Blood protein data quality assessment and correction method, system, equipment and medium

Abstract

The present invention relates to the field of blood protein analysis, and provides a blood protein data quality assessment and correction method, system, equipment and medium, including protein separation of the blood to be tested to obtain the blood protein to be tested, data characterization to obtain a protein expression matrix, selecting a reference marker to obtain a blood protein marker, screening to obtain a first intermediate marker; obtaining control protein data according to the grouping matrix, performing a Wilcoxon test and screening to obtain a second intermediate marker; performing a Pearson correlation analysis and screening to obtain a characterization marker, calculating the coefficient of variation, and marking the protein expression matrix; obtaining a characterization marker expression matrix, obtaining a correction parameter through the characterization marker expression matrix, and correcting the protein expression matrix through the correction parameter to obtain a correction matrix, thereby completing the correction of blood protein data. The present invention can correct and mark blood protein data.

Description

Technical Field

[0001] The present invention relates to the technical field of blood protein analysis, and in particular to a method, system, equipment and medium for evaluating and correcting blood protein data quality. Background Art

[0002] In modern medicine and biological research, accurate analysis of various proteins in blood is crucial. In the current analysis of various blood proteins, errors caused by various contaminations are unavoidable. Among them, errors caused by contamination of coagulation-related proteins, platelets, and red blood cells are the most common sources of error. Different extraction and processing methods of experimental samples, storage environments and times, reagents used, types of coagulants, and centrifugation times and speeds can all lead to errors, and the errors caused by these sources vary greatly. These errors need to be reduced by strictly controlling experimental variables or uniformly processing samples. However, for large, multicenter clinical cohorts or relatively rare samples, it is impossible to guarantee that the pre-analytical processing of samples is completely consistent, nor can it be guaranteed that all samples will be processed uniformly.

[0003] Existing technologies typically only issue warnings for heavily contaminated samples, indicating that they may not be usable. However, some samples are limited in number, and eliminating them may result in insufficient sample volume. Furthermore, various early warning platforms utilize fixed marker detection methods, but these markers vary significantly depending on blood processing methods, such as neat plasma and magnetic bead-enriched plasma. Using fixed markers for detection would significantly limit its scope of application. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a blood protein data quality assessment and correction method, system, device and medium to achieve early warning of blood protein contamination and accurate correction of related data.

[0005] The present invention provides a method for evaluating and correcting blood protein data quality, comprising:

[0006] S1: obtaining blood to be tested, performing protein separation on the blood to be tested to obtain blood proteins to be tested, performing data characterization on the blood proteins to be tested to obtain a protein expression matrix, and obtaining a grouping matrix;

[0007] S2: selecting a benchmark marker, obtaining a blood protein marker according to a protein expression matrix, and screening the blood protein marker according to the benchmark marker to obtain a first intermediate marker;

[0008] S3: obtaining control protein data according to the grouping matrix, performing a Wilcoxon test on the first intermediate marker and the control protein data, and screening the first intermediate marker to obtain a second intermediate marker;

[0009] S4: performing a Pearson correlation analysis on the second intermediate marker, screening the second intermediate marker to obtain a characterization marker, calculating the coefficient of variation of the characterization marker, and marking the protein expression matrix according to the coefficient of variation;

[0010] S5: Obtaining a characterization marker expression matrix of the characterization marker, obtaining correction parameters through the characterization marker expression matrix, and correcting the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of the blood protein data.

[0011] According to the blood protein data quality assessment and correction method provided by the present invention, step S1 specifically includes:

[0012] S11: obtaining blood to be tested, and performing protein separation on blood proteins in the blood to be tested by a high-abundance protein depletion method to obtain the blood proteins to be tested;

[0013] S12: performing data characterization on the blood protein to be tested by liquid chromatography-mass spectrometry to obtain the protein expression matrix and the grouping matrix.

[0014] According to the blood protein data quality assessment and correction method provided by the present invention, step S2 specifically includes:

[0015] S21: selecting the reference marker according to the blood protein to be tested, and obtaining the blood protein marker from the protein expression matrix;

[0016] S22: Obtain an intersection of the blood protein marker and the reference marker, and use the intersection of the blood protein marker and the reference marker as the first intermediate marker.

