Method and device for processing mass spectrometry flow data

Abstract

The present application provides a method and device for processing mass spectrometry data, which relates to the field of biomedical technology. It includes comparing the sample information summary table with the sample information statistics table of the current sub-project, determining the newly added samples of the current sub-project, and updating the sample information statistics table of the current sub-project based on the newly added samples of the current sub-project; based on the sample analysis record table and the sample information statistics table of the current sub-project, analyzing the unanalyzed samples in the new sample statistics table of the current sub-project, updating the sample analysis record table of the current sub-project based on the analysis progress, and recording the analysis results in the project management binary file of the current sub-project. In this way, the analysis process of mass spectrometry data can be regularized, thereby realizing fully automated analysis of mass spectrometry data, which can greatly improve the efficiency, accuracy and timeliness of sample analysis.

Description

Technical Field

[0001] The present application relates to the field of biomedical technology, and in particular to a method and device for processing mass spectrometry flow data. Background Art

[0002] With the rapid development of experimental technology in the field of life sciences, the automation and intelligence level of scientific instruments has been increasing, and the data output capacity has made a qualitative leap. At the same time, the requirements of life science research for analysis and testing have put forward higher standards and requirements in terms of sample number, analysis cycle, analysis items and data accuracy, and the information output of biological laboratories has increased exponentially.

[0003] In the traditional mass spectrometry data analysis process, there are many analysis steps, and each analysis step will introduce too many subjective human judgment factors, which seriously affect the data analysis results. In addition, when there is too much data, manual analysis is slow and data is prone to errors. Therefore, information loss or inefficiency will occur in the transmission or acceptance of each link. Especially in commercial mass spectrometry sample detection projects, sample analysis efficiency is a key consideration, and the sample analysis results will also affect the subsequent clinical detection judgment of the sample individual.

[0004] High-dimensional immune technology includes flow cytometry and mass spectrometry flow cytometry, especially mass spectrometry flow cytometry, which is a flow technology that uses the principle of mass spectrometry to perform multi-parameter detection on single cells. It inherits the high-speed analysis characteristics of traditional flow cytometers and has the high resolution capability of mass spectrometry detection. It is a new development direction of flow cytometry. Mass spectrometry flow cytometry can achieve accurate immunophenotyping of cell populations, comprehensive analysis of intracellular signal transduction networks, analysis of functional connections between cell subpopulations, and high-throughput multi-parameter detection of a large number of samples. It has broad application prospects in research in many fields such as hematopoiesis, immunity, stem cells, cancer, and drug screening. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method and device for processing mass spectrometry flow data to solve the technical problem of complex analysis process in the prior art.

[0006] In a first aspect, the present invention provides a method for processing mass spectrometry flow data, comprising:

[0007] Determine newly added samples of the current sub-project based on the comparison between the sample information summary table and the sample information statistics table of the current sub-project, and update the sample information statistics table of the current sub-project based on the newly added samples of the current sub-project, wherein the sample information summary table records data of the current project, the current project includes at least one sub-project, and the at least one sub-project includes the current sub-project;

[0008] Based on the sample analysis record table and sample information statistics table of the current sub-project, analyze the unanalyzed samples in the sample new statistics table of the current sub-project, update the sample analysis record table of the current sub-project based on the analysis progress, and record the analysis results in the project management binary file of the current sub-project.

[0009] In an optional implementation, a sub-project folder is pre-established in the server, and the sub-project folder includes a sample information statistics table of the sub-project, a sample analysis record table of the sub-project, and a project management binary file of the sub-project.

[0010] In an optional implementation, the sample analysis record table includes an original data analysis table, a sample data analysis table, an abnormal original data analysis table, and an abnormal sample data analysis table;

[0011] Among them, the experimental samples measured by the Cytof instrument are raw data; the sample data are obtained by processing the raw data.

[0012] In an optional implementation, each raw data is corrected to generate an intermediate file through a normalization step, and each intermediate file is debarked through a debarcode step to separate each sample data.

[0013] In an optional implementation, the raw data analysis table records each raw data analysis information including the raw data number, raw data file name, project ID, Panel, IsNormed, IsDebarcode, and sample name; the sample data analysis table records each sample data analysis information including sample ID, IsGated, gated file name, Panel, IsCalc, IsReport, and raw data number; the abnormal raw data analysis table records the raw offline data that failed analysis, including the raw data number and the reason for analysis failure; the abnormal sample data analysis table records the sample data that failed analysis, including the sample ID and the reason for analysis failure.

