Data analysis method and system, electronic equipment and medium

Abstract

The invention provides a data analysis method and system, electronic equipment and a medium, and the data analysis method comprises the steps: carrying out the analysis processing according to an original file of capillary electrophoresis, so as to obtain electrophoresis data; the electrophoresis data are analyzed, peaks of the electrophoresis data and information corresponding to the peaks are obtained, and the information corresponding to the peaks comprises various types of peak information, molecular weight sizes and / or genotypes; and screening and analyzing the peaks by using the peaks and the information corresponding to the peaks to obtain a data analysis result. According to the data analysis method, the electrophoresis data of capillary electrophoresis can be subjected to automatic batch analysis, and the efficiency and accuracy of electrophoresis data analysis are improved.

Description

Technical Field

[0001] The present application belongs to the field of biotechnology and relates to a data analysis method, and in particular to a data analysis method, system, electronic equipment and medium. Background Art

[0002] Capillary electrophoresis is an experimental method for separating and analyzing molecules. It uses capillaries as separation channels and high-voltage direct current electric fields as driving forces. It is an electrophoretic separation and analysis method that achieves separation based on the differences in mobility and distribution behavior between components in the sample. It has the characteristics of fast analysis speed, short running time, low sample consumption, and wide application range. It is mainly used for DNA / RNA analysis and is widely used in forensic identification, food analysis, drug analysis, environmental monitoring and other fields. Molecular genetic markers are genetic markers based on nucleotide sequence variations in genetic material between individuals, which are a direct reflection of genetic polymorphism at the DNA level. Molecular genetic markers have the following advantages: extremely rich genomic variation, the number of molecular markers is almost unlimited, and DNA from different stages of biological development and different tissues can be used for marker analysis. It is often used in forensic identification, genetic breeding, variety identification, individual identification and other fields. Capillary electrophoresis and molecular genetic markers are usually combined together to solve various problems in many fields.

[0003] However, the current analysis of capillary electrophoresis data usually requires the addition of an internal standard to the target sample. The labeling of the internal standard currently requires manual interpretation and analysis, and is easily interfered by external factors. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a data analysis method, system, electronic device and medium for solving the problems of low efficiency and manual processing errors in electrophoresis data analysis in the prior art.

[0005] In a first aspect, the present application provides a data analysis method, which includes: performing parsing processing based on an original file of capillary electrophoresis to obtain electrophoresis data; using the electrophoresis data for analysis to obtain peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight and / or genotype; using the peaks and the information corresponding to the peaks to screen and analyze the peaks to obtain data analysis results.

[0006] In the present application, the original file of capillary electrophoresis is used to obtain electrophoresis data, peaks and information corresponding to the peaks are obtained according to the electrophoresis data, and the peaks and information corresponding to the peaks are used to screen and analyze the peaks to obtain data analysis results. This data analysis method can automatically perform batch analysis on the electrophoresis data of capillary electrophoresis, thereby improving the efficiency and accuracy of electrophoresis data analysis.

[0007] In an implementation of the first aspect, performing parsing processing according to the original file of capillary electrophoresis to obtain electrophoresis data includes: performing parsing and conversion according to the original file of capillary electrophoresis to obtain converted data; performing data extraction according to the converted data to obtain the electrophoresis data.

[0008] In an implementation of the first aspect, using the electrophoresis data for analysis to obtain the peak of the electrophoresis data and the information corresponding to the peak includes: preprocessing and analyzing the electrophoresis data to obtain the peak of the electrophoresis data; processing the peak of the electrophoresis data using a correspondence model between the electrophoresis time and the molecular weight of a standard to obtain the molecular weight corresponding to the peak; and obtaining the genotype of the peak based on the molecular weight corresponding to the peak.

[0009] In an implementation of the first aspect, the peaks of the electrophoresis data are processed using a correspondence model between the electrophoresis time and the molecular weight of a standard to obtain the molecular weight corresponding to the peak, including: screening and analyzing the peaks of the electrophoresis data using peak screening rules to obtain candidate peaks; annotating the candidate peaks with internal standards to obtain peaks annotated with internal standards; analyzing unannotated peaks using the peaks annotated with internal standards to update the peaks annotated with internal standards; and processing non-internal standard target peaks using the correspondence model between the electrophoresis time and the molecular weight of the standard to obtain the molecular weight of the non-internal standard target peaks.

[0010] In an implementation of the first aspect, obtaining the genotype of the peak according to the molecular weight corresponding to the peak includes: judging the polymorphic site corresponding to the molecular weight to be converted according to the molecular weight corresponding to the peak; performing mapping processing according to the polymorphic site corresponding to the molecular weight to be converted to obtain the genotype of the peak.

[0011] In an implementation of the first aspect, the peaks are screened and analyzed using the peaks and the information corresponding to the peaks to obtain data analysis results, including: obtaining the signal intensity corresponding to the peaks and screening the peaks to obtain the screened peaks; establishing an offset correction model based on the screened peaks and the genotypes corresponding to the screened peaks; correcting the unmapped peaks using the offset correction model to obtain the genotypes of the unmapped peaks; and obtaining the data analysis results based on the screened peaks and the unmapped peaks.

[0012] In an implementation of the first aspect, the data analysis method further includes: generating a data analysis report using the data analysis results and visually displaying the report.