[0017] According to the blood protein data quality assessment and correction method provided by the present invention, step S3 specifically includes:

[0018] S31: extracting the control protein data from the grouping matrix, and obtaining the first intermediate protein data through the first intermediate marker;

[0019] S32: performing a Wilcoxon test on the first intermediate protein data and the control protein data to obtain a first test parameter;

[0020] S33: Screening the first intermediate marker according to the first test parameter to obtain a second intermediate marker.

[0021] According to the blood protein data quality assessment and correction method provided by the present invention, step S4 specifically includes:

[0022] S41: determining a correlation threshold, and obtaining second intermediate protein data of the second intermediate marker;

[0023] S42: performing a Pearson correlation analysis on the second intermediate protein data to obtain a correlation parameter, retaining markers in the second intermediate markers whose correlation parameters are greater than a correlation threshold, and obtaining the characterization marker;

[0024] S43: Calculate the mean difference and standard deviation of the characterizing markers, calculate the coefficient of variation based on the mean difference and the standard deviation, and mark the protein expression matrix according to the coefficient of variation to determine the contamination degree of the blood protein to be tested.

[0025] According to the blood protein data quality assessment and correction method provided by the present invention, step S5 specifically includes:

[0026] S51: obtaining protein data representing the marker, and obtaining the marker expression matrix based on the protein data;

[0027] S52: performing logarithmic transformation, Pearson correlation analysis, and robust linear regression on the marker expression matrix to obtain the correction parameters including the pollution level vector, the local constraint factor vector, and the robust regression coefficient;

[0028] S53: constructing a correction model using the correction parameters, correcting the protein expression matrix using the correction model to obtain a correction matrix, and completing the correction of the blood protein data.

[0029] According to the blood protein data quality assessment and correction method provided by the present invention, the correction model is expressed as:

[0030]

[0031]

[0032] Where Y is the row vector of the protein expression matrix, To find the base 2 logarithm of the content in the brackets, is the intercept term, is the kth robust regression coefficient, is the kth pollution level vector, s is the set of types of markers, is the error term, is the row vector of the correction matrix, is the global constraint parameter, is the kth local constraint factor vector of the protein.

[0033] The present invention also provides a blood protein data quality assessment and correction system, comprising:

[0034] Protein expression matrix module: used to obtain blood to be tested, perform protein separation on the blood to be tested to obtain blood proteins to be tested, perform data characterization on the blood proteins to be tested, obtain a protein expression matrix, and obtain a grouping matrix;

[0035] A first intermediate marker module: used to select a benchmark marker, obtain a blood protein marker according to a protein expression matrix, and screen the blood protein marker according to the benchmark marker to obtain a first intermediate marker;

[0036] A second intermediate marker module is configured to obtain control protein data according to the grouping matrix, perform a Wilcoxon test on the first intermediate marker and the control protein data, and screen the first intermediate marker to obtain a second intermediate marker;

[0037] A coefficient of variation module is used to perform Pearson correlation analysis on the second intermediate marker, screen the second intermediate marker, obtain a characterization marker, calculate the coefficient of variation of the characterization marker, and mark the protein expression matrix according to the coefficient of variation;

[0038] Correction matrix module: used to obtain the characterization marker expression matrix of the characterization marker, obtain correction parameters through the characterization marker expression matrix, and correct the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of blood protein data.

[0039] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any of the above-described blood protein data quality assessment and correction methods are implemented.

[0040] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described blood protein data quality assessment and correction methods.

[0041] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects:

[0042] The blood protein data quality assessment and correction method, system, equipment and medium provided by the present invention screen the protein expression matrix, identify characterization markers closely related to contamination, and calculate the coefficient of variation to issue an early warning for samples with more serious contamination. In addition, appropriate correction parameters are calculated based on the specific situation of the sample to correct the protein expression matrix, thereby reducing errors caused by contamination and improving sample availability, thereby saving experimental funds and simplifying the experimental process.