[0014] In an optional embodiment, each sample information in the sample information statistics table includes project number, formal experimental sample number, sample ID, disease label, pathology, pathological stage, pathological remarks, data loading location, gender, age, sample name, medical record number or bed number or other, loaded data name, PANEL, barcode, sample type, sample arrival date, project name, customer name + location, project manager, blood volume, average viable cell count, activity, remarks, disease history, family medical history, sample collection date, inspection agency, sample collection volume, test type, and report version.

[0015] In an optional implementation, the project management binary file also includes the analysis step status of the original data and the result corresponding to each step; and the analysis step status of the sample data and the result corresponding to each step.

[0016] In a second aspect, the present invention provides a mass spectrometry data processing device, comprising:

[0017] a comparison module, configured to compare the sample information summary table with the sample information statistics table of the current sub-project, determine the newly added samples of the current sub-project, and update the sample information statistics table of the current sub-project based on the newly added samples of the current sub-project, wherein the sample information summary table records the data of the current project, the current project includes at least one sub-project, and the at least one sub-project includes the current sub-project;

[0018] The analysis module is used to analyze the unanalyzed samples in the new sample statistics table of the current sub-project based on the sample analysis record table and sample information statistics table of the current sub-project, update the sample analysis record table of the current sub-project based on the analysis progress, and record the analysis results in the project management binary file of the current sub-project.

[0019] In an optional implementation, a sub-project folder is pre-established in the server, and the sub-project folder includes a sample information statistics table of the sub-project, a sample analysis record table of the sub-project, and a project management binary file of the sub-project.

[0020] In an optional implementation, the sample analysis record table includes an original data analysis table, a sample data analysis table, an abnormal original data analysis table, and an abnormal sample data analysis table;

[0021] Among them, the experimental samples measured by the Cytof instrument are raw data; the sample data are obtained by processing the raw data.

[0022] In an optional implementation, each original data is corrected to generate an intermediate file through a normalization step, and each intermediate file is debarked through a debarcode step to separate each sample data.

[0023] In an optional implementation, the raw data analysis table records each raw data analysis information including the raw data number, raw data file name, project ID, Panel, IsNormed, IsDebarcode, and sample name; the sample data analysis table records each sample data analysis information including sample ID, IsGated, gated file name, Panel, IsCalc, IsReport, and raw data number; the abnormal raw data analysis table records the raw offline data that failed analysis, including the raw data number and the reason for analysis failure; the abnormal sample data analysis table records the sample data that failed analysis, including the sample ID and the reason for analysis failure.

[0024] In an optional embodiment, each sample information in the sample information statistics table includes project number, formal experimental sample number, sample ID, disease label, pathology, pathological stage, pathological remarks, data loading location, gender, age, sample name, medical record number or bed number or other, loaded data name, PANEL, barcode, sample type, sample arrival date, project name, customer name + location, project manager, blood volume, average viable cell count, activity, remarks, disease history, family medical history, sample collection date, inspection agency, sample collection volume, test type, and report version.

[0025] In an optional implementation, the project management binary file also includes the analysis step status of the original data and the result corresponding to each step; and the analysis step status of the sample data and the result corresponding to each step.

[0026] In a third aspect, the present invention provides an electronic device, comprising: a processing device and a storage device;

[0027] The storage device stores a computer program, and when the computer program is executed by the processor, the method of any one of the aforementioned embodiments is executed for camera calibration and / or mass spectrometry flow data processing.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method of any one of the aforementioned implementation modes are executed.

[0029] The embodiment of the present invention provides a method and device for processing mass spectrometry data. By comparing the sample information summary table with the sample information statistics table of the current sub-project, the newly added samples of the current sub-project are determined, and the sample information statistics table of the current sub-project is updated based on the newly added samples of the current sub-project. The sample information summary table records the data of the current project, and the current project includes at least one sub-project, and at least one sub-project includes the current sub-project; based on the sample analysis record table and the sample information statistics table of the current sub-project, the unanalyzed samples in the new sample statistics table of the current sub-project are analyzed, and the sample analysis record table of the current sub-project is updated based on the analysis progress, and the analysis results are recorded in the project management binary file of the current sub-project. In this way, the analysis process of mass spectrometry data can be regularized, thereby realizing fully automated analysis of mass spectrometry data, which can greatly improve the efficiency, accuracy and timeliness of sample analysis. 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 An exemplary electronic device for implementing the method for processing mass spectrometry streaming data according to an embodiment of the present invention;