[0013] In a second aspect, the present application provides a data analysis system, which includes: a file parsing module, which is used to perform parsing processing based on the original file of capillary electrophoresis to obtain electrophoresis data; a data analysis module, which is used to use the electrophoresis data for analysis to obtain peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight size and / or genotype; a result acquisition module, which is used to use the peaks and the information corresponding to the peaks to screen and analyze the peaks to obtain data analysis results.

[0014] In a third aspect, the present application provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory so that the electronic device performs a data analysis method as described in any one of the first aspects.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data analysis method described in any one of the first aspects.

[0016] As described above, the data analysis method, system, electronic device and medium of the present application have the following beneficial effects:

[0017] The peaks and peak-related information are obtained using electrophoresis data, and the peaks and peak-related information are analyzed at various levels using algorithms to achieve data analysis results from the original capillary electrophoresis file. The electrophoresis data can be analyzed and processed automatically, in batches, simply, quickly, accurately and effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A Shown is a schematic diagram of an application scenario of the data analysis method described in this application.

[0019] Figure 1B Shown are structural diagrams of the client-cloud interaction scenarios in these implementations.

[0020] Figure 2 Shown is a flow chart of the data analysis method described in an embodiment of the present application.

[0021] Figure 3 Shown is a schematic diagram of the electrophoresis data described in the examples of this application.

[0022] Figure 4 Shown is a schematic diagram of the electrophoresis data of the internal standard channel described in the examples of the present application.

[0023] Figure 5 Shown is a schematic diagram of the LIZ channel for automatic internal standard annotation described in an embodiment of the present application.

[0024] Figure 6 Schematic showing the model built for automatic internal labeling.

[0025] Figure 7 Shown is a schematic diagram of the polymorphic sites described in the examples of this application.

[0026] Figure 8 Shown is a schematic diagram of the polymorphic sites described in the examples of this application.

[0027] Fig. 9 Shown is a schematic diagram of the polymorphic sites described in the examples of this application.

[0028] Fig.10 Shown is a structural schematic diagram of the data analysis system described in an embodiment of the present application.

[0029] Fig.11 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.

[0030] Component number description

[0031] 1 Electrophoresis apparatus

[0032] 11. Sample collection equipment

[0033] 12 Local Processor

[0034] 13 Display Terminal

[0035] 2-end-cloud interactive system

[0036] 20 Terminal

[0037] 21 Cloud Server

[0038] 100 Data Analysis System

[0039] 110 File parsing module

[0040] 120 Data Analysis Module

[0041] 130 Result Acquisition Module

[0042] 200 Electronic equipment

[0043] 210 Memory

[0044] 220 processors

[0045] 230 Display

[0046] Steps S11 to S13 DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] It should be noted that in the embodiments of the present application, the words "optionally" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "optionally" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "optionally" or "for example" is intended to present related concepts in a specific way.

[0049] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0050] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0051] At present, the analysis of capillary electrophoresis data still relies on the supporting software of various manufacturers, which is easily affected by the ban on sales. Second, capillary electrophoresis analysis is to determine the molecular weight information of the target sample, and usually an internal standard needs to be added to the target sample. The labeling of the internal standard currently requires manual interpretation and research, which is easily interfered by external factors and the reproducibility of capillary electrophoresis itself is poor. Third, capillary electrophoresis gives molecular weight information, which needs to be manually sorted and summarized into genotype information at different sites. On the other hand, the process of capillary electrophoresis data analysis and the genotype results of the samples to be tested also need to be displayed in an intuitive, concise and clear visual report, so that researchers, engineers and other users can clearly and specifically understand the experimental effects of capillary electrophoresis and the genotypes of the polymorphic molecular genetic sites of the samples.

[0052] At least to address the above-mentioned problems, an embodiment of the present application provides a data analysis method, which includes: performing parsing processing based on the original file of capillary electrophoresis to obtain electrophoresis data; using the electrophoresis data for analysis to obtain peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight and / or genotype; using the peaks and the information corresponding to the peaks to screen and analyze the peaks to obtain data analysis results.

[0053] In the embodiment of the present application, the original file of capillary electrophoresis is used to obtain electrophoresis data, peaks and information corresponding to the peaks are obtained according to the electrophoresis data, and the peaks and the information corresponding to the peaks are used to screen and analyze the peaks to obtain data analysis results. This data analysis method can automatically perform batch analysis on the electrophoresis data of capillary electrophoresis, thereby improving the efficiency and accuracy of electrophoresis data analysis.

[0054] Figure 1A The electrophoresis apparatus 1 can be used to implement the data analysis method provided in the embodiment of the present application, but the application scenario of the data analysis method provided in the embodiment of the present application is not limited to Figure 1A The electrophoresis apparatus 1 shown. Figure 1A As shown, the electrophoresis apparatus 1 includes a sample collection device 11, a local processor 12 and a display terminal 13. The data analysis method provided in the embodiment of the present application can be applied to the local processor 12.

[0055] in, Figure 1A The local processor 12 in the embodiment may be a local processor or a local processor cluster composed of multiple local processors or a cloud computing center, etc., which is not limited to the specifics herein. Figure 1A Only one sample collection device 11, one local processor 12 and one display terminal 13 are shown, but it should be understood that Figure 1AThe examples are only used to understand the present solution, and the specific numbers of local processors 12 and display terminals 13 should be flexibly determined based on actual conditions.

