Cluster mutation detection method and device based on plane distance

Abstract

The present application belongs to the field of biological information technology, and particularly relates to a cluster mutation detection method and device based on plane distance. The present application obtains a clustering condition demarcation value through sample data analysis, determines a range parameter and a mutation parameter, processes the sample data to be detected and merges clusters to obtain a clustering result, and verifies the result. The present application performs clustering based on plane distance recognition, adopts a completely different cluster mutation recognition algorithm, has higher performance and higher recognition accuracy compared with the existing multiple sliding windows, and only needs to provide an API to conveniently perform online detection on a webpage based on the Java developed algorithm, without the need to configure or download any matching environment or software, thereby greatly improving the convenience.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular relates to a cluster mutation detection method and device based on plane distance. Background Art

[0002] Mutation refers to the occurrence of multiple mutations in a localized region of the genome. These mutations may affect different genes and pathways, leading to the acquisition of multiple functional abnormalities within the same tumor cell. Detection of clustered mutations can help develop more effective treatment strategies, thereby improving patient clinical outcomes and profoundly impacting all aspects of tumor biology. Therefore, systematically acquiring clustered mutation data and associated genes is crucial.

[0003] Existing cluster mutation identification methods currently rely heavily on Python and R bindings, requiring the download of extensive runtime environments and supporting software. These methods are cumbersome and can lead to significant errors, making detection convenience and effectiveness difficult to guarantee.

[0004] Therefore, it is of great significance to develop a new cluster mutation detection method and device. Summary of the Invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide a cluster mutation detection method and device based on plane distance. This method is developed based on Java and only requires an API to conveniently perform online detection of web pages. It does not require the configuration or download of any supporting environment or software, greatly improving the convenience and detection effect.

[0006] In order to solve the above technical problems, the present invention discloses a cluster mutation detection method based on plane distance, comprising the following steps:

[0007] Step 1: Screen the sample data and analyze the sample data to obtain the cutoff value that meets the clustering conditions;

[0008] Step 2: Define the range parameter 1 within which mutation sites can cluster, and define the minimum number of mutations required to achieve clustering, parameter 2;

[0009] Step 3: Process the data of the sample to be tested, merge the clusters according to the key nodes and neighbor nodes, and obtain the clustering results;

[0010] Step 4: Calculate the silhouette coefficient of the clustering results to verify the clustering effect, and verify the results based on the cluster mutation characteristics.

[0011] Among them, step 1 specifically includes:

[0012] Step 1.1: Obtain sample data that has been verified to have a large number of cluster mutations; filter sample data with SNP mutation type; retain the gene column, chromosome column, and starting mutation site column data as input test data;

[0013] Step 1.2: Convert the sample data to obtain a rainforest map, draw a k-distance curve to obtain the DBSCAN parameter eps, perform DBSCAN clustering, calculate the silhouette coefficient to test the clustering quality, and obtain the cutoff point.

[0014] Preferably, the Silhouette Coefficient is used to evaluate the quality of clustering. The following is a brief description of the algorithm steps:

[0015] A. Calculate the silhouette coefficient for each sample:

[0016] For each cluster, calculate the silhouette coefficient of each sample in the cluster in turn;

[0017] For each sample, calculate its average distance to other samples in the same cluster as the closeness a;

[0018] For each sample, calculate the minimum value of its average distance to other clusters as the separation degree b;

[0019] Calculate the silhouette coefficient according to the formula: If a and b are equal, the silhouette coefficient is 0;

[0020] B. Calculate the average silhouette coefficient:

[0021] Add up the silhouette coefficients of all samples and calculate the total number of samples;

[0022] Calculate the average silhouette coefficient: average Silhouette = total Silhouette / totalSamples;

[0023] Returns the mean silhouette coefficient;

[0024] For each cluster, if it contains only one sample, its silhouette coefficient is not calculated. The silhouette coefficient range is (-1, 1). If the silhouette coefficient of the clustering result is closer to 1, it means the clustering effect is better. If it is close to 0, it means the sample is at the edge position. If it is a negative number, it means the clustering effect is poor.