[0032] Figure 2 is a flow chart of a method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0033] Figure 3 is a flowchart of another method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0034] Figure 4 is a flowchart of another method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0035] Figure 5 is a flowchart of another method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0036] Figure 6 is a flowchart of another method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0037] Figure 7 is an example of another method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0038] Figure 8It is a logical relationship of data in a method for processing mass spectrometry streaming data according to an embodiment of the present invention;

[0039] Fig. 9 is a logical relationship of a table in a method for processing mass spectrometry flow data according to an embodiment of the present invention;

[0040] Fig.10 It is a schematic diagram of a mass spectrometry streaming data processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0044] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0045] First, refer to Figure 1 An exemplary electronic device 100 for implementing the method for processing mass spectrometry streaming data according to an embodiment of the present invention is described.

[0046] like Figure 1 As shown, the electronic device 100 includes one or more processing devices 102, one or more storage devices 104, an input device 106, an output device 108, and an image acquisition device 110, and these components are interconnected through a bus system 112 and / or other forms of connection mechanisms (not shown). It should be noted that Figure 1 The components and structures of the electronic device 100 shown are merely exemplary and non-limiting. The electronic device may also have other components and structures as required.

[0047] The processing device 102 may be a central processing unit (CPU), a graphics processing unit (GPU), or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.

[0048] The storage device 104 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on a computer-readable storage medium, and the processing device 102 may run the program instructions to implement the client functions (implemented by the processor) and / or other desired functions in the embodiments of the present invention described below. Various applications and various data, such as various data used and / or generated by the application, may also be stored in the computer-readable storage medium.

[0049] The input device 106 may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.

[0050] The output device 108 may output various information (eg, images or sounds) to the outside (eg, a user), and may include one or more of a display, a speaker, and the like.

[0051] For example, the exemplary electronic device for implementing the method for processing mass spectrometry streaming data according to the embodiment of the present invention may be implemented on a mobile terminal such as a smart phone, a tablet computer, or the like.

[0052] According to an embodiment of the present invention, an embodiment of a method for processing mass spectrometry flow data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0053] Figure 2 FIG. 4 is a flow chart of a method for processing mass spectrometry data according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0054] S210, based on the comparison between the sample information summary table and the sample information statistics table of the current sub-project, determine the newly added samples of the current sub-project, and update the sample information statistics table of the current sub-project based on the newly added samples of the current sub-project. The sample information summary table records the data of the current project, the current project includes at least one sub-project, and the at least one sub-project includes the current sub-project.

[0055] A subproject folder may be pre-established in the server for each subproject, and the subproject folder includes a sample information statistics table of the subproject, a sample analysis record table of the subproject, and a project management binary file of the subproject.

[0056] The sample analysis record table may include an original data analysis table, a sample data analysis table, an abnormal original data analysis table, and an abnormal sample data analysis table; wherein, the experimental sample measured by mass cytometry is the original data; the sample data is obtained by processing the original data. For example, the detection of mass cytometry can be achieved by a Cytof instrument.

[0057] Among them, each original data can be corrected to generate an intermediate file through the normalization step; and each intermediate file can be disassembled through the Debarcode step to separate each sample data. The normalization step can be called a normalization step, which includes a series of data normalization methods for the data, and the normalization method can include batch correction, normalization, etc. During the experiment, multiple samples are tested on the machine at the same time, and a file is generated including the data information of multiple samples. The process of splitting multiple sample data files from this sample is called Debarcode, that is, decoding.

[0058] In some embodiments, the raw data analysis table records each raw data analysis information including the raw data number, raw data file name, project ID, Panel, IsNormed, IsDebarcode, and sample name; the sample data analysis table records each sample data analysis information including sample ID, IsGated, gated file name, Panel, IsCalc, IsReport, and raw data number; the abnormal raw data analysis table records the raw offline data that failed to be analyzed, including the raw data number and the reason for the analysis failure; the abnormal sample data analysis table records the sample data that failed to be analyzed, including the sample ID and the reason for the analysis failure.

[0059] Panel is a set of antibody proteins labeled with heavy metal isotopes provided during the experiment. Different panels will be used according to customer requirements during the experiment.

[0060] IsNormed is a column name in the original data table. Its value is True or False. True means that the original data is correct when Normed, and False means that an error occurs when Normed.