[0043] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 It is a flow chart of the blood protein data quality assessment and correction method provided by the present invention.

[0046] Figure 2 It is a structural diagram of the blood protein data quality assessment and correction system provided by the present invention.

[0047] Figure 3 It is a structural schematic diagram of the blood protein data quality assessment and correction device provided by the present invention.

[0048] Reference numerals:

[0049] 100, protein expression matrix module; 200, first intermediate marker module; 300, second intermediate marker module; 400, coefficient of variation module; 500, correction matrix module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0050] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0051] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0052] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0053] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0054] The following combination Figures 1 to 3 Describe the specific embodiment of the present invention:

[0055] Figure 1 The present invention provides a flow chart of the blood protein data quality assessment and correction method. First, protein separation is performed to obtain the blood protein to be tested, and data characterization is performed to obtain a protein expression matrix and a grouping matrix. Next, blood protein markers are obtained and screened based on the benchmark markers to obtain a first intermediate marker. Next, a Wilcoxon test is performed on the first intermediate marker and the control protein data, and screening is performed to obtain a second intermediate marker. Next, a Pearson correlation analysis is performed, and the second intermediate marker is screened to obtain a characterization marker and calculate the coefficient of variation. Finally, correction parameters are obtained, and the protein expression matrix is corrected using the correction parameters to obtain a correction matrix.

[0056] The present invention provides a method for evaluating and correcting blood protein data quality, comprising:

[0057] S1: obtaining blood to be tested, performing protein separation on the blood to be tested to obtain blood proteins to be tested, performing data characterization on the blood proteins to be tested to obtain a protein expression matrix, and obtaining a grouping matrix;

[0058] Furthermore, the purpose of this stage is to perform protein separation on the blood to be tested, thereby performing data characterization and obtaining a protein expression matrix, and also obtaining a grouping matrix. Specifically, step S1 specifically includes:

[0059] S11: obtaining blood to be tested, and performing protein separation on blood proteins in the blood to be tested by a high-abundance protein depletion method to obtain the blood proteins to be tested;

[0060] S12: performing data characterization on the blood protein to be tested by liquid chromatography-mass spectrometry to obtain the protein expression matrix and the grouping matrix.

[0061] With respect to the above steps, the specific implementation plan in this embodiment is as follows:

[0062] First, extract the blood to be tested from the human body or animal body. After obtaining the blood to be tested, add a reasonable concentration of anticoagulant to the blood to prevent it from coagulation. Then place the blood to be tested in a water bath at 37°C and mix it. Select a suitable antibody column according to the amount of blood to be tested and place the antibody column at room temperature for a period of time to make its temperature reach room temperature. Here, the antibody column is filled with antibody resin slurry loaded with antibodies. Then remove the nut on the antibody column and add a certain amount of blood to be tested to the antibody resin slurry in the antibody column. Invert the antibody column back and forth several times until the antibody resin slurry and the blood to be tested are completely mixed. Let the antibody column stand at room temperature for 30 minutes for incubation. During this period, the liquid in the antibody column needs to be gently vortexed every few minutes to ensure that the antibody resin slurry and the blood to be tested are always mixed evenly. After incubation, open the seal at the bottom of the antibody column and loosen the nut. Place the antibody column in a collection tube and centrifuge the collection tube at 1000 g for 2 minutes. The filtrate obtained is the blood protein to be tested. This completes the protein separation using the high-abundance protein depletion method.

[0063] Subsequently, liquid chromatography-mass spectrometry (LC-MS) is used to characterize the data of the blood protein to be tested: first, the blood protein to be tested is pretreated, including enzymatic hydrolysis, to degrade the blood protein to be tested into enzyme segments. Then, suitable chromatographic columns and mobile phase conditions are selected to elute and separate the degraded blood protein to be tested at a certain flow rate. The eluted and separated enzyme segments are sent to the mass spectrometer, where charged droplets are formed through the capillary and volatilized under a high electric field to form gaseous ions. The mass-to-charge ratio, abundance, etc. are analyzed by the mass spectrometer. The analyzed data are compared and paired with the protein database including blood protein data to complete the data characterization of the blood protein to be tested and obtain a protein expression matrix. The protein expression matrix includes the protein names and protein expression data of all proteins in the blood protein to be tested, including the protein names and protein expression data of pollutants. In addition, it is necessary to analyze standard samples under relatively ideal conditions to obtain a grouping matrix, which also includes protein names and protein expression data.

