Method, apparatus and storage medium for flow cytometric lymphocyte subset analysis

Abstract

The application discloses a flow lymphocyte subpopulation analysis method, equipment and a storage medium thereof, and belongs to the technical field of flow cytometry. The flow lymphocyte subpopulation analysis method comprises the following steps: acquiring flow cytometry original data, determining an SSC.A probability density curve distributed on SSC.A, and then determining a lymphocyte threshold value in the flow cytometry original data; extracting lymphocyte data in the flow cytometry original data based on the lymphocyte threshold value; determining a fluorescence probability density curve distributed on fluorescence signals of the lymphocyte data, and then determining subpopulation cell threshold values of each lymphocyte subpopulation in the lymphocyte data; identifying subpopulation cell data in the lymphocyte data based on the subpopulation cell threshold values; and detecting the subpopulation cell data according to a preset lymphocyte subpopulation anomaly detection rule, so as to generate a lymphocyte subpopulation analysis result. The application solves the technical problem of low detection efficiency of flow lymphocytes in the prior art.

Description

Technical Field

[0001] This application relates to the field of flow cytometry technology, and in particular to a flow cytometry method, apparatus and storage medium for analyzing lymphocyte subsets. Background Technology

[0002] Lymphocytes are the main cell group that constitutes the body's immune system and performs immune functions, participating in cellular and humoral immunity. Lymphocyte subset detection is an important clinical examination used to evaluate the body's immune status. It often reflects the functional status of various immune cells in terms of quantity and proportion, and is widely used in the diagnosis, treatment, and prognosis of tumors, AIDS, and autoimmune diseases.

[0003] Flow cytometry (FCM) is a routine clinical method for analyzing the immunophenotypic characteristics of lymphocytes. FCM analyzes the intensity of scattered light and fluorescence signals produced by fluorescently labeled single cells (microparticles) flowing at high speed under high-energy laser irradiation, thereby qualitatively classifying and quantitatively analyzing the cells and achieving the analysis or sorting of target cells. However, the conventional analysis and detection of flow cytometry data for lymphocytes mainly relies on manual analysis and review by technicians, resulting in low efficiency in flow cytometry lymphocyte detection.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a method, device, and storage medium for flow cytometry lymphocyte subset analysis, aiming to solve the technical problem of low detection efficiency of conventional methods for flow cytometry lymphocytes.

[0006] To achieve the above objectives, this application provides a flow cytometry method for lymphocyte subset analysis, the flow cytometry method for lymphocyte subset analysis comprising:

[0007] Obtain raw flow cytometry data, determine the SSC.A probability density curve of the raw flow cytometry data on SSC.A, and determine the lymphocyte threshold in the raw flow cytometry data based on the SSC.A probability density curve.

[0008] Based on the lymphocyte threshold, lymphocyte data are extracted from the raw flow cytometry data;

[0009] Determine the fluorescence probability density curve of the lymphocyte data on the fluorescence signal, and determine the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data based on the fluorescence probability density curve.

[0010] Based on the subpopulation cell threshold, subpopulation cell data in the lymphocyte data are identified;

[0011] Based on preset rules for detecting abnormal lymphocyte subsets, the subpopulation cell data are detected to generate lymphocyte subset analysis results.

[0012] Optionally, the step of determining the lymphocyte threshold in the raw flow cytometry data based on the SSC.A probability density curve includes:

[0013] The SSC.A threshold is determined based on the troughs in the SSC.A probability density curve.

[0014] Extract data from the raw flow cytometry data where SSC.A is less than the SSC.A threshold, and use them as the first cell data; then determine the CD45 probability density curve of the first cell data on CD45.

[0015] The CD45 threshold is determined based on the trough in the CD45 probability density curve, and the SSC.A threshold and the CD45 threshold are used as the lymphocyte threshold.

[0016] Optionally, the step of determining the SSC.A threshold based on the troughs in the SSC.A probability density curve includes:

[0017] Determine whether there is a trough on the probability density curve of the SSC.A;

[0018] If so, the minimum value of SSC.A located at the trough on the SSC.A probability density curve shall be taken as the SSC.A threshold.

[0019] If not, the SSC.A value at the termination point of the peak on the SSC.A probability density curve is taken as the SSC.A threshold.

[0020] Optionally, the step of determining the CD45 threshold based on the troughs in the CD45 probability density curve includes:

[0021] Determine whether there is a trough on the CD45 probability density curve;

[0022] If so, the CD45 maximum value located at the trough on the CD45 probability density curve is taken as the CD45 threshold.

[0023] If not, the CD45 value at the starting point of the peak on the CD45 probability density curve is taken as the CD45 threshold.

[0024] Optionally, the fluorescence signal includes CD3, and the step of determining the fluorescence probability density curve of the lymphocyte data distribution on the fluorescence signal, and determining the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data based on the fluorescence probability density curve includes:

[0025] Determine the CD3 probability density curve of the distribution of the lymphocyte data on CD3;

[0026] The CD3 value at the trough of the CD3 probability density curve is used as the CD3 threshold, and the CD3 threshold is used as the subpopulation cell threshold.

[0027] Optionally, the fluorescence signal further includes a first signal, the first signal including at least one of CD4, CD8, CD19, and CD16+56. After the step of using the CD3 value at the trough of the CD3 probability density curve as a CD3 threshold and using the CD3 threshold as the subpopulation cell threshold, the signal further includes:

[0028] Determine a first probability density curve of the distribution of the lymphocyte data on the first signal;

[0029] Identify the first highest peak in the first probability density curve, and determine the first fluorescence value of the first trough after the first highest peak, and use the first fluorescence value as the threshold of the subpopulation cells.

[0030] Optionally, before the step of using the first fluorescence value as the threshold for the subpopulation of cells, the method further includes:

[0031] Determine whether the first fluorescence value is within the preset range of fluorescence signals of the subpopulation cells;

[0032] If so, then the step of using the first fluorescence value as the threshold of the subpopulation cells is performed;

[0033] If not, then based on the CD3 threshold, extract the second cell data from the lymphocyte data;

[0034] A second probability density curve is determined on the first signal to represent the distribution of the second cell data. A second highest peak in the second probability density curve is identified, and a second fluorescence value is determined for the first trough after the second highest peak. The second fluorescence value is then used as the threshold of the subpopulation cells.

[0035] Optionally, the subpopulation cell thresholds include: CD3 threshold, CD4 threshold, CD8 threshold, CD19 threshold, and CD16+56 threshold, and the step of identifying subpopulation cell data in the lymphocyte data based on the subpopulation cell thresholds includes:

[0036] Data in the lymphocyte data where CD3 is greater than the CD3 threshold are used as T lymphocyte data;

[0037] The T lymphocyte data with CD4 values ​​greater than the CD4 threshold are used as the Th cell subset data.

[0038] Data from the T lymphocyte data where CD8 is greater than the CD8 threshold are used as Ts cell subset data;

[0039] Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD19 is greater than the CD19 threshold are used as B lymphocyte data.

[0040] Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD16+56 is greater than the CD16+56 threshold are used as NK lymphocyte data.

[0041] Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD4 is greater than the CD4 threshold are used as monocyte data.

[0042] Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD8 is less than or equal to the CD8 threshold are considered as CD3-positive and CD8-negative cell data.

[0043] Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD4 is less than or equal to the CD4 threshold are considered as CD3-positive and CD4-negative cell data.

[0044] The T lymphocyte data, the Th cell subset data, the Ts cell subset data, the B lymphocyte data, the NK lymphocyte data, the monocyte data, the CD3-positive CD8-negative cell data, and the CD3-positive CD4-negative cell data are all used as the subpopulation cell data.

[0045] Optionally, the step of detecting the subpopulation cell data according to preset lymphocyte subpopulation abnormality detection rules to generate lymphocyte subpopulation analysis results includes:

[0046] Determine whether the subpopulation cell data meets the lymphocyte subpopulation anomaly detection rules, wherein the lymphocyte subpopulation anomaly detection rules include at least one of the following rules:

[0047] The sum of the percentages of T lymphocytes, B lymphocytes, and NK lymphocytes is within a first preset range;

[0048] The combined percentage of Th cells and Ts cells is less than or equal to the percentage of T lymphocytes.

[0049] The difference between the percentage of Th cells in the subset and the percentage of CD3-positive and CD8-negative cells is within a second preset range;

[0050] The difference between the percentage of Ts subpopulation cells and the percentage of CD3-positive and CD4-negative cells was within the third preset range;

[0051] The number of lymphocytes is greater than a preset lymphocyte count threshold;

[0052] The percentage of B lymphocytes is less than the preset threshold for B lymphocyte percentage.

[0053] The proportion of mononuclear cells is less than the preset mononuclear cell proportion threshold;

[0054] If not, the subpopulation cell data is determined to be abnormal, and the lymphocyte subpopulation analysis results are generated based on the abnormality of the subpopulation cell data.