[0056] In some other implementations, the electrophoresis apparatus 1 may not include the display terminal 13, but only include a local processor 12 with a display function and a sample collection device 11. The data analysis method provided in the embodiment of the present application can be applied to the local processor 12. The local processor 12 with a display function may include a tablet computer, a laptop computer, a PDA, a mobile phone, or a personal computer, which is not limited here.

[0057] In still other implementations, the data analysis method described in the present application may be applied to end-cloud interaction scenarios. Figure 1B The following is a schematic diagram of the structure of the client-cloud interaction scenario in these implementations. Figure 1B As shown, the terminal-cloud interaction system 2 includes a terminal 20 and a cloud server 21. The terminal 20 and the cloud server 21 can communicate with each other, and the communication method is not limited to wired or wireless.

[0058] Among them, the terminal 20 can be mobile or fixed, for example, the terminal 20 can be a wireless terminal or a wired terminal, and the wireless terminal can refer to a device with wireless transceiver function, which can be deployed outdoors, in biological laboratories and medical laboratories. The terminal 20 can be a mobile phone, a tablet computer, a laptop computer, etc., which is not limited here. The cloud server 21 can include one or more servers, or one or more processing nodes, or one or more virtual machines running on the server. The cloud server 21 can also be called a server cluster, a management platform, a data processing center, etc., which is not limited in the embodiments of the present application.

[0059] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0060] The following examples of the present application provide a data analysis method, for example, Figure 1A The local processor 12 shown or Figure 1B It is implemented by the cloud server 21 shown. Figure 2 The flowchart of the data analysis method described in the embodiment of the present application is shown as follows: Figure 2 As shown, the data analysis method includes steps S11 to S13.

[0061] Step S11, performing analysis processing on the original file of capillary electrophoresis to obtain electrophoresis data.

[0062] In one embodiment of the present application, the original file of capillary electrophoresis is parsed and processed to obtain electrophoresis data, including: parsing and converting the original file of capillary electrophoresis to obtain converted data, and extracting data based on the converted data to obtain the electrophoresis data. Specifically, the original file of capillary electrophoresis is parsed, and the binary original file is converted into a numeric type or a character type according to the format standard of the original file. The converted data is extracted to obtain electrophoresis data, and the electrophoresis data includes sample information, dye channel information and corresponding electrophoresis information required for subsequent analysis.

[0063] The following will explain the parsing process in detail with reference to the embodiments and drawings:

[0064] Example 1: Analysis of raw files of capillary electrophoresis.

[0065] The original file of capillary electrophoresis is a file in ".fsa" format, which is a standard file format developed by Applied Biosystems (ABI) for storing capillary electrophoresis data. The 1-256 binary bytes of the ".fsa" file store the header information of the file. By parsing the header information of the file, the dye channel name and the corresponding electrophoresis data can be obtained.

[0066] Step S12, using the electrophoresis data to perform analysis, obtaining peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight and / or genotype. Optionally, in actual application, the various types of peak information include various types of peak information obtained from a one-dimensional spectrum of the electrophoresis data, including but not limited to the electrophoresis time, peak height, peak width and peak skewness of the peak.

[0067] Figure 3 Shown is a schematic diagram of the electrophoresis data described in the examples of this application. Figure 3 As shown, the original file in ".fsa" format is parsed to obtain the dye channel name and the corresponding electrophoresis time and electrophoresis data. The dye channels include FAM channel, VIC channel, NED channel, PET channel and internal standard LIZ channel.

[0068] In one embodiment of the present application, the electrophoresis data is used for analysis to obtain the peak of the electrophoresis data and the information corresponding to the peak, including: preprocessing and analyzing the electrophoresis data using a one-dimensional spectrum data processing method to obtain the peak of the electrophoresis data; processing the peak of the electrophoresis data using a correspondence model between the electrophoresis time and the molecular weight of a standard to obtain the molecular weight corresponding to the peak; and obtaining the genotype of the peak based on the molecular weight corresponding to the peak.

[0069] Specifically, the preprocessing of the electrophoresis data includes smoothing, denoising, baseline correction and peak extraction of the electrophoresis data of each channel to obtain the peak of the electrophoresis data. The electrophoresis time and molecular weight of the standard are modeled, and the peak of the electrophoresis data is processed using the corresponding model of the electrophoresis time and molecular weight of the standard to obtain the molecular weight corresponding to the peak. There is a corresponding relationship between the preset channel or sample and the polymorphic site, and the typing of the polymorphic site is associated with the molecular weight. The genotype of the peak is obtained according to the molecular weight corresponding to the peak and the relationship between the typing of the polymorphic site and the molecular weight.

[0070] The following will explain the analysis process in detail with reference to specific embodiments and drawings:

[0071] Example 2: Analyze the electrophoresis data to obtain the molecular weight.

[0072] Figure 4 Shown is a schematic diagram of the electrophoresis data of the internal standard channel described in the embodiment of the present application. Figure 4 As shown, the spectrum includes the peak height, peak width, peak apex and left and right endpoint information.