[0064] S2: selecting a benchmark marker, obtaining a blood protein marker according to a protein expression matrix, and screening the blood protein marker according to the benchmark marker to obtain a first intermediate marker;

[0065] Furthermore, the purpose of this stage is to screen the blood protein markers according to the benchmark markers to obtain a first intermediate marker. Specifically, step S2 includes:

[0066] S21: selecting the reference marker according to the blood protein to be tested, and obtaining the blood protein marker from the protein expression matrix;

[0067] S22: Obtain an intersection of the blood protein marker and the reference marker, and use the intersection of the blood protein marker and the reference marker as the first intermediate marker.

[0068] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0069] First, a benchmark marker is selected based on the blood protein to be tested. The benchmark marker is a common marker that may cause systemic bias. Systemic bias is the systemic influence caused by pre-analytical variables of the blood protein to be tested, such as storage time, centrifugation time, centrifugation speed, different thrombin, etc. In the present application, since coagulation reaction-related proteins are related to thrombin and platelets, and factors such as centrifugation time and centrifugation speed can affect platelets and red blood cells, in this embodiment, marker proteins related to coagulation reaction-related proteins, platelets and red blood cells are selected as benchmark markers. In addition, other proteins that may interfere with the analysis of the blood protein to be tested can also be selected as benchmark markers based on the characteristics of the blood protein to be tested.

[0070] Since the protein expression matrix includes the protein names of the blood proteins to be tested and the proteins that characterize the systematic bias, the blood protein markers can be identified by the protein names. The benchmark markers also include the protein names of the benchmark markers. The purpose of correction and quality assessment of this method is to perform quality assessment and correction on the systematic bias represented by the benchmark markers. Therefore, it is necessary to first exclude the influence caused by proteins other than proteins that characterize the systematic bias. Therefore, the intersection of blood proteins and benchmark markers is obtained, and the intersection of blood protein markers and benchmark markers is retained, that is, the part of the blood protein markers that is the same as the benchmark markers is retained, so that the intersection of blood protein markers and benchmark markers is used as the first intermediate marker.

[0071] S3: obtaining control protein data according to the grouping matrix, performing a Wilcoxon test on the first intermediate marker and the control protein data, and screening the first intermediate marker to obtain a second intermediate marker;

[0072] Furthermore, the purpose of this stage is to perform a Wilcoxon test and screening based on the first intermediate marker and the control protein data to obtain a second intermediate marker. Specifically, step S3 includes:

[0073] S31: extracting the control protein data from the grouping matrix, and obtaining the first intermediate protein data through the first intermediate marker;

[0074] S32: performing a Wilcoxon test on the first intermediate protein data and the control protein data to obtain a first test parameter;

[0075] S33: Screening the first intermediate marker according to the first test parameter to obtain a second intermediate marker.

[0076] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0077] First, the protein expression data and protein names of the standard samples under relatively ideal conditions can be obtained from the grouping matrix. Since there will also be markers related to coagulation reaction-related proteins, platelets and red blood cells, the protein expression data of these markers are used as control protein data. Based on the first intermediate marker, the protein expression data of the first intermediate marker can also be obtained from the protein expression matrix as the first intermediate protein data.