[0055] Optionally, prior to the step of generating the lymphocyte subset analysis results, the method further includes:

[0056] If the subpopulation cell data does not meet the rule that the proportion of monocytes is less than a preset monocyte proportion threshold, then the lymphocyte threshold is corrected according to the preset threshold compensation value, the lymphocyte threshold is lowered, and the step of extracting lymphocyte data from the original flow cytometry data based on the lymphocyte threshold is performed.

[0057] This application also provides an electronic device, the electronic device comprising: a memory, a processor, and a flow cytometry lymphocyte subset analysis program stored in the memory and executable on the processor, the flow cytometry lymphocyte subset analysis program being configured to implement the steps of the flow cytometry lymphocyte subset analysis method described above.

[0058] This application also provides a storage medium, which is a computer-readable storage medium, on which a flow cytometry lymphocyte subset analysis program is stored. The flow cytometry lymphocyte subset analysis program is executed by a processor to implement the steps of the above-described flow cytometry lymphocyte subset analysis method.

[0059] This application discloses a flow cytometry method for lymphocyte subset analysis. By acquiring raw flow cytometry data, the method determines the SSC.A probability density curve of the raw data on the surface of the cells (SSC.A), and based on the SSC.A probability density curve, determines the lymphocyte threshold in the raw flow cytometry data. Then, based on the lymphocyte threshold, lymphocyte data is extracted from the raw flow cytometry data. By calculating the lymphocyte threshold, the method accurately distinguishes lymphocyte data from non-lymphocyte data in the raw flow cytometry data, thus accurately separating lymphocyte data. Finally, the method determines the fluorescence signal of the lymphocyte data. The system analyzes the distribution of fluorescence probability density curves and determines the subpopulation cell thresholds for each lymphocyte subset in the lymphocyte data based on these curves. Subpopulation cell data is then identified based on these thresholds. Furthermore, by using the subpopulation cell thresholds for each lymphocyte subset, subpopulation cell data, such as T lymphocyte data, Th cell subsets, Ts cell subsets, B lymphocyte data, and NK lymphocyte data, are precisely separated. Finally, according to pre-defined lymphocyte subset anomaly detection rules, the subpopulation cell data is detected to generate lymphocyte subset analysis results. These pre-defined rules enable comprehensive detection of lymphocyte data from the overall data to specific subpopulations, providing timely warnings for incorrectly grouped cells or samples with abnormal detection results. This assists in manual review and improves the efficiency of processing abnormal detection results. By objectively analyzing and detecting all cell data, the influence of human subjective factors on cell clustering is avoided, thus improving the accuracy of flow cytometry lymphocyte subset analysis. Furthermore, it can realize automatic real-time analysis and detection of sample cell data, i.e., raw flow cytometry data, which is applicable to various complex situations such as large sample cell numbers and variable cell types, greatly improving detection efficiency. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the structure of an electronic device in the hardware operating environment involved in the embodiments of this application;

[0061] Figure 2 This is a schematic flowchart of the flow cytometry lymphocyte subset analysis method involved in the embodiments of this application;

[0062] Figure 3 This is a schematic diagram illustrating a scenario of the flow cytometry lymphocyte subset analysis method involved in the embodiments of this application;

[0063] Figure 4 This is a schematic diagram showing the start and end points of the peaks on the probability density curve involved in the embodiments of this application;

[0064] Figure 5 This is a schematic diagram illustrating the lymphocyte subset analysis results involved in an embodiment of this application;

[0065] Figure 6 This is a schematic diagram illustrating a more complete embodiment of the present application.

[0066] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0068] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0069] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application.

[0070] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0071] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0072] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a flow cytometry lymphocyte subset analysis program.

[0073] exist Figure 1 In the illustrated electronic device, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the electronic device of this application can be disposed in the electronic device, and the electronic device calls the flow cytometry lymphocyte subset analysis program stored in the memory 1005 through the processor 1001 and performs the following operations:

[0074] Obtain raw flow cytometry data, determine the SSC.A probability density curve of the raw flow cytometry data on SSC.A, and determine the lymphocyte threshold in the raw flow cytometry data based on the SSC.A probability density curve.

[0075] Based on the lymphocyte threshold, lymphocyte data are extracted from the raw flow cytometry data;

[0076] Determine the fluorescence probability density curve of the lymphocyte data on the fluorescence signal, and determine the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data based on the fluorescence probability density curve.

[0077] Based on the subpopulation cell threshold, subpopulation cell data in the lymphocyte data are identified;

[0078] Based on preset rules for detecting abnormal lymphocyte subsets, the subpopulation cell data are detected to generate lymphocyte subset analysis results.

[0079] Further, the operation of determining the lymphocyte threshold in the raw flow cytometry data based on the SSC.A probability density curve includes:

[0080] The SSC.A threshold is determined based on the troughs in the SSC.A probability density curve.

[0081] Extract data from the raw flow cytometry data where SSC.A is less than the SSC.A threshold, and use them as the first cell data; then determine the CD45 probability density curve of the first cell data on CD45.

[0082] The CD45 threshold is determined based on the trough in the CD45 probability density curve, and the SSC.A threshold and the CD45 threshold are used as the lymphocyte threshold.

[0083] Further, the operation of determining the SSC.A threshold based on the troughs in the SSC.A probability density curve includes:

[0084] Determine whether there is a trough on the probability density curve of the SSC.A;

[0085] If so, the minimum value of SSC.A located at the trough on the SSC.A probability density curve shall be taken as the SSC.A threshold.

[0086] If not, the SSC.A value at the termination point of the peak on the SSC.A probability density curve is taken as the SSC.A threshold.

[0087] Furthermore, the operation of determining the CD45 threshold based on the troughs in the CD45 probability density curve includes:

[0088] Determine whether there is a trough on the CD45 probability density curve;

[0089] If so, the CD45 maximum value located at the trough on the CD45 probability density curve is taken as the CD45 threshold.

[0090] If not, the CD45 value at the starting point of the peak on the CD45 probability density curve is taken as the CD45 threshold.

[0091] Further, the fluorescence signal includes CD3, and the operation of determining the fluorescence probability density curve of the lymphocyte data distribution on the fluorescence signal, and determining the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data based on the fluorescence probability density curve includes:

[0092] Determine the CD3 probability density curve of the distribution of the lymphocyte data on CD3;

[0093] The CD3 value at the trough of the CD3 probability density curve is used as the CD3 threshold, and the CD3 threshold is used as the subpopulation cell threshold.

[0094] Furthermore, the processor 1001 can call the flow cytometry lymphocyte subset analysis program stored in the memory 1005 and also perform the following operations:

[0095] The fluorescence signal further includes a first signal, which includes at least one of CD4, CD8, CD19, and CD16+56. After the operation of using the CD3 value at the trough of the CD3 probability density curve as a CD3 threshold and using the CD3 threshold as the subpopulation cell threshold, the method further includes:

[0096] Determine a first probability density curve of the distribution of the lymphocyte data on the first signal;

[0097] Identify the first highest peak in the first probability density curve, and determine the first fluorescence value of the first trough after the first highest peak, and use the first fluorescence value as the threshold of the subpopulation cells.

[0098] Furthermore, the processor 1001 can call the flow cytometry lymphocyte subset analysis program stored in the memory 1005 and also perform the following operations:

[0099] Prior to the operation of using the first fluorescence value as the threshold for the subpopulation of cells, the method further includes:

[0100] Determine whether the first fluorescence value is within the preset range of fluorescence signals of the subpopulation cells;

[0101] If so, then the operation of using the first fluorescence value as the threshold of the subpopulation cells is performed;

[0102] If not, then based on the CD3 threshold, extract the second cell data from the lymphocyte data;

[0103] A second probability density curve is determined on the first signal to represent the distribution of the second cell data. A second highest peak in the second probability density curve is identified, and a second fluorescence value is determined for the first trough after the second highest peak. The second fluorescence value is then used as the threshold of the subpopulation cells.

[0104] Further, the subpopulation cell thresholds include: CD3 threshold, CD4 threshold, CD8 threshold, CD19 threshold, and CD16+56 threshold. The operation of identifying subpopulation cell data in the lymphocyte data based on the subpopulation cell thresholds includes:

[0105] Data in the lymphocyte data where CD3 is greater than the CD3 threshold are used as T lymphocyte data;

[0106] The T lymphocyte data with CD4 values ​​greater than the CD4 threshold are used as the Th cell subset data.

[0107] Data from the T lymphocyte data where CD8 is greater than the CD8 threshold are used as Ts cell subset data;

[0108] Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD19 is greater than the CD19 threshold are used as B lymphocyte data.

[0109] Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD16+56 is greater than the CD16+56 threshold are used as NK lymphocyte data.

[0110] Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD4 is greater than the CD4 threshold are used as monocyte data.

[0111] Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD8 is less than or equal to the CD8 threshold are considered as CD3-positive and CD8-negative cell data.

