Mass spectrum detection quality control method, device, equipment and medium

Abstract

The invention provides a mass spectrum detection quality control method, device and equipment and a medium, and relates to the technical field of mass spectrum detection quality control, and the method comprises the following steps: carrying out quality evaluation on target mass spectrum data to obtain a quality evaluation result of the target mass spectrum data; extracting an abnormal to-be-evaluated index from the quality evaluation result to obtain an abnormal to-be-evaluated index; matching a target control strategy corresponding to the abnormal to-be-evaluated index from a configured comparison table of different to-be-evaluated indexes and different related attributes and a comparison table of different related attributes and different control strategies; and based on the target control strategy and the quality evaluation result, generating a quality control parameter of the target mass spectrum detection equipment so as to control the target mass spectrum detection equipment to perform mass spectrum detection. According to the invention, comprehensive mass spectrum data quality evaluation can be carried out, and a user can be helped to quickly and accurately complete mass spectrum experiment parameter and flow optimization.

Description

Technical Field

[0001] The present application relates to the technical field of mass spectrometry detection quality control, and in particular to a mass spectrometry detection quality control method, device, equipment and medium. Background Art

[0002] DIA-based proteomics technology can identify and screen potential biomarkers and drug targets in large cohort samples, which is of great significance for early diagnosis, treatment monitoring and prognosis evaluation of diseases. However, in the actual operation process, when analyzing large-scale clinical samples, problems such as long data acquisition cycle, reduced instrument system stability, low repeatability of mass spectrometry experiments, and poor quality of collected mass spectrometry data will be faced. Therefore, it is particularly important to establish a full-process automated instrument quality control system to ensure the stability of the instrument system and the reliability of the generated proteomics data during large cohort analysis.

[0003] Existing software for proteomics mass spectrometry data quality assessment include MSQC, QuaMeter, RawBeans, PTXQC, DO-MS, etc. However, the above methods are mainly aimed at the quality control of traditional DDA mass spectrometry data, and only determine which mass spectrometry data have problems by extracting and analyzing a series of indicators, but cannot provide substantial reasons for mass spectrometry data deviations and troubleshooting ideas, so they cannot substantially solve related problems, resulting in significant limitations of the above methods.

[0004] More importantly, there is currently a lack of a methodological system for comprehensive quality control and evaluation of DIA mass spectrometry data. Most laboratories evaluate the stability and repeatability of instrument systems based on the identification results of mass spectrometry data, such as the number of identified peptides and proteins. This process lacks detailed comprehensive quality control at the quantitative level for sample processing, chromatographic separation, and mass spectrometry acquisition. There is a lack of unified and efficient quality control methods and systems, and it is impossible to objectively, accurately, and comprehensively evaluate the system stability, repeatability, and heterogeneity of DIA mass spectrometry data. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a quality control method, device, equipment and medium for mass spectrometry detection, which are used to solve the above-mentioned problems existing in the prior art, and can accurately, comprehensively and objectively evaluate the quality of DIA mass spectrometry data, screen out abnormal mass spectrometry data, and generate quality control parameters to perform quality control on DIA mass spectrometry data.

[0006] In a first aspect, a quality control method for mass spectrometry detection is provided, which may include:

[0007] Obtain target mass spectrum data obtained by detecting a mass spectrum sample with a target mass spectrum detection device; wherein the target mass spectrum data includes at least one indicator to be evaluated; perform quality evaluation on the target mass spectrum data to obtain a quality evaluation result of the target mass spectrum data; extract abnormal indicators to be evaluated from the quality evaluation result to obtain abnormal indicators to be evaluated; match the target related attributes corresponding to the abnormal indicators to be evaluated from a comparison table of different configured indicators to be evaluated and different related attributes; match the target control strategy corresponding to the target related attributes from a comparison table of different configured related attributes and different control strategies; generate quality control parameters of the target mass spectrum detection device based on the target control strategy and the quality evaluation result to control the target mass spectrum detection device to perform mass spectrum detection.

[0008] In an optional implementation, before performing quality assessment on the target mass spectrum data, the method further includes:

[0009] A pre-constructed evaluation matrix is ​​obtained; wherein the evaluation matrix includes a plurality of first-level evaluation indicator parameters; each first-level evaluation indicator parameter includes a plurality of second-level evaluation indicator parameters; and any indicator to be evaluated corresponds to a second-level evaluation indicator parameter.

[0010] In an optional implementation, performing quality assessment on the target mass spectrum data to obtain a quality assessment result of the target mass spectrum data includes:

[0011] For any first-level evaluation index parameter, all second-level evaluation index parameters contained in the first-level evaluation index parameter are used as target second-level evaluation index parameters; for any target second-level evaluation index parameter, the configured scoring rule of the target second-level evaluation index parameter is obtained; based on the scoring rule, the first scoring result of the target mass spectrum data under the target second-level evaluation index parameter is determined; the first scoring results of the target mass spectrum data under different target second-level evaluation index parameters are added and averaged to obtain the second scoring result of the target mass spectrum data under the first-level evaluation index parameter; based on each second scoring result under different first-level evaluation index parameters and each first scoring result under the corresponding target second-level evaluation index parameter, the quality evaluation result of the target mass spectrum data is obtained.

[0012] In an optional implementation, the scoring rule includes a stability scoring rule; and the configuration method of the scoring rule of the target secondary evaluation indicator parameter includes:

[0013] Obtain a preset stability threshold judgment rule for the target secondary evaluation index parameter; extract the to-be-evaluated index corresponding to the target secondary evaluation index parameter from multiple target historical mass spectrum data to obtain a first target to-be-evaluated index; wherein the target historical mass spectrum data is the historical mass spectrum data of the to-be-evaluated index without any abnormality in the quality evaluation result; arrange each first target to-be-evaluated index in descending order of value to obtain the arranged first target to-be-evaluated index; extract the median of the arranged first target to-be-evaluated index as the first median; calculate the standard deviation of the arranged first target to-be-evaluated index to obtain the first standard deviation; calculate the stability threshold of the target secondary evaluation index parameter based on the first median and the first standard deviation; obtain the stability scoring rule of the target secondary evaluation index parameter based on the stability threshold and the stability threshold judgment rule.

[0014] In an optional implementation, determining a first scoring result of the target mass spectrometry data under the target secondary evaluation index parameter based on the scoring rule includes:

[0015] The indicator to be evaluated in the target mass spectrum data corresponding to the target secondary evaluation index parameter is used as the second target indicator to be evaluated; the value of the second target indicator to be evaluated is compared with the size of the stability threshold to obtain a first comparison result; based on the stability threshold judgment rule and the first comparison result, the stability scoring result of the target mass spectrum data under the target secondary evaluation index parameter is obtained; the stability scoring result is used as the first scoring result of the target mass spectrum data under the target secondary evaluation index parameter.

[0016] In an optional implementation, the quality assessment result includes: each indicator to be evaluated is abnormal or non-abnormal; after obtaining the quality assessment result of the target mass spectrometry data, the method further includes: generating a two-dimensional heat map matrix of the quality assessment result based on the evaluation matrix and the quality assessment result; wherein, the abnormal indicators to be evaluated and the non-abnormal indicators to be evaluated are distinguished and displayed in the two-dimensional heat map matrix.

[0017] In an optional implementation, any first-level evaluation index parameter corresponds to a mass spectrometry detection process; based on the target control strategy and the quality assessment result, the quality control parameters of the target mass spectrometry detection device are generated, including: for any second-level evaluation index parameter, the mass spectrometry detection process corresponding to the first-level evaluation index parameter to which the second-level evaluation index parameter belongs is used as the mass spectrometry detection process where the second-level evaluation index parameter is located; the mass spectrometry detection process where the second-level evaluation index parameter corresponding to the abnormal indicator to be evaluated is located is used as the target mass spectrometry detection process for the abnormal indicator to be evaluated; according to the control strategy and the quality assessment result, optimized control parameters are generated; and the optimized control parameters are used as the quality control parameters of the target mass spectrometry detection device in the target mass spectrometry detection process.

[0018] In a second aspect, a quality control device for mass spectrometry detection is provided, which may include:

[0019] An acquisition unit, used for acquiring target mass spectrum data obtained by a target mass spectrum detection device for detecting a mass spectrum sample; wherein the target mass spectrum data includes at least one indicator to be evaluated;

[0020] An evaluation unit, used to perform quality evaluation on the target mass spectrum data to obtain a quality evaluation result of the target mass spectrum data;

[0021] The analysis unit is used to extract the abnormal indicators to be evaluated from the quality evaluation results to obtain the abnormal indicators to be evaluated; match the target related attributes corresponding to the abnormal indicators to be evaluated from the configured comparison table of different indicators to be evaluated and different related attributes; match the target control strategy corresponding to the target related attribute from the configured comparison table of different related attributes and different control strategies;

[0022] A control unit is used to generate quality control parameters of the target mass spectrometry detection device based on the target control strategy and the quality assessment result, so as to control the target mass spectrometry detection device to perform mass spectrometry detection.

[0023] In a third aspect, an electronic device is provided, the electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0024] Memory, used to store computer programs;

[0025] The processor is used to implement any method step described in the first aspect when executing the program stored in the memory.

[0026] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.

[0027] The secondary evaluation indicators of this application cover the entire process of mass spectrometry data generation, with a wide coverage range, and can comprehensively reflect the problems of each generation process of mass spectrometry data; this application sets up multiple primary evaluation indicators that the existing technology does not have when evaluating mass spectrometry data, and can comprehensively, objectively, conveniently and systematically evaluate the quality of the collected DIA mass spectrometry data; this application eliminates the interference of human factors, and by evaluating the stability, repeatability and heterogeneity of mass spectrometry data, it can more efficiently, accurately, comprehensively and objectively evaluate the quality of mass spectrometry data; combined with the correlation analysis result matching control strategy, it can realize direct comprehensive and efficient data quality control and optimization of mass spectrometry data, improve the depth of sample proteome identification, and ensure the reliability of the quality of large cohort proteomics data and the accuracy of downstream biomarker analysis.