[0073] The internal standard of the standard sample was automatically labeled on the internal standard peak channel using the internal standard automatic labeling algorithm, and a model was built using the correspondence between the internal standard peak electrophoresis time and the molecular weight. Figure 5 The schematic diagram of the LIZ channel with automatic internal standard marking described in the embodiment of the present application is shown. Figure 5 As shown, the electrophoresis time and molecular weight of 16 standards are modeled, and the model equation of electrophoresis time and molecular weight is:

[0074] s=-49.69763704+0.033700804×t+3.04754E-05×t 2 -7.86392E-09×t 3 +8.77774E-13×t 4 -3.56E-17×t 5 ,

[0075] Where s is the molecular weight and t is the electrophoresis time.

[0076] For example, if the coordinates of any peak vertex are (4269, 9046.11), then the electrophoresis time of the peak is 4269 and the peak height is 9046.11. Substituting the above electrophoresis time and molecular weight model equation into the molecular weight is 278.751476.

[0077] It should be noted that the model equation of electrophoresis time and molecular weight described in the embodiment of the present application is only the equation obtained by modeling in this embodiment. The model equation applicable to other internal standard substances depends on the electrophoresis data of other internal standard substances, and the present application is not limited to this.

[0078] Embodiment 3, internal standard automatic labeling algorithm.

[0079] In the process of analyzing the electrophoresis data, the molecular weight of the peak of the electrophoresis data needs to be automatically marked on the internal standard peak channel by the internal standard automatic marking algorithm, and the corresponding relationship between the electrophoresis time of the internal standard peak and the molecular weight is used to model. The internal standard automatic marking algorithm includes: filtering and screening the peaks in the internal standard channel according to preset rules to obtain candidate peaks. Select several peaks with later electrophoresis time from the candidate peaks and mark them as internal standards to obtain the peaks marked with internal standards. For the remaining unmarked peaks, mark them in order from large to small until all internal standards are marked. Use the corresponding model of the electrophoresis time of the standard to process the non-internal standard target peak to obtain the molecular weight of the non-internal standard target peak. The non-internal standard target peak is the peak for which the corresponding molecular weight needs to be obtained and is not marked as an internal standard. For the peaks marked with internal standards, the molecular weight and electrophoresis time of the peaks marked with internal standards are used to construct equations using some or all of the rules of polynomial fitting, linear equation fitting, and average slope fitting to infer the electrophoresis time corresponding to the next peak to be marked with internal standards, and the peak closest to the electrophoresis time of the peak marked with internal standards is selected as the candidate marked peak. The first-order derivative or second-order derivative of the candidate marked peak within the corresponding electrophoresis time range is calculated using the model of the peaks of the marked internal standards and the electrophoresis time model of the peaks of the marked internal standards. If the first-order derivative remains near a value, or oscillates very slightly within the set threshold, or the second-order derivative is 0 and the absolute value is less than the set performance threshold, the candidate marked peak is marked as the internal standard. Otherwise, the candidate marked peak is eliminated in this internal standard marking, and the candidate marked peak is reselected until the conditions are met.

[0080] Specifically, the peak screening analysis of the electrophoresis data using the peak screening rule includes: assuming that the internal standard is LIZ500, there are 16 standard products, and the corresponding molecular weights are "35, 50, 75, 100, 139, 150, 160, 200, 250, 300, 340, 350, 400, 450, 490, 500." Figure 4 As shown, the IS column is the annotated internal standard information. Peaks less than 0 are filtered out, and peaks greater than 0 are annotated by the internal standard automatic labeling algorithm.

[0081] In some possible implementations, a predetermined peak screening rule is used to remove excessive peaks. For example, peak screening rule A1 is used to remove excessive peaks, and peaks are sorted by peak height, and peaks with heights higher than 2 times the 90th percentile of the candidate peak height or 5 times the median peak height are removed. Peak screening rule B1 is used to remove excessively low peaks, and peaks are sorted by peak height, and half of the peak heights lower than the 10th percentile of the candidate peak height or 10% of the peak height lower than the median peak height are removed. Assuming that the peak heights of the five peaks A, B, C, D, and E are 1, 2, 3, 4, and 5, respectively, peaks with heights higher than 2 times the 90th percentile of the five peaks are removed, that is, peak heights higher than 2 times the 90th percentile of 4.6 are removed, and excessive peaks with heights higher than 9.2 are removed. Eliminate half of the peak heights below the 10th percentile of the five peak heights, that is, eliminate half of the peak heights below the 10th percentile of 1.4, and eliminate peaks that are too low with a height below 0.7. Optionally, select the first excessively high peak with the shortest electrophoresis time as the marker peak a1, filter the peaks before a1, and filter the peaks whose electrophoresis time with the a1 peak is less than the first threshold, and filter the excessively low peaks again according to rule B1 among the retained peaks. Compare the number of filtered peaks with the number of internal standards of the standard to determine whether the number of retained peaks is greater than the number of internal standards of the standard. If not, relax the requirements of peak screening rule A1 and peak screening rule B1, and repeat the above steps until the number of filtered peaks is greater than the number of internal standards of the standard.