[0061] IsDebarcode is a column name in the original data table. The value is True or False. True means that the original data is correctly debarcoded, and False means that an error occurs when the original data is debarcoded.

[0062] Gated is the process of preprocessing when analyzing sample files.

[0063] IsGated is a column name in the sample analysis information table. Its value is True or False. True means that the sample data is correct when gated, and False means that an error occurs when gated the sample data.

[0064] Calc, Report is the process of performing subgroup calculations on sample data.

[0065] IsCalc is a column name in the sample analysis information table. Its value is True or False. True means that the sample data is correct when calculating the subgroup proportions, and False means that an error occurs when calculating the subgroup proportions.

[0066] IsReport is a column name in the sample analysis information table, with a value of True or False; True means that the sample data is correct when calculating the subpopulation proportions for reporting, and False means that an error occurs when calculating the subpopulation proportions for reporting;

[0067] Barcode is a coding technology in biology.

[0068] In some embodiments, each sample information in the sample information statistics table includes project number, formal experimental sample number, sample ID, disease label, pathology, pathological stage, pathological remarks, data upload location, gender, age, sample name, medical record number or bed number or other, uploaded data name, PANEL, barcode, sample type, sample arrival date, project name, customer name + location, project manager, blood volume, average viable cell count, activity, remarks, disease history, family medical history, sample collection date, testing agency, sample collection volume, test type, and report version.

[0069] A sample information summary table can be established, which can include experimental statistical information of each sub-project, such as project name, sample name, etc. Take a sub-project as an example. The sub-project can include a sample information statistics table, which can include project number, formal experimental sample number, sample ID, sample disease label, pathology, pathological stage, pathological remarks, data upload location, gender, age, sample arrival date, project name, customer name + location, project manager, blood volume, average live cell count, activity, remarks, disease history, family history, sample collection date, inspection agency, sample collection volume, test type, and report version.

[0070] The sub-project may also include an original data analysis table for recording original data analysis information; the original data analysis table may include: original data number, original data file name, sample name, project ID, Panel table, IsNormed, IsDebarcode;

[0071] The sub-item may also include a sample data analysis table for recording sample analysis information; the sample data analysis table may include sample ID, IsGated, Gated file name, Panel table, IsCalc (calculate the proportion of each subgroup), IsReport (calculate the subgroup proportion for automatic reporting);

[0072] This sub-item may also include an abnormal raw data analysis table, which is used to record the raw offline data that failed to be analyzed: raw data number, reason for analysis failure;

[0073] This sub-project may also include an abnormal sample data analysis table, which is used to record the sample data that failed analysis: sample ID, reason for analysis failure;

[0074] The sub-project may also include a binary file of analysis results: the analysis step status of the original data and the result corresponding to each step, the analysis step status of the sample data and the result corresponding to each step.

[0075] Among them, the sample total table and the sample information statistics table in the sub-project are compared to obtain the new sub-project; the original data of the new sample is processed (Normed, Debarcode) to obtain the processed sample. This process updates the original data analysis table and the abnormal original data analysis table, and the analysis results are recorded in the binary file; the sample is processed (Gated, IsCalc, IsReport) to obtain the sample data. This process updates the sample data analysis table and the abnormal sample data analysis table, and the analysis results are recorded in the binary file.

[0076] S220, based on the sample analysis record table and sample information statistics table of the current sub-project, analyze the unanalyzed samples in the sample new statistics table of the current sub-project, update the sample analysis record table of the current sub-project based on the analysis progress, and record the analysis results in the project management binary file of the current sub-project.

[0077] The project management binary file also includes the analysis step status of the original data and the result corresponding to each step; and the analysis step status of the sample data and the result corresponding to each step.

[0078] In the specific analysis process, if the sample included in the subproject has never been analyzed, a standard process analysis is created for the sample. When performing standard analysis on each sample, the corresponding standard analysis steps are executed according to the analysis step requirements of the sample recorded in the sample history analysis record table of the current subproject; when the sample included in the subproject has been analyzed, and the sample status parameters in the sample history analysis record table are consistent with the status parameters recorded in the subproject itself, the analysis of the sample is skipped. When the analysis of all samples in the subproject is completed, a binary file of the Project object of the subproject is generated, which records the analysis results and process information of all samples that exist in the subproject up to now.

[0079] Through the embodiments of the present invention, the analysis process of mass spectrometry data can be regularized, thereby realizing fully automated analysis of mass spectrometry data, which can greatly improve the efficiency, accuracy and timeliness of sample analysis.

