value, a candidate Tm value and a target maximum value point from the initial Tm value and the maximum value point corresponding to the cluster set; the target Tm value is an initial Tm value corresponding to the amplitude value of which the profile coefficient is greater than a set threshold value, the candidate Tm value is an initial Tm value corresponding to the amplitude value of which the profile coefficient is less than or equal to the set threshold value, and the target maximum value point is a maximum value point corresponding to the amplitude value of which the profile coefficient is greater than the set threshold value; determining a second melting curve and an initial negative melting curve of which all maximum points are target maximum points as target negative melting curves; and determining a first melting curve containing the Tm value or candidate Tm value corresponding to the hetero peak as a candidate melting curve of the hetero peak to be removed.
Optionally, the optimization module 503 is specifically configured to: determining a target correlation coefficient and a relevant negative melting curve corresponding to the candidate melting curve; the target correlation coefficient is the maximum value of the correlation coefficient between the fluorescence intensity curve corresponding to the candidate melting curve and the fluorescence intensity curve corresponding to each target negative melting curve, and the correlation negative melting curve is the target negative melting curve corresponding to the target correlation coefficient; determining a target hybrid peak in the candidate melting curve according to the threshold interval to which the target correlation coefficient belongs and the related negative melting curve corresponding to the candidate melting curve; and performing smoothing treatment on the target miscellaneous peak on the candidate melting curve by adopting a preset filtering algorithm to obtain an optimized target melting curve.
Optionally, the optimization module 503 is further configured to:
when the target correlation coefficient belongs to a first threshold interval, determining melting peaks corresponding to all initial Tm values in the candidate melting curve as target mixed peaks;
when the target correlation coefficient belongs to a second threshold interval, carrying out hybrid peak investigation on candidate Tm values in the candidate melting curve in sequence from large amplitude to small amplitude to obtain a target hybrid peak in the candidate melting curve; wherein any numerical value in the second threshold interval is smaller than any numerical value in the first threshold interval, the candidate Tm value is an initial Tm value which is not determined to belong to a hybrid peak, the target hybrid peak comprises a melting peak corresponding to the candidate Tm value which does not meet the preset melting peak requirement, and the melting peak requirement is related to the determined target Tm value set;
when the target correlation coefficient belongs to a third threshold interval, determining a target hybrid peak in the candidate melting curve based on the size relation between the maximum amplitude of the candidate Tm value in the candidate melting curve and the amplitude of the corresponding position of the relevant negative melting curve; wherein any value within the third threshold interval is less than any value within the second threshold interval;
when the target correlation coefficient belongs to a fourth threshold interval, determining that a target miscellaneous peak of the candidate melting curve is empty; wherein any value within the fourth threshold interval is less than any value within the third threshold interval.
Alternatively, melting peaks are required to be included in the determined set of target Tm values, there are at least a preset number of associated Tm values within a preset range of the candidate Tm values that are greater than the magnitude of the candidate Tm value, and the candidate Tm value is greater than half of the smallest magnitude of the associated Tm values.
Optionally, the optimization module 503 is further configured to: when the maximum amplitude of the candidate Tm values in the candidate melting curve is less than or equal to the amplitude of the corresponding position of the related negative melting curve, carrying out hybrid investigation on the candidate Tm values in the candidate melting curve according to the sequence of the amplitudes from large to small to obtain a target hybrid in the candidate melting curve; and when the maximum amplitude of the candidate Tm values in the candidate melting curve is larger than the amplitude of the corresponding position of the related negative melting curve, determining that the target hetero-peak of the candidate melting curve is empty.
The melting curve optimization device provided in this embodiment has the same implementation principle and technical effect as those of the foregoing embodiment of the melting curve optimization method, and for brief description, reference may be made to the corresponding contents in the foregoing embodiment of the melting curve optimization method where no mention is made in the embodiment of the melting curve optimization device.
As shown in fig. 6, an electronic device 600 provided in an embodiment of the present invention includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores a computer program operable on the processor 601, and when the electronic device 600 is operated, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the computer program to implement the melting curve optimization method.
Specifically, the memory 602 and the processor 601 can be general memories and processors, and are not limited thereto.
Embodiments of the present invention further provide a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the computer program performs the melting curve optimization method described in the foregoing method embodiments. The storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk.
In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of melt curve optimization, comprising:
obtaining an initial melting curve and an initial Tm value corresponding to fluorescence intensity data to be processed; wherein the initial melting curves comprise a first melting curve with an initial Tm value, a second melting curve without the initial Tm value and with a maximum value point, and an initial negative melting curve without the initial Tm value and without the maximum value point;
based on a preset rule, carrying out cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve to obtain a target negative melting curve and a candidate melting curve from which a foreign peak is to be removed; wherein the preset rule is related to a melting curve definition;
and removing the hybrid peak of the candidate melting curve based on the similarity between the candidate melting curve and the target negative melting curve to obtain an optimized target melting curve.
2. The melting curve optimization method of claim 1, wherein the performing cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve based on a preset rule to obtain a target negative melting curve and a candidate melting curve from which a peak is to be removed comprises:
screening out a Tm value corresponding to a hetero peak from the initial Tm value of the first melting curve according to the preset rule;
carrying out clustering analysis based on the contour coefficient on the cluster set to obtain a clustering result; wherein the cluster set comprises the amplitude of the initial Tm value in the first melting curve except the Tm value corresponding to the hetero-peak and the amplitude of the maximum point in the second melting curve;
and obtaining a target negative melting curve and a candidate melting curve of the impurity peak to be removed according to the clustering result.