[0082] In some other possible implementations, the number of internal standards greater than or equal to 1 / 3 of the standard is n, and the n peaks with the largest molecular weight are used as internal standards. For example, if the number of internal standards of the standard is 16, the largest 6 peaks are determined as internal standards. Based on the average peak height m1 of the n peaks, according to the peak screening rule A2, 80% of the peaks with a peak height higher than m1 are eliminated, and according to the peak screening rule B2, 30% of the peaks with a peak height lower than m1 are eliminated.

[0083] In some other possible implementations, peaks whose electrophoresis time between peak a1 and the first too low peak is less than the second threshold are eliminated, for example, peaks whose electrophoresis time between peak a1 and the first too low peak is less than 50 are eliminated. Alternatively, the peak between the first peak of the candidate peak below the third threshold and a1 is eliminated, for example, the peak between the peak below 50% of the median of the candidate peaks and a1 is eliminated.

[0084] The candidate peaks are labeled with internal standards, and obtaining the peaks labeled with internal standards includes: selecting several peaks with later electrophoresis time from the candidate peaks and marking them as internal standards, and obtaining the peaks labeled with internal standards. For the remaining unlabeled peaks, the peaks are labeled in descending order until all the internal standards are labeled. For example, six peaks with later electrophoresis time from the candidate peaks are selected and marked as internal standards, such as Figure 4As shown, the peak heights of these 6 peaks are 340, 350, 400, 450, 490, and 500, respectively, and the corresponding electrophoresis times are 5058, 5191, 5861, 6485, 7001, and 7109, respectively.

[0085] In some possible implementations, the polynomial fitting calculation process is as follows: by gradually increasing the order of the polynomial fitting, the data is fitted with a polynomial, and its derivative in a specified interval is calculated. The specified interval is the maximum m+1 internal standard range, where m is the number of labeled internal standards. Through a series of conditions (such as all derivative values ​​are positive, derivative values ​​are within a reasonable range, standard deviation limits, etc.), the most suitable polynomial fitting is filtered and optimized, and the peak closest to the predicted value is selected as the candidate peak for marking the m+1 internal standard. In order to make the fitting result have a higher accuracy within a certain range and minimize the error between the fitting point and the target point, an optimal polynomial with a stable derivative and meeting the requirements is required. It should be noted that the order of the polynomial fitting needs to be consistent with the actual data, for example, it is meaningless if it exceeds the m+1 order. Furthermore, the initial order j is set to m+1 / 5 but not more than m-3, and the order of the polynomial fitting is gradually increased until the fitting is stopped at m-3; further explained in detail, the iteration stop condition does not necessarily require the order of fitting to reach a preset value (such as m-3), as long as other preset conditions are met, such as the difference between the fitting point and the target point (electrophoresis time) is less than a preset threshold (such as 10), it can also be exited. Further examples are given to illustrate the filtering conditions of the optimal polynomial whose derivatives are stable and meet the requirements, for example, ensuring that all derivative values ​​are positive, ensuring that the first derivative value (i.e., the m+1th internal standard) is within a certain range (the difference with the median of the derivative cannot exceed 30%, such as within the range of the geometric mean ± one standard deviation with other internal standards), and for another example, the standard deviation of the derivative corresponding to the internal standard is within a reasonable range (such as cannot exceed 1, etc.), and the present application is not limited to this.

[0086] In some other possible implementations, the calculation process of the linear equation fitting is as follows: by gradually increasing the number of fitting internal standards, a linear equation that meets the requirements is found, so that the error between the fitting point and the target point is minimized. It should be noted that the number of fitting internal standards is not the more the better, because the entire fitting process is to mark the internal standards from large to small, and the larger the number of fitting internal standards is, the more the internal standards with larger molecular weight play a role in the entire internal standard automatic marking process. In actual situations, due to the influence of factors such as electrophoretic offset, the influence of internal standards closer to the internal standard to be measured should be considered. Similarly, the number of fitting internal standards is not the smaller the better, and the fewer internal standards are affected by outliers and the less information is retained. Specifically, the number of fitting internal standards is selected to increase successively for iterative processing, and the number of fitting internal standards stops when it reaches a preset value, or the difference (electrophoresis time) between the fitting point and the target point is less than the preset threshold value. For example, the number of fitting internal standards increases from 4 to 9, stops when it reaches a preset value of 9, or stops when the difference in electrophoresis time between the fitting point and the target point is less than 5.

[0087] In some other possible implementations, the calculation process of the average slope fitting is as follows: for several marked internal standards that are closest to the internal standard to be marked, the ratio between the molecular weight difference and the electrophoresis time difference of two adjacent internal standards is calculated, and the weighted average value of each ratio value is calculated. Specifically, the closer the distance to the internal standard to be marked, the greater the weight, and the electrophoresis time is predicted by the molecular weight of the nearest internal standard to the internal standard to be marked and the average ratio. An average ratio that meets the requirements is found by gradually increasing the number of internal standards, so that the error between the predicted point and the target point is minimized. Specifically, the number of fitted internal standards is selected to increase successively for iterative processing, and the number of fitted internal standards is stopped when it reaches a preset value, or the difference (electrophoresis time) between the fitting point and the target point is less than a preset threshold value. For example, the number of fitted internal standards continues to increase, and stops when it reaches a preset value of 7, or stops when the difference in electrophoresis time between the fitting point and the target point is less than 2.5.

