A method, apparatus, and electronic device for evaluating the effectiveness of SNP typing based on silhouette coefficients.

Abstract

This application provides a method, apparatus, and electronic device for evaluating the effectiveness of SNP genotyping based on silhouette coefficients, relating to the field of SNP genotyping technology, and alleviating the technical problems in evaluating the effectiveness of SNP genotyping. The method includes: acquiring endpoint fluorescence intensity data for a target SNP locus; performing clustering based on the endpoint fluorescence intensity data using a clustering algorithm to obtain a first clustering result; filtering samples in the first clustering result using individual silhouette coefficients and a preset silhouette coefficient threshold to obtain a sample filtering result; and clustering the sample filtering result using the clustering algorithm to obtain a second clustering result.

Description

Technical Field

[0001] This application relates to the field of SNP genotyping technology, and in particular to a method, apparatus, and electronic device for evaluating the effectiveness of SNP genotyping based on silhouette coefficients. Background Technology

[0002] Single nucleotide polymorphism (SNP) is a polymorphism in nucleic acid sequences caused by a change in a single nucleotide. Generally, an SNP site has only two alleles, hence it is also called a biallelic gene. SNPs occur frequently in the genome, with an average of one polymorphic site per 1000 base pairs. They are important evidence for studying genetic variation in plant and animal strains, the relationship between diseases and individual differences, and the different responses of individuals to drug resistance. Currently, various detection techniques have been developed, including direct sequencing, amplification curve analysis, and high-resolution melting curve analysis (HRM). Among these, amplification curve analysis is frequently used due to its advantages such as simple operation (requiring only one polymerase chain reaction (PCR) reaction), high speed, high throughput, and easy interpretation of results.

[0003] However, whether the classification is done manually or automatically using genotype characteristics, there is still the problem of evaluating the effectiveness of the classification results. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and electronic device for evaluating the effectiveness of SNP typing based on silhouette coefficients, so as to alleviate the technical problems in evaluating the effectiveness of classification results.

[0005] In a first aspect, embodiments of this application provide a method for evaluating the effectiveness of SNP typing based on silhouette coefficients, the method comprising:

[0006] Obtain endpoint fluorescence intensity data for the target SNP site;

[0007] Based on the endpoint fluorescence intensity data, a clustering algorithm is used to perform clustering to obtain the first clustering result;

[0008] The samples in the first clustering result are filtered by individual silhouette coefficients and preset silhouette coefficient thresholds to obtain sample filtering results;

[0009] The clustering algorithm is used to cluster the sample screening results to obtain a second clustering result.

[0010] In one possible implementation, the step of acquiring endpoint fluorescence intensity data for the target SNP site includes:

[0011] The endpoint fluorescence intensity data for the target SNP site in each channel were acquired using the amplification curve method.

[0012] In one possible implementation, the step of clustering based on the endpoint fluorescence intensity data using a clustering algorithm to obtain a first clustering result includes:

[0013] The endpoint fluorescence intensity data are preprocessed to obtain the preprocessing result;

[0014] The preprocessed results are clustered using a clustering algorithm to obtain the first clustering result.

[0015] In one possible implementation, the step of preprocessing the endpoint fluorescence intensity data to obtain a preprocessing result includes:

[0016] The endpoint fluorescence intensity data is normalized to obtain the normalized result, and the normalized result is used as the preprocessing result.

[0017] In one possible implementation, the step of clustering the preprocessed results using a clustering algorithm to obtain a first clustering result includes:

[0018] Remove blank samples and unamplified sample data from the preprocessing results;

[0019] Clustering analysis is performed based on the overall profile coefficient using a clustering algorithm to obtain the first clustering result; wherein the first clustering result includes the number of categories, the result, and the overall and individual profile coefficients.

[0020] In one possible implementation, the step of filtering samples in the first clustering result using individual silhouette coefficients and a preset silhouette coefficient threshold to obtain sample filtering results includes:

[0021] Based on the first clustering result, determine whether the overall profile coefficient meets the preset profile coefficient index threshold; if the overall profile coefficient does not meet the preset profile coefficient index threshold, then the site is classified as Class 1 or cannot be separated, and manual division or re-experimentation is performed subsequently; if it meets the threshold, proceed to the next step.