3. The method for optimizing melting curves according to claim 2, wherein the step of screening the Tm value corresponding to the hetero-peak from the initial Tm values of the first melting curve according to the predetermined rule comprises:
and determining the initial Tm value of the first melting curve, which does not satisfy the occurrence of a melting peak in a fluorescence intensity decreasing region, as the Tm value corresponding to the hetero peak.
4. The method for optimizing a melting curve according to claim 2, wherein obtaining a target negative melting curve and a candidate melting curve from which a peak is to be removed according to the clustering result comprises:
screening out a target Tm value, a candidate Tm value and a target maximum value point from the initial Tm value and the maximum value point corresponding to the cluster set according to the clustering result; the target Tm value is an initial Tm value corresponding to a amplitude value of which the profile coefficient is greater than a set threshold value, the candidate Tm value is an initial Tm value corresponding to a amplitude value of which the profile coefficient is less than or equal to the set threshold value, and the target maximum value point is a maximum value point corresponding to an amplitude value of which the profile coefficient is greater than the set threshold value;
determining a second melting curve and the initial negative melting curve of which all maximum points are the target maximum points as a target negative melting curve;
and determining a first melting curve comprising the Tm value corresponding to the hybrid peak or the candidate Tm value as a candidate melting curve of the hybrid peak to be removed.
5. The method of claim 1, wherein the removing the hybrid peak from the candidate melting curve based on the similarity with the target negative melting curve to obtain an optimized target melting curve comprises:
determining a target correlation coefficient and a related negative melting curve corresponding to the candidate melting curve; wherein the target correlation coefficient is a maximum value of correlation coefficients between fluorescence intensity curves corresponding to the candidate melting curves and fluorescence intensity curves corresponding to the target negative melting curves, and the correlation negative melting curves are target negative melting curves corresponding to the target correlation coefficients;
determining a target hybrid peak in the candidate melting curve according to the threshold interval to which the target correlation coefficient belongs and the related negative melting curve corresponding to the candidate melting curve;
and performing smoothing treatment on the target miscellaneous peak on the candidate melting curve by adopting a preset filtering algorithm to obtain an optimized target melting curve.
6. The method of optimizing a melting curve according to claim 5, wherein the determining a target peak in the candidate melting curve according to the threshold interval to which the target correlation coefficient belongs and the corresponding negative melting curve of the candidate melting curve comprises:
when the target correlation coefficient belongs to a first threshold interval, determining melting peaks corresponding to all initial Tm values in the candidate melting curve as target hybrid peaks;
when the target correlation coefficient belongs to a second threshold interval, carrying out hybrid peak investigation on candidate Tm values in the candidate melting curve in sequence from large amplitude to small amplitude to obtain a target hybrid peak in the candidate melting curve; wherein any numerical value in the second threshold interval is smaller than any numerical value in the first threshold interval, the candidate Tm value is an initial Tm value that is not yet determined to belong to a hybrid peak, the target hybrid peak includes a melting peak corresponding to the candidate Tm value that does not meet a preset melting peak requirement, and the melting peak requirement is related to the determined target Tm value set;
when the target correlation coefficient belongs to a third threshold interval, determining a target hybrid peak in the candidate melting curve based on the magnitude relation between the maximum amplitude of the candidate Tm value in the candidate melting curve and the amplitude of the corresponding position of the relevant negative melting curve; wherein any value within the third threshold interval is less than any value within the second threshold interval;
when the target correlation coefficient belongs to a fourth threshold interval, determining that a target miscellaneous peak of the candidate melting curve is empty; wherein any value within the fourth threshold interval is less than any value within the third threshold interval.
7. The method of claim 6, wherein the melting peak requirement comprises that there are at least a preset number of associated Tm values in the determined set of target Tm values, which are within a preset range of the candidate Tm values and larger than the amplitude of the candidate Tm values, and the candidate Tm values are larger than half of the minimum amplitude of the associated Tm values;
the determining a target hetero-peak in the candidate melting curve based on the magnitude relation between the maximum amplitude of the candidate Tm value in the candidate melting curve and the amplitude of the corresponding position of the related negative melting curve comprises the following steps:
when the maximum amplitude of the candidate Tm values in the candidate melting curve is smaller than or equal to the amplitude of the corresponding position of the related negative melting curve, carrying out hybrid peak investigation on the candidate Tm values in the candidate melting curve in sequence from large to small according to the amplitude, and obtaining a target hybrid peak in the candidate melting curve;
and when the maximum amplitude of the candidate Tm values in the candidate melting curve is larger than the amplitude of the corresponding position of the related negative melting curve, determining that the target hetero-peak of the candidate melting curve is empty.
8. A melt curve optimization device, comprising:
the acquisition module is used for acquiring an initial melting curve and an initial Tm value corresponding to the fluorescence intensity data to be processed; wherein the initial melting curves comprise a first melting curve with an initial Tm value, a second melting curve without the initial Tm value and with a maximum value point, and an initial negative melting curve without the initial Tm value and without the maximum value point;
the analysis module is used for carrying out cluster analysis on the initial Tm value of the first melting curve and the maximum value point of the second melting curve based on a preset rule to obtain a target negative melting curve and a candidate melting curve from which a foreign peak is to be removed; wherein the preset rule is related to a melting curve definition;
and the optimization module is used for removing the hybrid peak of the candidate melting curve based on the similarity between the candidate melting curve and the target negative melting curve to obtain an optimized target melting curve.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the melt curve optimization method of any one of claims 1-7 when executing the computer program.
10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the melt curve optimization method of any of claims 1-7.
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