[0112] Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD4 is less than or equal to the CD4 threshold are considered as CD3-positive and CD4-negative cell data.

[0113] The T lymphocyte data, the Th cell subset data, the Ts cell subset data, the B lymphocyte data, the NK lymphocyte data, the monocyte data, the CD3-positive CD8-negative cell data, and the CD3-positive CD4-negative cell data are all used as the subpopulation cell data.

[0114] Furthermore, the operation of detecting the subpopulation cell data according to preset lymphocyte subpopulation abnormality detection rules to generate lymphocyte subpopulation analysis results includes:

[0115] Determine whether the subpopulation cell data meets the lymphocyte subpopulation anomaly detection rules, wherein the lymphocyte subpopulation anomaly detection rules include at least one of the following rules:

[0116] The sum of the percentages of T lymphocytes, B lymphocytes, and NK lymphocytes is within a first preset range;

[0117] The combined percentage of Th cells and Ts cells is less than or equal to the percentage of T lymphocytes.

[0118] The difference between the percentage of Th cells in the subset and the percentage of CD3-positive and CD8-negative cells is within a second preset range;

[0119] The difference between the percentage of Ts subpopulation cells and the percentage of CD3-positive and CD4-negative cells was within the third preset range;

[0120] The number of lymphocytes is greater than a preset lymphocyte count threshold;

[0121] The percentage of B lymphocytes is less than the preset threshold for B lymphocyte percentage.

[0122] The proportion of mononuclear cells is less than the preset mononuclear cell proportion threshold;

[0123] If not, the subpopulation cell data is determined to be abnormal, and the lymphocyte subpopulation analysis results are generated based on the abnormality of the subpopulation cell data.

[0124] Furthermore, the processor 1001 can call the flow cytometry lymphocyte subset analysis program stored in the memory 1005 and also perform the following operations:

[0125] Prior to the operation of generating the lymphocyte subset analysis results, the method further includes:

[0126] If the subpopulation cell data does not meet the rule that the proportion of monocytes is less than a preset monocyte proportion threshold, then the lymphocyte threshold is corrected according to the preset threshold compensation value, the lymphocyte threshold is lowered, and the operation of extracting lymphocyte data from the original flow cytometry data based on the lymphocyte threshold is performed.

[0127] Based on the above structure, various embodiments of the flow cytometry lymphocyte subset analysis method are proposed.

[0128] Reference Figure 2 , Figure 2 This is a schematic flowchart of the first embodiment of the flow cytometry lymphocyte subset analysis method of this application.

[0129] In this embodiment, the execution subject of the flow cytometry lymphocyte subset analysis method can be an electronic device, which can be a local device or a network device. No limitation is made in this embodiment. For ease of description, the execution subject is omitted in the following description of each embodiment. In this embodiment, the flow cytometry lymphocyte subset analysis method includes:

[0130] Step S10: Obtain raw flow cytometry data, determine the SSC.A probability density curve of the raw flow cytometry data on SSC.A, and determine the lymphocyte threshold in the raw flow cytometry data based on the SSC.A probability density curve.

[0131] In one feasible embodiment, in order to detect lymphocyte subsets, flow cytometry data of sample cells obtained from FCM (hereinafter referred to as raw flow cytometry data for distinction) can be acquired, and then the probability density distribution curve of the raw flow cytometry data on SSC.A (hereinafter referred to as SSC.A probability density curve for distinction) can be plotted; and through the obtained SSC.A probability density curve, the lymphocyte threshold in the raw flow cytometry data can be determined, and cell clustering can be achieved through the lymphocyte threshold, so as to accurately separate the lymphocytes in the sample cells.

[0132] Optionally, raw flow cytometry data can be obtained by performing FCM on the sample cells, referring to... Figure 3 The sample cells are prepared into a cell suspension, and the cells in the suspension are labeled with fluorescent antibodies and driven into a cell stream under high pressure. A laser beam of a specific wavelength is then directly irradiated into the cells within the high-pressure driven cell stream. The resulting light signals are received by multiple receivers: one is the scattered light signal received in the straight direction of the laser beam (forward scattering), and the others are the light signals received in the direction perpendicular to the laser beam, including scattered light signals (side scattering) and fluorescence signals. The suspended cells or particles in the flow stream can cause the laser beam to scatter and emit fluorescence. The scattered light signals and fluorescence signals are received by the corresponding receivers, thus obtaining the raw flow cytometry data.

[0133] SSC represents the granularity of cells. The more irregular the cell, the more protrusions on the cell surface, and the more organelles or particulate matter inside the cell that can cause laser scattering, the larger the SSC value. This value can be used to compare the granularity of cells. Among them, SSC.A represents the area of ​​the electron wave.

[0134] The lymphocyte threshold represents the boundary between lymphocyte populations and other cell populations in a sample cell. It can include the SSC.A threshold. Since the SSC.A value of lymphocytes in a sample cell is lower than that of monocytes, the SSC.A threshold can represent the boundary between lymphocyte populations and monocyte populations. By using the SSC.A threshold, lymphocyte populations and monocyte populations in the sample cell can be partitioned to accurately identify lymphocyte populations and lymphocyte data.

[0135] Optionally, the lymphocyte threshold may include: SSC.A threshold and CD45 threshold; by setting the SSC.A gate, lymphocytes and monocytes in the sample cells are grouped; and then by setting the CD45 gate, fragmented cell groups in the lymphocyte group are further separated, thereby improving the accuracy of cell grouping.

[0136] Probability density curves, including SSC.A probability density curves, CD45 probability density curves, and fluorescence probability density curves, are plotted with the values ​​of each variable as the x-axis (e.g., SSC.A, CD45, and fluorescence signals such as CD3, CD4, CD8, CD19, and CD16+56) and the probability or density of the original data at each variable value as the y-axis. Probability density curves can be plotted using the flowDensity algorithm and combined with the gatedFD_lym() function to identify different lymphocyte populations.

[0137] In one feasible implementation, since different laboratories or hospitals may have different naming conventions for the signal parameters used in FCM, such as FSC.A, SSC.A, CD45, CD3, CD4, CD8, CD19, CD16+56, etc., the signal parameters of the raw flow cytometry data are converted before determining the SSC.A probability density curve of the raw flow cytometry data. For example, a preset mapping table is established, including signal parameter names and description information. Based on the description information, the original parameter names in the raw flow cytometry data are converted into corresponding and fixed signal parameter names. The raw flow cytometry data can also be format converted, such as uniformly converting it to FCS2.0, FCS3.0, FCS3.1, etc. The format conversion method can be performed by preset functions, etc., and this embodiment does not limit this.

[0138] In another feasible implementation, before determining the SSC.A probability density curve of the flow cytometry raw data distribution on SSC.A, the distribution range of SSC.A and CD45 of the lymphocyte population can be obtained through big data statistics as a preset screening range to achieve preliminary screening of the flow cytometry raw data. Then, the flow cytometry raw data within the preset screening range are extracted as the screened flow cytometry raw data. The steps of determining the SSC.A probability density curve of the screened flow cytometry raw data distribution on SSC.A and subsequent steps are then performed. The preset screening range is a relatively large range of lymphocyte population distribution on SSC.A and CD45, which can perform preliminary screening of the flow cytometry raw data to remove some non-lymphocyte cell data, thereby reducing the amount of subsequent data processing and increasing the flow cytometry lymphocyte subset analysis rate.

[0139] Step S20: Based on the lymphocyte threshold, extract lymphocyte data from the raw flow cytometry data;

[0140] In one feasible embodiment, the lymphocyte threshold is used as a dividing point for cell clustering, thereby accurately extracting lymphocyte data from the raw flow cytometry data (hereinafter referred to as lymphocyte data for distinction).

[0141] Optionally, the lymphocyte threshold includes the SSC.A threshold, thereby using data in the raw flow cytometry data that are less than the SSC.A threshold as lymphocyte data.

[0142] Optionally, the lymphocyte threshold includes the SSC.A threshold and the CD45 threshold, thereby using data from the original flow cytometry data that are less than the SSC.A threshold and greater than the CD45 threshold as lymphocyte data.

[0143] Optionally, the lymphocyte threshold includes an SSC.A threshold and a CD45 threshold. Then, data in the original flow cytometry data that are less than the SSC.A threshold and greater than the CD45 threshold are used as the first lymphocyte data. Then, clustered data in the first lymphocyte data are determined as lymphocyte data, wherein the clustered data are continuous data with a density within a preset range on the probability density curve.

[0144] Step S30: Determine the fluorescence probability density curve of the lymphocyte data on the fluorescence signal, and determine the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data based on the fluorescence probability density curve.