[0080] The following is a further description of the embodiments of the present invention in conjunction with some drawings. Figure 3-6 As shown:

[0081] See also Figure 3 , Figure 3 1 is a flow chart of a method for fully automatic analysis of mass spectrometry data provided by an embodiment of the present invention. The method for fully automatic analysis of mass spectrometry data comprises the following steps:

[0082] S1: Create a sub-project folder, a sub-project sample information statistics table file, a sub-project sample analysis record table file, a sub-project operation log file, and a sub-project Project binary file, that is, a project management binary file, on the storage server.

[0083] S2: When analyzing the current sub-project, first update the sample information statistics table of the sub-project. Read the sample information statistics table of the sub-project, then read the sample information summary table in the database (that is, the sample information summary table of the current project), clean the sample data summary table, select the required sample information, combine the sample information summary table of the current sub-project, find the newly added samples of the sub-project, and record the newly added samples and sample information in the sample information statistics table of the sub-project.

[0084] S3: Execute step S2 first. Load the Project binary file of the sub-project, read the sample analysis record table of the sub-project, and modify the data analysis attributes recorded in the Project object to the corresponding data analysis attributes recorded in the sub-project sample analysis record table.

[0085] S4: Execute step S3 first. Perform the corresponding analysis steps on the data recorded in the Project object. Generate the analysis results corresponding to each step.

[0086] S5: The analysis results are backed up and the original offline data and sample data analysis step status and results contained in the sub-project are saved as a Project binary file.

[0087] As an improvement of the above technical solution, the summary information of each sub-project includes sub-project number, sub-project name, sub-project status, and sub-project manager.

[0088] As an improvement of the above technical solution, each sample information in the sample information statistics table includes project number, formal experimental sample number, sample ID, disease label, pathology, pathological stage, pathological remarks, data upload location, gender, age, sample name, medical record number or bed number or other, upload data name, PANEL, barcode, sample type, sample arrival date, project name, customer name + location, project manager, blood volume (ml), average viable cell count (x10^6 cells), activity (= average number of viable cells / total number of cells), remarks, disease history, family medical history, sample collection date, inspection agency, sample collection volume, test type, and report version.

[0089] As an improvement of the above technical solution, the sample analysis record sheet includes 4 sheets, namely, original data analysis sheet, sample data analysis sheet, abnormal original data analysis sheet, and abnormal sample data analysis sheet; the original data analysis sheet records each original offline data analysis information including original data number, original data file name, project ID, Panel, IsNormed, IsDebarcode, and sample name; the sample data analysis sheet records each sample data analysis information including sample ID, IsGated, Gated file name, Panel, IsCalc, IsReport, and original data number; the abnormal original data analysis sheet records the original offline data that failed to be analyzed, including the original data number and the reason for the analysis failure; the abnormal sample data analysis sheet records the sample data that failed to be analyzed, including the sample ID and the reason for the analysis failure.

[0090] like Figure 4 As shown, Figure 4 The process of updating the sample information statistics table in a sub-project includes steps.

[0091] S401: Read all samples and all information of the samples recorded in the sub-project from the database;

[0092] S402: Read the sample statistics table recorded under the sub-project path;

[0093] S403: Determine whether the sample captured from the database already exists in the sub-project sample statistics table, and if not, add it to the sub-project sample statistics table;

[0094] S404: Generate a new sub-item statistics table.

[0095] like Figure 5 As shown, Figure 5 It is the process and steps to modify the data analysis properties recorded in the Project object in a subproject.

[0096] S501: Load the Project object of the sub-project, and load the sample analysis record table of the sub-project.

[0097] S502: Update the analysis status IsNormed, IsDebarcode of the offline data in the Project. Change the IsNormed, IsDebarcode attribute value of each offline data in the Project to the corresponding IsNormed, IsDebarcode attribute value of the offline data in the sample analysis record sheet original data analysis sheet.

[0098] S503: Update the analysis status IsGated, IsCalc, IsReport of the sample data in the Project. Change the IsGated, IsCalc, IsReport attribute values ​​of each sample data in the Project to the corresponding IsGated, IsCalc, IsReport attribute values ​​of the sample data in the sample analysis record sheet.

[0099] like Figure 6 As shown, Figure 6 It is the process and steps of analyzing the newly added data process in a sub-project.

[0100] S601: Extract the offline data corresponding to the newly added sample and save it in the Project object.