[0028] This application can not only be applied to the quantitative quality control and heterogeneity assessment of mass spectrometry detection in a single laboratory, but also to the transfer of experimental methods between different laboratories and the evaluation and comparison of instrument performance, giving full play to the ability of proteomics analysis based on DIA acquisition methods. This application intuitively displays the quality control results of the overall stability and heterogeneity analysis of mass spectrometry data through a two-dimensional heat map matrix and an isolation forest model score map. This application determines evaluation rules based on a large cohort of target mass spectrometry data, performs quality assessment on the target mass spectrometry data based on the evaluation rules, and outputs optimization suggestions for each target mass spectrometry data based on a pre-set control strategy and quality assessment results, thereby achieving quality control of mass spectrometry data and helping to obtain high-quality mass spectrometry data.

[0029] The present application can monitor the quality of mass spectrometry data in real time, and based on the quality assessment results of the current mass spectrometry data, timely adjust the control parameters of the mass spectrometry detection equipment, thereby optimizing the next mass spectrometry data generation process and realizing real-time and efficient quality control of mass spectrometry data. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flow chart of a quality control method for mass spectrometry detection provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of a mass spectrometry data generation process corresponding to each secondary evaluation index parameter provided in an embodiment of the present application;

[0033] Figure 3 A two-dimensional matrix heat map of the overall quality control results of mass spectrometry data provided in the embodiments of the present application;

[0034] Figure 4 A schematic diagram of heterogeneity quality assessment results of mass spectrometry data provided in an embodiment of the present application;

[0035] Figure 5 A schematic diagram of the structure of a quality control device for mass spectrometry detection provided in an embodiment of the present application;

[0036] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] The quality control method for mass spectrometry detection provided in the embodiment of the present application can be applied in a server or in a terminal with strong computing power.

[0039] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0040] Figure 1 A flow chart of a quality control method for mass spectrometry detection provided in an embodiment of the present application. Figure 1 As shown, the method may include:

[0041] Step S110, obtaining target mass spectrum data obtained by detecting a mass spectrum sample using a target mass spectrum detection device; performing quality assessment on the target mass spectrum data to obtain a quality assessment result of the target mass spectrum data.

[0042] In the embodiments of the present application, any target mass spectrum data is obtained by preprocessing the raw mass spectrum data collected by the mass spectrum detection device in the DIA mode; the raw mass spectrum data can be collected by any mass spectrometer in the DIA mode. The target mass spectrum detection device can be an Orbitrap type mass spectrometer from Thermo Fisher Scientific, a timsTOF type mass spectrometer from Bruker, a TripleTOF or ZenoTOF type mass spectrometer from SCIEX, etc.

[0043] In the embodiment of the present application, the original mass spectrometry data is preprocessed, that is, the mass spectrometry data in raw or .d or .wiff format are uniformly converted into .ms1 and / or .ms2 format files. Specifically, the format conversion of the original mass spectrometry data can be specifically carried out in the following steps: preprocessing the original mass spectrometry data according to the type and path of the original mass spectrometry data input by the user; specifically, for the original file in .raw format generated by the Orbitrap type mass spectrometer of Thermo Fisher Scientific, the original mass spectrometry data is preprocessed by Python language, the primary spectrum and secondary spectrum information are extracted, and .ms1 and .ms2 files are output; for the original folder in .d format generated by the timsTOF type mass spectrometer of Bruker, the folder contains multiple files, and Python and C languages ​​are used to quickly access and extract information from the .tdf and .tdf_bin files therein, and store the spectrum information as .ms1 and .ms2 files; for the original files in .wiff and .wiff2 formats generated by the TripleTOF and ZenoTOF type mass spectrometers of SCIEX, the original files are preprocessed by C++ language, the primary spectrum and secondary spectrum information are extracted, and .ms1 and .ms2 files are output.

[0044] In the embodiment of the present application, the target mass spectrometry data includes protein identification and quantification results obtained by parsing proteomics data through a proteomics search engine.

[0045] In the embodiment of the present application, the target mass spectrometry data includes 47 indicators to be evaluated. These 47 indicators to be evaluated involve the entire process of mass spectrometry detection, covering sample processing, chromatographic separation, mass spectrometry acquisition, and identification result steps. Through these 47 indicators to be evaluated, a comprehensive and high-quality quality evaluation of the mass spectrometry data can be performed; the indicators to be evaluated included in the target mass spectrometry data include: enzymatic missed cleavage rate S1, polypeptide length S2, proportion of contaminating proteins S3, chromatographic retention time C1, chromatographic half-peak width C2, chromatographic peak width C3, chromatographic tailing ratio C4, number of polypeptide parent ions identified per minute C5, number of peptide parent ions identified per minute The identification rate C6, the ratio of 1-charged ions to 2-charged ions in the collected primary spectra IS1, the ratio of 3-charged ions to 2-charged ions in the collected primary spectra IS2, the ratio of 4-charged ions to 2-charged ions in the collected primary spectra IS3, the median of the mass-to-charge ratio distribution of the identified peptide precursor ions IS4, the number of secondary spectra collected in each cycle W1, the median of the DIA acquisition window size W2, the average number of peptide precursor ions identified in each secondary spectrum W3, the average number of repeated identifications of peptide precursor ions W4, the ratio of the number of identified peptide precursor ions to the number of identified secondary spectra W 5. Number of primary spectrum points collected for each chromatographic peak W6, number of secondary spectrum points collected for each chromatographic peak W7, proportion of primary spectrum collection time M1, primary spectrum ion injection time M2, secondary spectrum ion injection time M3, primary spectrum mass spectrum cycle time M4, secondary spectrum mass spectrum cycle time M5, primary spectrum peak intensity M6, primary spectrum peak number M7, secondary spectrum peak intensity M8, secondary spectrum peak number M9, identified polypeptide isotope peak characteristic ratio M10, polypeptide parent ion signal-to-noise ratio M11, primary parent ion mass deviation M12, secondary daughter ion mass The following are the data: quantity deviation M13, polypeptide precursor ion intensity M14, polypeptide intensity M15, protein intensity M16, number of primary spectra collected M17, number of secondary spectra collected M18, secondary spectrum identification rate M19, secondary spectrum identification rate collected in each DIA window M20, number of identified secondary spectra ID1, number of identified polypeptide precursor ions ID2, number of identified polypeptides ID3, number of identified proteomes ID4, number of identified proteins ID5, average number of polypeptides identified per proteome ID6 and average number of polypeptide precursor ions identified per proteome ID7.

[0046] In an embodiment of the present application, a method for obtaining an indicator to be evaluated for target mass spectrometry data includes: enzymatic missed cleavage rate S1: extracting the identification results for the target mass spectrometry data reported by a proteomics search engine, and counting the proportion of polypeptide sequences containing missed cleavage sites in all identified polypeptide sequences in the target mass spectrometry data; polypeptide length S2: extracting the identification results for the target mass spectrometry data reported by a proteomics search engine, and counting the distribution and median of the amino acid lengths contained in all identified polypeptide sequences in the target mass spectrometry data; the proportion of contaminating proteins S3: extracting the identification and quantitative results for the target mass spectrometry data reported by a proteomics search engine, Calculate the ratio of the sum of the intensities of contaminating proteins in the target mass spectrometry data to the sum of the intensities of all identified proteins; Chromatographic retention time C1: Extract the identification and quantitative results of the proteomics search engine, and extract the retention time of the internal standard peptide in the target mass spectrometry data from the results; Chromatographic half-peak width C2: Extract the identification and quantitative results of the target mass spectrometry data reported by the proteomics search engine, and calculate the median value of the half-peak full width of all identified peptide chromatographic peaks in the target mass spectrometry data; Chromatographic peak width C3: Extract the identification and quantitative results of the target mass spectrometry data reported by the proteomics search engine, and calculate the median value of the half-peak full width of all identified peptide chromatographic peaks in the target mass spectrometry data The median value of the full peak width; Chromatographic tailing ratio C4: Extract the identification and quantitative results of the target mass spectrometry data reported by the proteomics search engine, and count the proportion of peptides with a full peak width of >1 minute in all identified peptide chromatographic peaks in the target mass spectrometry data; The number of peptide precursor ions identified per minute C5: Extract the identification and quantitative results of the target mass spectrometry data reported by the proteomics search engine, and count the median value of the number of peptide precursor ions identified per minute in the target mass spectrometry data; Identification rate per minute C6: First, extract the secondary spectrum information and the proteomics search engine identification results from the target mass spectrometry data, and use The spectrum identification rate per minute was calculated by the following formula: Spectrum identification rate = number of identified secondary spectra / number of collected secondary spectra, and the median value of the identification rate per minute was calculated; the ratio of 1-charge ions to 2-charge ions in the collected primary spectra IS1: firstly, the primary spectrum information was extracted from the target mass spectrum data, and the ratio of the number of 1-charge isotope cluster ions to the number of 2-charge isotope cluster ions was calculated; the ratio of 3-charge ions to 2-charge ions in the collected primary spectrum IS2: firstly, the primary spectrum information was extracted from the target mass spectrum data, and the ratio of the number of 3-charge isotope cluster ions to the number of 2-charge isotope cluster ions was calculated;