[0145] In one feasible embodiment, after obtaining lymphocyte data, it is necessary to analyze lymphocyte subsets within the lymphocyte population, and then plot the fluorescence probability density curves of the lymphocyte data distribution on various fluorescence signals. The fluorescence signals include one or more of CD45, CD3, CD4, CD8, CD19, and CD16+56; the fluorescence probability density curves include one or more of CD45, CD3, CD4, CD8, CD19, and CD16+56. By obtaining the fluorescence probability density curves, the subset cell thresholds of each lymphocyte subset in the lymphocyte data can be determined, and cell grouping can be achieved through these thresholds, accurately separating each lymphocyte subset from the lymphocyte population. The lymphocyte subsets include T lymphocytes, B lymphocytes, NK lymphocytes, etc., and T lymphocytes can be further divided into the T lymphocyte subset Th cells and the T lymphocyte subset Ts cells.

[0146] Optionally, the subpopulation cell thresholds include one or more of the following: CD45, CD3, CD4, CD8, CD19, and CD16+56 thresholds.

[0147] Optionally, the lymphocyte threshold is determined using the SSC.A probability density curve, wherein the lymphocyte threshold includes: the SSC.A threshold; monocyte and granulocyte data are removed from the raw flow cytometry data using the SSC.A threshold; then the fluorescence probability density curve of the lymphocyte data on CD45 in the fluorescence signal is determined, i.e., the CD45 probability density curve; the CD45 threshold is determined using the CD45 probability density curve; then fragmented cells are removed from the lymphocyte data using the CD45 threshold; then the fluorescence probability density curve of the lymphocyte data on one or more of CD3, CD4, CD8, CD19, and CD16+56 in the fluorescence signal is determined, and the subpopulation cell threshold of each lymphocyte subset in the lymphocyte data is determined based on the fluorescence probability density curve.

[0148] Step S40: Based on the subpopulation cell threshold, identify subpopulation cell data in the lymphocyte data;

[0149] In one feasible embodiment, cell clustering is performed using a subpopulation cell threshold as a dividing point, thereby accurately extracting cell data from each subpopulation in the lymphocyte data.

[0150] Optionally, the subpopulation cell threshold includes at least one of the following: CD3 threshold, CD4 threshold, CD8 threshold, CD19 threshold, and CD16+56 threshold.

[0151] Optionally, the subpopulation cell data includes at least one of the following: T lymphocyte data, Th cell subpopulation data, Ts cell subpopulation data, B lymphocyte data, NK lymphocyte data, monocyte data, CD3-positive CD8-negative cell data, and CD3-positive CD4-negative cell data; wherein, the T lymphocyte data, Th cell subpopulation data, Ts cell subpopulation data, B lymphocyte data, and NK lymphocyte data are lymphocyte subpopulation data, while the monocyte data, CD3-positive CD8-negative cell data, and CD3-positive CD4-negative cell data are used to detect abnormalities in lymphocyte subpopulations.

[0152] Step S50: Detect the subpopulation cell data according to the preset lymphocyte subpopulation abnormality detection rules to generate lymphocyte subpopulation analysis results.

[0153] In one feasible embodiment, after obtaining the subpopulation cell data, the data needs to be tested to confirm whether there are any abnormalities in the sample cells or whether there is a detection failure in the flow cytometry lymphocyte subpopulation analysis; therefore, according to the preset lymphocyte subpopulation abnormality detection rules, the subpopulation cell data is tested and lymphocyte subpopulation analysis results are generated.

[0154] Optionally, the lymphocyte subset analysis results may include analysis result graphs, statistical results, abnormal warning information, etc.

[0155] Optionally, the lymphocyte subset analysis results can include schematic diagrams of the results of each step in the flow cytometry lymphocyte subset analysis method. For example, after obtaining the lymphocyte threshold, a two-dimensional scatter plot of the raw flow cytometry data can be plotted based on SSC.A and CD45. Then, in the two-dimensional scatter plot, the boundaries of lymphocyte populations within the range of the SSC.A and CD45 thresholds can be delineated using elliptical gating. After obtaining the subset cell threshold, a two-dimensional scatter plot of lymphocyte data can be plotted based on pairwise combinations of CD3, CD4, CD8, CD19, and CD16+56 in the fluorescence signal, and the threshold boundary lines for positive and negative expression of fluorescence signals can be drawn. This allows cell populations expressing specific fluorescence signals to be identified, such as cell populations simultaneously expressing CD3 and CD4. By visualizing the results of each step, abnormal data can be quickly identified, improving the efficiency of lymphocyte subset detection.

[0156] Optionally, in the subpopulation cell threshold, if the signal threshold is greater than the signal threshold in the subpopulation cell threshold, it indicates that the cell is positive for the signal and can be represented as "+"; if it is less than or equal to any signal threshold in the subpopulation cell threshold, it indicates that the cell is negative for the signal and can be represented as "-".

[0157] In this embodiment, by acquiring raw flow cytometry data, the SSC.A probability density curve of the raw flow cytometry data on the SSC.A is determined, and based on the SSC.A probability density curve, the lymphocyte threshold in the raw flow cytometry data is determined; then, based on the lymphocyte threshold, lymphocyte data in the raw flow cytometry data is extracted; by calculating the lymphocyte threshold, the lymphocyte data in the raw flow cytometry data is accurately distinguished from other cell data, so as to accurately separate the lymphocyte data; and then the fluorescence probability density curve of the lymphocyte data on the fluorescence signal is determined. The system uses fluorescence probability density curves to determine the subpopulation thresholds of each lymphocyte subset in the lymphocyte data. Based on these thresholds, it identifies subpopulation cell data within the lymphocyte data. Further, by using these thresholds, it precisely separates subpopulation cell data, such as T lymphocytes, Th cells, Ts cells, B lymphocytes, and NK lymphocytes. Then, according to pre-defined lymphocyte subset anomaly detection rules, it detects subpopulation cell data to generate lymphocyte subset analysis results. These pre-defined rules enable comprehensive detection of lymphocyte data from the overall data to specific subpopulations, providing timely warnings for incorrectly grouped cells or samples with abnormal detection results. This assists in manual review and improves the efficiency of processing abnormal detection results. By objectively analyzing and detecting all cell data, the influence of human subjective factors on cell clustering is avoided, thus improving the accuracy of flow cytometry lymphocyte subset analysis. Furthermore, it can realize automatic real-time analysis and detection of sample cell data, i.e., raw flow cytometry data, which is applicable to various complex situations such as large sample cell numbers and variable cell types, greatly improving detection efficiency.

[0158] Furthermore, based on the first embodiment described above, a second embodiment of the flow cytometry lymphocyte subset analysis method of this application is proposed. In this embodiment, step S10, determining the lymphocyte threshold in the raw flow cytometry data based on the SSC.A probability density curve, includes:

[0159] Step S11: Determine the SSC.A threshold based on the troughs in the SSC.A probability density curve;

[0160] In one feasible embodiment, after the SSC.A probability density curve is plotted, the peaks and troughs in the SSC.A probability density curve are identified. Since the peaks in the probability density curve represent the location of each cell population, and the troughs between two peaks represent the boundaries between cell populations, and the threshold is used to distinguish the boundary points between two cell populations, the SSC.A threshold is determined based on the SSC.A value at the trough.

[0161] In one feasible implementation, step S11, determining the SSC.A threshold based on the troughs in the SSC.A probability density curve, includes:

[0162] Step S111: Determine whether there is a trough on the probability density curve of SSC.A;

[0163] Step S112: If yes, then the minimum value of SSC.A located at the trough on the probability density curve of SSC.A is taken as the threshold of SSC.A.

[0164] In one feasible embodiment, it is determined whether there are troughs on the SSC.A probability density curve. Generally, there is one trough on the SSC.A probability density curve, which is the boundary between the lymphocyte population and the non-lymphocyte population (monocyte population). If there are multiple troughs, it indicates that the monocytes in the data are distributed in more than one cluster, thus forming troughs within the monocyte population. Since the SSC.A value of lymphocytes is less than that of monocytes, the SSC.A values ​​corresponding to multiple troughs can be determined, and the minimum value is determined from the SSC.A values ​​corresponding to each trough, that is, the minimum SSC.A value located at the trough, which can be used as the SSC.A threshold.

[0165] Step S113: If not, then the SSC.A value at the termination point of the peak on the SSC.A probability density curve is taken as the SSC.A threshold.

[0166] In one feasible embodiment, if there is no trough on the SSC.A probability density curve, it indicates that there are too few or too few monocytes and granulocytes in the data, and only one peak of lymphocytes is formed, which cannot form a trough; therefore, the SSC.A value at the end point of the peak on the SSC.A probability density curve is used as the SSC.A threshold.

[0167] The termination point of the peak can be the initial flat point after the peak is formed, that is, the slope of the curve is relatively large before this point; the curve after the peak point on the probability density curve can be divided into multiple sub-segments of a preset length, and the coordinate point of the minimum SSC.A value among the sub-segments with a slope within the second preset slope range can be used as the termination point of the peak.

