Protein sequencing data processing method, related method and device

Abstract

The invention provides a protein sequencing data processing method, a related method and a related device, and relates to the technical field of protein sequencing. The protein sequencing data processing method comprises the following steps: determining the occurrence frequency of each protein signal intensity value in the protein sequencing data by acquiring the protein sequencing data of a target protein in a target slice, and calculating the occurrence probability of each protein signal intensity value according to the frequency; calculating a protein signal intensity threshold value for carrying out intensity division on the protein signal intensity based on the protein signal intensity value and the probability; and determining the sequencing site with the protein signal intensity value greater than the protein signal intensity threshold as the foreground site corresponding to the target protein. The protein signal intensity threshold value is calculated based on the protein signal intensity value and the probability, and the protein signal intensity value and the probability are used as defining factors of the protein signal intensity threshold value, so that the threshold value for distinguishing the positive signal from the negative signal can be reasonably and accurately determined, and the foreground site can be more accurately determined.

Description

Technical Field

[0001] The present application relates to the technical field of protein sequencing, and in particular to a method for processing protein sequencing data, and related methods and devices. Background Art

[0002] Spatial proteomics sequencing technology is a new technology for studying the spatial expression patterns of proteins in tissues. Spatial proteomics technology can be divided into three categories based on the differences in quantitative methods: spatial proteomics technology based on marker signal imaging, spatial proteomics technology based on mass spectrometry, and spatial proteomics technology based on sequencing. Among them, spatial proteomics technology based on sequencing has been widely used due to its advantages of high research efficiency and high resolution.

[0003] It should be noted that the detected antibody protein signal intensity includes two situations: first, protein positive signal, i.e. specific binding; second, protein negative signal, i.e. non-specific binding, i.e. negative signal; it should be pointed out that the industry often uses a threshold to distinguish protein positive signals from protein negative signals, so as to determine the prospect sites that characterize specific binding, and thus analyze the relevant data of the prospect sites. However, in the related technology, there is no reasonable and accurate method for determining the threshold for distinguishing positive signals from negative signals. Therefore, the accurate determination of prospect sites has become a major problem that needs to be solved in the industry. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method for processing protein sequencing data, related methods and devices, aiming to reasonably and accurately determine the threshold for distinguishing positive signals from negative signals, so as to more accurately determine the prospect sites.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for processing protein sequencing data, the method comprising:

[0006] Acquiring protein sequencing data of a target protein in a target slice, wherein the protein sequencing data includes protein signal intensity values ​​of the target protein measured at a plurality of sequencing sites;

[0007] Determine the frequency of occurrence of each protein signal intensity value in the protein sequencing data, and calculate the probability of occurrence of each protein signal intensity value according to the frequency;

[0008] A protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and the probability calculation;

[0009] Determine the sequencing site whose protein signal intensity value is greater than the protein signal intensity threshold as the foreground site corresponding to the target protein.

[0010] In some embodiments of the present application, the protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and the probability calculation includes:

[0011] Dividing the protein signal intensity values into a first type of protein signal intensity values and a second type of protein signal intensity values with any target protein signal intensity value as the threshold;

[0012] Calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on the probability;

[0013] Determining the protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the between-class variance value.

[0014] In some embodiments of the present application, the calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on the probability includes:

[0015] Calculating the sum of multiple probabilities corresponding to the first type of protein signal intensity values to obtain a first probability sum, and calculating the sum of multiple probabilities corresponding to the second type of protein signal intensity values to obtain a second probability sum;

[0016] Calculating a first protein signal intensity mean according to the first probability sum, the first type of protein signal intensity values and the corresponding probabilities, and calculating a second protein signal intensity mean according to the second probability sum, the second type of protein signal intensity values and the corresponding probabilities;

[0017] Calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on the first probability sum, the second probability sum, the first protein signal intensity mean and the second protein signal intensity mean.

[0018] In some embodiments of the present application, the dividing the protein signal intensity values into a first type of protein signal intensity values and a second type of protein signal intensity values with any target protein signal intensity value as the threshold includes:

[0019] Determining any protein signal intensity value among multiple protein signal intensity values as the target protein signal intensity value;

[0020] Dividing the protein signal intensity values with numerical values less than the target protein signal intensity value into the first type of protein signal intensity values, and dividing the protein signal intensity values with numerical values not less than the target protein signal intensity value into the second type of protein signal intensity values.

[0021] In some embodiments of the present application, determining a protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the between-class variance value includes:

[0022] Determining the between-class variance value corresponding to each target protein signal intensity value;

[0023] Determining the target protein signal intensity value with the largest between-class variance value as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak.

[0024] In some embodiments of the present application, calculating the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value based on the first probability sum, the second probability sum, the first protein signal intensity mean, and the second protein signal intensity mean includes:

[0025] Calculating the square of the difference between the first protein signal intensity mean and the second protein signal intensity mean;

[0026] Calculating the continued product of the first probability sum, the second probability sum, and the square of the difference to obtain the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value.

[0027] In some embodiments of the present application, determining a protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and the probability includes:

[0028] Dividing the protein signal intensity values into a first type of protein signal intensity value and a second type of protein signal intensity value with any target protein signal intensity value as the threshold;

[0029] Calculating a first information entropy corresponding to the first type of protein signal intensity value and a second information entropy corresponding to the second type of protein signal intensity value based on the probability;

[0030] Determining a protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the sum of the first information entropy and the second information entropy.

[0031] To achieve the above object, a second aspect of the embodiments of the present application proposes an antigen protein distribution region detection method, and the method includes:

[0032] Obtaining a biological section to be detected;

[0033] Infiltrating the biological section with a solution of a preset antibody protein, and cleaning the infiltrated biological section to obtain a target section;

[0034] Performing protein sequencing on the target section to obtain protein sequencing data;

[0035] Process the protein sequencing data by using the method for processing protein sequencing data according to any one of the embodiments of the first aspect to obtain the foreground sites of the preset antibody protein;

[0036] Determine the distribution region of the antigen protein in the biological section according to the foreground sites of the preset antibody protein.