[0078] Then, the Wilcoxon test can be performed on the first intermediate protein data and the control protein data. The Wilcoxon test is a non-parametric statistical test method that can test whether two samples come from the same distribution, thereby measuring whether there is a significant difference in the protein expression data of the proteins in the first intermediate protein data and the control protein data. If it is judged that the two do not come from the same distribution, that is, there is a significant difference, it means that the protein expression data in the first intermediate protein data may not only come from systematic bias, but may also come from other factors, such as external bacterial contamination. Here, the Wilcoxon test has a null hypothesis, that is, there is a significant difference between the first intermediate protein data and the control protein data. In the Wilcoxon test, the p-value of the protein expression data of the corresponding markers in the first intermediate protein data and the control protein data is obtained, and the p-value is used as the first test parameter. If the first test parameter is too small, here it is less than 0.05, then the null hypothesis cannot be rejected, and it is considered that there is a significant difference in the protein expression data in the first intermediate protein data and the control protein data. The marker corresponding to the protein expression data in the first intermediate marker needs to be eliminated. Otherwise, the null hypothesis can be rejected, and the marker corresponding to the protein expression data in the first intermediate marker is retained. This process is performed in sequence for all protein expression data in the first intermediate marker to obtain the second intermediate marker.

[0079] S4: performing a Pearson correlation analysis on the second intermediate marker, screening the second intermediate marker to obtain a characterization marker, calculating the coefficient of variation of the characterization marker, and marking the protein expression matrix according to the coefficient of variation;

[0080] Furthermore, the purpose of this stage is to perform Pearson analysis on the second intermediate marker to screen for characterization markers, calculate the coefficient of variation of the characterization markers, and finally label the protein expression matrix. Specifically, step S4 includes:

[0081] S41: determining a correlation threshold, and obtaining second intermediate protein data of the second intermediate marker;

[0082] S42: performing a Pearson correlation analysis on the second intermediate protein data to obtain a correlation parameter, retaining markers in the second intermediate markers whose correlation parameters are greater than a correlation threshold, and obtaining the characterization marker;

[0083] S43: Calculate the mean difference and standard deviation of the characterizing markers, calculate the coefficient of variation based on the mean difference and the standard deviation, and mark the protein expression matrix according to the coefficient of variation to determine the contamination degree of the blood protein to be tested.

[0084] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0085] First, a value is empirically determined as a correlation threshold. Here, the correlation threshold is 0.9. Second intermediate protein data for the second intermediate marker is then obtained. The method for obtaining the second intermediate protein data is the same as that for obtaining the first intermediate protein data. Pearson correlation analysis is then performed on the protein expression data in the second intermediate protein data. Pearson correlation analysis can measure the linear correlation between protein expression data and reflect it through a correlation parameter. Pearson correlation analysis can be used to obtain the correlation parameter between the protein expression data in the second intermediate protein data. When the correlation parameter of the protein expression data of a marker in the second intermediate marker is greater than the correlation threshold, the protein expression data of the marker can be considered to be derived from systematic bias and thus retained. Otherwise, it is deleted as external contamination, thereby obtaining a characterization marker.

[0086] Finally, the mean deviation and standard deviation of the protein expression data of the characterizing marker are calculated. In addition, the mean deviation and standard deviation of the protein expression data of other normal proteins in the blood protein to be tested need to be calculated, so as to calculate the respective coefficients of variation by taking the ratio of their respective standard deviations and mean deviations. If the difference in the coefficients of variation is large, it means that the degree of contamination of the blood protein to be tested is high and it needs to be marked to complete the assessment of the degree of contamination.

[0087] S5: Obtaining a characterization marker expression matrix of the characterization marker, obtaining correction parameters through the characterization marker expression matrix, and correcting the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of the blood protein data.

[0088] Furthermore, the purpose of this stage is to obtain correction parameters, and then perform correction by using the correction parameters to obtain a correction matrix, thereby completing the correction of the blood protein data. Specifically, step S5 specifically includes:

[0089] S51: obtaining protein data representing the marker, and obtaining the marker expression matrix based on the protein data;

[0090] S52: performing logarithmic transformation, Pearson correlation analysis, and robust linear regression on the marker expression matrix to obtain the correction parameters including the pollution level vector, the local constraint factor vector, and the robust regression coefficient;

[0091] S53: constructing a correction model using the correction parameters, correcting the protein expression matrix using the correction model to obtain a correction matrix, and completing the correction of the blood protein data.