[0168] The starting point of the peak can be the last flat point before the peak is formed, that is, the slope of the curve increases sharply after the point and the peak is formed. The curve before the peak point on the probability density curve can be divided into multiple sub-segments of a preset length, and the coordinate point of the maximum SSC.A value in the sub-segments with the slope within the first preset slope range can be used as the starting point of the peak.

[0169] For example, refer to Figure 4 Let Q be the probability density curve. AThe peak point Q on the probability density curve; A The curve on the front side is divided into multiple sub-segments of preset length, resulting in L1, L2, L3, etc. (some sub-segments are not shown in the figure). The slope of each sub-segment is calculated. Among them, the sub-segments within the first preset slope range include L1 and L2. Then, the maximum value of SSC.A in L1 and L2 is determined to be Q. B The SSC.A value at that location, and thus Q B This serves as the starting point of the peak. Furthermore, the peak point Q on the probability density curve is... A The curve on the rear side is divided into multiple sub-segments of preset length, resulting in L4, L5, L6, L7, etc. (some sub-segments are not shown in the figure). The slope of each sub-segment is calculated. Among them, the sub-segments within the second preset slope range include L5, L6, and L7. Then, the minimum value of SSC.A among L5, L6, and L7 is determined to be Q. C The SSC.A value at that location, and thus Q C As the endpoint of the peak.

[0170] Step S12: Extract data from the original flow cytometry data where SSC.A is less than the SSC.A threshold, and use them as first cell data; determine the CD45 probability density curve of the first cell data on CD45.

[0171] In one feasible embodiment, since the SSC.A value of lymphocytes is less than that of monocytes, data with SSC.A values ​​less than the SSC.A threshold in the original flow cytometry data are extracted as first cell data to remove monocyte and granulocyte data from the original flow cytometry data, thereby achieving the separation of monocytes, granulocytes, and lymphocytes. However, the first cell data may also contain cell data of fragmented cells and lymphocytes. Therefore, the CD45 probability density curve of the first cell data on CD45 is plotted.

[0172] Step S13: Determine the CD45 threshold based on the trough in the CD45 probability density curve, and use the SSC.A threshold and the CD45 threshold as the lymphocyte threshold.

[0173] In one feasible embodiment, after the CD45 probability density curve is plotted, the peaks and troughs in the CD45 probability density curve are identified, and then the CD45 threshold is determined based on the CD45 value at the trough. The SSC.A threshold and the CD45 threshold are used as the lymphocyte threshold.

[0174] Optionally, after determining the SSC.A and CD45 thresholds, the flowDensity() function can be used to define the boundaries of the lymphocyte population within the threshold range using elliptical gating, thus achieving automatic gating. Then, by using the polygonGate() and Subset() functions, lymphocyte data can be extracted from the raw flow cytometry data.

[0175] In one feasible implementation, step S13, determining the CD45 threshold based on the troughs in the CD45 probability density curve, includes:

[0176] Step S131: Determine whether there is a trough on the CD45 probability density curve;

[0177] Step S132: If yes, then the maximum value of CD45 located at the trough on the CD45 probability density curve is taken as the CD45 threshold.

[0178] In one feasible embodiment, it is determined whether there is a trough on the CD45 probability density curve. If the CD45 probability density curve has two peaks, that is, only one trough, it indicates that fragment cells and lymphocytes are clustered separately. Therefore, the CD45 value of this trough can be used as the CD45 threshold to distinguish between lymphocyte populations and fragment cell populations. If there are more than two peaks in the CD45 probability density curve, since the CD45 value of lymphocytes is greater than that of fragment cells, that is, the peak at the rightmost end of the CD45 probability density curve is the peak of lymphocytes. Therefore, the maximum CD45 value at the trough of the CD45 probability density curve is used as the CD45 threshold.

[0179] Step S133: If not, then the CD45 value at the starting point of the peak on the CD45 probability density curve is taken as the CD45 threshold.

[0180] In one feasible embodiment, if the CD45 probability density curve is a single peak, it indicates that the fragmented cells are not clustered and there is only a cluster of lymphocytes. In this case, the CD45 value at the starting point of the peak on the CD45 probability density curve is taken as the CD45 threshold.

[0181] The termination point of the peak can be the initial flat point after the peak is formed, that is, the slope of the curve is relatively large before this point; the curve after the peak point on the probability density curve can be divided into multiple sub-segments of a preset length, and the coordinate point of the minimum SSC.A value among the sub-segments with a slope within the second preset slope range can be used as the termination point of the peak.

[0182] In this embodiment, based on the distribution characteristics of lymphocyte, monocyte, and fragmented cell data on SSC.A and SD45 in the raw flow cytometry data, the SSC.A threshold and CD45 threshold are determined by the troughs in the plotted SSC.A probability density curve and CD45 probability density curve, respectively, thereby obtaining the lymphocyte threshold. Based on the inherent characteristics of the raw flow cytometry data, the lymphocyte threshold is set accurately and automatically, thus enabling automatic gating for flow cytometry lymphocyte subset analysis and improving the efficiency of flow cytometry lymphocyte subset analysis. Furthermore, the obtained lymphocyte threshold can accurately extract lymphocyte data from the raw flow cytometry data to avoid the influence of other cells on flow cytometry lymphocyte subset analysis, further improving the accuracy of flow cytometry lymphocyte subset analysis.

[0183] Furthermore, based on the first and / or second embodiments described above, a third embodiment of the flow cytometry lymphocyte subset analysis method of this application is proposed. In this embodiment, the fluorescence signal includes CD3. Step S30, determining the fluorescence probability density curve of the lymphocyte data distribution on the fluorescence signal, and determining the subset cell threshold of each lymphocyte subset in the lymphocyte data based on the fluorescence probability density curve, includes:

[0184] Step S31: Determine the CD3 probability density curve of the lymphocyte data distribution on CD3;

[0185] In one feasible embodiment, after extracting lymphocyte data from the raw flow cytometry data, it is necessary to further separate the subpopulation cell data of each lymphocyte subset in the lymphocyte data; and then plot the CD3 probability density curve of the lymphocyte data distribution on CD3.

[0186] Step S32: The CD3 value at the trough of the CD3 probability density curve is used as the CD3 threshold, and the CD3 threshold is used as the subpopulation cell threshold.

[0187] In one feasible embodiment, conventionally, the probability density curve of a lymphocyte population on CD3 has only one trough. Therefore, this trough is the CD3 threshold, i.e., the dividing point, between CD3 positive and negative expression in lymphocytes. The CD3 value at the trough in the CD3 probability density curve is then used as the CD3 threshold, and this CD3 threshold is used as the threshold for the subpopulation of cells.

[0188] Alternatively, the flowDensity() and deGate() functions can be used to find the threshold between CD3 negative and positive expression of lymphocytes.

[0189] In one feasible implementation, if there are two or more troughs in the CD3 probability density curve, the CD3 value at the trough within a preset CD3 range is used as the CD3 threshold; wherein, the preset CD3 range can be set according to big data statistics.

[0190] In this embodiment, the CD3 probability density curve representing the distribution of lymphocyte data on CD3 is determined. The CD3 values ​​at the troughs of the CD3 probability density curve are then used as the CD3 threshold, and this CD3 threshold is used as the threshold for subpopulation cells. By accurately determining the CD3 threshold in the lymphocyte data through the troughs in the CD3 probability density curve, the cell data showing CD3-negative and CD3-positive expression can be identified based on the CD3 threshold. This enables precise separation of different lymphocyte subpopulations in the lymphocyte data, thereby improving the accuracy of flow cytometry lymphocyte subpopulation analysis.

[0191] In one feasible embodiment, the fluorescence signal further includes a first signal, the first signal including at least one of CD4, CD8, CD19, and CD16+56. After step S32, which uses the CD3 value at the trough of the CD3 probability density curve as the CD3 threshold and uses the CD3 threshold as the subpopulation cell threshold, the method further includes:

[0192] Step S33: Determine the first probability density curve of the lymphocyte data distribution on the first signal;

[0193] In one feasible embodiment, after obtaining the CD3 threshold, the CD3 threshold can be used to determine the T lymphocyte data that are positive for CD3 expression in the lymphocyte data. To further segment the lymphocytes to separate B lymphocytes, NK lymphocytes, etc., a first probability density curve is plotted on the distribution of the lymphocyte data at a first signal, wherein the first signal includes at least one of CD4, CD8, CD19, and CD16+56. Probability density curves corresponding to each signal in the first signal can be plotted, thereby determining the portion of the lymphocyte data that is negative or positive for each of the first signals.

[0194] Optionally, the first probability density curve includes at least one of the following: CD4 probability density curve, CD8 probability density curve, CD19 probability density curve, and CD16+56 probability density curve.

[0195] Step S34: Identify the first highest peak in the first probability density curve, determine the first fluorescence value of the first trough after the first highest peak, and use the first fluorescence value as the threshold of the subpopulation cells.