[0037] To achieve the above object, a third aspect of the embodiments of the present application provides a device for processing protein sequencing data, including:

[0038] A first acquisition unit, configured to acquire protein sequencing data of a target protein in a target section, where the protein sequencing data includes protein signal intensity values of the target protein measured at a plurality of sequencing sites;

[0039] A first determination unit, configured to determine the frequency of occurrence of each protein signal intensity value in the protein sequencing data, and calculate the probability of occurrence of each protein signal intensity value according to the frequency;

[0040] A threshold division unit, configured to calculate a protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and the probability calculation;

[0041] A second determination unit, configured to determine that the sequencing site where the protein signal intensity value is greater than the protein signal intensity threshold is the foreground site corresponding to the target protein.

[0042] To achieve the above object, a fourth aspect of the embodiments of the present application provides a device for detecting the distribution region of an antigen protein, including:

[0043] A second acquisition unit, configured to acquire a biological section to be detected;

[0044] A section cleaning unit, configured to infiltrate the biological section with a solution of a preset antibody protein, and clean the infiltrated biological section to obtain a target section;

[0045] A protein sequencing unit, configured to perform protein sequencing on the target section to obtain protein sequencing data;

[0046] A sequencing data processing unit, configured to process the protein sequencing data by using the method for processing protein sequencing data according to any one of the embodiments of the first aspect to obtain the foreground sites of the preset antibody protein;

[0047] A third determination unit, configured to determine the distribution region of the antigen protein in the biological section according to the foreground sites of the preset antibody protein.

[0048] To achieve the above object, a fifth aspect of the embodiments of the present application provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the processing method of protein sequencing data described in the first aspect or the detection method of antigen protein distribution region described in the second aspect is implemented.

[0049] To achieve the above object, a sixth aspect of the embodiments of the present application provides a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the processing method of protein sequencing data described in the first aspect or the detection method of antigen protein distribution region described in the second aspect is implemented.

[0050] To achieve the above object, a seventh aspect of the embodiments of the present application provides a computer program product. The computer program product includes a computer program, and the computer program is read and executed by a processor of a computer device, so that the computer device executes the processing method of protein sequencing data described in the first aspect or the detection method of antigen protein distribution region described in the second aspect.

[0051] The processing method of protein sequencing data, related methods and devices provided by the embodiments of the present application determine the frequency of occurrence of each protein signal intensity value in the protein sequencing data by obtaining the protein sequencing data of the target protein in the target section, and calculate the probability of occurrence of each protein signal intensity value according to the frequency; wherein, the protein sequencing data includes the protein signal intensity values of the target protein measured at multiple sequencing sites; further, based on the protein signal intensity value and the probability calculation, a protein signal intensity threshold for dividing the protein signal intensity into strong and weak is determined; furthermore, the sequencing site where the protein signal intensity value is greater than the protein signal intensity threshold is determined as the foreground site corresponding to the target protein. Since the protein signal intensity threshold for dividing the protein signal intensity into strong and weak is calculated based on the protein signal intensity value and the probability, using the protein signal intensity value and the probability as the defining factors of the protein signal intensity threshold helps to reasonably and accurately determine the threshold for distinguishing positive signals and negative signals, so as to more accurately determine the foreground site. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings are used to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification. They are used together with the embodiments of the present application to explain the technical solutions of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0053] Figure 1 is a flowchart of the processing method of protein sequencing data provided by the embodiments of the present application;

[0054] Figure 2 is Figure 1 a flowchart of step S103 in

[0055] Figure 3 is Figure 2 the flowchart of step S201 in

[0056] Figure 4 is Figure 2 the flowchart of step S202 in

[0057] Figure 5 is Figure 4 the flowchart of step S403 in

[0058] Figure 6 is Figure 2 the flowchart of step S203 in

[0059] Figure 7 a schematic diagram of the correspondence between the protein signal intensity threshold and the between-class variance value;

[0060] Figure 8 is Figure 1 the flowchart of step S103 in

[0061] Figure 9 the flowchart of the antigen protein distribution region detection method provided by the embodiments of the present application;

[0062] Figure 10 a schematic diagram of the spatial distribution of the target section after removing protein negative signals according to the protein signal intensity threshold;

[0063] Figure 11 the flowchart of the protein sequencing data processing device provided by the embodiments of the present application;

[0064] Figure 12 the flowchart of the antigen protein distribution region detection device provided by the embodiments of the present application;

[0065] Figure 13 a schematic diagram of the hardware structure of the electronic device provided by the embodiments of the present application. Detailed implementation manners

[0066] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] Before further elaborating on the embodiments of the present application, the nouns and terms involved in the embodiments of the present application are described. The nouns and terms involved in the embodiments of the present application are applicable to the following explanations:

[0068] Spatial proteomics sequencing technology is a new technology for studying the spatial expression patterns of proteins in tissues. Spatial proteomics technology can be divided into three categories based on the differences in quantitative methods: spatial proteomics technology based on marker signal imaging, spatial proteomics technology based on mass spectrometry, and spatial proteomics technology based on sequencing. Among them, spatial proteomics technology based on sequencing has been widely used due to its advantages of high research efficiency and high resolution.

[0069] Spatial proteomics refers to the scientific field that studies the structure, assembly and function of proteins in three-dimensional space. Proteins are important components of living organisms, and their structure and function are essential for maintaining life activities. The purpose of spatial proteomics research is to reveal the expression patterns of proteins in physiological and pathological processes by comprehensively analyzing the expression of proteins on the cell surface in tissues. It should be understood that spatial proteomics is a new type of omics that studies the spatial expression patterns of proteins in tissues.

[0070] It should be noted that the principle of spatial proteomics is to cut out tissue regions or cells of interest through high-precision laser capture microdissection (LCM) technology; where the region of interest (ROI) is the area to be processed in the form of a box, circle, ellipse, irregular polygon, etc. from the processed image in machine vision and image processing. Furthermore, the optimized ultra-micro sample is non-destructively extracted and enzymatically cleaved into peptides, and then the expression characteristics of proteins at different spatial locations are analyzed using high-sensitivity mass spectrometry.

[0071] It should be pointed out that in order to analyze the expression characteristics of cell surface antigen proteins at different spatial locations in spatial proteomics, it is necessary to use antibody proteins with labeling signals to bind to cell surface antigen protein epitopes to mark the location of antigen proteins. In this process, there may be specific binding and non-specific binding between antibody proteins and antigen proteins. Therefore, the detected antibody protein signal intensity also includes two situations: first, protein positive signal, that is, specific binding; second, protein negative signal, that is, non-specific binding, that is, negative signal; it should be pointed out that the industry often uses a threshold to distinguish between protein positive signals and protein negative signals, so as to determine the prospect sites that characterize specific binding, thereby analyzing the relevant data of prospect sites. However, in the related technology, there is no reasonable and accurate method for determining the threshold for distinguishing positive signals from negative signals. Therefore, the accurate determination of prospect sites has also become a major problem that needs to be solved in the industry.