[0047] The ratio of quadruple-charged ions to doubly-charged ions in the collected primary spectrum IS3: First, extract the primary spectrum information from the target mass spectrometry data, and count the ratio of the number of quadruple-charged isotope cluster ions to the number of doubly-charged isotope cluster ions; The median of the mass-to-charge ratio distribution of the identified polypeptide precursor ions IS4: Extract the identification results for the target mass spectrometry data reported by the proteomics search engine, count the mass-to-charge ratios (m / z) of all identified polypeptide precursor ions, and calculate the median value; The number of secondary spectra collected in each cycle W1: First, extract the secondary spectrum information from the target mass spectrometry data, and count the number of secondary spectra collected in a mass spectrometry cycle; DIA acquisition window The median of the size of the opening W2: First, extract the secondary spectrum information from the target mass spectrum data, count the size of each DIA acquisition window ion range, and calculate the median value; the average number of peptide precursor ions identified in each secondary spectrum W3: First, extract the secondary spectrum information from the target mass spectrum data and extract the proteomics search engine identification results, calculate the total number of identified peptide precursor ions (including the number of repeated identifications) and the ratio of the number of identified secondary spectra, and obtain the average number of peptide precursor ions identified in each secondary spectrum; the average number of repeated identifications of peptide precursor ions W4: First, extract the secondary spectrum information from the target mass spectrum data and extract the proteomics search engine identification results The identification results of the proteomics search engine are used to calculate the ratio of the total number of identified polypeptide parent ions (including the number of repeated identifications) and the number of non-redundant polypeptide parent ions to obtain the average number of repeated identifications of polypeptide parent ions; the ratio of the number of identified polypeptide parent ions to the number of identified secondary spectra W5: first extract the secondary spectrum information from the target mass spectrometry data and extract the proteomics search engine identification results, calculate the ratio of the number of identified polypeptide parent ions to the number of identified secondary spectra; the number of primary spectrum points collected for each chromatographic peak W6: extract the identification and quantitative results for the target mass spectrometry data reported by the proteomics search engine, and count the primary spectrum points collected for each chromatographic peak in the target mass spectrometry data The median value of the number of spectrum points; The number of secondary spectrum points collected for each chromatographic peak W7: Extract the identification and quantitative results of the target mass spectrometry data reported by the proteomics search engine, and count the median value of the number of secondary spectrum points collected for each chromatographic peak in the target mass spectrometry data; The proportion of primary spectrum acquisition time M1: First, extract the primary spectrum and secondary spectrum information from the target mass spectrometry data, count the acquisition time of each primary spectrum and secondary spectrum, and calculate the proportion of the total primary spectrum acquisition time; Primary spectrum ion injection time M2: First, extract the primary spectrum information from the target mass spectrometry data, count the ion injection time of each primary spectrum, and calculate its median value;

[0048] Secondary spectrum ion injection time M3: first extract the secondary spectrum information from the target mass spectrum data, count the ion injection time of each secondary spectrum, and calculate its median value; primary spectrum mass spectrum cycle time M4: first extract the primary spectrum information from the target mass spectrum data, count the scanning time of each primary spectrum, and calculate the median value of the cycle time of each primary spectrum acquired; secondary spectrum mass spectrum cycle time M5: first extract the secondary spectrum information from the target mass spectrum data, count the scanning time of each secondary spectrum, and calculate the median value of the cycle time of each secondary spectrum acquired; primary spectrum peak intensity M6: first extract the primary spectrum information from the target mass spectrum data, count the sum of the peak intensities of each primary spectrum, and calculate its median value; primary spectrum peak number M7: first extract the primary spectrum information from the target mass spectrum data, count the number of peaks of each primary spectrum, and calculate its median value; secondary spectrum peak intensity M8: first Extract secondary spectrum information from target mass spectrometry data, count the sum of the peak intensities of each secondary spectrum, and calculate the median value; Number of secondary spectrum peaks M9: First, extract secondary spectrum information from target mass spectrometry data, count the number of peaks of each secondary spectrum, and calculate the median value; Characteristic ratio of identified polypeptide isotope peaks M10: First, extract primary spectrum information and extract proteomics search engine identification results from target mass spectrometry data, and count the ratio of identified parent ion isotope peak clusters to all collected ion isotope peak clusters; Signal-to-noise ratio of polypeptide parent ions M11: Extract identification and quantification results for target mass spectrometry data reported by proteomics search engines, and count the median value of signal-to-noise ratio of polypeptide parent ions in target mass spectrometry data; Primary parent ion mass deviation M12: Extract identification and quantification results for target mass spectrometry data reported by proteomics search engines, and count the median value of primary parent ion mass deviation in target mass spectrometry data;

[0049] Secondary product ion mass deviation M13: Extract the identification and quantification results of the target mass spectrometry data reported by the proteomics search engine, and count the median value of the secondary parent ion mass deviation in the target mass spectrometry data; Peptide parent ion intensity M14: Extract the identification and quantification results of the target mass spectrometry data reported by the proteomics search engine, and count the median value of the peptide parent ion intensity in the target mass spectrometry data; Peptide intensity M15: Extract the identification and quantification results of the target mass spectrometry data reported by the proteomics search engine, and count the median value of the peptide intensity; Protein intensity M16: Extract the identification and quantification results of the target mass spectrometry data reported by the proteomics search engine, and count the median value of the protein intensity; Number of collected primary spectra M17: First extract the primary spectrum information from the target mass spectrometry data, and count the total number of primary spectra collected in the target mass spectrometry data; Number of collected secondary spectra M18: First extract the secondary spectrum information from the target mass spectrometry data, and count the total number of secondary spectra collected in the target mass spectrometry data; Secondary spectrum identification rate M19: First extract the secondary spectrum information and extract proteomics from the target mass spectrometry data Search engine identification results, calculate the ratio of the number of identified secondary spectra to the number of collected secondary spectra; identification rate M20 of secondary spectra collected in each DIA window: first extract primary spectrum information from the target mass spectrometry data and extract proteomics search engine identification results, calculate the ratio of the number of identified secondary spectra to the number of collected secondary spectra in each DIA window range; number of identified secondary spectra ID1: extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and count the total number of secondary spectra of the peptide parent ions identified in the target mass spectrometry data; number of identified peptide parent ions ID2: extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and count the total number of peptide parent ions identified in the target mass spectrometry data; number of identified peptides ID3: extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and count the total number of peptide sequences identified in the target mass spectrometry data; number of identified proteomes ID4: extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and count the total number of proteomes identified in the target mass spectrometry data;

[0050] Number of identified proteins ID5: Extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and count the total number of proteins identified in the target mass spectrometry data; average the number of peptides identified in each proteome group ID6: Extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and extract the ratio of the number of identified peptides to the number of identified proteomes; average the number of peptide precursor ions identified in each proteome group ID7: Extract the identification results for the target mass spectrometry data reported by the proteomics search engine, and extract the ratio of the number of identified peptide precursor ions to the number of identified proteomes.

[0051] In the embodiment of the present application, the target mass spectrometry data is quality evaluated to obtain the quality evaluation result of the target mass spectrometry data, including:

[0052] The quality of the target mass spectrometry data is evaluated by a pre-constructed evaluation matrix to obtain the quality evaluation result of the target mass spectrometry data; or, the quality of the target mass spectrometry data is evaluated by a pre-constructed evaluation matrix combined with an isolation forest model; the stability and repeatability of the target mass spectrometry data are evaluated based on the evaluation matrix; and the heterogeneity level of the target mass spectrometry data is evaluated based on the isolation forest model.

[0053] In an embodiment of the present application, the evaluation matrix includes multiple first-level evaluation index parameters, and multiple second-level evaluation index parameters included in each first-level evaluation index parameter; any indicator to be evaluated corresponds to one second-level evaluation index parameter; for example, the evaluation matrix includes protein intensity as a second-level evaluation index parameter, and the target mass spectrometry data also includes protein intensity as an indicator to be evaluated, and the two are corresponding; its purpose is to judge whether the indicator to be evaluated in the target mass spectrometry data meets the preset standard based on the scoring rules corresponding to the second-level evaluation index parameters. If not, it means that the indicator to be evaluated is unqualified (or abnormal) or there is an abnormality.

[0054] In the embodiment of the present application, a total of 47 secondary evaluation index parameters are included. According to the relationship between the secondary evaluation index parameters and the mass spectrometry data acquisition process, the primary evaluation index parameters total 6, such as Figure 2As shown in the figure, they are: sample processing, chromatographic separation, ion source, DIA window setting, primary and secondary mass spectrometry signals, and identification results; the six primary evaluation index parameters cover the entire process of proteomics mass spectrometry data detection, including sample processing, chromatographic separation, mass spectrometry acquisition, and identification result steps. According to the DIA data acquisition principle, the mass spectrometry acquisition step is subdivided into ion source, DIA window setting, primary and secondary mass spectrometry signals; sample processing includes three secondary evaluation index parameters, namely: S1: enzymatic missed cleavage rate, S2: polypeptide length, S3: total ion current; chromatographic separation includes six secondary evaluation index parameters, namely: C1: chromatographic ineffective acquisition time, C2: chromatographic half-peak width, C3: chromatographic peak width, C4: chromatographic Tailing ratio, C5: the number of peptide precursor ions identified per minute, C6: identification rate per minute; the ion source includes 4 secondary evaluation index parameters, namely: IS1: the ratio of 1-charge ions to 2-charge ions in the collected primary spectrum, IS2: the ratio of 3-charge ions to 2-charge ions, IS3: the ratio of 4-charge ions to 2-charge ions, IS4: the median of the mass-to-charge ratio distribution of the identified peptide precursor ions; the DIA window setting includes 7 secondary evaluation index parameters, namely: W1: the number of secondary spectra collected in each cycle, W2: the median of the DIA acquisition window size, W3: the average number of peptide precursor ions identified in each secondary spectrum, W4: the average number of repeated identifications of peptide precursor ions, W5: the number of peptides identified The ratio of the number of parent ions to the number of identified secondary spectra, W6: the number of primary spectrum points collected for each chromatographic peak, W7: the number of secondary spectrum points collected for each chromatographic peak; the primary and secondary mass spectrometry signals include 20 secondary evaluation index parameters, namely: M1: the proportion of primary spectrum acquisition time, M2: primary spectrum ion injection time, M3: secondary spectrum ion injection time, M4: primary spectrum mass spectrum cycle time, M5: secondary spectrum mass spectrum cycle time, M6: primary spectrum peak intensity, M7: primary spectrum peak number, M8: secondary spectrum peak intensity, M9: secondary spectrum peak number, M10: identified polypeptide isotope peak characteristic ratio, M11: polypeptide parent ion signal-to-noise ratio, M12: primary parent ion Mass deviation, M13: secondary product ion mass deviation, M14: polypeptide parent ion intensity, M15: polypeptide intensity, M16: protein intensity, M17: number of primary spectra collected, M18: number of secondary spectra collected, M19: secondary spectrum identification rate, M20: secondary spectrum identification rate collected in each DIA window; The identification results include 7 secondary evaluation index parameters, namely: ID1: number of identified secondary spectra, ID2: number of identified polypeptide parent ions, ID3: number of identified polypeptides, ID4: number of identified proteomes, ID5: number of identified proteins, ID6: average number of polypeptides identified per proteome, ID7: average number of polypeptide parent ions identified per proteome.