[0025] Preferably, the rainforest map in step 1.2 is obtained by sorting the chromosome column and mutation site column and performing log transformation on the distance values;

[0026] Preferably, the demarcation point in step 1.2 is 1000 bp.

[0027] Wherein, the range parameter 1 and the minimum mutation number parameter 2 in step 2 are obtained by geometric principle and rainforest map display principle; the range parameter 1 is 1000√2; and the minimum mutation number parameter 2 is 1.

[0028] Wherein, step 3 specifically comprises:

[0029] Step 3.1: processing the input mutation data of the sample to be tested, classifying the data by sequence, recording each SNP starting mutation position x on the sequence and the distance from the previous SNP value y into a node object instance;

[0030] Step 3.2: mapping all node object instances into a rainforest map, and calculating the nearest neighbor nodes according to the positions in the map;

[0031] Step 3.3: storing the nodes meeting the parameter 1 (the range radius is less than or equal to 1000√2) and parameter 2 (the number of nodes in the range is at least 1 except for the node itself) conditions into a node type set, and marking as a key node set;

[0032] Step 3.4: traversing the key nodes in the above set, and calculating all nodes within 1000√2 as the neighbor node set of the current key node;

[0033] Step 3.5: if the neighbor node set of a key node contains a key node other than itself, merging the neighbor sets of the two key nodes as a cluster (the neighbor node set contains a key node), and obtaining the clustering result by depth-first traversal of the above structure.

[0034] Wherein, the specific method of result verification in step 4 is:

[0035] 4.1: verifying the mutation type, which is mainly C>T and C>G;

[0036] 4.2: verifying the TCW sequence, and enriching the context of the TCW sequence, wherein W represents base A or T;

[0037] 4.3: judging whether it has continuous synthesis ability, and the continuous mutations in the cluster should be on the same strand; that is, continuous 6 C>T mutations or continuous 6 G>A mutations;

[0038] 4.4: judging whether it is in cis, that is, the mutations occurring at adjacent positions on the same chromosome, which are often on the same read;

[0039] Preferably, the screening results are verified based on the above-mentioned known cluster mutation conclusions (due to sample data limitations, at least the above three conditions are met), and then compared with the literature data on the existing verification of cluster mutation results to determine whether the test results meet the cluster mutation characteristics.

[0040] The present invention also provides a device for cluster mutation detection based on plane distance, which is used to implement the above-mentioned cluster mutation detection method.

[0041] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned cluster mutation detection method when executed by a processor.

[0042] The present invention also provides a device, comprising: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the device performs the above-mentioned cluster mutation detection method.

[0043] Beneficial effects: The Kataegis and Omikli cluster mutation detection method developed by the Java algorithm based on two-dimensional spatial distance provided by the present invention performs clustering based on plane distance recognition and adopts a completely different cluster mutation recognition algorithm. Compared with the existing multiple sliding windows, this algorithm has higher performance and higher recognition accuracy. Moreover, the algorithm developed based on Java only needs to provide an API to conveniently perform online detection of web pages, without the need to configure or download any supporting environment or software, which greatly improves the convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more apparent.

[0045] Figure 1 It is a flow chart of the method of the present invention;

[0046] Figure 2 It is a schematic diagram of the two major parameters of the clustering algorithm defined in an embodiment of the present invention;

[0047] Figure 3 This is a visualization diagram of the cluster mutation online detection sample chromosome 12 obtained by using the interface developed by this algorithm in an embodiment of the present invention;

[0048] Figure 4 It is the silhouette coefficient and the average number of cluster mutations calculated for the final clustering result in the embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0050] Embodiment 1:

[0051] Comparison data comes from the article "Signatures of mutational processes in human cancer." The sample is BRCA PD4103a (GRCh37) from the Cosmic database.