[0072] The main objective of the embodiments of this application is to propose a method for processing protein sequencing data, related methods and devices, aiming to reasonably and accurately determine the threshold for distinguishing positive signals and negative signals, so as to more precisely determine the foreground sites.

[0073] The following will be further described with reference to the accompanying drawings.

[0074] To achieve the above objective, the embodiments of this application propose a method for processing protein sequencing data. Among them, the method for processing protein sequencing data may include, but is not limited to, the following steps S101 to step S104.

[0075] Step S101: Obtain the protein sequencing data of the target protein in the target section. The protein sequencing data includes the protein signal intensity values of the target protein measured at multiple sequencing sites.

[0076] Step S102: Determine the frequency of occurrence of each protein signal intensity value in the protein sequencing data, and calculate the probability of occurrence of each protein signal intensity value according to the frequency.

[0077] Step S103: Based on the protein signal intensity value and the probability calculation, determine the protein signal intensity threshold for dividing the protein signal intensity into strong and weak.

[0078] Step S104: Determine that the sequencing sites with protein signal intensity values greater than the protein signal intensity threshold are the foreground sites corresponding to the target protein.

[0079] Through the method for processing protein sequencing data shown in steps S101 to S104, by obtaining the protein sequencing data of the target protein in the target section, the frequency of occurrence of each protein signal intensity value in the protein sequencing data is determined, and the probability of occurrence of each protein signal intensity value is calculated according to the frequency. Among them, the protein sequencing data includes the protein signal intensity values of the target protein measured at multiple sequencing sites. Further, based on the protein signal intensity value and the probability calculation, the protein signal intensity threshold for dividing the protein signal intensity into strong and weak is determined. Furthermore, it is determined that the sequencing sites with protein signal intensity values greater than the protein signal intensity threshold are the foreground sites corresponding to the target protein. Since the protein signal intensity threshold for dividing the protein signal intensity into strong and weak is calculated based on the protein signal intensity value and the probability, using the protein signal intensity value and the probability as the defining factors of the protein signal intensity threshold helps to reasonably and accurately determine the threshold for distinguishing positive signals and negative signals, so as to more precisely determine the foreground sites.

[0080] In step S101 of some embodiments, protein sequencing data of the target protein in the target slice is obtained. The protein sequencing data includes the protein signal intensity values of the target protein measured at multiple sequencing sites. It should be emphasized that in spatial proteomics, in order to analyze the expression characteristics of cell surface antigen proteins at different spatial positions, antibody proteins with labeled signals need to be used to bind to the epitopes of cell surface antigen proteins in the target slice to label the positions of the antigen proteins. It should be noted that the target slice is a tissue slice serving as the sequencing target, and the target protein is an antibody protein with a labeled signal, where the labeled signal can be a fluorescent type of labeled signal or other types of labeled signals. Additionally, it should be clear that the protein sequencing data is the protein sequencing data measured after the antigen protein and the antibody protein are fully bound. The target slice contains multiple sequencing sites. By detecting the intensity of the labeled signal in the target protein for these sequencing sites, the protein signal intensity values of the target protein measured at multiple sequencing sites can be obtained. In this way, the protein sequencing data of the target protein in the target slice can be obtained.

[0081] In step S102 of some embodiments, the frequency of occurrence of each protein signal intensity value in the protein sequencing data is determined, and the probability of occurrence of each protein signal intensity value is calculated based on the frequency. It should be noted that the protein signal intensity values measured at different sequencing sites in the target slice may be the same or different. Based on this, based on the protein signal intensity values of each detection site, the frequency of occurrence of each protein signal intensity value in the protein sequencing data can be determined. On the basis of clarifying the corresponding occurrence frequencies of each protein signal intensity value, the probability of occurrence of each protein signal intensity value can be further calculated according to the frequency. It should be pointed out that the purpose of determining each protein signal intensity value and the probability of occurrence of each protein signal intensity value in the protein sequencing data is to facilitate the determination of the protein signal intensity threshold in the subsequent steps.

[0082] In some more specific embodiments, the protein signal intensity values of some sequencing sites are relatively close and belong to the same intensity range; the protein signal intensity values of other sequencing sites vary greatly and belong to different intensity ranges. On this basis, the embodiments of the present application can determine the frequency of occurrence of each intensity range in the protein sequencing data and calculate the probability of occurrence of each intensity range according to the frequency. In this way, the data processing steps can be simplified while maintaining a high accuracy rate, further improving the efficiency.

[0083] In some more specific embodiments, if the number of protein signal intensity values is m, the multiple protein signal intensity values can be expressed as (n1, n2, n3, ……, n m ), then the occurrence frequencies corresponding to these m protein signal intensity values of (n1, n2, n3, ……, n m ) can be expressed as (f1, f2, f3, ……, f m)。After determining the occurrence frequencies corresponding to the m protein signal intensity values as (f1, f2, f3, ……, f m )), the occurrence probabilities corresponding to the m protein signal intensity values can be determined as (p1, p2, p3, ……, p m ) based on the following analytical expressions (1) and (2):

[0084]

[0085]

[0086] In step S103 of some embodiments, a protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and probability calculation is determined. It should be noted that the protein signal intensity threshold is used to divide the protein signal intensity into strong and weak. Through the protein signal intensity threshold, the protein positive signal and the protein negative signal can be distinguished. The protein positive signal represents the labeled signal of the target protein that has specifically bound to the antigen protein of the cells in the target section, and the protein negative signal represents the labeled signal of the target protein that has undergone non-specific binding. It should be understood that the purpose of the protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and probability calculation is to determine the foreground sites representing specific binding through the protein signal intensity threshold, so as to analyze the relevant data of the foreground sites and clarify the expression characteristics of the cell surface antigen protein at different spatial positions.

[0087] In step S104 of some embodiments, the sequencing sites with protein signal intensity values greater than the protein signal intensity threshold are determined as the foreground sites corresponding to the target protein. It should be noted that precisely because the protein signal intensity threshold is used to divide the protein signal intensity into strong and weak, the sequencing sites with protein signal intensity values greater than the protein signal intensity threshold can be determined as the foreground sites corresponding to the target protein through the protein signal intensity threshold. In this way, the relevant data of the foreground sites can be analyzed, and the expression characteristics of the cell surface antigen protein at different spatial positions can be clarified.