[0055] In the embodiment of the present application, the target mass spectrum data is quality evaluated to obtain the quality evaluation result of the target mass spectrum data, including:

[0056] Using an evaluation matrix, quality evaluation is performed on the target mass spectrometry data to obtain a quality evaluation result of the target mass spectrometry data; alternatively, using an evaluation matrix, quality evaluation is performed on the target mass spectrometry data to obtain a first quality evaluation result of the target mass spectrometry data; using an isolation forest model, quality evaluation is performed on the target mass spectrometry data to obtain a second quality evaluation result of the target mass spectrometry data; based on the first quality evaluation result and the second quality evaluation result, a quality evaluation result of the target mass spectrometry data is obtained.

[0057] In the embodiment of the present application, the target mass spectrometry data is quality evaluated using an evaluation matrix to obtain a quality evaluation result or a first quality evaluation result of the target mass spectrometry data, including:

[0058] For any first-level evaluation index parameter, all second-level evaluation index parameters contained in the first-level evaluation index parameter are used as target second-level evaluation index parameters; for any target second-level evaluation index parameter, the configured scoring rule of the target second-level evaluation index parameter is obtained; based on the scoring rule, the first scoring result of the target mass spectrum data under the target second-level evaluation index parameter is determined; the first scoring results of the target mass spectrum data under different target second-level evaluation index parameters are added and averaged to obtain the second scoring result of the target mass spectrum data under the first-level evaluation index parameter; based on each second scoring result under different first-level evaluation index parameters and each first scoring result under the corresponding target second-level evaluation index parameter, the quality assessment result (or first quality assessment result) of the target mass spectrum data is obtained.

[0059] In the embodiment of the present application, the scoring rule of the first quality assessment result includes a stability scoring rule; and the method for determining the stability scoring rule includes:

[0060] From multiple target historical mass spectrometry data, extract the indicators to be evaluated corresponding to the target secondary evaluation indicator parameters to obtain the first target indicators to be evaluated; arrange the first target indicators to be evaluated in descending order of numerical value to obtain the arranged first target indicators to be evaluated; extract the median of the arranged first target indicators to be evaluated as the first median; calculate the standard deviation of the arranged first target indicators to be evaluated to obtain the first standard deviation; calculate the stability threshold of the target secondary evaluation indicator parameters based on the first median and the first standard deviation; obtain the stability scoring rule of the target secondary evaluation indicator parameters based on the stability threshold and the stability threshold judgment rule.

[0061] In the embodiment of the present application, the target historical mass spectrometry data is the historical mass spectrometry data of the indicators to be evaluated that do not have abnormalities in the quality evaluation results. The historical mass spectrometry data is also obtained by preprocessing the original mass spectrometry data collected by the target mass spectrometry detection device in the DIA mode; the historical mass spectrometry data can be obtained by preprocessing the original mass spectrometry data collected by the target mass spectrometry detection device at the last moment, or by preprocessing the original mass spectrometry data collected in advance by the target mass spectrometry detection device.

[0062] Specifically, based on the first median and the first standard deviation, the stability threshold of the target secondary evaluation index parameter is calculated, including: adding 2 times the first standard deviation to the first median to obtain the first stability threshold; subtracting 2 times the first standard deviation from the first median to obtain the second stability threshold; wherein the first stability threshold is greater than the second stability threshold.

[0063] In the embodiment of the present application, the stability threshold judgment rule is used to indicate whether it is abnormal or normal when it is greater than or less than the first stability threshold and / or the second stability threshold.

[0064] In an embodiment of the present application, the stability threshold judgment rule of any target secondary evaluation index parameter includes using the first stability threshold as the stability threshold of the target secondary evaluation index parameter, or using the second stability threshold as the stability threshold of the target secondary evaluation index parameter, or using the range formed by the first stability threshold and the second stability threshold as the stability threshold of the target secondary evaluation index parameter; at the same time, the stability threshold judgment rule also includes that it is abnormal when it is greater than the stability threshold or it is abnormal when it is less than the stability threshold or it is not within the range formed by the first stability threshold and the second stability threshold.

[0065] In an embodiment of the present application, any target secondary evaluation indicator parameter corresponds to an indicator to be evaluated, and a comparison table of different indicators to be evaluated and different related attributes is pre-configured, so that the related attribute corresponding to the indicator to be evaluated corresponding to any target secondary evaluation indicator parameter can be obtained; the related attribute indicates whether the indicator to be evaluated is positively correlated with the quality assessment result (that is, the larger the indicator to be evaluated, the higher the quality assessment result) or negatively correlated (that is, the smaller the indicator to be evaluated, the higher the quality assessment result) or limitedly correlated (that is, the indicator to be evaluated is within a certain range, and the higher the quality assessment result).

[0066] When the relevant attribute of the indicator to be evaluated corresponding to the target secondary evaluation index parameter is positively correlated, the stability threshold judgment rule corresponding to the target secondary evaluation index parameter includes: data less than the second stability threshold is abnormal data; when the relevant attribute of the indicator to be evaluated corresponding to the target secondary evaluation index parameter is negatively correlated, the stability threshold judgment rule corresponding to the target secondary evaluation index parameter includes: data greater than the second stability threshold is abnormal data; when the relevant attribute of the indicator to be evaluated corresponding to the target secondary evaluation index parameter is limited correlation, the stability threshold judgment rule corresponding to the target secondary evaluation index parameter includes: data greater than the second stability threshold and less than the first stability threshold is non-abnormal data, and data less than the second stability threshold or greater than the first stability threshold is abnormal data.

[0067] In the embodiment of the present application, the stability threshold judgment rule is determined according to the correlation relationship between the experimental mass spectrometry data and the standard mass spectrometry data caused by the mass spectrometry parameters; the specific steps include:

[0068] Obtain a pre-prepared standard mass spectrometry sample and standard mass spectrometry data of the standard mass spectrometry sample; wherein the standard mass spectrometry data is obtained by performing mass spectrometry analysis on a mass spectrometer in a DIA mode and under empirical mass spectrometry parameters; the empirical mass spectrometry parameters are pre-set; the empirical mass spectrometry parameters are the best mass spectrometry parameters for the mass spectrometer determined based on experience; the standard mass spectrometry data is standard mass spectrometry data in which all indicators to be evaluated are normal (i.e., the quality evaluation result is normal); multiple reference values ​​are determined based on the value range of the mass spectrometry parameters; the mass spectrometry parameters of the mass spectrometer are set to the determined reference values, other mass spectrometry parameters of the mass spectrometer are kept unchanged, and mass spectrometry analysis is performed to obtain multiple experimental mass spectrometry data; wherein the multiple reference values ​​include a minimum value, an intermediate value, and a maximum value within a value range; the difference between the experimental mass spectrometry data and the standard mass spectrometry data (the quality evaluation result) is calculated. The method comprises the following steps: determining the difference in estimated results of the mass spectrum parameter under different reference values ​​of the experimental mass spectrum data and the standard mass spectrum data according to the calculated difference; determining the error size and error direction of the experimental mass spectrum data and the standard mass spectrum data caused by the mass spectrum parameter when the mass spectrum parameter moves away from the corresponding empirical mass spectrum parameter in different directions (i.e., increases or decreases) based on the obtained deviation degree; obtaining a correlation relationship based on the error direction of the experimental mass spectrum data and the standard mass spectrum data caused when the mass spectrum parameter moves away from the corresponding empirical mass spectrum parameter in different directions; wherein the correlation relationship includes positive correlation, negative correlation and limited correlation; limited correlation means that when within a certain range, the error with the standard mass spectrum data is infinitely close, but when it is less than the minimum value within the range or greater than the maximum value within the range, the error will become larger.