[0022] Based on the first clustering result, determine whether the individual silhouette coefficient meets the preset silhouette coefficient index threshold; if there are samples whose individual silhouette coefficient does not meet the preset silhouette coefficient index threshold, remove the samples that do not meet the preset silhouette coefficient index threshold from the first clustering result to obtain the sample screening result.

[0023] In one possible implementation, the step of clustering the sample screening results using the clustering algorithm to obtain a second clustering result includes:

[0024] The clustering algorithm is used to cluster and analyze the sample screening results to obtain multiple cluster centers and genotype features. Based on each cluster center and each genotype feature, a second clustering result is obtained.

[0025] Secondly, a device for evaluating the effectiveness of SNP typing based on silhouette coefficients is provided, including:

[0026] The acquisition module is used to acquire endpoint fluorescence intensity data for the target SNP site;

[0027] The first clustering module is used to perform clustering based on the endpoint fluorescence intensity data using a clustering algorithm to obtain the first clustering result;

[0028] The filtering module is used to filter samples in the first clustering result by individual silhouette coefficient and preset silhouette coefficient index threshold to obtain sample filtering results;

[0029] The second clustering module is used to cluster the sample screening results using the clustering algorithm to obtain the second clustering result.

[0030] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect above.

[0031] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in the first aspect above.

[0032] The embodiments of this application bring the following beneficial effects:

[0033] This application provides a method, apparatus, and electronic device for evaluating the effectiveness of SNP genotyping based on silhouette coefficient. The method acquires endpoint fluorescence intensity data for a target SNP locus, performs clustering based on the endpoint fluorescence intensity data using a clustering algorithm to obtain a first clustering result, filters samples in the first clustering result using individual silhouette coefficients and a preset silhouette coefficient threshold to obtain a sample filtering result, and then clusters the sample filtering result using a clustering algorithm to obtain a second clustering result. In this step, by using the silhouette coefficient as an indicator and cyclically utilizing the clustering algorithm, the problem of evaluating the effectiveness of SNP genotyping is effectively alleviated.

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating the effectiveness evaluation method for SNP typing based on profile coefficients provided in this application embodiment;

[0037] Figure 2 Another flowchart illustrating the SNP typing validity evaluation method based on profile coefficients provided in this application embodiment;

[0038] Figure 3 In the SNP typing validity evaluation method based on silhouette coefficient provided in the embodiments of this application, the initial SNP typing result is used.

[0039] Figure 4 In the SNP typing validity evaluation method based on silhouette coefficient provided in the embodiments of this application, the SNP typing results are obtained after removing individual samples with individual silhouette coefficients <0.9;

[0040] Figure 5 A schematic diagram of a SNP typing validity evaluation device based on profile coefficient provided in this application embodiment;

[0041] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0044] Currently, whether manual designation or automatic interpretation using genotype features is used, there is a problem of evaluating the effectiveness of classification results, as well as the resulting issue of removing outliers (or values ​​with unclear genotyping features) to improve genotyping results.

[0045] Based on this, embodiments of this application provide a method, apparatus, and electronic device for evaluating the effectiveness of SNP typing based on profile coefficients. This method can alleviate the technical problems in evaluating the effectiveness of SNP typing.

[0046] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 This is a flowchart illustrating a method for evaluating the effectiveness of SNP typing based on silhouette coefficients, provided in an embodiment of this application. Figure 1 As shown, the method includes:

[0048] Step S110: Obtain endpoint fluorescence intensity data for the target SNP site.

[0049] It should be noted that the fluorescence intensity data are the final fluorescence intensity data of the amplification curve after final processing. Before this, the effects of baseline, crosstalk between channels, and inter-well errors should have been removed.

[0050] Step S120: Clustering is performed based on the endpoint fluorescence intensity data using a clustering algorithm to obtain the first clustering result.

[0051] Step S130: The samples in the first clustering result are filtered by individual silhouette coefficients and preset silhouette coefficient index thresholds to obtain the sample filtering results.