[0088] The following is a detailed description of step S103.

[0089] Refer to Figure 2 , in some embodiments of the present application, the protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and probability calculation in step S103 may include, but is not limited to, the following steps S201 to S203.

[0090] Step S201, divide the protein signal intensity values into the first type of protein signal intensity values and the second type of protein signal intensity values with any target protein signal intensity value as the threshold;

[0091] Step S202: Calculate the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value based on probability.

[0092] Step S203: Determine the protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the between-class variance value among multiple target protein signal intensity values.

[0093] In step S201 of some embodiments, the protein signal intensity values are divided into the first-class protein signal intensity value and the second-class protein signal intensity value with any one of the target protein signal intensity values as the threshold. It should be noted that among multiple target protein signal intensity values, with any one of the target protein signal intensity values as the threshold, these target protein signal intensity values can be divided into a class with stronger target protein signals and a class with weaker target protein signals, and these two classes of target protein signal intensity values are the first-class protein signal intensity value and the second-class protein signal intensity value respectively.

[0094] In some more specific embodiments, the class with stronger target protein signals among the first-class protein signal intensity value and the second-class protein signal intensity value can be determined as the positive protein signal intensity value to represent the target protein signal intensity value of the specific binding of the antigen protein and the antibody protein; the class with weaker target protein signals among the first-class protein signal intensity value and the second-class protein signal intensity value is the negative protein signal intensity value to represent the target protein signal intensity value of the non-specific binding of the antigen protein.

[0095] Refer to Figure 3 , according to some more specific embodiments of the present application, step S201 divides the protein signal intensity values into the first-class protein signal intensity value and the second-class protein signal intensity value with any one of the target protein signal intensity values as the threshold, which may include, but are not limited to, the following steps S301 to S302.

[0096] Step S301: Determine any one of the protein signal intensity values among multiple protein signal intensity values as the target protein signal intensity value;

[0097] Step S302: Divide the protein signal intensity values with numerical values less than the target protein signal intensity value into the first-class protein signal intensity value, and divide the protein signal intensity values with numerical values not less than the target protein signal intensity value into the second-class protein signal intensity value.

[0098] In step S301 of some embodiments, any one of the protein signal intensity values among multiple protein signal intensity values is determined as the target protein signal intensity value. It should be noted that if the number of protein signal intensity values is m, the multiple protein signal intensity values can be expressed as (n1, n2, n3, ……, n m ). From (n1, n2, n3, ……, n m)Determine any one of the m protein signal intensity values as the target protein signal intensity value t, that is, t is determined from (n1, n2, n3, ……, n m ).

[0099] In step S302 of some embodiments, the protein signal intensity values less than the target protein signal intensity value are classified as the first-class protein signal intensity values, and the protein signal intensity values not less than the target protein signal intensity value are classified as the second-class protein signal intensity values. It should be noted that after determining any one of the m protein signal intensity values as the target protein signal intensity value t from (n1, n2, n3, ……, n m ), further, among the m protein signal intensity values of (n1, n2, n3, ……, n m ), the protein signal intensity values less than the target protein signal intensity value t are classified as the first-class protein signal intensity values C1, and among the m protein signal intensity values of (n1, n2, n3, ……, n m ), the protein signal intensity values not less than the target protein signal intensity value t are classified as the second-class protein signal intensity values C2.

[0100] Through the embodiments of the present application shown in steps S301 to S302, determine any one of the multiple protein signal intensity values as the target protein signal intensity value, then classify the protein signal intensity values less than the target protein signal intensity value as the first-class protein signal intensity values, and classify the protein signal intensity values not less than the target protein signal intensity value as the second-class protein signal intensity values. In this way, the multiple protein signal intensity values can be efficiently classified into the first-class protein signal intensity values with weaker intensity and the second-class protein signal intensity values with stronger intensity according to the target protein signal intensity value t.

[0101] In step S202 of some embodiments, the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value is calculated based on probability. It should be noted that data of the same class are more similar, so the within-class variance value is small; data of different classes are less similar, so the between-class variance value is large. Based on this, it is necessary to calculate the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value based on probability. Just because data of different classes are less similar and the between-class variance value is large. Therefore, the between-class variance value can be used in subsequent steps to determine the protein signal intensity threshold for dividing the protein signal intensity from multiple target protein signal intensity values. It should be understood that by adding the analytical expressions of the between-class variance value and the within-class variance value for summation, it can be proved that the sum of the between-class variance value and the within-class variance value is a constant value. Therefore, it is determined that the larger the between-class variance value, the smaller the within-class variance value. According to the between-class variance value, the protein signal intensity threshold for dividing the protein signal intensity can be determined among multiple target protein signal intensity values, and the determination of the protein signal intensity threshold may not require the intervention of the within-class variance value.

[0102] Referring to Figure 4 , according to some more specific embodiments of the present application, step S202 calculating the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value based on probability may include, but is not limited to, the following steps S401 to S403.

[0103] Step S401, calculating the sum of multiple probabilities corresponding to the first-class protein signal intensity value to obtain the first probability sum, and calculating the sum of multiple probabilities corresponding to the second-class protein signal intensity value to obtain the second probability sum;

[0104] Step S402, calculating the first protein signal intensity mean value according to the first probability sum, the first-class protein signal intensity value and the corresponding probability, and calculating the second protein signal intensity mean value according to the second probability sum, the second-class protein signal intensity value and the corresponding probability;

[0105] Step S403, calculating the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value based on the first probability sum, the second probability sum, the first protein signal intensity mean value and the second protein signal intensity mean value.