[0069] For example, the correlation analysis between the number of primary spectrum points collected for each chromatographic peak and the mass spectrometry detection results includes:

[0070] Obtain a pre-prepared standard mass spectrometry sample and standard mass spectrometry data of the standard mass spectrometry sample; wherein the standard mass spectrometry data is obtained by performing mass spectrometry analysis on a mass spectrometer in DIA mode and under empirical mass spectrometry parameters; the empirical mass spectrometry parameters are pre-set; the empirical mass spectrometry parameters are the best mass spectrometry parameters for the mass spectrometer determined based on experience; the number of primary spectrum points collected for each chromatographic peak in the empirical mass spectrometry parameters is 6; obtain the number of primary spectrum points collected for each chromatographic peak of the mass spectrometer and the value range of the number of primary spectrum points collected for each chromatographic peak; assuming that the value range is 1-10; determine that 3 reference values ​​are 1, 5, and 10 respectively; set the number of primary spectrum points collected for each chromatographic peak of the mass spectrometer to 1, 5, and 10 respectively, keep other mass spectrometry parameters unchanged, and perform mass spectrometry analysis to obtain experimental mass spectrum data corresponding to 1, 5, and 10; calculate the difference between the experimental mass spectrum data corresponding to 1, 5, and 10 and the standard mass spectrum data, and determine the number of primary spectrum points collected for each chromatographic peak under different reference values ​​based on the calculated difference. The degree of deviation between the experimental mass spectrum data and the standard mass spectrum data obtained; based on the obtained degree of deviation, determine the error size and error direction of the experimental mass spectrum data and the standard mass spectrum data caused by the number of secondary spectrum collection in each cycle when the number of primary spectrum points collected by each chromatographic peak moves away from the corresponding empirical mass spectrum parameters (i.e., 6) in different directions; based on the error direction of the experimental mass spectrum data and the standard mass spectrum data caused when the number of primary spectrum points collected by each chromatographic peak moves away from the corresponding empirical mass spectrum parameters in different directions, the correlation relationship is obtained to be restricted correlation, that is, it needs to be within a restricted range; the error between the experimental mass spectrum data and the standard mass spectrum data caused by the number of primary spectrum points collected by each chromatographic peak is used as the degree of correlation between the number of primary spectrum points collected by each chromatographic peak corresponding to the number of secondary spectrum collection in each cycle and the mass spectrum detection result; based on the restricted correlation relationship and the degree of correlation between the number of primary spectrum points collected by each chromatographic peak and the mass spectrum detection result, the correlation analysis result of the number of primary spectrum points collected by each chromatographic peak is obtained.

[0071] For example, a total of 5 mass spectrometry detection results are included (one of which is the target mass spectrometry data, and the other 4 are non-target detection data), and the target secondary evaluation index parameter is the number of identified proteomes. The number of identified proteomes obtained from the 5 mass spectrometry detection results is extracted to obtain the first target indicator to be evaluated, which are 5010, 5040, 5020, 5030, and 5050, respectively; they are arranged from large to small as 5050, 5040, 5030, 5020, and 5010; the first median is 5030, and the first standard deviation is 15.81; 5030-2*15.81=4998.38, 5030+2*15.81=5061.62, to obtain the first stability threshold and the second stability threshold.

[0072] The number of identified proteomes is positively correlated with the mass spectrometry detection results, that is, the more identified proteins, the better. Therefore, for this secondary evaluation index, the first stability threshold is 5061.62, the second stability threshold is 4998.38, and the stability threshold of the positive correlation is the second stability threshold; if the index to be evaluated is less than 4998.38, it means that the index to be evaluated is abnormal; if the index to be evaluated is greater than 4998.38, it means that the index to be evaluated is normal; thus, it is realized to use the threshold combined with the judgment rule to judge whether the index to be evaluated is abnormal (that is, whether the quality assessment is unqualified (or abnormal)).

[0073] In the embodiment of the present application, based on the scoring rules, determining the first scoring result of the target mass spectrometry data under the target secondary evaluation index parameter includes:

[0074] The indicator to be evaluated in the target mass spectrometry data corresponding to the target secondary evaluation index parameter is used as the second target indicator to be evaluated; the value of the second target indicator to be evaluated is compared with the size of the stability threshold to obtain a first comparison result; based on the stability threshold judgment rule and the first comparison result, a stability scoring result of the target mass spectrometry data under the target secondary evaluation index parameter is obtained; the stability scoring result is used as the first scoring result of the target mass spectrometry data under the target secondary evaluation index parameter.

[0075] In the embodiment of the present application, based on the stability threshold judgment rule and the first comparison result, the stability scoring result of the target mass spectrometry data under the target secondary evaluation index parameter is obtained, including:

[0076] Any target secondary evaluation index parameter corresponds to an index to be evaluated, and a comparison table of different indicators to be evaluated and different related attributes is pre-configured, so that the related attributes corresponding to the indicators to be evaluated corresponding to any target secondary evaluation index parameter can be obtained; when the related attributes corresponding to the indicators to be evaluated corresponding to any target secondary evaluation index parameter are positively correlated, the second target indicator to be evaluated is greater than or equal to the median, and the score is 5 points; less than or equal to the second stability threshold, the score is 1 point; between the median and the second stability threshold, linear scoring is performed. When the target secondary evaluation index parameter is negatively correlated with the mass spectrometry detection result, the second target indicator to be evaluated is less than or equal to the median, and the score is 5 points; greater than or equal to the first stability threshold, the score is 1 point; between the first stability threshold and the median, linear scoring is performed. When the target secondary evaluation index parameter is limitedly correlated with the mass spectrometry detection result, the second target indicator to be evaluated is close to or equal to the median of the secondary evaluation index parameter, and the score is 5 points; close to or equal to the stability threshold, the score is 1 point; between the stability threshold and the median, linear scoring is performed; if it is greater than the first stability threshold or less than the second stability threshold, both are 1 point.

[0077] In the embodiment of the present application, when scoring linearly, the closer to the median, the higher the score; the scoring result can be determined based on the difference between the actual score and the median and the proportional relationship between the highest score and the lowest score.

[0078] For example, if the target secondary evaluation index parameter is the number of secondary spectra collected in each cycle, the number of secondary spectra collected in each cycle is limitedly correlated with the mass spectrometry detection result, assuming that the first stability threshold of the secondary evaluation index parameter is 70, the second stability threshold is 30, and the median value is 50; assuming that the number of secondary spectra collected in each cycle extracted from the target mass spectrometry data is 50, then the score of the secondary evaluation index is 5 points; assuming that the number of secondary spectra collected in each cycle extracted from the target mass spectrometry data is 20, then the score of the secondary evaluation index is 1 point; assuming that the number of secondary spectra collected in each cycle extracted from the target mass spectrometry data is 40, then the score of the secondary evaluation index is 3 points, and the linear scoring calculation method is: 1+(5-1)*(40-30) / (50-30)=3.

[0079] In another embodiment of the present application, the scoring rules further include: a repetitive scoring rule; and a method for determining the repetitive scoring rule, including:

[0080] Obtain the repeatability threshold judgment rule of the preset target secondary evaluation index parameter; extract the to-be-evaluated index corresponding to the target secondary evaluation index parameter from the target historical mass spectrometry data to obtain the second target to-be-evaluated index; calculate the Pearson correlation coefficient between each second target to-be-evaluated index and the second target to-be-evaluated index corresponding to other non-target mass spectrometry data, and arrange all the obtained Pearson correlation coefficients in descending order to obtain the arranged second target to-be-evaluated index correlation coefficient; extract the median of the arranged second target to-be-evaluated index correlation coefficient as the second median; calculate the standard deviation of the arranged second target to-be-evaluated index correlation coefficient to obtain the second standard deviation; calculate the repeatability threshold of the target secondary evaluation index parameter based on the second median and the second standard deviation; obtain the repeatability scoring rule of the target secondary evaluation index parameter based on the repeatability threshold and the repeatability threshold judgment rule.

[0081] Specifically, based on the second median and the second standard deviation, the repeatability threshold of the target secondary evaluation index parameter is calculated, including: subtracting 2 times the second standard deviation from the second median to obtain the repeatability threshold.

[0082] For example, a total of 5 target mass spectrometry data are included (1 of which is the target mass spectrometry data, and the remaining 4 are non-target detection data), and the target secondary evaluation index parameter is protein intensity. The protein intensity identified in the target mass spectrometry data and the 4 non-target detection data is extracted to obtain the second target indicator to be evaluated, and the Pearson correlation coefficient of the second target indicator to be evaluated between the target mass spectrometry data and the 4 non-target detection data is calculated; they are arranged from large to small as 0.99, 0.98, 0.96, and 0.95; the second median is 0.97, and the second standard deviation is 0.02; 0.97-2*0.02=0.93 to obtain the repeatability threshold.

[0083] In the embodiment of the present application, the repeatability threshold evaluation rule includes: when it is greater than or equal to the repeatability threshold, it is normal; when it is less than the repeatability threshold, it is abnormal.

[0084] For example, if the repeatability threshold of the target secondary evaluation index parameter is 0.93, if the index to be evaluated is less than 0.93, it means that the index to be evaluated of the target mass spectrometry data is abnormal; otherwise, it means that the index to be evaluated is normal; at this time, if the index to be evaluated is 0.9, it means that the index to be evaluated is abnormal; thereby realizing the use of the threshold combined with the judgment rule to determine whether there is an abnormality in the index to be evaluated (that is, whether the quality assessment is unqualified (or abnormal)).

[0085] In another embodiment of the present application, the threshold evaluation rule may also indicate the rating; for example, greater than the first stability threshold is excellent, less than the first stability threshold but greater than the second stability threshold is qualified, less than the second stability threshold is unqualified (or abnormal), and equal to the first stability threshold is super excellent.