[0052] like Figure 1 As shown in Figure 2, the Kataegis and Omikli cluster mutation detection method developed using a Java algorithm based on plane distance mainly includes the following four steps:

[0053] 1. Obtain sample data that has been verified to have a large number of clustered mutations; screen sample data for SNP mutations; retain the gene column, chromosome column, and starting mutation site column data as input test data. Table 1 shows the input test data for this example. Sort the sample data by chromosome and mutation site, and plot the k-distance curve using a log-transformed rainforest plot to obtain the DBSCAN parameter eps. After DBSCAN clustering, the silhouette coefficient is calculated to be 0.96, and the minimum average value for each chromosome region is calculated to be 1000 bp.

[0054] Table 1

[0055] GENE_SYMBOL CHROMOSOME GENOME_START COL23A1 chr5 177805673 CACHD1 chr1 65017293

[0056] 2. Based on the demarcation point of 1000bp obtained in step 1, two key parameters are determined: parameter 1, which defines the range within which mutation sites in the map can be clustered, and parameter 2, which defines the minimum number of mutations required to achieve clustering. The above parameters are obtained using geometric principles and rainforest map display principles. For details, refer to Figure 2 ;

[0057] Depend on Figure 2 It can be seen that, taking the demarcation point of 1000bp as an example, according to the definition of the rainforest map, the next SNP must be on the y=x line with the previous SNP position y=0 as the starting point; when the previous SNP transmutation cluster is small (0bp), the allowable range of the next adjacent SNP transmutation distance will be reduced to 1000bp ( Figure 2 Figure c); When the transmutation distance of the previous SNP is large, this clustering method will allow a larger transmutation distance of the next adjacent SNP - 1000√2 ( Figure 2This shows that this type of algorithm can dynamically limit the maximum distance range of the next mutation site based on the actual distance of the mutation site, while ensuring that the maximum value includes the 1000bp demarcation point, which can relatively reduce detection errors.

[0058] 3. Process the input mutation data of the sample to be tested, classify the data by sequence, record the starting mutation position x of each SNP on the sequence and the distance y from the previous SNP into a node object instance, and record the corresponding gene name into the current node instance; map all the above node object instances into a rainforest map, calculate the nearest adjacent nodes according to the position in the map, and store the nodes that are within the range of parameter 1 (range radius is less than or equal to 1000√2) and meet the conditions of parameter 2 into the node type set and mark them as the key node set; traverse the key nodes in the above set, and calculate all nodes within the range of 1000√2 as the neighbor node set of the current key node. If the neighbor node set of a key node contains key nodes other than itself, the neighbor node sets of the two key nodes are merged into one cluster (the neighbor node set contains the key node), and the above structure is traversed in depth-first order to obtain the clustering result; Table 2 shows the clustering result of this embodiment using chromosome 12 as an example.

[0059] Table 2

[0060] Gene Position Cluster_Number Type Strand GRIP1 66847196-66848938 6 Kataegis - PTPRB 70993642-70994342 6 Kataegis - GRIP1 66854364-66856770 6 Kataegis - PTPRB 70985092-70986204 10 Kataegis - TSPAN8 71722858-71723706 7 Kataegis - FICD 108913672-108913942 5 Kataegis + LGR5 71850868-71852522 11 Kataegis + TSPAN8 71773055-71773270 2 Omikli - SRGAP1 64297537-64297812 3 Omikli + MY01H 109884221-109884756 2 Omikli + TSPAN8 71742442-71742459 3 Omikli - LGR5 71862778-71862785 2 Omikli + HELB 66729876-66731227 4 Omikli + LGR5 71864737-71865531 4 Omikli + HELB 66726752-66727147 2 Omikli + LGR5 71867643-71867824 2 Omikli + GRIP1 66850692-66851595 3 Omikli - SRGAP1 64269477-64269683 3 Omikli + TRHDE 72798975-72799548 2 Omikli +

[0061] 4. Calculate the silhouette coefficient of the clustering results to test the clustering effect of the algorithm. Figure 4 It is the silhouette coefficient and the average number of mutations in the cluster calculated for the final clustering result. As can be seen from the figure, the silhouette coefficient is 0.9186.