[0092] The expression of the correction model is:

[0093]

[0094]

[0095] Where Y is the row vector of the protein expression matrix, To find the base 2 logarithm of the content in the brackets, is the intercept term, is the kth robust regression coefficient, is the kth pollution level vector, s is the set of types of markers, is the error term, is the row vector of the correction matrix, is the global constraint parameter, is the kth local constraint factor vector of the protein.

[0096] Regarding the above steps, the specific implementation methods in this embodiment are as follows:

[0097] First, the protein expression data of the characterization markers are obtained from the protein expression matrix, and then the characterization marker expression matrix is formed by the protein expression data of the characterization markers. In addition, a set of characterization marker types can be obtained. Then, the k-th protein expression data in the characterization marker expression matrix is logarithmically transformed with base 2, and the average value of the logarithmically transformed protein expression data in the characterization marker expression matrix is obtained. The ratio of the logarithmically transformed k-th protein expression data in the characterization marker expression matrix to the average value of the logarithmically transformed protein expression data is obtained, and the pollution level vector of the k-th protein expression data in the characterization marker expression matrix, that is, the k-th pollution level vector, can be obtained.

[0098] Then, the local constraint factor vector is solved, that is, the protein to be corrected and the protein expression data of the protein to be corrected are selected from the protein expression matrix, and then the protein expression data of the protein to be corrected and the protein expression data in the characterization marker expression matrix are subjected to Pearson correlation analysis to obtain multiple Pearson correlation coefficients between the k-th protein expression data in the characterization marker expression matrix and the protein expression data of each protein to be corrected. These Pearson correlation coefficients are sorted from large to small, and the top 5% of the Pearson correlation coefficients are taken to calculate their truncated mean, so as to obtain the local constraint factor vector of the k-th protein expression data in the characterization marker expression matrix, that is, the k-th local constraint factor vector of the protein.

[0099] For the robust regression coefficient, robust linear regression is used to estimate the function of the k-th pollution level vector and the protein expression data of the protein to be corrected, and the robust regression coefficient representing the k-th protein expression data in the marker expression matrix, that is, the k-th robust regression coefficient, can also be obtained. The intercept term can be used to evaluate the quality of the correction parameters through the equation relationship in the correction model. After obtaining the correction parameters including the pollution level vector, the local constraint factor vector and the robust regression coefficient, the correction model can be constructed. The expression of the correction model is:

[0100]

[0101]

[0102] Where Y is the row vector of the protein expression matrix, To find the base 2 logarithm of the content in the brackets, is the intercept term, is the kth robust regression coefficient, is the kth pollution level vector, s is the set of types of markers, is the error term determined empirically, is the row vector of the correction matrix after correction, is the global constraint parameter, here it is 1, is the kth local constraint factor vector of the protein. The error term is determined empirically, and the correction model can also be used to evaluate the quality of the correction parameters. In the process of correction by the correction model, the content of the protein expression matrix is used row by row to obtain Correction is then performed to obtain each row of corrected protein expression data as a row vector of the correction matrix, and the correction matrix is formed by each row of corrected protein expression data. In this way, the corrected blood protein data can be obtained from the correction matrix, thereby completing the correction of the blood protein data.

[0103] The present invention can effectively warn and flag severely contaminated blood protein data, and correct the blood protein data to minimize the impact of systemic bias. In actual use, for 140 blood protein data sets, this application effectively warned and corrected 37 of them that were contaminated, thus saving these samples.

[0104] The blood protein data quality assessment and correction device provided by the present invention is described below. The blood protein data quality assessment and correction device described below and the blood protein data quality assessment and correction method described above can be referenced to each other.

