Batch analysis and processing method and system for flow cytometer data

Abstract

The present invention discloses a method and system for batch processing of flow cytometer data, and establishes three modes, namely "sharing mode", "adaptive mode" and "independent mode", for batch processing of data. A large amount of data is batch analyzed through the "sharing mode", and the data processing results are further intelligently adjusted through the adaptive algorithm, so that the processing results are more accurate. Finally, the reporter can manually process the samples with unsatisfactory processing results through the "independent" mode, which not only ensures the processing efficiency and saves a lot of manpower and time costs, but also ensures the correctness of the processing results of each sample.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular to a method, system, computer equipment and storage medium for batch analysis and processing of flow cytometer data. Background Art

[0002] Flow Cytometer (FCM) is a commonly used single-cell, multi-parameter, high-throughput detection instrument. The sample is compressed and focused on a line through sheath fluid, so that the cells line up and pass through the laser detection area one by one. The detector receives the scattered light signal and fluorescence signal emitted by the cell to analyze the physical, chemical, and biological characteristics of the cell.

[0003] Flow cytometry is known as the "CT of the laboratory" and is widely used in clinical testing. Since the number of clinical samples is large and the analysis is relatively complex, but the analysis targets are generally fixed, batch processing of samples can reduce a lot of analysis work. However, since the test results of each sample will be slightly different, if a fixed template is used for analysis, the analysis results of some samples will have large errors.

[0004] There are currently no related articles or patents that study this. The commercial streaming data analysis software flowJo proposes a concept called "magnetic gate", which can automatically process a single sample to a certain extent, but the algorithm's processing accuracy is low. This method is only applicable to situations where the change from the original circle gate is small, and cannot correctly process situations where the change is large. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system, computer equipment and storage medium for batch analysis and processing of flow cytometer data in view of the problems existing in the prior art.

[0006] The technical solution to achieve the purpose of the present invention is: a method for batch analysis and processing of flow cytometer data, the method comprising the following steps:

[0007] Step 1, set the sample analysis mode;

[0008] Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template;

[0009] Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results in the analysis template.

[0010] Furthermore, the sample analysis mode in step 1 includes an independent mode, a shared mode, and an adaptive mode;

[0011] In the independent mode, the sample is analyzed separately, and the analysis results are modified and saved in real time to the analysis template of the sample;

[0012] In the sharing mode, when modifying the sample analysis, only the public analysis template is changed, and the analysis template of the sample itself is not modified;

[0013] In the adaptive mode, the analysis result of the sample is adjusted using an adaptive algorithm, and the analysis result is saved in real time to the analysis template of the sample.

[0014] Furthermore, it is characterized in that in the newly created experiment in step 1, an analysis template is established for the experiment and each sample, specifically including:

[0015] Generate an empty public analysis template for the experiment and generate a corresponding empty analysis template for each sample;

[0016] Or, copy the public analysis template of an existing experiment as the public analysis template of the newly created experiment, and generate a corresponding empty analysis template for each sample.

[0017] Furthermore, in step 3, the sample data in the experiment is analyzed and processed by different sample analysis modes according to user needs, specifically including:

[0018] (1) The current sample analysis mode is shared mode

[0019] If the user selects the independent mode: determine whether the current mode is the first switch. If so, copy the public analysis template information to the analysis template of the sample. Otherwise, call the analysis template of the sample to analyze and display the sample.

[0020] If the user selects the adaptive mode: determine whether the current mode is the first switch. If so, copy the public analysis template information to the analysis template of the sample. Otherwise, call the analysis template of the sample to analyze and display the sample, and then call the adaptive algorithm to perform adaptive analysis on the sample.

[0021] (2) The current sample analysis mode is independent mode

[0022] If the user selects the sharing mode: the public analysis template is called, and no processing is done on the sample analysis template;

[0023] If the user selects the adaptive mode: the adaptive algorithm is called to perform adaptive analysis on the sample, and the analysis results are saved in the analysis template of the sample;

[0024] (3) The current sample analysis mode is adaptive mode

[0025] If the user selects the sharing mode: the public analysis template is called, and no processing is done on the sample analysis template;

[0026] If the user selects independent mode: the samples are analyzed, displayed and saved according to the currently used analysis template.