[0052] In this step, the clustering results are evaluated, and unqualified samples are removed based on the individual silhouette coefficients and the set silhouette coefficient thresholds.

[0053] Step S140: Cluster the sample screening results using a clustering algorithm to obtain the second clustering result.

[0054] In practical applications, cluster analysis and classification can be performed again on the remaining samples.

[0055] In this embodiment, a clustering algorithm is first used to initially classify the fluorescence intensity data of each channel. Then, the silhouette coefficient is used as an indicator to evaluate the genotyping results, ultimately achieving the goal of accurate genotyping of SNP sites. By using the silhouette coefficient as an indicator and cyclically utilizing the clustering algorithm, the problem of evaluating the effectiveness of SNP genotyping is effectively solved, and it is easy to understand and implement.

[0056] The steps described above will be explained in detail below.

[0057] In some embodiments, step S110 above may include the following steps:

[0058] Step a) Use the amplification curve method to collect the endpoint fluorescence intensity data of each channel for the target SNP site.

[0059] For example, such as Figure 2 As shown, the endpoint fluorescence intensity data for a specific SNP site in each channel were acquired using the amplification curve method. The fluorescence intensity data presented are the final processed endpoint fluorescence intensity data of the amplification curve, after which the effects of baseline, crosstalk between channels, and inter-well errors have been removed.

[0060] In some embodiments, step S120 above may include the following steps:

[0061] Step b) involves preprocessing the endpoint fluorescence intensity data to obtain the preprocessing results;

[0062] Step c) Use a clustering algorithm to cluster the preprocessed results to obtain the first clustering result.

[0063] In practical applications, adaptive clustering analysis can be performed on the preprocessed data based on clustering algorithms to obtain the first clustering result more efficiently.

[0064] In some embodiments, step b) above may include the following steps:

[0065] Step d) Normalize the endpoint fluorescence intensity data to obtain the normalized result, and use the normalized result as the preprocessing result.

[0066] Because the frequencies of different alleles are inconsistent, the frequencies of SNP genotypes are also inconsistent. Therefore, the fluorescence intensity at the endpoint of each channel should be normalized separately. The normalization method can be the min-max normalization method. Let's assume the maximum endpoint fluorescence intensity of a certain channel is X. max The minimum endpoint fluorescence intensity is X min The endpoint fluorescence intensity of this channel in a certain sample is X. i Then its normalized endpoint fluorescence intensity value X(i) is:

[0067]

[0068] In some embodiments, step c) above may include the following steps:

[0069] Step e) Remove blank samples and unamplified sample data from the preprocessing results;

[0070] Step f) uses a clustering algorithm to perform clustering and analysis based on the overall profile coefficient to obtain the first clustering result; wherein the first clustering result includes the number of categories, the result, and the overall and individual profile coefficients.

[0071] For example, such as Figure 2 As shown, blank samples and unamplified samples can be removed first to eliminate unnecessary interference in later clustering. Specific blank samples or samples without amplification in any channel can be excluded before clustering. Then, after removing blank and unamplified samples, adaptive clustering analysis is performed to determine the optimal number of categories and classification results based on the overall silhouette coefficient.

[0072] Assuming a given SNP locus has n alleles, the maximum possible number of homozygous or heterozygous clusters is: The minimum possible cluster size is 1, meaning all clusters consist of heterozygotes or homozygotes of a certain type. Conventional clustering analysis methods can be used, such as the classic K-means clustering method. Suppose that N samples to be classified have been divided into m classes using a certain clustering algorithm. For each sample vector in each class, the silhouette coefficient s(i) is calculated:

[0073]

[0074] Where a(i) is the average distance from the sample vector to other points in its cluster, b(i) is the minimum average distance from the sample vector to all points in all other clusters, and max{a(i),b(i)} represents taking the larger of a(i) and b(i). Therefore, s takes values ​​in the range [-1, 1]. Then the overall silhouette coefficient...

[0075]

[0076] Maximum overall profile coefficient for:

[0077]

[0078] The corresponding number of categories is the optimal number of categories. The classification results corresponding to this number of categories and the silhouette coefficients of all individuals are saved.