[0101] S602: The newly added offline data executes the Normalization molecular step. If the execution is successful, the IsNormed attribute value of the offline data is set to True, and a Normalized fcs file is generated; otherwise, the IsNormed attribute value of the offline data is set to FALSE, and the offline data is recorded in the abnormal original data table.

[0102] S603: Perform the Debarcode step on the fcs file of the original offline data nomrd after successful Normalization. If the execution is successful, set the IsDebarcode attribute value of the offline data to True, and generate a sample fcs file contained in the original offline data; otherwise, set the IsDebarcode attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table.

[0103] S604: Execute the AutoGate step on the sample data generated by debarocode. If the execution is successful, set the IsGated attribute value of the sample data to True, and generate a sample fcs file after the AutoGated sample data; otherwise, set the IsGated attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table.

[0104] S605: Execute the AutoCalc step on the sample data generated by AutoGate. If the execution is successful, set the IsCalc attribute value of the sample data to True, and generate the analysis result xlsx file of the sample data after AutoCalc; otherwise, set the IsCalc attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table.

[0105] S606: Execute the AutoReport step for the sample data analysis results generated by AutoCalc. If the execution is successful, set the IsReport attribute value of the sample data to True, and generate a sample report PDF file after the AutoReport of the sample data; otherwise, set the IsReport attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table.

[0106] The purpose of the present invention is to provide a fully automatic analysis method for mass spectrometry flow data, which can automatically process the original offline data to generate sample data, calculate the sample data to generate analysis results and reports, thereby reducing the subjective judgment factors introduced by the manual analysis steps and the problem of no unified analysis standards.

[0107] As an example, Figure 7 As shown, the above-mentioned mass spectrometry data processing method can be implemented by the following steps:

[0108] Analyze the current sub-project to determine whether the sub-project folder exists in the storage server;

[0109] If they do not exist, create the subproject folder, sample information table, sample analysis record table, operation log, and Project binary file; update the sample information table;

[0110] If it exists, update the sample information table;

[0111] Find the newly added offline data; execute the Normalization molecular step. If the execution is successful, set the IsNormed attribute value of the offline data to True, and generate the Normalized fcs file; otherwise, set the IsNormed attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table. Execute the Debarcode step on the fcs file of the original offline data nomrd after successful Normalization. If the execution is successful, set the IsDebarcode attribute value of the offline data to True, and generate the sample fcs file contained in the original offline data; otherwise, set the IsDebarcode attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table; record the above processing results in run_log.

[0112] Execute the AutoGate step for the newly added sample data. If the execution is successful, set the IsGated attribute value of the sample data to True, and generate the sample fcs file after the sample data is AutoGated; otherwise, set the IsGated attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table. Execute the AutoCalc step for the sample data generated by AutoGate. If the execution is successful, set the IsCalc attribute value of the sample data to True, and generate the analysis result xlsx file after the sample data is AutoCalc; otherwise, set the IsCalc attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table. Generate analysis results and record them in the xlsx file.

[0113] Execute the AutoReport step for the sample data analysis results generated by AutoCalc. If the execution is successful, set the IsReport attribute value of the sample data to True, and generate a sample report PDF file after the AutoReport of the sample data; otherwise, set the IsReport attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table.

[0114] Finally, a sample report PDF file is generated;

[0115] Among them, Figure 8 As shown, the data includes offline data and sample data. The offline data is the original data of the experiment, and the sample data is the data obtained after the offline data is normalized and debarcoded.

[0116] like Fig. 9 As shown, the table includes a sample information summary table, a sample information table, and a sample analysis record table (run_log); the sample information summary table is used to record the experimental information of all samples; each sub-project has its own sample information table, which can be obtained from the experimental summary table; the sample analysis record table (run_log) includes an original data analysis table and an abnormal sample data analysis table, wherein the original data analysis table and the abnormal original data analysis table are used to record the processing results of the newly added offline data, and the sample data analysis table and the abnormal sample data analysis table are used to record the processing results of the newly added sample data.

[0117] Fig.10 A schematic diagram of a mass spectrometry data processing device according to an embodiment of the present invention. Fig.10 As shown, the device may include:

[0118] The comparison module 701 is used to compare the sample information summary table with the sample information statistics table of the current sub-project, determine the newly added samples of the current sub-project, and update the sample information statistics table of the current sub-project based on the newly added samples of the current sub-project, wherein the sample information summary table records the data of the current project, the current project includes at least one sub-project, and the at least one sub-project includes the current sub-project;

[0119] Analysis module 702 is used to analyze the unanalyzed samples in the new sample statistics table of the current sub-project based on the sample analysis record table and sample information statistics table of the current sub-project, update the sample analysis record table of the current sub-project based on the analysis progress, and record the analysis results in the project management binary file of the current sub-project.