[0027] A flow cytometer data batch analysis and processing system, the system comprising:

[0028] A mode setting module, used to set the sample analysis mode;

[0029] A new module is used to create a new experiment. The experiment includes N flow cytometer sample data to be analyzed in batches, and an analysis template is created for the experiment and each sample. The analysis template corresponding to the experiment is recorded as a public analysis template;

[0030] The analysis module is used to analyze and process the sample data in the experiment by switching different sample analysis modes according to user needs, and save the analysis results in an analysis template.

[0031] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0032] Step 1, set the sample analysis mode;

[0033] Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template;

[0034] Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results in the analysis template.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0036] Step 1, set the sample analysis mode;

[0037] Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template;

[0038] Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results in the analysis template.

[0039] Compared with the prior art, the present invention has the following significant advantages:

[0040] 1) The present invention can perform batch processing of the same analysis items on a large number of samples through a batch processing mode, saving a lot of analysis time, and can increase the comparability of data and reduce errors introduced by human operations.

[0041] 2) The present invention can perform differentiated processing on different samples through an adaptive algorithm, making the analysis results more accurate.

[0042] 3) The present invention allows the user to perform expert processing on samples that are still unsatisfactory after batch processing and adaptive processing through the setting of independent mode, thereby ensuring the correctness of the results.

[0043] 4) Through the combination of batch processing, adaptive algorithm processing, and expert manual processing, the experimental data can be automatically processed, while allowing manual intervention to adjust to obtain more accurate results. The effective combination of the three can save time and effort, increase the comparability of data, and ensure the accuracy of the processing results.

[0044] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Create a new experimental flow chart for an embodiment.

[0046] Figure 2 The flowchart of switching from shared mode to independent mode / adaptive mode in one embodiment is shown.

[0047] Figure 3 The flowchart of switching from independent mode to shared mode / adaptive mode in one embodiment is shown.

[0048] Figure 4 The flowchart of switching from adaptive mode to shared mode / independent mode in one embodiment is shown.

[0049] Figure 5 The figure is a comparison chart of the calculation results of the method of the present invention and the existing software in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In one embodiment, the present invention provides a method for batch analysis and processing of flow cytometer data, the method comprising the following steps:

[0052] Step 1, set the sample analysis mode;

[0053] Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template;

[0054] Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results in the analysis template.

[0055] Further, in one of the embodiments, the sample analysis mode in step 1 includes an independent mode, a shared mode, and an adaptive mode;

[0056] In the independent mode, the sample is analyzed separately, and the analysis results are modified and saved in real time to the analysis template of the sample;

[0057] In the sharing mode, when modifying the sample analysis, only the public analysis template is changed, and the analysis template of the sample itself is not modified;

[0058] In the adaptive mode, the analysis result of the sample is adjusted using an adaptive algorithm, and the analysis result is saved in real time to the analysis template of the sample.

[0059] Further, in one embodiment, in combination Figure 1 In the new experiment described in step 1, an analysis template is created for the experiment and each sample, including:

[0060] Generate an empty public analysis template for the experiment and generate a corresponding empty analysis template for each sample;

[0061] Or, copy the public analysis template of an existing experiment as the public analysis template of the newly created experiment, and generate a corresponding empty analysis template for each sample.

[0062] Further preferably, in one of the embodiments, the analysis template stores sample analysis tool parameters such as graphics, gating conditions, statistical parameters, etc. used for sample analysis.

[0063] Further preferably, in one of the embodiments, the analysis mode of each sample in step 2 is the shared mode by default.

[0064] Further, in one embodiment, step 3 analyzes and processes the sample data in the experiment through different sample analysis modes according to user needs, specifically including:

[0065] (1) The current sample analysis mode is the sharing mode, such as Figure 2 Shown

[0066] If the user selects the independent mode: determine whether the current mode is the first switch. If so, copy the public analysis template information to the analysis template of the sample. Otherwise, call the analysis template of the sample to analyze and display the sample.

[0067] If the user selects the adaptive mode: determine whether the current mode is the first switch. If so, copy the public analysis template information to the analysis template of the sample. Otherwise, call the analysis template of the sample to analyze and display the sample, and then call the adaptive algorithm to perform adaptive analysis on the sample.