[0079] In some embodiments, step S130 above may include the following steps:

[0080] Step g): Based on the first clustering result, determine whether the overall profile coefficient meets the preset profile coefficient index threshold; if the overall profile coefficient does not meet the preset profile coefficient index threshold, then the site is classified as Class 1 or cannot be separated, and manual division or re-experimentation is performed subsequently; if it meets the threshold, proceed to the next step h).

[0081] Step h): Based on the first clustering result, determine whether the individual silhouette coefficient meets the preset silhouette coefficient index threshold; if there are samples whose individual silhouette coefficients do not meet the preset silhouette coefficient index threshold, remove the samples that do not meet the preset silhouette coefficient index threshold from the first clustering result to obtain the sample screening result.

[0082] It should be noted that a silhouette coefficient value closer to 1 indicates better clustering performance, while a value closer to -1 indicates worse clustering performance. Based on actual analysis data, a silhouette coefficient threshold S can be set. * For example, S * =0.9.

[0083] like Figure 2 As shown, step g) is executed first, which involves examining whether the overall silhouette coefficient meets the threshold requirement based on the clustering results. Specifically: comparison With S * The size of the value, if If the overall silhouette coefficient does not meet the classification threshold requirement, meaning the original sample data is either unseparable or all samples belong to one class, then a manual classification procedure or a new experiment can be performed. If the overall silhouette coefficient meets the classification threshold, the original sample data is separable. Then, proceed to the next step (h), which examines whether the individual silhouette coefficients meet the threshold, removing unqualified samples. Specifically: compare... The corresponding individual profile coefficients and S * The numerical value, when the individual's silhouette coefficient * If the sample does not meet the requirements, remove the non-compliant sample and classify it into the unknown cluster. If the silhouette coefficients of all individuals are ≥S * Then there are no unknown clusters. ​

[0084] In some embodiments, step S140 above may include the following steps:

[0085] Step i) Use a clustering algorithm to cluster and analyze the sample screening results to obtain multiple cluster centers and genotype features, and classify them according to each cluster center and each genotype feature to obtain the second clustering result.

[0086] For example, such as Figure 2 As shown, adaptive clustering analysis is first performed again on the remaining samples; then, they are classified according to each cluster center and each genotype characteristic.

[0087] Specifically, based on the biological characteristics of each genotype, when homozygous, the fluorescence intensity at the endpoint of one channel will be significantly higher than that of other channels, i.e., closer to the coordinate axis corresponding to that channel. When heterozygous, the fluorescence intensity at the endpoints of two channels will be relatively close, i.e., closer to the interface between the two orthogonal coordinate axes. Therefore, by comprehensively examining each cluster center, when a cluster center is close to a certain coordinate axis, it is considered homozygous for the channel corresponding to that coordinate axis; otherwise, it is considered heterozygous. Outside of homozygous and heterozygous samples are pre-specified blank sample types and unknown clusters determined in h) (Note: optional; if they exist, they are marked; otherwise, they are left blank).

[0088] The following example illustrates the effectiveness of the testing method: PCR amplification experiments were performed on multiple template reagents using the Bozhi Real-Time PCR detection system, and SNP genotyping was performed using the method described above. Here, we take one sample of SNP locus with two alleles as an example, and set the silhouette coefficient threshold S. * =0.9.

[0089] Following the aforementioned steps, samples without amplification in any channel were first designated as blank samples. Then, K-means adaptive clustering analysis was performed. It was found that the overall silhouette coefficient was highest when the number of clusters was 3. Therefore, 3 was selected as the number of clusters, yielding preliminary clustering results. They can be categorized, but in terms of individual profile coefficients, four samples have a profile coefficient <0.9, meaning they fall within the boundary region between the two classes. These are classified as unknown types. Regarding the SNP genotyping results, such as... Figure 3 As shown. Based on this, after removing the four unknown type samples, we continue with adaptive clustering analysis using K-means. At this point, the overall silhouette coefficient is still the largest when the number of clusters is 3. Compared to before removing unknown samples, there was a significant improvement, and at this point, the silhouette coefficients of all samples were >0.9. Regarding the final genotyping results, as... Figure 4 As shown.