[0106] In step S401 of some embodiments, the sum of multiple probabilities corresponding to the first-class protein signal intensity value is calculated to obtain the first probability sum, and the sum of multiple probabilities corresponding to the second-class protein signal intensity value is calculated to obtain the second probability sum. It should be noted that the occurrence probabilities corresponding to these m protein signal intensity values (n1, n2, n3,..., n m ) can be expressed as (p1, p2, p3,..., p m)。Therefore, the sum of multiple probabilities corresponding to the first type of protein signal intensity value C1 to obtain the first probability sum P1(t) can be expressed as the analytical formula (3):

[0107]

[0108] In addition, since all protein signal intensity values are divided into two categories: the first type of protein signal intensity value C1 and the second type of protein signal intensity value C2, therefore, the sum of multiple probabilities corresponding to the second type of protein signal intensity value C2 to obtain the second probability sum P2(t) can be expressed as the analytical formula (4):

[0109]

[0110] In step S402 of some embodiments, calculate the first protein signal intensity mean value according to the first probability sum, the first type of protein signal intensity value, and the corresponding probability, and calculate the second protein signal intensity mean value according to the second probability sum, the second type of protein signal intensity value, and the corresponding probability. It should be noted that the first protein signal intensity mean value refers to the average value of the protein signal intensity values assigned to the first type of protein signal intensity value C1, denoted as The second protein signal intensity mean value refers to the average value of the protein signal intensity values assigned to the second type of protein signal intensity value C2, denoted as

[0111] It should be pointed out that after calculating the sum of multiple probabilities corresponding to the first type of protein signal intensity value C1 to obtain the first probability sum P1(t), the first protein signal intensity mean value can be calculated according to the first probability sum P1(t), the first type of protein signal intensity value C1, and the corresponding probability (p1, p2, p3,..., t) through the analytical formula (5)

[0112]

[0113] Similarly, after calculating the sum of multiple probabilities corresponding to the second type of protein signal intensity value C2 to obtain the second probability sum P2(t), the second protein signal intensity mean value can be calculated according to the second probability sum P2(t), the second type of protein signal intensity value C2, and the corresponding probability (t,..., p m ) through the analytical formula (6)

[0114]

[0115] In step S403 of some embodiments, the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value is calculated based on the first probability sum, the second probability sum, the first protein signal intensity mean value, and the second protein signal intensity mean value. It should be noted that after obtaining the first protein signal intensity mean value and the second protein signal intensity mean value , the between-class variance value between the first-class protein signal intensity value C1 and the second-class protein signal intensity value C2 can be further calculated based on the first probability sum P1(t), the second probability sum P2(t), the first protein signal intensity mean value and the second protein signal intensity mean value .

[0116] In the embodiments of the present application illustrated by steps S401 to S403, first, the sum of multiple probabilities corresponding to the first-class protein signal intensity value is calculated to obtain the first probability sum, and the sum of multiple probabilities corresponding to the second-class protein signal intensity value is calculated to obtain the second probability sum; then, the first protein signal intensity mean value is calculated according to the first probability sum, the first-class protein signal intensity value, and the corresponding probability, and the second protein signal intensity mean value is calculated according to the second probability sum, the second-class protein signal intensity value, and the corresponding probability; further, the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value is calculated based on the first probability sum, the second probability sum, the first protein signal intensity mean value, and the second protein signal intensity mean value. In this way, the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value can be efficiently calculated based on each protein signal intensity value and its frequency, which helps to accurately determine the threshold for distinguishing positive signals and negative signals, so as to more precisely determine the foreground sites.

[0117] Referring to Figure 5 , according to some more specific embodiments of the present application, step S403 of calculating the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value based on the first probability sum, the second probability sum, the first protein signal intensity mean value, and the second protein signal intensity mean value may include, but is not limited to, the following steps S501 to S502.

[0118] Step S501, calculate the square of the difference between the first protein signal intensity mean value and the second protein signal intensity mean value;

[0119] Step S502, calculate the continuous product of the first probability sum, the second probability sum, and the square of the difference to obtain the between-class variance value between the first-class protein signal intensity value and the second-class protein signal intensity value.

[0120] In step S501 of some embodiments, calculate the squared difference between the mean of the first protein signal intensity and the mean of the second protein signal intensity. It should be noted that, in order to calculate the between-class variance value σ between the first type of protein signal intensity value and the second type of protein signal intensity value 2 , it is necessary to calculate the mean of the first protein signal intensity through analytical formula (7) and the mean of the second protein signal intensity and calculate the squared difference therebetween:

[0121]

[0122] In step S502 of some embodiments, calculate the product of the first probability sum, the second probability sum, and the squared difference to obtain the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value. It should be noted that after calculating the squared difference between the mean of the first protein signal intensity and the mean of the second protein signal intensity , the product of the first probability sum P1(t), the second probability sum P2(t), and the squared difference can be further calculated through analytical formula (8). In this way, the between-class variance value between the first type of protein signal intensity value C1 and the second type of protein signal intensity value C2 is obtained:

[0123]

[0124] Through the embodiments of the present application shown in steps S501 to S502, first calculate the squared difference between the mean of the first protein signal intensity and the mean of the second protein signal intensity, and then calculate the product of the first probability sum, the second probability sum, and the squared difference to obtain the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value. In this way, the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value can be efficiently calculated, which helps to accurately determine the threshold for distinguishing positive signals and negative signals, so as to more precisely determine the foreground sites.

[0125] In step S203 of some embodiments, determine the protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the between-class variance value. It should be noted that after calculating the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value based on the frequency of each protein signal intensity value in the protein sequencing data, since the data of different categories are more dissimilar and the between-class variance value is large, therefore, according to the between-class variance value, the protein signal intensity threshold for dividing the protein signal intensity into strong and weak can be determined among multiple target protein signal intensity values.

[0126] Reference Figure 6 , according to some more specific embodiments of the present application, step S203 determines a protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the between-class variance value, which may include, but is not limited to, the following steps S601 to step S602.

[0127] Step S601, determine the between-class variance value corresponding to each target protein signal intensity value;

[0128] Step S602, determine the target protein signal intensity value with the largest between-class variance value as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak.

[0129] In step S601 of some embodiments, determine the between-class variance value corresponding to each target protein signal intensity value. It should be noted that for each value of the target protein signal intensity value t corresponding to (n1, n2, n3,..., n m ), determine the between-class variance value (σ1 2 , σ2 2 , σ3 2 ,..., σ m 2 ) corresponding to each target protein signal intensity value t.

[0130] In step S602 of some embodiments, determine the target protein signal intensity value with the largest between-class variance value as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak. It should be noted that for the m between-class variance values (σ1 m ) corresponding to the target protein signal intensity value t from (n1, n2, n3,..., n 2 , σ2 2 , σ3 2 ,..., σ m 2 ), determine the largest between-class variance value σ max 2 , and determine the target protein signal intensity value corresponding to the largest between-class variance value σ max 2 as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak.