[0088] Select some or all of the rules of polynomial fitting, linear equation fitting, and average slope fitting to construct an equation and infer the electrophoresis time corresponding to the next internal standard to be labeled. Specifically, assume that the internal standard of the standard is: 35, 50, 75, 100, 139, 150, 160, 200, 250, 300, 340, 350, 400, 450, 490, 500. The known internal standards are: 340, 350, 400, 450, 490, 500, then the internal standard to be measured is 300. After a round of fitting processing, the internal standard to be measured is 300. At this time, the known internal standards are: 300, 340, 350, 400, 450, 490, 500, and the next internal standard to be measured is 250.

[0089] Select the peak with the smallest difference from the fitting point, which is also the closest peak, as the candidate annotation peak. Use the annotated internal standard and the corresponding electrophoresis time modeling to calculate the first-order derivative or second-order derivative within the electrophoresis time range corresponding to the candidate annotation peak. If the corresponding first-order derivative remains near a certain value and is less than the set threshold value after the candidate annotation peak is annotated with the internal standard modeling, or the second-order derivative remains at 0 and its absolute value is less than the set minimum threshold, then the candidate annotation peak is annotated with the internal standard. Otherwise, the peak is eliminated and the candidate annotation peak is reselected until the conditions are met. For example, if the first-order derivative of the candidate annotation peak after modeling is near the mean of the first-order derivative of the annotated internal standard, and is less than the set threshold value of 10. Or if the second-order derivative of the candidate annotation peak is 0, and its absolute value is less than the set minimum threshold value of 0.1, then the candidate annotation peak is selected to be annotated with the internal standard. Otherwise, it is deleted in this internal standard annotation and selected again in the subsequent internal standard annotation process until the annotation conditions are met.

[0090] Figure 6 Shown is a schematic diagram of the LIZ channel for automatic internal standard annotation described in an embodiment of the present application. Figure 6 Shown is a schematic diagram of the model for automatic internal standard modeling. Figure 6 As shown, the R2 value of the automatic internal standard annotation model is 0.99998, and the p value of the model test is 1.079E-34, which is much less than 0.001, indicating that the model has good significance and better prediction performance.

[0091] Embodiment 4: The genotype of the peak is obtained according to the molecular weight corresponding to the peak.

[0092] The polymorphic site corresponding to the molecular weight to be converted is judged according to the molecular weight corresponding to the peak. The polymorphic site corresponding to the molecular weight to be converted is mapped to obtain the genotype of the peak. Specifically, there is a corresponding relationship between the polymorphic site and the channel or sample of capillary electrophoresis. The molecular weight range of each polymorphic site is inferred according to the typing of the polymorphic site and the reference molecular weight information. According to the molecular weight corresponding to the peak, it is judged which polymorphic site the molecular weight to be converted corresponds to, and then the mapping process is performed according to the polymorphic site to obtain the genotype closest to the peak.

[0093] The process of determining the polymorphic site corresponding to the molecular weight to be converted according to the molecular weight corresponding to the peak is as follows: According to the experimental design of capillary electrophoresis, that is, the correspondence between the channel and the polymorphic site, it is predicted that the original file of capillary electrophoresis has 3 STR polymorphic sites (STR-01, STR-02, STR-03) on the FAM channel. Figure 7 Shown is a schematic diagram of the polymorphic sites described in the examples of this application. Figure 7As shown, the reference molecular weight of STR locus STR-01 is 138.24, and the corresponding number of repeats is 17. It is known that there are 11 STR typings for this polymorphic locus, of which the maximum and minimum numbers of repeats are 13 and 23, and the repeating unit is GAAA. It is inferred that the molecular weight range of this polymorphic locus is 122.24-162.24. Similarly, the molecular weight ranges of STR-02 and STR-03 are 187.9-239.9 and 266.9-302.9, respectively. For example, the molecular weight to be converted is 142.27, which belongs to STR-01. It should be noted that the experimental design of capillary electrophoresis generally does not allow overlap between different STR loci in the same channel. If the experimental design cannot avoid overlap, the overlapping loci (such as PCR amplification fragments) will be separated for capillary electrophoresis before the experiment.

[0094] The process of mapping the polymorphic sites corresponding to the molecular weight to be converted to obtain the genotype of the peak is as follows: Figure 8 Shown is a schematic diagram of the polymorphic sites described in the examples of this application, such as Figure 8 As shown, the polymorphic locus STR-01 has 11 types (s1 to s11, repeat number 13 to 23). According to its reference molecular weight 138.34 and the corresponding type s5 (repeat number 17), the reference molecular weights corresponding to the 11 types s1-s11 are 122.24, 126.24, 130.24, 134.24, 138.24, 142.24, 146.24, 150.24, 154.24, 158.24, 162.24, respectively. It is determined that the genotype corresponding to the molecular weight to be converted 142.27 is s6 (repeat number 18).

[0095] It should be noted that for the case where the molecular weight to be converted is between the molecular weights corresponding to two adjacent genotypes, Fig. 9 Shown is a schematic diagram of the polymorphic sites described in the examples of this application, such as Fig. 9 As shown, the molecular weight of STR-02 locus is 217.85, which is between typing s8 and s9, and the repeat numbers are 19 and 20, and the molecular weights are 215.9 and 219.9. Considering the influence of factors such as errors, it can be considered that the molecular weight has a deviation of 0.5 and / or exceeds the range. Specifically, the molecular weight in the range of 217.4-218.4 can be considered to be between typing s8 and s9, marked as the molecular weight to be determined, and the genotype is determined in the next screening analysis.