[0086] In the embodiment of the present application, based on the scoring rules, determining the first scoring result of the target mass spectrometry data under the target secondary evaluation index parameter includes:

[0087] The indicator to be evaluated in the target mass spectrum data corresponding to the target secondary evaluation index parameter is used as the third target indicator to be evaluated; the value of the third target indicator to be evaluated is compared with the stability threshold to obtain a first comparison result; based on the stability threshold judgment rule and the first comparison result, a first stability scoring result of the target mass spectrum data under the target secondary evaluation index parameter is obtained; the value of the third target indicator to be evaluated is compared with the repeatability threshold to obtain a second comparison result; based on the repeatability threshold judgment rule and the second comparison result, a first repeatability scoring result of the target mass spectrum data under the target secondary evaluation index parameter is obtained; based on the first stability scoring result and the first repeatability scoring result, a first scoring result of the target mass spectrum data under the target secondary evaluation index parameter is obtained.

[0088] In the embodiment of the present application, the quality assessment result includes: each indicator to be assessed is abnormal or non-abnormal (ie, normal).

[0089] In an embodiment of the present application, after obtaining the quality assessment result of the target mass spectrum data, the method further includes:

[0090] Based on the evaluation matrix and the quality evaluation results, a two-dimensional heat map matrix of the quality evaluation results is generated to be displayed using a display device; in the two-dimensional heat map matrix, indicators to be evaluated with abnormalities and indicators to be evaluated without abnormalities are distinguished, such as Figure 3 As shown, Figure 3 The indicator evaluation results in are the quality evaluation results.

[0091] In another embodiment of the present application, the target mass spectrum data is quality evaluated using the isolation forest model to obtain a second quality evaluation result of the target mass spectrum data, including:

[0092] The target mass spectrum data and all the target historical mass spectrum data are input into a pre-built isolation forest model to detect anomalies. In the isolation forest, based on all the input target mass spectrum data, features are randomly selected to segment the target mass spectrum data until all the target mass spectrum data are segmented into isolated sample data. At this time, for each isolated tree in the isolation forest, each sample data in the sample data set is located on the only leaf node of the isolated tree. Under this random segmentation strategy, the abnormal sample data usually has a path with a smaller path depth in the travel path from the root node of the isolation tree to the leaf node. The travel path corresponding to the deepest leaf node in the isolation tree has the largest path depth. The sample data segmented into isolation in the leaf node are usually those that are the least abnormal.

[0093] Calculate the path length of each target mass spectrometry data from the root node of the isolation tree to each leaf node, and calculate the average path length of all target historical mass spectrometry data in the isolation forest; calculate the anomaly detection score of the target mass spectrometry data based on the path length and the average path length; the lower the score, the easier it is for the data point to be isolated and the greater the possibility of anomaly. Based on the anomaly detection score of the target mass spectrometry data, the second quality assessment result of the target mass spectrometry data is obtained, such as Figure 4 As shown, the target mass spectrometry data whose obtained anomaly detection score is lower than the anomaly detection threshold is marked as anomaly data to be excluded.

[0094] In the embodiment of the present application, abnormal data screening is performed based on the isolation forest model, which is based on all secondary evaluation index parameters after standardization of the target historical mass spectrometry data center to obtain a threshold for screening outliers. After screening out the target abnormal data, it is annotated to achieve accurate control of the quality of proteomics data and ensure the credibility of subsequent biomarker analysis.

[0095] In the embodiment of the present application, the following method can also be used to screen abnormal data based on the isolation forest model:

[0096] First, by analyzing the correlation between each secondary evaluation index parameter and the mass spectrometry data identification and quantitative results, the weight coefficient of the feature is determined; then, the central standardized data of the target mass spectrometry data for the secondary evaluation index parameter is multiplied by the weight coefficient of the secondary evaluation index parameter, which is the feature data of the secondary evaluation index parameter input into the isolation forest model. The training of the weight coefficient helps to assign higher weight coefficients to quality control features with higher correlation with the results, and lower weight coefficients to quality control features with lower correlation with the results, which helps to improve the accuracy of model-based screening of abnormal data.

[0097] In one embodiment of the present application, analyzing the correlation between the secondary evaluation index parameter and the mass spectrometry data identification and quantitative results, and determining the weight coefficient of the secondary evaluation index parameter includes:

[0098] Obtain a pre-prepared standard mass spectrometry sample and standard mass spectrometry data of the standard mass spectrometry sample; wherein the standard mass spectrometry data is obtained by performing mass spectrometry analysis on a mass spectrometer in a DIA mode and under empirical mass spectrometry parameters; the empirical mass spectrometry parameters are pre-set; the empirical mass spectrometry parameters are the best mass spectrometry parameters for the mass spectrometer determined based on experience; for any secondary evaluation index parameter, obtain the mass spectrometry data type corresponding to the secondary evaluation index parameter, the mass spectrometry parameters of the mass spectrometer that affect the generation of mass spectrometry data of this type, and the corresponding chromatographic parameters; calculate the difference between the experimental mass spectrometry data and the standard mass spectrometry data, and determine the mass spectrometry or chromatographic parameters obtained under different set values ​​according to the calculated difference. The degree of deviation between the experimental mass spectrometry data and the standard mass spectrometry data; based on the obtained degree of deviation, determine the error between the experimental mass spectrometry data and the standard mass spectrometry data caused by the parameter when the mass spectrometry or chromatographic parameter moves away from the corresponding empirical mass spectrometry parameter in different directions; according to the error between the experimental mass spectrometry data and the standard mass spectrometry data caused by the mass spectrometry or chromatographic parameter, calculate the correlation between the secondary evaluation index parameter corresponding to the parameter and the mass spectrometry data identification and quantitative results; the default weight coefficient of each secondary evaluation index parameter is 1, and according to the distribution of the degree of correlation between each secondary evaluation index parameter and the mass spectrometry data identification and quantitative results, standardize the deviation to obtain a weight coefficient distribution between 0 and 1. Based on the degree of correlation between each secondary evaluation index parameter and the mass spectrometry data identification and quantitative results, determine the weight coefficient of the secondary evaluation index parameter.

[0099] In one embodiment of the present application, determining the weight coefficient of the secondary evaluation index parameter based on the correlation between the secondary evaluation index parameter and the mass spectrometry data identification and quantitative results may also include:

[0100] Determine the generation process of the mass spectrometry data where the secondary evaluation index parameter is located, and analyze the importance of the generation process to the quality control results; determine the weight coefficient of the quality control feature based on the degree of correlation between the secondary evaluation index parameter and the mass spectrometry data identification and quantitative results and the importance of the generation process where the secondary evaluation index parameter is located to the quality control results.

[0101] Step S120: extracting abnormal indicators to be evaluated from the quality evaluation results to obtain abnormal indicators to be evaluated; and matching target related attributes corresponding to the abnormal indicators to be evaluated from a comparison table of different indicators to be evaluated and different related attributes configured.

[0102] In the embodiment of the present application, the correlation relationship between the relevant attributes corresponding to the indicator to be evaluated, that is, the target secondary evaluation indicator parameters corresponding to the indicator to be evaluated and the mass spectrometry detection results, is a positive correlation, a negative correlation, and a limited correlation; the limited correlation means that when it is within a certain range, the error with the standard mass spectrometry data is infinitely close, but when it is less than the minimum value within the range or greater than the maximum value within the range, the error will become larger.

[0103] In the embodiment of the present application, the indicators to be evaluated that are abnormal are extracted from the quality evaluation results, the indicators to be evaluated that are evaluated as abnormal are obtained, and the abnormal evaluation indicators are obtained.

[0104] Step S130: Match the target control strategy corresponding to the target related attribute from the comparison table of different related attributes and different control strategies configured; and generate the quality control parameters of the target mass spectrometry detection device based on the target control strategy and the quality assessment result.

[0105] In an embodiment of the present application, a control strategy is set in advance based on the correlation in the correlation analysis results, and a correlation analysis result and a control strategy table is generated; if the correlation is positive, that is, the higher the value of the abnormal indicator to be evaluated, the higher the quality of the mass spectrometry detection result, and the control strategy is to increase the value of the abnormal indicator to be evaluated; if the correlation is negative, that is, the higher the value of the abnormal indicator to be evaluated, the lower the quality of the mass spectrometry detection result, and the control strategy is to reduce the value of the abnormal indicator to be evaluated; if the correlation is a restricted correlation, the control strategy is to limit the value of the abnormal indicator to be evaluated within a corresponding restricted range; the restricted range is generally obtained by adding or subtracting 2 times the first standard deviation from the first median.

[0106] In an embodiment of the present application, the control strategy also includes: standard control parameters for each indicator to be evaluated; the standard control parameters are obtained based on the analysis of the target historical mass spectrometry data, and the standard control parameters are used to indicate the standard value or standard range of the indicator to be evaluated.

[0107] In the embodiment of the present application, the control strategy of each indicator to be evaluated may include:

[0108] Enzyme hydrolysis missed cleavage rate: The lower the missed cleavage rate, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the sample processing steps should be optimized to make the indicator to be evaluated greater than 0 and less than the median + 2 times the standard deviation. Peptide length: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. Too high or too low will affect the identification results. When the indicator to be evaluated is "unqualified (or abnormal)", the sample processing steps should be optimized to make the indicator to be evaluated greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation. The proportion of contaminating proteins: the lower the proportion of contaminating proteins, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the sample processing steps should be optimized so that the indicator to be evaluated is greater than 0 and less than the median + 2 times the standard deviation. Chromatographic retention time: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the chromatographic separation steps should be optimized so that the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation.