[0090] Figure 5A schematic diagram of a device for evaluating the effectiveness of SNP typing based on profile coefficients is provided. Figure 5 As shown, the SNP typing validity evaluation device 500 based on silhouette coefficients includes:

[0091] The acquisition module 501 is used to acquire endpoint fluorescence intensity data for the target SNP site;

[0092] The first clustering module 502 is used to perform clustering based on the endpoint fluorescence intensity data using a clustering algorithm to obtain the first clustering result;

[0093] The filtering module 503 is used to filter the samples in the first clustering result by individual profile coefficient and preset profile coefficient index threshold to obtain the sample filtering result;

[0094] The second clustering module 504 is used to cluster the sample screening results using a clustering algorithm to obtain the second clustering result.

[0095] In some embodiments, the acquisition module 501 is specifically used for:

[0096] The endpoint fluorescence intensity data for the target SNP site in each channel were acquired using the amplification curve method.

[0097] In some embodiments, the first clustering module 502 is specifically used for:

[0098] The endpoint fluorescence intensity data were preprocessed to obtain the preprocessed results;

[0099] The preprocessed results are clustered using a clustering algorithm to obtain the first clustering result.

[0100] In some embodiments, the first clustering module 502 is further configured to:

[0101] The endpoint fluorescence intensity data were normalized to obtain the normalized result, which was then used as the preprocessing result.

[0102] In some embodiments, the first clustering module 502 is further configured to:

[0103] Remove blank samples and unamplified sample data from the preprocessing results;

[0104] Clustering algorithms are used to perform clustering and analysis based on the overall profile coefficient to obtain the first clustering result; the first clustering result includes the number of categories, the result, and the overall and individual profile coefficients.

[0105] In some embodiments, the filtering module 503 is specifically used for:

[0106] Based on the first clustering result, determine whether the overall profile coefficient meets the preset profile coefficient index threshold; if the overall profile coefficient does not meet the preset profile coefficient index threshold, then the site is classified as Class 1 or cannot be separated, and manual division or re-experimentation is performed subsequently; if it meets the threshold, proceed to the next step.

[0107] Based on the first clustering result, determine whether the individual silhouette coefficient meets the preset silhouette coefficient index threshold; if there are samples whose individual silhouette coefficients do not meet the preset silhouette coefficient index threshold, remove the samples that do not meet the preset silhouette coefficient index threshold from the first clustering result to obtain the sample screening result.

[0108] In some embodiments, the second clustering module 504 is specifically used for:

[0109] Clustering algorithms are used to cluster and analyze the sample screening results, resulting in multiple cluster centers and genotype features. The samples are then classified according to each cluster center and each genotype feature to obtain the second clustering result.

[0110] The SNP typing effectiveness evaluation device based on profile coefficient provided in this application has the same technical features as the SNP typing effectiveness evaluation method based on profile coefficient provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0111] An electronic device provided in this application embodiment, such as Figure 6 As shown, the electronic device 600 includes a processor 602 and a memory 601. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method provided in the above embodiments.

[0112] See Figure 6 The electronic device also includes a bus 603 and a communication interface 604. The processor 602, the communication interface 604 and the memory 601 are connected through the bus 603. The processor 602 is used to execute executable modules, such as computer programs, stored in the memory 601.

[0113] The memory 601 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 604 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0114] Bus 603 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0115] The memory 601 is used to store programs. After receiving an execution instruction, the processor 602 executes the program. The method executed by the apparatus defined by the process disclosed in any of the preceding embodiments of this application can be applied to the processor 602 or implemented by the processor 602.

[0116] Processor 602 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 602 or by instructions in software form. The processor 602 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can 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 can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 601, and processor 602 reads the information from memory 601 and, in conjunction with its hardware, completes the steps of the above method.

[0117] Corresponding to the above-described SNP typing validity evaluation method based on profile coefficients, this application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the above-described SNP typing validity evaluation method based on profile coefficients.

[0118] The SNP classification validity evaluation device based on profile coefficients provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0119] 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 units 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.