[0196] In one feasible embodiment, the highest peak in the first probability density curve (hereinafter referred to as the first highest peak for distinction) is identified, and the fluorescence value at the first trough after the first highest peak (hereinafter referred to as the first fluorescence value for distinction) is used as the subpopulation cell threshold.

[0197] Optionally, the first highest peak in the first probability density curve can be identified by the flowDensity() function, and the first fluorescence value of the first trough after the first highest peak can be determined, and the first fluorescence value can be used as the threshold of the subpopulation cells.

[0198] Optionally, if the first probability density curve is a CD4 probability density curve, then a CD4 threshold is obtained; if the first probability density curve is a CD8 probability density curve, then a CD8 threshold is obtained; if the first probability density curve is a CD19 probability density curve, then a CD19 threshold is obtained; if the first probability density curve is a CD16+56 probability density curve, then a CD16+56 threshold is obtained; and the CD4 threshold, CD8 threshold, CD19 threshold, and CD16+56 threshold are all used as subpopulation cell thresholds.

[0199] In one feasible embodiment, prior to step S34, which uses the first fluorescence value as the threshold for the subpopulation of cells, the method further includes:

[0200] Step S341: Determine whether the first fluorescence value is within the preset range of fluorescence signals of the subpopulation cells;

[0201] Step S342: If yes, then perform the step of using the first fluorescence value as the threshold of the subpopulation cells;

[0202] In one feasible embodiment, it is determined whether the first fluorescence value is within a preset range of subpopulation cell fluorescence signals, wherein the preset range of subpopulation cell fluorescence signals is the range of lymphocyte subpopulations in each fluorescence signal obtained based on big data statistics. If yes, it indicates that the first fluorescence value meets the general conditions of big data statistics, and then the step of using the first fluorescence value as a subpopulation cell threshold, and subsequent steps, are executed.

[0203] Step S343: If not, extract the second cell data from the lymphocyte data based on the CD3 threshold;

[0204] In one feasible embodiment, if not, data on CD3-negative and CD3-positive expression in lymphocyte data are obtained based on a CD3 threshold, respectively, as second cell data.

[0205] Step S344: Determine the second probability density curve of the second cell data distribution on the first signal, identify the second highest peak in the second probability density curve, and determine the second fluorescence value of the first trough after the second highest peak, and use the second fluorescence value as the threshold of the subpopulation cells.

[0206] In one feasible embodiment, a second probability density curve of the distribution of second cell data on the first signal is plotted, and then the highest peak in the second probability density curve (hereinafter referred to as the second highest peak for distinction) is identified, and the second fluorescence value of the first trough after the second highest peak is determined, and the second fluorescence value is used as the threshold of the subpopulation cells.

[0207] Optionally, data showing CD3-positive expression (i.e., lymphocytes with expression levels greater than the CD3 threshold) from the lymphocyte data are acquired as second-cell data. A second probability density curve is then plotted on CD4 or CD8, identifying the highest peak in the second probability density curve and determining the second fluorescence value of the first trough following the second highest peak. This second fluorescence value is used as the CD4 or CD8 threshold, and the CD4 and / or CD8 thresholds are then used as the subset cell thresholds. Since the CD4 and CD8 thresholds serve as the dividing points between the Th and Ts cell subsets of T lymphocytes, respectively, and both the Th and Ts cell subsets are CD3-positive, calculating the CD4 and CD8 thresholds using data showing CD3-positive expression from the lymphocyte data improves the accuracy of these threshold calculations.

[0208] Optionally, data showing CD3-negative expression (i.e., lymphocytes with a CD3 threshold less than or equal to the threshold) from the lymphocyte data are acquired as second cell data. A second probability density curve is then plotted on CD19 or CD16+56, identifying the highest peak in the second probability density curve and determining the second fluorescence value of the first trough following the second highest peak. This second fluorescence value is used as the CD16 threshold or CD16+56 threshold, and further used as the subpopulation cell threshold. Since the CD16 threshold and CD16+56 threshold serve as the dividing points between B lymphocytes and NK lymphocytes, respectively, and both B lymphocytes and NK lymphocytes are CD3-negative, calculating the CD16 and CD16+56 thresholds using data showing CD3-negative expression from the lymphocyte data improves the accuracy of these threshold calculations.

[0209] In this embodiment, a first probability density curve is determined based on the distribution of lymphocyte data on the first signal; the first highest peak in the first probability density curve is identified, and the first fluorescence value of the first trough after the first highest peak is determined. This first fluorescence value is used as the subpopulation cell threshold. By accurately determining the cell thresholds of each subpopulation in the lymphocyte data through the troughs in the first probability density curve, the cell data that are negative or positive for each first signal can be identified based on the subpopulation cell thresholds. This achieves precise separation of each lymphocyte subpopulation in the lymphocyte data, thereby improving the accuracy of flow cytometry lymphocyte subpopulation analysis.

[0210] Furthermore, based on the first, second, and / or third embodiments described above, a fourth embodiment of the flow cytometry lymphocyte subset analysis method of this application is proposed. In this embodiment, the subset cell thresholds include: CD3 threshold, CD4 threshold, CD8 threshold, CD19 threshold, and CD16+56 threshold. Step S40, the step of identifying subset cell data in the lymphocyte data based on the subset cell thresholds, includes:

[0211] Step S41: Data in the lymphocyte data where CD3 is greater than the CD3 threshold are used as T lymphocyte data;

[0212] In one feasible embodiment, T lymphocytes are positive for CD3, that is, CD3 positivity corresponds to T lymphocytes; therefore, data in the lymphocyte data where CD3 is greater than the CD3 threshold are used as T lymphocyte data.

[0213] Step S42: Data in the T lymphocyte data where CD4 is greater than the CD4 threshold are used as Th cell subset data;

[0214] In one feasible embodiment, the T lymphocyte subset Th cells are positive for CD4, that is, CD3 positive and CD4 positive correspond to the T lymphocyte subset Th cells; therefore, the T lymphocyte data with CD4 greater than the CD4 threshold are used as the T lymphocyte subset data.

[0215] Step S43: Data in the T lymphocyte data where CD8 is greater than the CD8 threshold are used as Ts cell subset data;

[0216] In one feasible embodiment, the T lymphocyte subset Ts cells are positive for CD8, that is, CD3 positive and CD8 positive correspond to the T lymphocyte subset Ts cells; therefore, the data of T lymphocytes with CD8 greater than the CD8 threshold are used as the Ts cell subset data.

[0217] Step S44: Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD19 is greater than the CD19 threshold are used as B lymphocyte data.

[0218] In one feasible embodiment, B lymphocytes are CD3 negative and CD19 positive, that is, CD3 negative and CD19 positive correspond to B lymphocytes; therefore, data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD19 is greater than the CD19 threshold are used as B lymphocyte data.

[0219] Step S45: Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD16+56 is greater than the CD16+56 threshold are used as NK lymphocyte data.

[0220] In one feasible embodiment, NK lymphocytes are negative for CD3 and positive for CD16+56, that is, CD3 negative and CD16+56 positive correspond to NK lymphocytes; therefore, data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD16+56 is greater than the CD16+56 threshold are used as NK lymphocyte data.

[0221] Step S46: Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD4 is greater than the CD4 threshold are used as monocyte data;

[0222] In one feasible embodiment, some unclassified monocyte data may remain in the lymphocyte data. Monocytes are CD3 negative and CD4 positive, i.e., CD3 negative and CD4 positive correspond to monocytes. Therefore, data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD4 is greater than the CD4 threshold are taken as monocyte data.

[0223] Step S47: Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD8 is less than or equal to the CD8 threshold are used as CD3-positive and CD8-negative cell data.

[0224] In one feasible embodiment, data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD8 is less than or equal to the CD8 threshold are used as CD3-positive and CD8-negative cell data, so as to use the CD3-positive and CD8-negative cell data to perform anomaly detection on the automatic analysis results of flow cytometry lymphocyte subset data.

[0225] Step S48: Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD4 is less than or equal to the CD4 threshold are taken as CD3-positive and CD4-negative cell data.

[0226] In one feasible embodiment, data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD4 is less than or equal to the CD4 threshold are used as CD3-positive and CD4-negative cell data, so as to use the CD3-positive and CD4-negative cell data to perform anomaly detection on the automatic analysis results of flow cytometry lymphocyte subset data.

[0227] Step S49: The T lymphocyte data, the Th cell subset data, the Ts cell subset data, the B lymphocyte data, the NK lymphocyte data, the monocyte data, the CD3-positive CD8-negative cell data, and the CD3-positive CD4-negative cell data are all used as the subpopulation cell data.