[0109] Chromatographic half-peak width: The lower the chromatographic half-peak width, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the chromatographic separation step should be optimized to make the range of the indicator to be evaluated greater than 0 and less than the median + 2 times the standard deviation; Chromatographic peak width: The lower the chromatographic peak width, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the chromatographic separation step should be optimized to make the range of the indicator to be evaluated greater than 0 and less than the median + 2 times the standard deviation; Chromatographic tailing ratio: The lower the chromatographic tailing ratio, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the chromatographic separation step should be optimized to make the range of the indicator to be evaluated greater than 0 and less than the median + 2 times the standard deviation; The number of identified polypeptide parent ions per minute: The higher the number of identified polypeptide parent ions per minute, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the chromatographic separation step should be optimized to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation;

[0110] Identification rate per minute: the higher the identification rate per minute, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the chromatographic separation step should be optimized to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation; the ratio of 1-charge ions to 2-charge ions in the collected primary spectrum: according to experimental statistics, the value range of the indicator to be evaluated should be within the range of median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the ion source part of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation and less than the median + 2 times the standard deviation; the ratio of 3-charge ions to 2-charge ions in the collected primary spectrum: according to experimental statistics, the value range of the indicator to be evaluated should be within the range of median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the ion source part of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation and less than the median + 2 times the standard deviation;

[0111] The ratio of quadruple-charged ions to dual-charged ions in the collected primary spectrum: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the ion source part of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation; The median of the mass-to-charge ratio distribution of the identified polypeptide precursor ion: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the ion source part of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation; The number of secondary spectra collected in each cycle: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When When the indicator to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation; Median of the DIA acquisition window size: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation; The average number of peptide precursor ions identified in each secondary spectrum: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation;

[0112] Average number of repeated identifications of peptide precursor ions: The lower the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than 1 and less than the median + 2 times the standard deviation; Ratio of the number of identified peptide precursor ions to the number of identified secondary spectra: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; Number of primary spectrum points collected for each chromatographic peak: According to literature reports and experimental statistics, the recommended range of the indicator to be evaluated is 4-7. When the indicator to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrometry acquisition should be adjusted to make the indicator to be evaluated in the range of 4-7; Number of secondary spectrum points collected for each chromatographic peak: According to literature reports and experimental statistics, the recommended range of the indicator to be evaluated is 4-7. According to the calculation, the recommended range of the index to be evaluated is 2-7. When the index to be evaluated is "unqualified (or abnormal)", the DIA window setting part of the mass spectrum acquisition should be adjusted to make the index to be evaluated in the range of 2-7; the proportion of the primary spectrum acquisition time: according to experimental statistics, the value range of the index to be evaluated should be within the median ± 2 times the standard deviation. When the index to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrum signal parts of the mass spectrum acquisition should be adjusted to make the index to be evaluated greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation; primary spectrum ion injection time: according to experimental statistics, the value range of the index to be evaluated should be within the median ± 2 times the standard deviation. When the index to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrum signal parts of the mass spectrum acquisition should be adjusted to make the index to be evaluated greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation;

[0113] Secondary spectrum ion injection time: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted so that the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation. Primary spectrum mass spectrometry cycle time: According to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted so that the indicator to be evaluated is greater than the median - 2 times the standard deviation. The difference is less than the median + 2 times the standard deviation; the secondary spectrum mass spectrum cycle time: according to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrum signal parts of the mass spectrum acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation and less than the median + 2 times the standard deviation; the primary spectrum peak intensity: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrum signal parts of the mass spectrum acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median - 2 times the standard deviation;

[0114] Number of primary spectrum peaks: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation; Secondary spectrum peak intensity: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation; Number of secondary spectrum peaks: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation; Identified polypeptide isotope peak characteristic ratio: The higher the value of the indicator to be evaluated, the better. The better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation; Signal-to-noise ratio of polypeptide parent ion: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median-2 times the standard deviation; Primary parent ion mass deviation: the lower the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the absolute value of the indicator to be evaluated greater than or equal to 0 and less than the median + 2 times the standard deviation. According to literature reports and experimental statistics, the absolute value range of the indicator to be evaluated is recommended to be 0-5ppm;

[0115] Secondary product ion mass deviation: the lower the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the absolute value of the indicator to be evaluated greater than or equal to 0 and less than the median + 2 times the standard deviation. According to literature reports and experimental statistics, the absolute value range of the indicator to be evaluated is recommended to be 0-5ppm; polypeptide parent ion intensity: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; polypeptide intensity: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; protein intensity: the value of the indicator to be evaluated The higher the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median-2 times the standard deviation; the number of primary spectra collected: according to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median-2 times the standard deviation and less than the median + 2 times the standard deviation; the number of secondary spectra collected: according to experimental statistics, the value range of the indicator to be evaluated should be within the median ± 2 times the standard deviation. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrometry signal parts of the mass spectrometry acquisition should be adjusted so that the range of the indicator to be evaluated is greater than the median-2 times the standard deviation and less than the median + 2 times the standard deviation;

[0116] Secondary spectrum identification rate: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrum signal parts of the mass spectrum acquisition should be adjusted to make the range of the indicator to be evaluated larger than the median-2 times the standard deviation; Secondary spectrum identification rate collected by each DIA window: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the primary and secondary mass spectrum signal parts of the mass spectrum acquisition should be adjusted to make the range of the indicator to be evaluated larger than the median-2 times the standard deviation; Number of identified secondary spectra: The higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrum acquisition , it should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; the number of identified peptide precursor ions: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; the number of identified peptides: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; the number of identified peptides: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features Adjustment: make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; number of identified protein groups: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated greater than the median - 2 times the standard deviation; number of identified proteins: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated greater than the median - 2 times Standard deviation; Average number of peptides identified in each proteome: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated larger than the median - 2 times the standard deviation; Average number of peptide parent ions identified in each proteome: the higher the value of the indicator to be evaluated, the better. When the indicator to be evaluated is "unqualified (or abnormal)", the indicator to be evaluated is related to sample processing, chromatographic separation and mass spectrometry acquisition. It should be adjusted in combination with other relevant "unqualified (or abnormal)" features to make the range of the indicator to be evaluated larger than the median - 2 times the standard deviation.

[0117] In another embodiment of the present application, the abnormal cause may be determined based on the quality evaluation result of each abnormal evaluation indicator, including:

[0118] The reasons for abnormalities in S1 / S2 / S3 are: low sample enzymatic hydrolysis efficiency and sample contamination; the reasons for abnormalities in C1 / C2 / C3 / C4 / C5 / C6 are: abnormal column connection, unreasonable chromatographic gradient setting, blockage or leakage of chromatographic separation system, and poor chromatographic column performance; the reasons for abnormalities in IS1 / IS2 / IS3 / IS4 are: ion source contamination and abnormal spray voltage; the reasons for abnormalities in W1 / W2 / W3 / W4 / W5 / W6 / W7 are: unreasonable setting of the number of DIA windows; the reasons for abnormalities in M1 / M2 / M3 / M4 / M5 are: improper ion injection time setting Reasonable, the mass spectrometry cycle time setting is unreasonable; the reasons for the abnormalities of M6 / M7 / M8 / M9 / M10 / M11 / M15 / M16 / M17 / M18 / M19 / M20 are: the sample amount is too low, the mass spectrometry system is contaminated and needs to be cleaned, the ion injection time setting is unreasonable, the ion intensity threshold setting is unreasonable, and the number of DIA windows is unreasonable; the reason for the abnormality of M12 / M13 is: the mass spectrometry system needs mass calibration; the reason for the abnormality of ID1 / ID2 / ID3 / ID4 / ID5 / ID6 / ID7 is: the overall identification results are small, which is related to each of the above situations.

[0119] In an embodiment of the present application, the control parameter corresponding to each abnormal indicator to be evaluated can be located through the control strategy. The control parameter is one or several specific control parameters on the mass spectrometry detection device, and the control strategy also includes how to adjust the control parameter (i.e., increase or decrease or other) according to the value of the abnormal indicator to be evaluated. Therefore, when an abnormal indicator to be evaluated appears, the mass spectrometry detection equipment can be adjusted quickly, efficiently and accurately, and the mass spectrometry data can be quickly and efficiently optimized and controlled to obtain high-quality mass spectrometry data.

[0120] In the embodiments of the present application, any primary evaluation index parameter corresponds to a mass spectrometry detection process, and the mass spectrometry detection process corresponding to it is used as the mass spectrometry detection process corresponding to the secondary evaluation index parameter it contains; the mass spectrometry detection process corresponding to each secondary evaluation index parameter is the mass spectrometry detection process of the corresponding indicator to be evaluated. For example, the primary evaluation index parameter A corresponds to the chromatographic separation process of mass spectrometry detection; A contains the secondary evaluation index parameter A1, A1 is the number of proteins; then the mass spectrometry detection process of the indicator to be evaluated - the number of proteins corresponding to A1 is the chromatographic separation process.

[0121] In the embodiment of the present application, based on the control strategy and the quality assessment result, the mass spectrometry detection control parameters of the mass spectrometry detection device corresponding to the target mass spectrometry data are generated, including:

[0122] For any secondary evaluation index parameter, the mass spectrometry detection process corresponding to the primary evaluation index parameter to which the secondary evaluation index parameter belongs is used as the mass spectrometry detection process where the secondary evaluation index parameter is located; the mass spectrometry detection process where the secondary evaluation index parameter corresponding to the abnormal indicator to be evaluated is located is used as the target mass spectrometry detection process for the abnormal indicator to be evaluated; based on the control strategy and quality assessment results, the optimized control parameters are generated; the optimized control parameters are used as the quality control parameters of the mass spectrometry detection equipment corresponding to the target mass spectrometry data in the target mass spectrometry detection process.

[0123] In the embodiment of the present application, based on the control strategy and the quality assessment results, the optimized control parameters are generated, including:

[0124] Whether to increase or decrease in the control strategy is used as the adjustment direction of the abnormal indicator to be evaluated; the value of the abnormal indicator to be evaluated in the quality assessment result is subtracted from the value of the standard control parameter in the control strategy to determine the adjustment amount; based on the adjustment direction and the adjustment amount, the optimized control parameters are obtained.