[0068] (2) The current sample analysis mode is independent mode, such as Figure 3 Shown

[0069] If the user selects the sharing mode: the public analysis template is called, and no processing is done on the sample analysis template;

[0070] If the user selects the adaptive mode: the adaptive algorithm is called to perform adaptive analysis on the sample, and the analysis results are saved in the analysis template of the sample;

[0071] (3) The current sample analysis mode is the adaptive mode, such as Figure 4 Shown

[0072] If the user selects the sharing mode: the public analysis template is called, and no processing is done on the sample analysis template;

[0073] If the user selects independent mode: the samples are analyzed, displayed and saved according to the currently used analysis template.

[0074] Furthermore, in one embodiment, the adaptive algorithm specifically comprises the following steps:

[0075] Manually gate samples for analysis:

[0076] 1) Manually circle the first gate and calculate the density centroid and its coordinates (x0, y0) inside the gate;

[0077] 2) Calculate the new density centroid and coordinates (x1, y1) within the gate;

[0078] 3) Move the door as a whole so that (x0, y0) moves to (x1, y1).

[0079] Furthermore, in one embodiment, the density centroid calculation process includes:

[0080] 1) Count the number of cells N in the gate;

[0081] 2) Divide the area inside the door into a*b grids;

[0082] 3) Count the number of cells that fall within each grid;

[0083] 4) Get the grid with the largest number of cells and set its center as the density centroid.

[0084] Furthermore, in one embodiment, the density centroid calculation process includes:

[0085] 1) For each point in the gate, find the number of points within the radius a and set it as the density of the point;

[0086] 2) Get the point with the largest density and set it as the density centroid.

[0087] A flow cytometer data batch analysis and processing system, the system comprising:

[0088] A mode setting module, used to set the sample analysis mode;

[0089] A new module is used to create a new experiment. The experiment includes N flow cytometer sample data to be analyzed in batches, and an analysis template is created for the experiment and each sample. The analysis template corresponding to the experiment is recorded as a public analysis template;

[0090] The analysis module is used to analyze and process the sample data in the experiment by switching different sample analysis modes according to user needs, and save the analysis results in an analysis template.

[0091] For the specific definition of the flow cytometer data batch analysis and processing system, please refer to the definition of the flow cytometer data batch analysis and processing method above, which will not be repeated here. Each module in the above-mentioned flow cytometer data batch analysis and processing system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0092] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0093] Step 1, set the sample analysis mode;

[0094] Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template;

[0095] Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results in the analysis template.

[0096] For the specific limitations of each step, please refer to the limitations of the flow cytometer data batch analysis and processing method above, which will not be repeated here.

[0097] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0098] Step 1, set the sample analysis mode;

[0099] Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template;

[0100] Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results in the analysis template.

[0101] For the specific limitations of each step, please refer to the limitations of the flow cytometer data batch analysis and processing method above, which will not be repeated here.

[0102] As a specific example, in one embodiment, the present invention is further verified and explained. Sample 1: 2020-12-03 150100.fcs, Sample 2: 2020-12-03 153703.fcs, manual gate analysis is performed on sample 1, and the gate is applied to sample 2 for intelligent analysis, and the calculation results of the present invention and commercial flow analysis software are compared as shown in the figure. Figure 5 As shown, Figure 5 (a) Figure A is the initial gate set manually by the user; Figure B is the result of intelligent processing of the sample using the method of the present invention; Figure C is the result of gating the data in Figure B by a flow cytometry expert; Figure 5 Figure A in (b) is the initial gate manually set by the user; Figure B is another sample that has been intelligently processed using the magnetic gate function after the gate has been applied; Figure C is the result of a flow cytometry expert gating the data in Figure B. It can be seen that the magnetic gate implemented by the commercial flow cytometry software method has a large error and is not accurate enough, while the accuracy of the present invention is very high.

[0103] The method for batch processing of flow cytometer data of the present invention establishes three modes of "sharing mode", "adaptive mode" and "independent mode" to batch process data. A large amount of data is batch analyzed through the "sharing mode", and the data processing results are further intelligently adjusted through the adaptive algorithm, so that the processing results are more accurate. Finally, the reporter and the reviewer can manually process the samples with unsatisfactory processing results through the "independent" mode, which not only ensures the efficiency of the processing, saves a lot of manpower and time costs, but also ensures the correctness of the processing results of each sample.