[0105] Figure 2The structural diagram of the blood protein data quality assessment and correction system is shown as an example. Figure 2 As shown, the method for performing the blood protein data quality assessment and correction as described above includes:

[0106] Protein expression matrix module 100: used to obtain blood to be tested, perform protein separation on the blood to be tested to obtain blood proteins to be tested, perform data characterization on the blood proteins to be tested to obtain a protein expression matrix, and obtain a grouping matrix;

[0107] The first intermediate marker module 200 is used to select a reference marker, obtain a blood protein marker according to a protein expression matrix, and screen the blood protein marker according to the reference marker to obtain a first intermediate marker;

[0108] A second intermediate marker module 300 is configured to obtain control protein data according to the grouping matrix, perform a Wilcoxon test on the first intermediate marker and the control protein data, and screen the first intermediate marker to obtain a second intermediate marker;

[0109] Coefficient of variation module 400: used to perform Pearson correlation analysis on the second intermediate marker, screen the second intermediate marker to obtain a characterization marker, calculate the coefficient of variation of the characterization marker, and label the protein expression matrix according to the coefficient of variation;

[0110] Correction matrix module 500: used to obtain the characterization marker expression matrix of the characterization marker, obtain correction parameters through the characterization marker expression matrix, and correct the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of blood protein data.

[0111] on the other hand, Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the blood protein data quality assessment and correction method, which includes:

[0112] S1: obtaining blood to be tested, performing protein separation on the blood to be tested to obtain blood proteins to be tested, performing data characterization on the blood proteins to be tested to obtain a protein expression matrix, and obtaining a grouping matrix;

[0113] S2: selecting a benchmark marker, obtaining a blood protein marker according to a protein expression matrix, and screening the blood protein marker according to the benchmark marker to obtain a first intermediate marker;

[0114] S3: obtaining control protein data according to the grouping matrix, performing a Wilcoxon test on the first intermediate marker and the control protein data, and screening the first intermediate marker to obtain a second intermediate marker;

[0115] S4: performing a Pearson correlation analysis on the second intermediate marker, screening the second intermediate marker to obtain a characterization marker, calculating the coefficient of variation of the characterization marker, and marking the protein expression matrix according to the coefficient of variation;

[0116] S5: Obtaining a characterization marker expression matrix of the characterization marker, obtaining correction parameters through the characterization marker expression matrix, and correcting the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of the blood protein data.

[0117] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the blood protein data quality assessment and correction method provided by the above methods, comprising:

[0118] S1: obtaining blood to be tested, performing protein separation on the blood to be tested to obtain blood proteins to be tested, performing data characterization on the blood proteins to be tested to obtain a protein expression matrix, and obtaining a grouping matrix;

[0119] S2: selecting a benchmark marker, obtaining a blood protein marker according to a protein expression matrix, and screening the blood protein marker according to the benchmark marker to obtain a first intermediate marker;

[0120] S3: obtaining control protein data according to the grouping matrix, performing a Wilcoxon test on the first intermediate marker and the control protein data, and screening the first intermediate marker to obtain a second intermediate marker;

[0121] S4: performing a Pearson correlation analysis on the second intermediate marker, screening the second intermediate marker to obtain a characterization marker, calculating the coefficient of variation of the characterization marker, and marking the protein expression matrix according to the coefficient of variation;

[0122] S5: Obtaining a characterization marker expression matrix of the characterization marker, obtaining correction parameters through the characterization marker expression matrix, and correcting the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of the blood protein data.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0124] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for evaluating and correcting blood protein data quality, characterized in that: include: S1: obtaining blood to be tested, performing protein separation on the blood to be tested to obtain blood proteins to be tested, performing data characterization on the blood proteins to be tested to obtain a protein expression matrix, and obtaining a grouping matrix; S2: selecting a benchmark marker, obtaining a blood protein marker according to a protein expression matrix, and screening the blood protein marker according to the benchmark marker to obtain a first intermediate marker; S3: obtaining control protein data according to the grouping matrix, performing a Wilcoxon test on the first intermediate marker and the control protein data, and screening the first intermediate marker to obtain a second intermediate marker; S4: performing a Pearson correlation analysis on the second intermediate marker, screening the second intermediate marker to obtain a characterization marker, calculating the coefficient of variation of the characterization marker, and marking the protein expression matrix according to the coefficient of variation; S5: Obtaining a characterization marker expression matrix of the characterization marker, obtaining correction parameters through the characterization marker expression matrix, and correcting the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of the blood protein data.