[0096] Step S13, screening and analyzing the peaks using the peaks and the information corresponding to the peaks to obtain data analysis results.

[0097] In some possible implementations, the process of screening and analyzing the peak using the peak and the information corresponding to the peak is as follows: obtain the signal intensity value RFU corresponding to the peak, and filter and screen the peak according to preset rules. Optionally, remove the peak with RFU higher than 30,000, remove the peak with RFU lower than 1,000, obtain the molecular weight information corresponding to the peak, select the peak of the molecular weight and genotype that can accurately correspond to this channel or all channels, and establish an offset correction model. Combined with the relevant information of the polymorphic site, filter and screen the peak that exceeds the range of the polymorphic site and exceeds a certain threshold. For the peak whose molecular weight is located in the middle of the corresponding molecular weight of two adjacent genotypes, combine the offset correction model and map it to one of the genotypes. For the peak that exceeds the range of the polymorphic site and does not exceed a certain threshold, map it to the corresponding genotype with the largest or smallest molecular weight. Further explanation is given below in conjunction with the accompanying drawings.

[0098] Example 5: Using the offset correction model to determine the genotype of the peak whose molecular weight is in the middle of the reference molecular weight.

[0099] like Fig. 9 As shown, in the polymorphic site STR-02, the molecular weight to be determined is 217.85, and the molecular weights corresponding to the two closest genotypes s8 and s9 are 215.9 and 219.9, respectively, and the repeat numbers are 19 and 20. It is known that the corresponding samples have other molecular weights of 142.27, 146.08, 138.38, 283.56, 291.58, 205.14, and 209.32 in this channel, and the reference molecular weights of the corresponding genotypes are determined to be 142.24, 146.24, 138.24, 282.9, 290.9, 203.9, and 207.9. A univariate linear equation is used for modeling, and the offset correction model y=0.9951*x+0.4272 of the theoretical molecular weight of the molecular weight and genotype (STR repeat number) is obtained. Substituting the molecular weight to be determined 217.85 into the offset correction model, the theoretical molecular weight is obtained to be 216.8603, and the converted STR repeat number is 19.24, which is closest to s8, that is, the typing corresponding to 217.85 is s8, and the genotype of the molecular weight to be determined is determined to be s8.

[0100] It should be noted that the method for establishing an offset correction model based on known molecular weight and theoretical molecular weight is not limited to univariate linear equation modeling, and other regression modeling methods such as ridge regression and lasso regression can also be used. The present application is not limited to this.

[0101] It should be further explained that the peaks that exceed the polymorphic site range and exceed a certain threshold are filtered and the peaks that exceed the polymorphic site range and do not exceed a certain threshold are mapped to the corresponding genotype with the largest or smallest molecular weight. The selection of the threshold can be judged in combination with the offset correction equation.

[0102] In one embodiment of the present application, the data analysis method further includes: generating a data analysis report using the data analysis result and visually displaying it. Specifically, the original file of the capillary electrophoresis is fully processed to obtain the data analysis result, and the data analysis report is generated according to the data analysis result and visually displayed.

[0103] In some possible implementations, such as Figure 7 As shown in the figure, the bottom solid line is the number of repeats of the STR locus, the middle is the STR locus and its theoretical size, the upper oblique dotted line is the reference molecular weight of the STR locus and its corresponding number of repeats, and the dotted line is the peak converted to genotyping or repeat number. The figure clearly shows the STR locus corresponding to each peak. Figure 8 As shown, according to the information of STR-01 locus, the number of repetitions corresponding to 142.27 and 146.08 is 18 and 19. Fig. 9 As shown in the figure, it shows an overview of the peak and site information in the FAM channel. The visual data analysis report has the effect of clearly and specifically showing the corresponding relationship between molecular weight and corresponding genotyping.

[0104] It should be noted that the STR loci in the above embodiments are only used to better illustrate the present application, and do not mean that the present application is limited to STR. As long as other polymorphic genetic loci involving molecular weight changes, such as indel mutations, can also be applied to the present application, which is not limited here.

[0105] Fig.10 The structure diagram of the data analysis system described in the embodiment of the present application is shown as follows: Fig.10 As shown, the data analysis system 100 includes a file parsing module 110 , a data analysis module 120 and a result acquisition module 130 .

[0106] The file parsing module 110 is used to perform parsing processing according to the original file of capillary electrophoresis to obtain electrophoresis data;

[0107] The data analysis module 120 is used to analyze the electrophoresis data to obtain peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight and / or genotype;

[0108] The result acquisition module 130 is used to perform screening analysis on the peaks using the peaks and the information corresponding to the peaks to obtain data analysis results.

[0109] It should be noted that the above modules 110 to 130 included in the data analysis system 100 are Figure 2 Steps S11 to S13 in the data analysis method shown correspond to each other and are not described in detail here.