[0104] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for batch analysis and processing of flow cytometer data, characterized in that: The method comprises the following steps: Step 1, set the sample analysis mode; Step 2, create a new experiment, the experiment includes N flow cytometry sample data to be analyzed in batches, and establish an analysis template for the experiment and each sample, and record the analysis template corresponding to the experiment as a public analysis template; Step 3: According to user needs, analyze and process the sample data in the experiment by switching different sample analysis modes, and save the analysis results to the analysis template; In step 1, the sample analysis modes include independent mode, shared mode, and adaptive mode; In the independent mode, the sample is analyzed separately, and the analysis results are modified and saved in real time to the analysis template of the sample; In the sharing mode, when modifying the sample analysis, only the public analysis template is changed, and the analysis template of the sample itself is not modified; In the adaptive mode, the analysis result of the sample is adjusted using an adaptive algorithm, and the analysis result is saved in real time to the analysis template of the sample; According to user needs, the sample data in the experiment is analyzed and processed through different sample analysis modes, specifically including: (1) The current sample analysis mode is shared mode If the user selects the independent mode: determine whether the current mode is the first switch. If so, copy the public analysis template information to the analysis template of the sample. Otherwise, call the analysis template of the sample to analyze and display the sample. If the user selects the adaptive mode: determine whether the current mode is the first switch. If so, copy the public analysis template information to the analysis template of the sample. Otherwise, call the analysis template of the sample to analyze and display the sample, and then call the adaptive algorithm to perform adaptive analysis on the sample. (2) The current sample analysis mode is independent mode If the user selects the sharing mode: the public analysis template is called, and no processing is done on the sample analysis template; If the user selects the adaptive mode: the adaptive algorithm is called to perform adaptive analysis on the sample, and the analysis results are saved in the analysis template of the sample; (3) The current sample analysis mode is adaptive mode If the user selects the sharing mode: the public analysis template is called, and no processing is done on the sample analysis template; If the user selects the independent mode: the samples are analyzed, displayed and saved according to the currently used analysis template; The adaptive algorithm specifically comprises the following steps: Manually gate samples for analysis: 1) Manually circle the first gate and calculate the density centroid and its coordinates (x0, y0) within the gate; 2) Calculate the new density centroid and coordinates (x1, y1) within the gate; 3) Move the door as a whole so that (x0, y0) moves to (x1, y1); The calculation process of the density centroid includes: 1) Count the number of cells N in the gate; 2) Divide the area inside the door into a*b grids; 3) Count the number of cells that fall within each grid; 4) Obtain the grid with the largest number of cells and set its center as the density centroid; Alternatively, the density centroid calculation process includes: 1) For each point in the gate, find the number of points within the radius a and set it as the density of the point; 2) Get the point with the largest density and set it as the density centroid.

2. The method for batch analysis and processing of flow cytometer data according to claim 1, characterized in that: In the new experiment described in step 1, an analysis template is created for the experiment and each sample, including: Generate an empty public analysis template for the experiment and generate a corresponding empty analysis template for each sample; Or, copy the public analysis template of an existing experiment as the public analysis template of the newly created experiment, and generate a corresponding empty analysis template for each sample.

3. The method for batch analysis and processing of flow cytometer data according to claim 2, characterized in that: The analysis template stores the graphics, gate conditions, and statistical parameters used for sample analysis.

4. The method for batch analysis and processing of flow cytometer data according to claim 3, characterized in that: The analysis mode for each sample in step 2 defaults to shared mode.

5. A flow cytometer data batch analysis and processing system based on the method according to any one of claims 1 to 4, characterized in that: The system comprises: A mode setting module, used to set the sample analysis mode; A new module is used to create a new experiment. The experiment includes N flow cytometer sample data to be analyzed in batches, and an analysis template is created for the experiment and each sample. The analysis template corresponding to the experiment is recorded as a public analysis template; The analysis module is used to analyze and process the sample data in the experiment by switching different sample analysis modes according to user needs, and save the analysis results in an analysis template.

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