[0062] 5. Verify the accuracy of the detected cluster mutation results using cluster mutation characteristics:

[0063] (1) Table 3 shows that the mutation types are mainly C>T and C>G;

[0064] Table 3

[0065] Mutation type C>G C>T G>C G>A C>A Frequency 29% 29% 11% 10% 6%

[0066] (2) The results in the Strand column in Table 2 indicate that all cluster mutation regions occur on the same chain, proving the processivity and cis arrangement of cluster mutations.

[0067] Example 2:

[0068] The computer-readable storage medium of this embodiment stores a computer program thereon, and when the program is executed by a processor, the steps in the cluster mutation detection method based on plane distance in Example 1 are implemented.

[0069] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0070] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0071] Example 3:

[0072] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the cluster mutation detection method based on plane distance in Example 1 are implemented.

[0073] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The memory can include read-only memory and random access memory, and provide instructions and data to the processor. A part of the memory can also include non-volatile random access memory. For example, the memory can also store information about the device type.

[0074] Those skilled in the art will appreciate that the disclosed embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware embodiments. Furthermore, the present solution may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0075] The present invention is described with reference to the flowcharts and / or block diagrams of the methods and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of the processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0078] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

Claims

1. A cluster mutation detection method based on plane distance, characterized in that: The following steps are involved: Step 1: Screen the sample data and analyze to obtain the cutoff value that meets the clustering conditions, including: Step 1.1: Obtain sample data that has been verified to have a large number of cluster mutations; screen the sample data and retain the gene column, chromosome column, and starting mutation site column data of the sample data with the mutation type of SNP as input detection data; Step 1.2: Convert the sample data of SNP mutation type in step 1.1 into a rainforest map, draw a k-distance curve to obtain the DBSCAN parameter eps, perform DBSCAN clustering, calculate the silhouette coefficient to test the clustering quality, and obtain the cutoff point; Among them, the demarcation point described in step 1.2 is 1000bp; Step 2: Define the range parameter 1 within which mutation sites can cluster, and define the minimum number of mutations required to achieve clustering, parameter 2; The range parameter 1 and the minimum mutation number parameter 2 described in step 2 are obtained by geometric principles and rainforest map display principles; The range parameter 1 is 1000√2; The minimum mutation number parameter 2 is 1; Step 3: Process the data of the sample to be tested, merge the clusters based on the key nodes and neighbor node sets, and obtain the clustering results, including: Step 3.1: Process the input mutation data of the sample to be tested, classify the data by sequence, record the starting mutation position x of each SNP in the sequence and the distance y from the previous SNP in a node object instance, and record the corresponding gene name in the current node instance; Step 3.2: Map all node instances into a rainforest graph and calculate the nearest adjacent nodes based on their positions in the graph; Step 3.3: Store the nodes that meet the conditions of parameter 2 within the range of parameter 1 into the node type set, and mark the node type set as the key node set; Step 3.4: Traverse the key nodes in the above set, calculate all nodes within the range of parameter 1 and store them in the set, which is recorded as the neighbor node set of the current key node; Step 3.5: If the neighbor node set of a key node contains a key node other than itself, the neighbor node sets of the two key nodes are merged into one cluster, and the above structure is traversed in depth-first order to obtain the clustering result; Step 4: Verify the clustering effect and verify the results based on the cluster mutation characteristics.

2. The detection method according to claim 1, wherein The rainforest map described in step 1.2 is obtained by sorting the chromosome column and the starting mutation site column of the sample data with the mutation type of SNP in step 1.1 and performing log transformation on the distance values.

3. The detection method according to claim 1, wherein The specific method for verifying the results described in step 4 is: 4.1: Verify mutation type; 4.2: Verify TCW sequence; 4.3: Determine whether it has the ability to continuously synthesize; 4.4: Determine whether it is arranged in cis order.

4. A device for cluster mutation detection based on plane distance, characterized in that: Used to implement the detection method according to any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the detection method according to any one of claims 1 to 3 is implemented.

6. A device comprising: A processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the device performs the detection method according to any one of claims 1 to 3.

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