[0110] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0111] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, each functional module / unit in each embodiment of the present application may be integrated in a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated in one module / unit. For example, the file parsing module 110 may be a separately established processing element, or it may be integrated in a chip of the above-mentioned device for implementation. In addition, it may be stored in the memory of the above-mentioned device in the form of program code, and the functions of the above-mentioned file parsing module 110 may be called and executed by a processing element of the above-mentioned device. The implementation of other modules is similar. In addition, all or part of these modules may be integrated together, or they may be implemented independently. The processing element described here may be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules may be completed by an integrated logic circuit of hardware in a processor element or instructions in the form of software.

[0112] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0113] An embodiment of the present application also provides an electronic device. Fig.11The structure diagram of the electronic device 200 described in the embodiment of the present application is shown. Fig.11 As shown, in this embodiment, the electronic device 200 includes a memory 210 and a processor 220 .

[0114] The memory 210 is used to store computer programs; preferably, the memory 210 includes: ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk, etc., various media that can store program codes.

[0115] Specifically, the memory 210 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 210 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application. It is understood that the memory 210 may be a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), which is used as an external cache. By way of exemplary but not limiting explanation, many forms of RAM are available, such as static random access memory (SRAM, StaticRandom Access Memory), synchronous static random access memory (SSRAM, Synchronous Static RandomAccess Memory). The memory described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable categories of memory.

[0116] The processor 220 is connected to the memory 210 and is used to execute the computer program stored in the memory 210 so that the electronic device 200 executes the data analysis method described in any embodiment of the present application.

[0117] Optionally, the processor 220 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, discrete hardware components.

[0118] Optionally, the electronic device 200 in this embodiment may further include a display 230. The display 230 is communicatively connected to the memory 210 and the processor 220, and is used to display a graphical user interface (GUI) interaction interface related to the data analysis method described in the embodiment of the present application.

[0119] The present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the data analysis method described in any embodiment of the present application is implemented.

[0120] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0121] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0122] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A data analysis method, characterized in that: include: Perform analysis and processing on the original files of capillary electrophoresis to obtain electrophoresis data; Analyzing the electrophoresis data to obtain peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight and / or genotype; The peaks are screened and analyzed using the peaks and the information corresponding to the peaks to obtain data analysis results.

2. The data analysis method according to claim 1, characterized in that: According to the original file of capillary electrophoresis, the electrophoresis data are analyzed and processed to obtain: Perform analysis and conversion according to the original file of capillary electrophoresis to obtain the converted data; Data extraction is performed based on the converted data to obtain the electrophoresis data.

3. The data analysis method according to claim 1, characterized in that: Analyzing the electrophoresis data to obtain peaks of the electrophoresis data and information corresponding to the peaks includes: Preprocessing and analyzing the electrophoresis data to obtain peaks of the electrophoresis data; Processing the peak of the electrophoresis data using a corresponding model between the electrophoresis time and the molecular weight of a standard substance to obtain the molecular weight corresponding to the peak; The genotype of the peak is obtained according to the molecular weight corresponding to the peak.

4. The data analysis method according to claim 3, characterized in that: The peak of the electrophoresis data is processed using the corresponding model of the electrophoresis time and the molecular weight of the standard to obtain the molecular weight corresponding to the peak, including: Using peak screening rules to screen and analyze the peaks of the electrophoresis data to obtain candidate peaks; Annotating the candidate peaks with internal standards to obtain peaks annotated with internal standards; Analyzing the unlabeled peaks using the peaks labeled with internal standards to update the peaks labeled with internal standards; The non-internal standard target peak is processed using the corresponding model between the electrophoresis time and the molecular weight of the standard to obtain the molecular weight of the non-internal standard target peak.

5. The data analysis method according to claim 1, characterized in that: Obtaining the genotype of the peak according to the molecular weight corresponding to the peak includes: Determine the polymorphic site corresponding to the molecular weight to be converted according to the molecular weight corresponding to the peak; Mapping is performed according to the polymorphic sites corresponding to the molecular weight to be converted to obtain the genotype of the peak.

6. The data analysis method according to claim 1, characterized in that: Screening and analyzing the peak using the peak and the information corresponding to the peak to obtain data analysis results includes: Obtaining the signal intensity corresponding to the peak and screening the peak to obtain the screened peak; Establishing an offset correction model according to the screened peaks and the genotypes corresponding to the screened peaks; Performing correction processing on the unmapped peak using an offset correction model to obtain the genotype of the unmapped peak; The data analysis results are obtained based on the filtered peaks and the unmapped peaks.

7. The data analysis method according to claim 1, characterized in that: Also includes: A data analysis report is generated using the data analysis results and displayed visually.

8. A data analysis system, characterized in that: include: A file parsing module, used for parsing the original file of capillary electrophoresis to obtain electrophoresis data; A data analysis module, used to analyze the electrophoresis data to obtain peaks of the electrophoresis data and information corresponding to the peaks, wherein the information corresponding to the peaks includes various types of peak information, molecular weight and / or genotype; The result acquisition module is used to screen and analyze the peak using the peak and the information corresponding to the peak to obtain a data analysis result.

9. An electronic device, characterized in that: The electronic device comprises: Memory for storing computer programs; A processor, wherein the processor is used to execute the computer program stored in the memory so that the electronic device performs the data analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data analysis method according to any one of claims 1 to 7 is implemented.

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