2. The blood protein data quality assessment and correction method according to claim 1, characterized in that: Step S1 specifically includes: S11: obtaining blood to be tested, and performing protein separation on blood proteins in the blood to be tested by a high-abundance protein depletion method to obtain the blood proteins to be tested; S12: performing data characterization on the blood protein to be tested by liquid chromatography-mass spectrometry to obtain the protein expression matrix and the grouping matrix.

3. The blood protein data quality assessment and correction method according to claim 1, characterized in that: Step S2 specifically includes: S21: selecting the reference marker according to the blood protein to be tested, and obtaining the blood protein marker from the protein expression matrix; S22: Obtain an intersection of the blood protein marker and the reference marker, and use the intersection of the blood protein marker and the reference marker as the first intermediate marker.

4. The blood protein data quality assessment and correction method according to claim 1, characterized in that: Step S3 specifically includes: S31: extracting the control protein data from the grouping matrix, and obtaining the first intermediate protein data through the first intermediate marker; S32: performing a Wilcoxon test on the first intermediate protein data and the control protein data to obtain a first test parameter; S33: Screening the first intermediate marker according to the first test parameter to obtain a second intermediate marker.

5. The blood protein data quality assessment and correction method according to claim 1, characterized in that: Step S4 specifically includes: S41: determining a correlation threshold, and obtaining second intermediate protein data of the second intermediate marker; S42: performing a Pearson correlation analysis on the second intermediate protein data to obtain a correlation parameter, retaining markers in the second intermediate markers whose correlation parameters are greater than a correlation threshold, and obtaining the characterization marker; S43: Calculate the mean difference and standard deviation of the characterizing markers, calculate the coefficient of variation based on the mean difference and the standard deviation, and mark the protein expression matrix according to the coefficient of variation to determine the contamination degree of the blood protein to be tested.

6. The blood protein data quality assessment and correction method according to claim 1, characterized in that: Step S5 specifically includes: S51: obtaining protein data representing the marker, and obtaining the marker expression matrix based on the protein data; S52: performing logarithmic transformation, Pearson correlation analysis, and robust linear regression on the marker expression matrix to obtain the correction parameters including the pollution level vector, the local constraint factor vector, and the robust regression coefficient; S53: constructing a correction model using the correction parameters, correcting the protein expression matrix using the correction model to obtain a correction matrix, and completing the correction of the blood protein data.

7. The blood protein data quality assessment and correction method according to claim 6, characterized in that: The expression of the correction model is: Where Y is the row vector of the protein expression matrix, To find the base 2 logarithm of the content in the brackets, is the intercept term, is the kth robust regression coefficient, is the kth pollution level vector, s is the set of types of markers, is the error term, is the row vector of the correction matrix, is the global constraint parameter, is the kth local constraint factor vector of the protein.

8. A blood protein data quality assessment and correction system, configured to execute the blood protein data quality assessment and correction method according to any one of claims 1 to 7, characterized in that: include: Protein expression matrix module: used to obtain blood to be tested, perform protein separation on the blood to be tested to obtain blood proteins to be tested, perform data characterization on the blood proteins to be tested, obtain a protein expression matrix, and obtain a grouping matrix; A first intermediate marker module: used to select a benchmark marker, obtain a blood protein marker according to a protein expression matrix, and screen the blood protein marker according to the benchmark marker to obtain a first intermediate marker; A second intermediate marker module is configured to obtain control protein data according to the grouping matrix, perform a Wilcoxon test on the first intermediate marker and the control protein data, and screen the first intermediate marker to obtain a second intermediate marker; A coefficient of variation module is used to perform Pearson correlation analysis on the second intermediate marker, screen the second intermediate marker, obtain a characterization marker, calculate the coefficient of variation of the characterization marker, and mark the protein expression matrix according to the coefficient of variation; Correction matrix module: used to obtain the characterization marker expression matrix of the characterization marker, obtain correction parameters through the characterization marker expression matrix, and correct the protein expression matrix through the correction parameters to obtain a correction matrix, thereby completing the correction of blood protein data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the blood protein data quality assessment and correction method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the blood protein data quality assessment and correction method according to any one of claims 1 to 7 are implemented.

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