[0125] Corresponding to the above method, the present application embodiment also provides a quality control device for mass spectrometry detection, such as Figure 5 As shown, the quality control device for mass spectrometry detection includes:

[0126] An acquisition unit 510 is used to acquire target mass spectrum data obtained by a target mass spectrum detection device for detecting a mass spectrum sample; wherein the target mass spectrum data includes at least one indicator to be evaluated;

[0127] An evaluation unit 520, configured to perform quality evaluation on the target mass spectrum data to obtain a quality evaluation result of the target mass spectrum data;

[0128] The analysis unit 530 is used to extract the abnormal indicators to be evaluated from the quality evaluation results to obtain the abnormal indicators to be evaluated; match the target related attributes corresponding to the abnormal indicators to be evaluated from the configured comparison table of different indicators to be evaluated and different related attributes; match the target control strategy corresponding to the target related attribute from the configured comparison table of different related attributes and different control strategies;

[0129] The control unit 540 is used to generate quality control parameters of the target mass spectrometry detection device based on the target control strategy and the quality assessment result, so as to control the target mass spectrometry detection device to perform mass spectrometry detection.

[0130] The functions of each functional unit of the quality control device for mass spectrometry detection provided in the above embodiments of the present application can be achieved through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the quality control device for mass spectrometry detection provided in the embodiments of the present application will not be repeated here.

[0131] The present application also provides an electronic device, such as Figure 6 As shown, it includes a processor 610 , a communication interface 620 , a memory 630 and a communication bus 640 , wherein the processor 610 , the communication interface 620 , and the memory 630 communicate with each other via the communication bus 640 .

[0132] Memory 630, for storing computer programs;

[0133] The processor 610 is used to implement the following steps when executing the program stored in the memory 630:

[0134] Obtaining target mass spectrum data obtained by a target mass spectrum detection device for detecting a mass spectrum sample; wherein the target mass spectrum data includes at least one indicator to be evaluated;

[0135] Performing quality assessment on the target mass spectrometry data to obtain a quality assessment result of the target mass spectrometry data;

[0136] From the quality assessment results, extract the indicators to be evaluated that have abnormalities to obtain the abnormal indicators to be evaluated; from the comparison table between different indicators to be evaluated and different related attributes configured, match the target related attributes corresponding to the abnormal indicators to be evaluated; from the comparison table between different related attributes configured and different control strategies, match the target control strategy corresponding to the target related attribute;

[0137] Based on the target control strategy and the quality assessment result, quality control parameters of the target mass spectrometry detection device are generated to control the target mass spectrometry detection device to perform mass spectrometry detection.

[0138] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0139] The communication interface is used for communication between the above electronic device and other devices.

[0140] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0141] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0142] The implementation methods and beneficial effects of the components of the electronic device in the above embodiments to solve the problems can be seen in Figure 1 The various steps in the illustrated embodiment are implemented, therefore, the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application are not repeated here.

[0143] In another embodiment provided in the present application, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is executed on a computer, the computer executes the quality control method of mass spectrometry detection in any of the above embodiments.

[0144] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute the quality control method for mass spectrometry detection in any one of the above embodiments.

[0145] Those skilled in the art will appreciate that the embodiments in the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt a complete hardware embodiment, a complete software embodiment, or a form of an embodiment combining software and hardware. Moreover, the present application may adopt a form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0150] Obviously, those skilled in the art can make various changes and modifications to the embodiments in the present application without departing from the spirit and scope of the embodiments in the present application. Thus, if these modifications and variations of the embodiments in the present application fall within the scope of the claims and their equivalents in the embodiments of the present application, the embodiments of the present application are also intended to include these modifications and variations.

Claims

1. A quality control method for mass spectrometry detection, characterized in that: The method comprises: Acquire target mass spectrum data obtained by a target mass spectrum detection device for detecting a mass spectrum sample; wherein the target mass spectrum data includes at least one indicator to be evaluated; Performing quality assessment on the target mass spectrum data to obtain a quality assessment result of the target mass spectrum data; Extracting abnormal indicators to be evaluated from the quality evaluation results to obtain abnormal indicators to be evaluated; Match the target related attribute corresponding to the abnormal indicator to be evaluated from the configured comparison table of different indicators to be evaluated and different related attributes; Matching the target control strategy corresponding to the target related attribute from the comparison table of different related attributes and different control strategies configured; Based on the target control strategy and the quality assessment result, quality control parameters of the target mass spectrometry detection device are generated to control the target mass spectrometry detection device to perform mass spectrometry detection.

2. The method according to claim 1, characterized in that Before performing quality assessment on the target mass spectrum data, the method further comprises: A pre-constructed evaluation matrix is ​​obtained; wherein the evaluation matrix includes a plurality of first-level evaluation indicator parameters; each first-level evaluation indicator parameter includes a plurality of second-level evaluation indicator parameters; and any indicator to be evaluated corresponds to a second-level evaluation indicator parameter.

3. The method according to claim 2, characterized in that Performing quality assessment on the target mass spectrum data to obtain a quality assessment result of the target mass spectrum data includes: For any first-level evaluation index parameter, all second-level evaluation index parameters contained in the first-level evaluation index parameter are used as target second-level evaluation index parameters; For any target secondary evaluation indicator parameter, obtain the configured scoring rule of the target secondary evaluation indicator parameter; Based on the scoring rule, determining a first scoring result of the target mass spectrometry data under the target secondary evaluation index parameter; The first scoring results of the target mass spectrum data under different target secondary evaluation index parameters are added and averaged to obtain the second scoring result of the target mass spectrum data under the primary evaluation index parameters; Based on the second scoring results under different primary evaluation index parameters and the first scoring results under the corresponding target secondary evaluation index parameters, the quality evaluation result of the target mass spectrometry data is obtained.

4. The method according to claim 3, characterized in that The scoring rules include stability scoring rules; The configuration method of the scoring rule of the target secondary evaluation indicator parameter includes: Obtaining a preset stability threshold judgment rule for the target secondary evaluation index parameter; Extracting the indicator to be evaluated corresponding to the target secondary evaluation indicator parameter from multiple target historical mass spectrum data to obtain a first target indicator to be evaluated; wherein the target historical mass spectrum data is the historical mass spectrum data of the indicator to be evaluated without abnormality in the quality evaluation result; Arrange the first target indicators to be evaluated in descending order of value to obtain the arranged first target indicators to be evaluated; Extracting the median of the first target indicator to be evaluated after the arrangement as a first median; calculating the standard deviation of the first target indicator to be evaluated after the arrangement to obtain a first standard deviation; Calculating a stability threshold of the target secondary evaluation indicator parameter based on the first median and the first standard deviation; Based on the stability threshold and the stability threshold judgment rule, a stability scoring rule for the target secondary evaluation index parameter is obtained.

5. The method according to claim 4, characterized in that Based on the scoring rule, determining a first scoring result of the target mass spectrum data under the target secondary evaluation index parameter includes: Using the to-be-evaluated index in the target mass spectrum data corresponding to the target secondary evaluation index parameter as the second target to-be-evaluated index; Comparing the value of the second target indicator to be evaluated with the stability threshold to obtain a first comparison result; Based on the stability threshold judgment rule and the first comparison result, obtaining a stability scoring result of the target mass spectrometry data under the target secondary evaluation index parameter; The stability scoring result is used as the first scoring result of the target mass spectrometry data under the target secondary evaluation index parameter.

6. The method according to claim 2, characterized in that The quality assessment results include: whether each indicator to be assessed is abnormal or non-abnormal; After obtaining the quality assessment result of the target mass spectrum data, the method further includes: Based on the evaluation matrix and the quality evaluation results, a two-dimensional heat map matrix of the quality evaluation results is generated; wherein the abnormal indicators to be evaluated and the non-abnormal indicators to be evaluated are distinguished and displayed in the two-dimensional heat map matrix.

7. The method according to claim 1, characterized in that Any first-level evaluation index parameter corresponds to a mass spectrometry detection process; Generating quality control parameters of the target mass spectrometry detection device based on the target control strategy and the quality assessment result includes: For any secondary evaluation index parameter, the mass spectrometry detection process corresponding to the primary evaluation index parameter to which the secondary evaluation index parameter belongs is used as the mass spectrometry detection process to which the secondary evaluation index parameter belongs; The mass spectrometry detection process where the secondary evaluation index parameter corresponding to the abnormal indicator to be evaluated is located is used as the target mass spectrometry detection process of the abnormal indicator to be evaluated; generating optimized control parameters according to the control strategy and the quality assessment result; The optimized control parameters are used as quality control parameters of the target mass spectrometry detection device in the target mass spectrometry detection process.

8. A quality control device for mass spectrometry detection, characterized in that: The device comprises: An acquisition unit, used for acquiring target mass spectrum data obtained by a target mass spectrum detection device for detecting a mass spectrum sample; wherein the target mass spectrum data includes at least one indicator to be evaluated; An evaluation unit, used to perform quality evaluation on the target mass spectrum data to obtain a quality evaluation result of the target mass spectrum data; The analysis unit is used to extract the abnormal indicators to be evaluated from the quality evaluation results to obtain the abnormal indicators to be evaluated; match the target related attributes corresponding to the abnormal indicators to be evaluated from the configured comparison table of different indicators to be evaluated and different related attributes; match the target control strategy corresponding to the target related attribute from the configured comparison table of different related attributes and different control strategies; A control unit is used to generate quality control parameters of the target mass spectrometry detection device based on the target control strategy and the quality assessment result, so as to control the target mass spectrometry detection device to perform mass spectrometry detection.

9. An electronic device, characterized in that: The electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing any of the methods described in claims 1-7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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