[0131] In the embodiment of the present application shown in steps S601 to S602, first determine the between-class variance value corresponding to each target protein signal intensity value, and then determine the target protein signal intensity value with the largest between-class variance value as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak. In this way, among multiple target protein signal intensity values, the target protein signal intensity value with the largest between-class variance value can be determined and used as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak, which helps to accurately determine the threshold for distinguishing positive signals and negative signals, so as to more precisely determine the foreground sites.

[0132] In the embodiment of the present application shown in steps S201 to S203, first divide the protein signal intensity values into the first type of protein signal intensity values and the second type of protein signal intensity values with any target protein signal intensity value as the threshold, and then calculate the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on probability; further, determine the protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the between-class variance value. Among them, since the data of different categories are more dissimilar, the between-class variance value is large. After calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values, the between-class variance value can be used to determine the protein signal intensity threshold. In this way, the threshold for distinguishing positive signals and negative signals can be reasonably and accurately determined, so as to more precisely determine the foreground sites.

[0133] Refer to Figure 7 , and a relatively specific embodiment of the above steps S201 to S203 is provided. Figure 6 The corresponding relationship between the protein signal intensity threshold and the between-class variance value is reflected in

[0134] Refer to Figure 8, in some embodiments of the present application, the protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and probability calculation is not limited to the embodiments shown in steps S201 to S203 above, and may further include the following steps S801 to S803.

[0135] Step S801: Divide the protein signal intensity values into the first type of protein signal intensity values and the second type of protein signal intensity values using any target protein signal intensity value as the threshold.

[0136] Step S802: Calculate the first information entropy corresponding to the first type of protein signal intensity values and the second information entropy corresponding to the second type of protein signal intensity values based on probability.

[0137] Step S803: Determine the protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the sum of the first information entropy and the second information entropy.

[0138] It should be noted that the protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and probability calculation in step S103 is not limited to the embodiments shown in steps S201 to S203 above. It is also possible to first divide the protein signal intensity values into the first type of protein signal intensity values and the second type of protein signal intensity values using any target protein signal intensity value as the threshold, calculate the mean value for the first type of protein signal intensity values and the mean value for the second type of protein signal intensity values, and then when the sum of the two parts of the mean values remains stable, the corresponding target protein signal intensity value is the protein signal intensity threshold. In addition, it may also include the embodiments shown in steps S801 to S803.

[0139] In step S801 of some embodiments, the protein signal intensity values are divided into the first type of protein signal intensity values and the second type of protein signal intensity values using any target protein signal intensity value as the threshold. It should be noted that any protein signal intensity value is determined as the target protein signal intensity value t from multiple protein signal intensity values, and the protein signal intensity values are divided into the first type of protein signal intensity values and the second type of protein signal intensity values using any target protein signal intensity value t as the threshold.

[0140] In step S802 of some embodiments, the first information entropy corresponding to the first type of protein signal intensity values and the second information entropy corresponding to the second type of protein signal intensity values are calculated based on probability. It should be noted that if the i-th protein signal intensity value is represented as p i , and the probability of being assigned to the first type of protein signal intensity values is represented as p A , where then the first information entropy H(A) corresponding to the first type of protein signal intensity values can be calculated according to formula (9):

[0141]

[0142] Similarly, the probability of assigning the protein signal intensity value to the second category is denoted as p B , where Then, the second information entropy H(B) corresponding to the protein signal intensity value of the second category can be calculated according to the analytical formula (10):

[0143]

[0144] In step S803 of some embodiments, a protein signal intensity threshold for dividing the protein signal intensity into strong and weak is determined among multiple target protein signal intensity values according to the sum of the first information entropy and the second information entropy. It should be noted that after calculating the first information entropy H(A) and the second information entropy H(B), it is necessary to further determine a protein signal intensity threshold for dividing the protein signal intensity into strong and weak among multiple target protein signal intensity values according to the sum of the first information entropy H(A) and the second information entropy H(B).

[0145] In some more specific embodiments, the sum of the first information entropy H(A) and the second information entropy H(B) can be expressed as:

[0146]

[0147]

[0148] Furthermore, the sum of the first information entropy H(A) and the second information entropy H(B) can be expressed by the analytical formula (11) as:

[0149]

[0150] On this basis, by determining each protein signal intensity value as a target protein signal intensity value and traversing all protein signal intensity values in sequence, the target protein signal intensity value that makes the sum of the first information entropy H(A) and the second information entropy H(B) reach the maximum value can be found, and it can be determined as the protein signal intensity threshold.

[0151] In the embodiments of the present application shown in steps S801 to S803, first, the protein signal intensity values are divided into the first type of protein signal intensity values and the second type of protein signal intensity values by using any target protein signal intensity value as a threshold; further, based on probability, the first information entropy corresponding to the first type of protein signal intensity values and the second information entropy corresponding to the second type of protein signal intensity values are calculated; furthermore, according to the sum of the first information entropy and the second information entropy, a protein signal intensity threshold for dividing the protein signal intensity is determined among multiple target protein signal intensity values. In this way, the threshold for distinguishing positive signals and negative signals can also be reasonably and accurately determined, so as to more precisely determine the foreground sites.

[0152] The antigen protein distribution region detection method of the present application will be described below.

[0153] Referring to Figure 9 , the embodiments of the present application propose an antigen protein distribution region detection method, and the antigen protein distribution region detection method may include, but is not limited to, the following steps S901 to S905.

[0154] Step S901, obtaining a biological section to be detected;

[0155] Step S902, infiltrating the biological section with a solution of a preset antibody protein, and washing the infiltrated biological section to obtain a target section;

[0156] Step S903, performing protein sequencing on the target section to obtain protein sequencing data;

[0157] Step S904, processing the protein sequencing data based on the processing method of the protein sequencing data to obtain the foreground sites of the preset antibody protein;

[0158] Step S905, determining the distribution region of the antigen protein in the biological section according to the foreground sites of the preset antibody protein.

[0159] It should be emphasized that in spatial proteomics, in order to analyze the expression characteristics of cell surface antigen proteins at different spatial positions, it is necessary to use antibody proteins with labeled signals to bind to the epitopes of cell surface antigen proteins in the target section to label the positions of the antigen proteins. It should be noted that the target section is a tissue section used as the sequencing target.