[0120] In some embodiments, a sub-project folder is pre-established in the server, and the sub-project folder includes a sample information statistics table of the sub-project, a sample analysis record table of the sub-project, and a project management binary file of the sub-project.

[0121] In some embodiments, the sample analysis record table includes an original data analysis table, a sample data analysis table, an abnormal original data analysis table, and an abnormal sample data analysis table;

[0122] Among them, the experimental samples measured by the Cytof instrument are raw data; the sample data are obtained by processing the raw data.

[0123] In some embodiments, each original data is corrected to generate an intermediate file through a normalization step, and each intermediate file is debarked through a debarcode step to separate each sample data.

[0124] In some embodiments, the raw data analysis table records each raw data analysis information including the raw data number, raw data file name, project ID, Panel, IsNormed, IsDebarcode, and sample name; the sample data analysis table records each sample data analysis information including sample ID, IsGated, gated file name, Panel, IsCalc, IsReport, and raw data number; the abnormal raw data analysis table records the raw offline data that failed to be analyzed, including the raw data number and the reason for the analysis failure; the abnormal sample data analysis table records the sample data that failed to be analyzed, including the sample ID and the reason for the analysis failure.

[0125] In some embodiments, each sample information in the sample information statistics table includes project number, formal experimental sample number, sample ID, disease label, pathology, pathological stage, pathological remarks, data upload location, gender, age, sample name, medical record number or bed number or other, uploaded data name, PANEL, barcode, sample type, sample arrival date, project name, customer name + location, project manager, blood volume, average viable cell count, activity, remarks, disease history, family medical history, sample collection date, testing agency, sample collection volume, test type, and report version.

[0126] In some embodiments, the project management binary file also includes the analysis step status of the original data and the result corresponding to each step; and the analysis step status of the sample data and the result corresponding to each step.

[0127] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0128] Furthermore, this embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method provided in the aforementioned method embodiment are executed.

[0129] The computer program products of the face recognition methods, devices and systems provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be repeated here.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0135] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention rather than to limit it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed by the present invention, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention.

Claims

1. A method for processing mass spectrometry data, characterized in that: include: Determine a newly added sample of the current sub-project based on a comparison between the sample information summary table and the sample information statistics table of the current sub-project, and update the sample information statistics table of the current sub-project based on the newly added sample of the current sub-project, wherein the sample information summary table records data of the current project, the current project includes at least one sub-project, and the at least one sub-project includes the current sub-project; Based on the sample analysis record table and sample information statistics table of the current sub-project, analyze the unanalyzed samples in the sample new statistics table of the current sub-project, update the sample analysis record table of the current sub-project based on the analysis progress, and record the analysis results in the project management binary file of the current sub-project; Among them, the current sub-project is analyzed to determine whether the sub-project folder exists in the storage server; if it does not exist, the sub-project folder, sample information table, sample analysis record table, operation log, and Project binary file are created; the sample information table is updated; if it exists, the sample information table is updated; the Project binary file is a project management binary file; Find the newly added offline data; execute the Normalization molecular step. If the execution is successful, set the IsNormed attribute value of the offline data to True, and generate the Normalized fcs file; otherwise, set the IsNormed attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table; execute the Debarcode step on the fcs file after the original offline data nomrd after successful Normalization; if the execution is successful, set the IsDebarcode attribute value of the offline data to True, and generate the sample fcs file contained in the original offline data; otherwise, set the IsDebarcode attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table; record the above processing results in run_log; Execute the AutoGate step for the newly added sample data; if the execution is successful, set the IsGated attribute value of the sample data to True, and generate a sample fcs file after the sample data is AutoGated; otherwise, set the IsGated attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table; execute the AutoCalc step for the sample data generated by AutoGate; if the execution is successful, set the IsCalc attribute value of the sample data to True, and generate an analysis result xlsx file after the sample data is AutoCalc; otherwise, set the IsCalc attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table; generate analysis results and record them in the xlsx file; Execute the AutoReport step for the sample data analysis results generated by AutoCalc; if the execution is successful, set the IsReport attribute value of the sample data to True, and generate a sample report PDF file after the sample data is AutoReported; otherwise, set the IsReport attribute value of the sample data to FALSE, record the sample data in the abnormal sample data table, and finally generate a sample report PDF file.