[0228] In one feasible embodiment, to achieve detailed detection of lymphocytes from the whole to the local level, T lymphocyte data, Th cell subset data, Ts cell subset data, B lymphocyte data, NK lymphocyte data, monocyte data, CD3-positive CD8-negative cell data, and CD3-positive CD4-negative cell data are all used as subpopulation cell data. Among them, T lymphocyte data, Th cell subset data, Ts cell subset data, B lymphocyte data, and NK lymphocyte data represent lymphocyte subpopulation data, while monocyte data, CD3-positive CD8-negative cell data, and CD3-positive CD4-negative cell data are used to detect abnormalities in lymphocyte subpopulations. Based on the cell proportion of each type of subpopulation cell data in the lymphocytes, the flow cytometry lymphocyte subpopulations are automatically detected, improving detection efficiency.

[0229] Furthermore, based on the first, second, third, and / or fourth embodiments described above, a fifth embodiment of the flow cytometry lymphocyte subset analysis method of this application is proposed. In this embodiment, step S50, which involves detecting the subset cell data according to preset lymphocyte subset abnormality detection rules to generate lymphocyte subset analysis results, includes:

[0230] Step S51: Determine whether the subpopulation cell data meets the lymphocyte subpopulation abnormality detection rules, wherein the lymphocyte subpopulation abnormality detection rules include at least one of the following rules:

[0231] The sum of the percentages of T lymphocytes, B lymphocytes, and NK lymphocytes is within a first preset range;

[0232] The combined percentage of Th cells and Ts cells is less than or equal to the percentage of T lymphocytes.

[0233] The difference between the percentage of Th cells in the subset and the percentage of CD3-positive and CD8-negative cells is within a second preset range;

[0234] The difference between the percentage of Ts subpopulation cells and the percentage of CD3-positive and CD4-negative cells was within the third preset range;

[0235] The number of lymphocytes is greater than a preset lymphocyte count threshold;

[0236] The percentage of B lymphocytes is less than the preset threshold for B lymphocyte percentage.

[0237] The proportion of mononuclear cells is less than the preset mononuclear cell proportion threshold;

[0238] In one feasible embodiment, after obtaining the subpopulation cell data, the results of each subpopulation cell data are statistically analyzed, which can include the number of cells in each subpopulation cell data and the cell proportions between each subpopulation cell data; then, the subpopulation cell data are detected using lymphocyte subpopulation abnormality detection rules to determine whether the subpopulation cell data meets the lymphocyte subpopulation abnormality detection rules; wherein, at least one of the following rules is included:

[0239] The sum of the percentages of T lymphocytes, B lymphocytes, and NK lymphocytes is within a first preset range, where the percentage is the ratio of each lymphocyte subset to the total number of lymphocytes.

[0240] Optionally, the first preset range can be 95-100%. Since T lymphocytes, B lymphocytes and NK lymphocytes are three subsets of lymphocytes, the sum of the ratios of the number of these three subsets of cells to the number of lymphocytes should theoretically be equal to 100%. However, there may be experimental errors in the actual detection process, so the first preset range is determined to be 95-100%.

[0241] In one feasible embodiment, the rule for detecting abnormal lymphocyte subsets may further include: the sum of the cell proportions of Th cells and Ts cells is less than or equal to the cell proportion of T lymphocytes; since Th cells and Ts cells are subsets of T lymphocytes, the sum of the ratios of Th cells and Ts cells to the total number of lymphocytes should theoretically be equal to the cell proportion of T lymphocytes. However, in actual detection, there may be experimental errors, so it is determined that the sum of the cell proportions of Th cells and Ts cells is less than or equal to the cell proportion of T lymphocytes.

[0242] In one feasible embodiment, the lymphocyte subset abnormality detection rule may further include: the difference between the percentage of Th cells in the subset and the percentage of CD3-positive and CD8-negative cells is within a second preset range; conventionally, the cell population exhibiting CD3-positive and CD4-positive (Th cell subset) and the cell population exhibiting CD3-positive and CD8-negative are the same cell population, therefore, their cell percentages will not differ too much, so the difference between the percentage of Th cells in the subset and the percentage of CD3-positive and CD8-negative cells is determined to be within a second preset range; wherein, the second preset range can be -20 to 20%.

[0243] In one feasible embodiment, the lymphocyte subset abnormality detection rule may further include: the difference between the percentage of Ts cells and the percentage of CD3-positive and CD4-negative cells is within a third preset range; conventionally, the cell population exhibiting CD3-positive and CD4-positive (subpopulation Ts cells) and the cell population exhibiting CD3-positive and CD4-negative are the same cell population, therefore, their cell percentages will not differ too much, thus determining that the difference between the percentage of Th cells and the percentage of CD3-positive and CD4-negative cells is within the third preset range; wherein, the second preset range can be -20 to 20%.

[0244] In one feasible embodiment, the lymphocyte subset anomaly detection rule may further include: the number of lymphocytes is greater than a preset lymphocyte count threshold. In the sample cells used for flow cytometry lymphocyte subset analysis, the number of lymphocytes is usually above a certain level. If the number of cells is too low, it may indicate a problem with cell partitioning or an abnormality in the sample cells. Therefore, it is determined that the number of lymphocytes is greater than the preset lymphocyte count threshold; for example, the lymphocyte count threshold is 2000.

[0245] In one feasible embodiment, the percentage of B lymphocytes is less than a preset B lymphocyte percentage threshold; the preset lymphocyte percentage threshold is determined based on the percentage of B lymphocyte subsets in lymphocytes in conventional lymphocytes, for example, the preset B lymphocyte percentage threshold is 50%.

[0246] In one feasible embodiment, the lymphocyte subset abnormality detection rule may further include: the proportion of monocytes is less than a preset monocyte proportion threshold; since monocytes are close to lymphocytes, distinguishing between monocytes and lymphocytes is a challenge in identifying lymphocyte populations, and there is a possibility that nearby monocytes may also be identified as lymphocytes. Therefore, the proportion of monocytes needs to be less than the preset monocyte proportion threshold; for example, the preset monocyte proportion threshold is 5%.

[0247] Step S52: If not, determine that the subpopulation cell data is abnormal, and generate the lymphocyte subpopulation analysis result based on the abnormality of the subpopulation cell data.

[0248] In one feasible embodiment, if no, indicating that there is an abnormality in the sample cells or a malfunction in the flow cytometry lymphocyte subset analysis step, then the subset cell data is determined to be abnormal, and lymphocyte subset analysis results can be generated based on the abnormality of the subset cell data.

[0249] Optionally, the lymphocyte subset analysis results may include analysis result graphs, statistical results, and abnormality warning information, and may also include schematic diagrams of the results of each step of the flow cytometry lymphocyte subset analysis method. The lymphocyte subset analysis results include the sample name of the tested cells, statistical results, and warning information. The warning information may indicate which lymphocyte subset abnormality detection rule the sample cells do not meet; if all rules are met, the warning information is empty. The statistical results may include the number of cells in each subset and the percentage of cells in each subset.

[0250] For example, refer to Figure 5 This is a graph showing the analysis results of the lymphocyte subset analysis. The graph contains nine two-dimensional scatter plots, each plotted based on pairwise combinations of lymphocyte SSC.A, CD45, CD3, CD4, CD8, CD19, and CD16+56. Different scatter plot colors, such as red, green, and gray, are used to label different lymphocyte subsets and non-lymphocytes (see reference). Figure 5 The scatter plot shows different shades of gray. The horizontal line 101 and the vertical line 102 in the 2D scatter plot represent the thresholds that distinguish between positive and negative fluorescence signals. The thresholds divide the plot into four quadrants that represent different cell populations expressing fluorescence signals. For example, the cell population in the first quadrant is double negative for both fluorescence signals. The numbers marked in each quadrant represent the ratio of the number of cells in that quadrant to the number of lymphocytes.

[0251] In one feasible implementation, prior to step S51, which generates the lymphocyte subset analysis results, the method further includes:

[0252] Step S521: If the subpopulation cell data does not meet the rule that the proportion of monocytes is less than a preset monocyte proportion threshold, then the lymphocyte threshold is corrected according to the preset threshold compensation value, the lymphocyte threshold is lowered, and the step of extracting lymphocyte data from the original flow cytometry data based on the lymphocyte threshold is executed.

[0253] In one feasible embodiment, because monocytes are close to lymphocytes, they are easily mistaken for lymphocytes, making the distinction between monocytes and lymphocytes a challenge in identifying lymphocyte populations. Under correct analysis conditions, monocytes will not be extracted into the lymphocyte population, meaning the proportion of monocytes is less than a preset monocyte proportion threshold. If the subpopulation cell data does not meet the rule that the proportion of monocytes is less than the preset monocyte proportion threshold, it indicates that a certain number of monocytes are mistakenly identified as lymphocytes. This is due to the SSC.A threshold being set too high, so the SSC.A threshold needs to be lowered. Then, based on a preset threshold compensation value, the lymphocyte threshold is corrected by lowering the lymphocyte threshold, and the steps of extracting lymphocyte data from the original flow cytometry data based on the lymphocyte threshold are performed, along with subsequent steps, to re-analyze the original flow cytometry data for lymphocyte subpopulations.