[0160] Steps S901 to S905 of some embodiments include obtaining a biological section to be detected, then infiltrating the biological section with a solution of a preset antibody protein, washing the infiltrated biological section to obtain a target section, and then performing protein sequencing on the target section to obtain protein sequencing data; further, processing the protein sequencing data according to the processing method of the protein sequencing data to obtain the foreground sites of the preset antibody protein; still further, determining the distribution region of the antigen protein in the biological section according to the foreground sites of the preset antibody protein. It should be noted that in order to obtain the protein sequencing data of the target section, the biological section to be detected can be obtained first, then the biological section can be infiltrated with a solution of the preset antibody protein, and the infiltrated biological section can be washed to obtain the target section. It should be emphasized that the target protein is an antibody protein with a labeling signal, where the labeling signal can be a fluorescence-type labeling signal or other types of labeling signals. It should be clear that the protein sequencing data is the protein sequencing data measured after the antigen protein and the antibody protein are fully combined. The target section contains multiple sequencing sites. By detecting the intensity of the labeling signal in the target protein for these sequencing sites, the protein signal intensity values of the target protein measured at multiple sequencing sites can be obtained. In this way, the protein sequencing data of the target protein in the target section can be obtained.

[0161] After processing the protein sequencing data according to the processing method of the protein sequencing data of the embodiments of the present application to obtain the foreground sites representing specific binding of the preset antibody protein, the distribution region of the antigen protein in the biological section can be determined according to the foreground sites of the preset antibody protein. In this way, it helps to analyze the data of the foreground sites of specific binding in a targeted manner.

[0162] Refer to Figure 10 , Figure 10 shows a schematic diagram of the spatial distribution of the target section of the mouse_CD4 corresponding sample after removing the protein negative signal according to the protein signal intensity threshold in some embodiments of the present application. It should be emphasized that the protein signal intensity threshold is used to divide the protein signal intensity into strong and weak. Through the protein signal intensity threshold, the protein positive signal and the protein negative signal can be distinguished. The protein positive signal represents the labeling signal of the target protein that has specifically bound to the antigen protein of the cells in the target section, and the protein negative signal represents the labeling signal of the target protein that has undergone non-specific binding. Among them, the foreground sites are the Figure 10 bright spot regions in. Determining the foreground sites representing specific binding helps to analyze the relevant data of the foreground sites and clarify the expression characteristics of the antigen protein corresponding to mouse_CD4 on the cell surface at different spatial positions.

[0163] The processing device for the protein sequencing data of the present application will be described below.

[0164] Refer toFigure 11 , according to some embodiments, the present application provides a processing device 1100 for protein sequencing data, including:

[0165] A first acquisition unit 1101, configured to acquire protein sequencing data of a target protein in a target slice, where the protein sequencing data includes protein signal intensity values of the target protein measured at a plurality of sequencing sites;

[0166] A first determination unit 1102, configured to determine the frequency of occurrence of each protein signal intensity value in the protein sequencing data, and calculate the probability of occurrence of each protein signal intensity value according to the frequency;

[0167] A threshold division unit 1103, configured to calculate a protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and probability calculation;

[0168] A second determination unit 1104, configured to determine that the sequencing site where the protein signal intensity value is greater than the protein signal intensity threshold is the foreground site corresponding to the target protein.

[0169] It can be seen that the content in the above embodiments of the protein sequencing data processing method is applicable to the embodiments of this protein sequencing data processing device. The functions specifically implemented by the embodiments of this protein sequencing data processing device are the same as those of the above embodiments of the protein sequencing data processing method, and the beneficial effects achieved are also the same as those of the above embodiments of the protein sequencing data processing method.

[0170] Next, an antigen protein distribution area detection device of the present application will be described.

[0171] Referring to Figure 12 , according to some embodiments, an antigen protein distribution area detection device 1200 of the present application includes:

[0172] A second acquisition unit 1201, configured to acquire a biological slice to be detected;

[0173] A slice cleaning unit 1202, configured to infiltrate the biological slice with a solution of a preset antibody protein, and clean the infiltrated biological slice to obtain a target slice;

[0174] A protein sequencing unit 1203, configured to perform protein sequencing on the target slice to obtain protein sequencing data;

[0175] A sequencing data processing unit, configured to process the protein sequencing data based on the protein sequencing data processing method to obtain the foreground sites of the preset antibody protein;

[0176] A third determination unit 1204, configured to determine the distribution area of the antigen protein in the biological slice according to the foreground sites of the preset antibody protein.

[0177] It can be seen that the content in the embodiments of the above antigen protein distribution area detection method is applicable to the embodiments of this antigen protein distribution area detection device. The functions specifically implemented in the embodiments of this antigen protein distribution area detection device are the same as those in the embodiments of the above antigen protein distribution area detection method, and the beneficial effects achieved are also the same as those in the embodiments of the above antigen protein distribution area detection method.

[0178] Referring to Figure 13 , Figure 13 illustrates the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0179] A processor 1301, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit, central processor), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application;

[0180] A memory 1302, which can be implemented in forms such as a read-only memory (ReadOnlyMemory, ROM), a static storage device, a dynamic storage device, or a random access memory (RandomAccessMemory, RAM). The memory 1302 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1302 and are called by the processor 1301 to execute the protein sequencing data processing method or the antigen protein distribution area detection method in the embodiments of this application;

[0181] An input / output interface 1303, which is used to implement information input and output;

[0182] A communication interface 1304, which is used to implement communication interaction between this device and other devices, and can achieve communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0183] A bus 1305, which transmits information between various components of the device (such as the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304);

[0184] Among them, the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304 achieve communication connections with each other inside the device through the bus 1305.

[0185] An embodiment of the present application also provides a computer program product, which includes a computer program. The processor of the computer device reads and executes the computer program, so that the computer device executes the method for processing protein sequencing data or the method for detecting the antigen protein distribution region described above.

[0186] Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "comprising" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0187] It should be understood that in the present disclosure, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or similar expressions refer to any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0188] It should be understood that in the description of the embodiments of the present application, the meaning of "a plurality (or multiple)" is more than two. Understandings such as greater than, less than, exceeding, etc. do not include the present number, and understandings such as above, below, within, etc. include the present number.

[0189] In several embodiments provided by the present disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0190] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, in each embodiment of the present disclosure, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0192] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present disclosure. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0193] It should also be understood that the various implementation manners provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0194] The above is a specific description of the embodiments of the present disclosure. However, the present disclosure is not limited to the above embodiments. Those skilled in the art can still make various equivalent deformations or substitutions without departing from the spirit of the present disclosure, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present disclosure.