[0120] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] If the aforementioned function is implemented as a software functional unit 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 a 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, server, or network device, etc.) to execute all or part of the steps of the SNP typing validity evaluation method based on profile coefficients 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.

[0124] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0125] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for evaluating the effectiveness of a SNP typing based on profile coefficients, characterized in that, The method comprises: Collecting end-point fluorescence intensity data of each channel for the target SNP site by using the amplification curve method; Normalizing the end-point fluorescence intensity data to obtain a normalization result, and taking the normalization result as a pretreatment result; Removing blank samples and non-amplification sample data in the pretreatment result; Performing clustering analysis based on the overall silhouette coefficient by using a clustering algorithm to obtain a first clustering result; wherein the first clustering result comprises the number of classifications, results, overall, and individual silhouette coefficients; Judging whether the overall silhouette coefficient meets a preset silhouette coefficient index threshold based on the first clustering result; if the overall silhouette coefficient does not meet the preset silhouette coefficient index threshold, the site is classified as class 1 or is not classifiable, and subsequent manual division or re-experimentation is performed; if the overall silhouette coefficient meets the preset silhouette coefficient index threshold, a next step is performed; Judging whether the individual silhouette coefficient meets the preset silhouette coefficient index threshold based on the first clustering result; if there is a sample whose individual silhouette coefficient does not meet the preset silhouette coefficient index threshold, removing the sample whose individual silhouette coefficient does not meet the preset silhouette coefficient index threshold from the first clustering result to obtain a sample screening result; Performing clustering and analysis on the sample screening result by using the clustering algorithm to obtain a plurality of clustering centers and genotype characteristics, and classifying each clustering center and each genotype characteristic to obtain a second clustering result.

2. A device for evaluating the effectiveness of a SNP typing based on profile coefficients, characterized in that, Comprise: An acquisition module, configured to acquire end-point fluorescence intensity data for a target SNP site; A first clustering module, configured to perform clustering based on the end-point fluorescence intensity data by using a clustering algorithm to obtain a first clustering result; A screening module, configured to screen samples in the first clustering result by using an individual silhouette coefficient and a preset silhouette coefficient index threshold to obtain a sample screening result; A second clustering module, configured to perform clustering on the sample screening result by using the clustering algorithm to obtain a second clustering result; The acquisition module is specifically configured to collect end-point fluorescence intensity data of each channel for the target SNP site by using the amplification curve method; The first clustering module is specifically configured to perform pretreatment on the end-point fluorescence intensity data to obtain a pretreatment result; The first clustering module is specifically configured to perform clustering on the pretreatment result by using the clustering algorithm to obtain a first clustering result; The first clustering module is further configured to perform normalization on the end-point fluorescence intensity data to obtain a normalization result, and take the normalization result as the pretreatment result; The first clustering module is further configured to remove blank samples and non-amplification sample data in the pretreatment result; The first clustering module is further configured to perform clustering and analysis based on the overall silhouette coefficient by using the clustering algorithm to obtain a first clustering result; wherein the first clustering result comprises the number of classifications, results, overall, and individual silhouette coefficients; The screening module is specifically configured to: determine whether the overall silhouette coefficient meets a preset silhouette coefficient index threshold based on the first clustering result; if the overall silhouette coefficient does not meet the preset silhouette coefficient index threshold, the site is all of Class 1 or is not separable, subsequent manual division or re-experiment is performed, and if the overall silhouette coefficient meets the preset silhouette coefficient index threshold, a next step is entered; determine whether the individual silhouette coefficient meets the preset silhouette coefficient index threshold based on the first clustering result; if there is a sample whose individual silhouette coefficient does not meet the preset silhouette coefficient index threshold, the sample whose individual silhouette coefficient does not meet the preset silhouette coefficient index threshold is removed from the first clustering result, and a sample screening result is obtained; The second clustering module is specifically configured to: cluster and analyze the sample screening result by using a clustering algorithm, obtain a plurality of clustering centers and genotype characteristics, and classify according to each clustering center and each genotype characteristic, and obtain a second clustering result.

3. An electronic device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method in claim 1.

4. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by the processor, the computer executable instructions cause the processor to execute the method in claim 1.

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