2. The method according to claim 1, characterized in that The sample analysis record table includes the original data analysis table, the sample data analysis table, the abnormal original data analysis table and the abnormal sample data analysis table; Among them, the experimental samples measured by mass spectrometry flow cytometry technology are raw data; the sample data are obtained by processing the raw data.

3. The method according to claim 2, characterized in that Through the normalization step, each raw data is corrected to generate an intermediate file, and through the Debarcode step, each intermediate file is disassembled to separate each sample data.

4. The method according to claim 2 or 3, characterized in that: The original data analysis table records each original data analysis information, including original data number, original data file name, project ID, Panel, IsNormed, IsDebarcode, and sample name; the sample data analysis table records each sample data analysis information, including sample ID, IsGated, Gated file name, Panel, IsCalc, IsReport, and original data number; the abnormal original data analysis table records the original offline data that failed to be analyzed, including the original data number and the reason for the analysis failure; The abnormal sample data analysis table records the sample data that failed analysis, including the sample ID and the reason for the analysis failure.

5. The method according to claim 1, characterized in that Each sample information in the sample information statistics table includes project number, formal experimental sample number, sample ID, disease label, pathology, data upload location, gender, age, sample name, medical record number or bed number, upload data name, Panel, barcode, sample type, sample arrival date, project name, customer name + location, project manager, blood volume, average live cell count, activity, remarks, disease history, sample collection date, inspection agency, sample collection volume, test type, and report version.

6. The method according to claim 1, characterized in that The project management binary file also includes the analysis step status of the original data and the result corresponding to each step; and the analysis step status of the sample data and the result corresponding to each step.

7. A mass spectrometry data processing device, characterized in that: include: a comparison module, configured to compare the sample information summary table with the sample information statistics table of the current sub-project, determine the newly added samples of the current sub-project, and update the sample information statistics table of the current sub-project based on the newly added samples of the current sub-project, wherein the sample information summary table records the data of the current project, the current project includes at least one sub-project, and the at least one sub-project includes the current sub-project; An analysis module, configured to analyze the unanalyzed samples in the new sample statistics table of the current sub-project based on the sample analysis record table and the sample information statistics table of the current sub-project, update the sample analysis record table of the current sub-project based on the analysis progress, and record the analysis results in the project management binary file of the current sub-project; Among them, the current sub-project is analyzed to determine whether the sub-project folder exists in the storage server; if it does not exist, the sub-project folder, sample information table, sample analysis record table, operation log, and Project binary file are created; the sample information table is updated; if it exists, the sample information table is updated; the Project binary file is a project management binary file; Find the newly added offline data; execute the Normalization molecular step. If the execution is successful, set the IsNormed attribute value of the offline data to True, and generate the Normalized fcs file; otherwise, set the IsNormed attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table; execute the Debarcode step on the fcs file after the original offline data nomrd after successful Normalization; if the execution is successful, set the IsDebarcode attribute value of the offline data to True, and generate the sample fcs file contained in the original offline data; otherwise, set the IsDebarcode attribute value of the offline data to FALSE, and record the offline data in the abnormal original data table; record the above processing results in run_log; Execute the AutoGate step for the newly added sample data; if the execution is successful, set the IsGated attribute value of the sample data to True, and generate a sample fcs file after the sample data is AutoGated; otherwise, set the IsGated attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table; execute the AutoCalc step for the sample data generated by AutoGate; if the execution is successful, set the IsCalc attribute value of the sample data to True, and generate an analysis result xlsx file after the sample data is AutoCalc; otherwise, set the IsCalc attribute value of the sample data to FALSE, and record the sample data in the abnormal sample data table; generate analysis results and record them in the xlsx file; Execute the AutoReport step for the sample data analysis results generated by AutoCalc; if the execution is successful, set the IsReport attribute value of the sample data to True, and generate a sample report PDF file after the sample data is AutoReported; otherwise, set the IsReport attribute value of the sample data to FALSE, record the sample data in the abnormal sample data table, and finally generate a sample report PDF file.

8. An electronic device, characterized in that: include: Processing device and storage device; The storage device stores a computer program, which, when executed by the processor, executes the method according to any one of claims 1 to 6, so as to be used for camera calibration and / or processing of mass spectrometry flow data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are executed.

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