Claims

1. A method for processing protein sequencing data, characterized in that, The method includes: Obtaining protein sequencing data of a target protein in a target slice, where the protein sequencing data includes protein signal intensity values of the target protein measured at multiple sequencing sites; Determining the frequency of occurrence of each protein signal intensity value in the protein sequencing data, and calculating the probability of occurrence of each protein signal intensity value based on the frequency; Calculating a protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and the probability; Determining that the sequencing sites with protein signal intensity values greater than the protein signal intensity threshold are the foreground sites corresponding to the target protein.

2. The method according to claim 1, characterized in that, The protein signal intensity threshold for dividing the protein signal intensity into strong and weak based on the protein signal intensity value and the probability includes: Dividing the protein signal intensity values into a first type of protein signal intensity values and a second type of protein signal intensity values with any target protein signal intensity value as the threshold; Calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on the probability; Determining the protein signal intensity threshold for dividing the protein signal intensity into strong and weak from multiple target protein signal intensity values according to the between-class variance value.

3. The method according to claim 2, characterized in that, The calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on the probability includes: Calculating the sum of multiple probabilities corresponding to the first type of protein signal intensity values to obtain a first probability sum, and calculating the sum of multiple probabilities corresponding to the second type of protein signal intensity values to obtain a second probability sum; Calculating a first protein signal intensity mean according to the first probability sum, the first type of protein signal intensity values and the corresponding probabilities, and calculating a second protein signal intensity mean according to the second probability sum, the second type of protein signal intensity values and the corresponding probabilities; Calculating the between-class variance value between the first type of protein signal intensity values and the second type of protein signal intensity values based on the first probability sum, the second probability sum, the first protein signal intensity mean and the second protein signal intensity mean.

4. The method according to claim 2, characterized in that, The dividing the protein signal intensity values into a first type of protein signal intensity values and a second type of protein signal intensity values with any target protein signal intensity value as the threshold includes: Determining any protein signal intensity value among multiple protein signal intensity values as the target protein signal intensity value; Dividing the protein signal intensity values with values less than the target protein signal intensity value into the first type of protein signal intensity values, and dividing the protein signal intensity values with values not less than the target protein signal intensity value into the second type of protein signal intensity values.

5. The method according to claim 2, characterized in that, The determining the protein signal intensity threshold for dividing the protein signal intensity into strong and weak from multiple target protein signal intensity values according to the between-class variance value includes: Determining the between-class variance value corresponding to each target protein signal intensity value; Determining the target protein signal intensity value with the largest between-class variance value as the protein signal intensity threshold for dividing the protein signal intensity into strong and weak.

6. The method according to claim 3, characterized in that, Calculating the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value based on the first probability sum, the second probability sum, the mean value of the first protein signal intensity, and the mean value of the second protein signal intensity includes: Calculating the square of the difference between the mean value of the first protein signal intensity and the mean value of the second protein signal intensity; Calculating the continuous product among the first probability sum, the second probability sum, and the square of the difference to obtain the between-class variance value between the first type of protein signal intensity value and the second type of protein signal intensity value.

7. The method according to claim 1, characterized in that, The protein signal intensity threshold for classifying the protein signal intensity as strong or weak based on the protein signal intensity value and the probability calculation includes: Dividing the protein signal intensity values into the first type of protein signal intensity values and the second type of protein signal intensity values with any target protein signal intensity value as the threshold; Calculating the first information entropy corresponding to the first type of protein signal intensity value and the second information entropy corresponding to the second type of protein signal intensity value based on the probability; Determining the protein signal intensity threshold for classifying the protein signal intensity as strong or weak from multiple target protein signal intensity values according to the sum of the first information entropy and the second information entropy.

8. A method for detecting the distribution region of antigen proteins, characterized in that, The method includes: Obtaining a biological section to be detected; Infiltrating the biological section with a solution of a preset antibody protein, and cleaning the infiltrated biological section to obtain a target section; Performing protein sequencing on the target section to obtain protein sequencing data; Processing the protein sequencing data based on the protein sequencing data processing method according to any one of claims 1 to 7 to obtain the foreground sites of the preset antibody protein; Determining the distribution region of the antigen protein in the biological section according to the foreground sites of the preset antibody protein.

9. A processing device for protein sequencing data, characterized in that, Including: A first obtaining unit, configured to obtain protein sequencing data of a target protein in a target section, where the protein sequencing data includes protein signal intensity values of the target protein measured at multiple sequencing sites; A first determining unit, configured to determine the frequency of occurrence of each protein signal intensity value in the protein sequencing data, and calculate the probability of occurrence of each protein signal intensity value according to the frequency; A threshold dividing unit, configured to calculate a protein signal intensity threshold for classifying the protein signal intensity as strong or weak based on the protein signal intensity value and the probability; A second determining unit, configured to determine that the sequencing sites with protein signal intensity values greater than the protein signal intensity threshold are the foreground sites corresponding to the target protein.

10. A detection device for antigen protein distribution area, characterized in that, Including: A second obtaining unit, configured to obtain a biological section to be detected; A section cleaning unit, configured to infiltrate the biological section with a solution of a preset antibody protein, and clean the infiltrated biological section to obtain a target section; A protein sequencing unit, configured to perform protein sequencing on the target section to obtain protein sequencing data; A sequencing data processing unit, configured to process the protein sequencing data based on the protein sequencing data processing method according to any one of claims 1 to 7 to obtain the foreground sites of the preset antibody protein; A third determination unit, configured to determine a distribution region of an antigen protein in the biological section according to a foreground site of the preset antibody protein.

11. An electronic device, including a memory and a processor, the memory stores a computer program, characterized in that, When the processor executes the computer program, the method for processing protein sequencing data according to any one of claims 1 to 7 or the method for detecting a distribution region of an antigen protein in claim 8 is implemented.

12. A computer-readable storage medium, the storage medium stores a computer program, characterized in that, When the computer program is executed by a processor, the method for processing protein sequencing data according to any one of claims 1 to 7 or the method for detecting a distribution region of an antigen protein in claim 8 is implemented.

13. A computer program product, the computer program product includes a computer program, the computer program is read and executed by a processor of a computer device, so that the computer device executes the processing method of protein sequencing data according to any one of claims 1 to 7 or the detection method of antigen protein distribution area in claim 8.

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