[0254] In one feasible implementation, the difference between the percentage of monocytes and a preset threshold for the percentage of monocytes (hereinafter referred to as the monocyte percentage difference for distinction) is calculated, and the magnitude between the monocyte percentage difference and a preset first percentage difference threshold is determined. If the monocyte percentage difference is less than the first percentage difference threshold, the SSC.A threshold is corrected using a preset first threshold compensation value. If the monocyte percentage difference is greater than or equal to the first percentage difference threshold, the SSC.A threshold is corrected using a preset second threshold compensation value, wherein the first threshold compensation value is greater than the second threshold compensation value. For example, the first percentage difference threshold is 8%, the first threshold compensation value is 0.95, and the second threshold compensation value is 0.9.

[0255] In this embodiment, by using preset lymphocyte subset anomaly detection rules, comprehensive detection of lymphocyte data from the whole to the part is achieved, thereby enabling timely warning of incorrect cell grouping or abnormal detection results, assisting manual review, and improving the efficiency of timely processing of abnormal detection results.

[0256] To aid in understanding the above technical solutions, a more complete embodiment of a specific flow cytometry lymphocyte subset analysis method is provided below for illustration. (Refer to...) Figure 6The process involves importing raw flow cytometry data and preprocessing it, including feature name recognition, data naming, and format standardization. Then, it distinguishes between lymphocyte and non-lymphocyte data to determine lymphocyte population locations and obtain boundary coordinates, i.e., lymphocyte thresholds. Lymphocyte data is extracted using these boundary coordinates. Finally, CD3, CD4, CD8, CD19, and CD16+56 thresholds are identified to achieve automatic gating. Finally, based on these CD3, CD4, CD8, CD19, and CD16+56 thresholds... The system identifies the subpopulation data of each lymphocyte subset within the lymphocyte data and calculates the percentage of cells in each subpopulation. It then identifies anomalies using pre-defined lymphocyte subset anomaly detection rules and performs reverse subpopulation correction based on the anomaly identification results, adjusting the lymphocyte threshold. After correcting the lymphocyte threshold, the system outputs the analysis results, which may include a subpopulation gating diagram, statistical results, and early warning information. This enables timely warnings for incorrectly subpopulated cells or samples with abnormal detection results, assisting in manual review and improving the efficiency of timely processing of abnormal detection results.

[0257] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0258] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0259] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A flow cytometry method for analyzing lymphocyte subsets, characterized in that, The flow cytometry lymphocyte subset analysis method includes the following steps: Obtain raw flow cytometry data and determine the SSC.A probability density curve of the raw flow cytometry data on SSC.A; The SSC.A threshold is determined based on the troughs in the SSC.A probability density curve. If there is a trough in the SSC.A probability density curve, the minimum SSC.A value at the trough is taken as the SSC.A threshold. If there is no trough in the SSC.A probability density curve, the SSC.A value at the termination point of the peak is taken as the SSC.A threshold. Extract data from the raw flow cytometry data where SSC.A is less than the SSC.A threshold, and use them as the first cell data; then determine the CD45 probability density curve of the first cell data on CD45. Based on the troughs in the CD45 probability density curve, a CD45 threshold is determined, and the SSC.A threshold and the CD45 threshold are used as lymphocyte thresholds. Specifically, if there is a trough in the CD45 probability density curve, the maximum CD45 value located at the trough is used as the CD45 threshold; if there is no trough in the CD45 probability density curve, the CD45 value at the starting point of the peak is used as the CD45 threshold. Data from the raw flow cytometry data that are less than the SSC.A threshold and greater than the CD45 threshold are identified as lymphocyte data. The fluorescence probability density curve of the lymphocyte data on the fluorescence signal is determined, and the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data is determined according to the fluorescence probability density curve, wherein the fluorescence signal includes: CD3, a first signal, and the first signal includes one or more of CD4, CD8, CD19 and CD16+56; Based on the subpopulation cell threshold, subpopulation cell data in the lymphocyte data are identified; According to the preset abnormal lymphocyte subset detection rules, the subset cell data are detected to generate lymphocyte subset analysis results. The lymphocyte subset abnormality detection rules include at least one of the following rules: The sum of the percentages of T lymphocytes, B lymphocytes, and NK lymphocytes is within a first preset range; The combined percentage of Th cells and Ts cells is less than or equal to the percentage of T lymphocytes. The difference between the percentage of Th cells in the subset and the percentage of CD3-positive and CD8-negative cells is within a second preset range; The difference between the percentage of Ts subpopulation cells and the percentage of CD3-positive and CD4-negative cells was within the third preset range; The number of lymphocytes is greater than a preset lymphocyte count threshold; The percentage of B lymphocytes is less than the preset threshold for the percentage of B lymphocytes. The proportion of monocytes is less than the preset threshold for the proportion of monocytes.

2. The flow cytometry method for analyzing lymphocyte subsets as described in claim 1, characterized in that, The fluorescence signal includes CD3. The step of determining the fluorescence probability density curve of the lymphocyte data distribution on the fluorescence signal, and determining the subpopulation cell threshold of each lymphocyte subpopulation in the lymphocyte data based on the fluorescence probability density curve, includes: Determine the CD3 probability density curve of the distribution of the lymphocyte data on CD3; The CD3 value at the trough of the CD3 probability density curve is used as the CD3 threshold, and the CD3 threshold is used as the subpopulation cell threshold.

3. The flow cytometry method for analyzing lymphocyte subsets as described in claim 2, characterized in that, The fluorescence signal further includes a first signal, the first signal including at least one of CD4, CD8, CD19, and CD16+56. After the step of using the CD3 value at the trough of the CD3 probability density curve as a CD3 threshold and using the CD3 threshold as the subpopulation cell threshold, the signal further includes: Determine a first probability density curve of the distribution of the lymphocyte data on the first signal; Identify the first highest peak in the first probability density curve, and determine the first fluorescence value of the first trough after the first highest peak, and use the first fluorescence value as the threshold of the subpopulation cells.

4. The flow cytometry method for analyzing lymphocyte subsets as described in claim 1, characterized in that, The subpopulation cell thresholds include: CD3 threshold, CD4 threshold, CD8 threshold, CD19 threshold, and CD16+56 threshold. The step of identifying subpopulation cell data in the lymphocyte data based on the subpopulation cell thresholds includes: Data in the lymphocyte data where CD3 is greater than the CD3 threshold are used as T lymphocyte data; The T lymphocyte data with CD4 values ​​greater than the CD4 threshold are used as the Th cell subset data. Data from the T lymphocyte data where CD8 is greater than the CD8 threshold are used as Ts cell subset data; Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD19 is greater than the CD19 threshold are used as B lymphocyte data. Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD16+56 is greater than the CD16+56 threshold are used as NK lymphocyte data. Data in the lymphocyte data where CD3 is less than or equal to the CD3 threshold and CD4 is greater than the CD4 threshold are used as monocyte data. Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD8 is less than or equal to the CD8 threshold are considered as CD3-positive and CD8-negative cell data. Data in the lymphocyte data where CD3 is greater than the CD3 threshold and CD4 is less than or equal to the CD4 threshold are considered as CD3-positive and CD4-negative cell data. The T lymphocyte data, the Th cell subset data, the Ts cell subset data, the B lymphocyte data, the NK lymphocyte data, the monocyte data, the CD3-positive CD8-negative cell data, and the CD3-positive CD4-negative cell data are all used as the subpopulation cell data.

5. The flow cytometry method for analyzing lymphocyte subsets as described in claim 1, characterized in that, The step of detecting the subpopulation cell data according to preset lymphocyte subpopulation abnormality detection rules to generate lymphocyte subpopulation analysis results includes: Determine whether the subpopulation cell data meets the lymphocyte subpopulation abnormality detection rules; If not, the subpopulation cell data is determined to be abnormal, and the lymphocyte subpopulation analysis results are generated based on the abnormality of the subpopulation cell data.

6. The flow cytometry method for analyzing lymphocyte subsets as described in claim 5, characterized in that, Prior to the step of generating the lymphocyte subset analysis results, the method further includes: If the subpopulation cell data does not meet the rule that the proportion of monocytes is less than a preset monocyte proportion threshold, then the lymphocyte threshold is corrected according to the preset threshold compensation value, the lymphocyte threshold is lowered, and the step of extracting lymphocyte data from the original flow cytometry data based on the lymphocyte threshold is performed.

7. An electronic device, characterized in that, The device includes: a memory, a processor, and a flow cytometry lymphocyte subset analysis program stored in the memory and executable on the processor, the flow cytometry lymphocyte subset analysis program being configured to implement the steps of the flow cytometry lymphocyte subset analysis method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the storage medium stores a flow cytometry lymphocyte subset analysis program, which, when executed by a processor, implements the steps of the flow cytometry lymphocyte subset analysis method as described in any one of claims 1 to 6.

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