A method, medium and system for processing flow cytometer background fluorescence data graphs

Abstract

The present invention provides a flow cytometer background fluorescence data graph processing method, medium and system, belonging to the technical field of flow cytometer, the method comprises: obtaining experimental parameters and optical signal data in the flow cytometer experiment process; in a pre-set experimental optical signal basic database, finding the experimental optical signal basic data group with the highest experimental parameter matching degree as a reference data group; the basic experiment is a flow cytometer experiment using unstained cells as experimental samples; optimizing the experimental optical signal data using the reference data group; using the optimized experimental optical signal data as the signal data for generating a flow cytometer graph. The method, medium and system can effectively reduce the flow cytometer graph error caused by background fluorescence and non-specifically bound fluorescence signals, and solve the technical problem that the prior art is difficult to accurately analyze the detection sample.

Description

Technical Field

[0001] The present invention belongs to the technical field of flow cytometers, and more particularly, relates to a method, medium, and system for processing background fluorescence data graphs of flow cytometers. Background Art

[0002] Flow cytometry systematically combines fluidics, optics, and electronic systems to detect various microparticles using their fluorescence and physical properties. It is a technique that effectively differentiates heterogeneous cell populations at the single-cell level. A flow cytometer is used to perform single-cell, multi-parameter, rapid qualitative and quantitative analysis or sorting on a single column of cells or biological particles in a fast linear flow state. Most flow cytometers use an argon ion gas laser to generate laser light (wavelength 488 nm). When the cells pass through the measurement area, the laser light irradiates the fluorescence-stained cells vertically, and the fluorescent dye is excited to generate fluorescence signals and scattered light signals. The scattered light signals are divided into forward scatter (FSC) and side scatter (SSC). FSC reflects the size of the cells, while SSC reflects the internal structure and granular properties of the cells. The more complex the internal structure of the cells, the stronger the SSC. When different fluorescent dyes are excited by the laser, they emit fluorescence of different wavelengths. These fluorescences, together with the SSC, pass through various filters along the 90° direction of the laser to reach the corresponding channels. The cells carrying the fluorescent dye pass through the measurement area and are irradiated by the laser to emit photons, generating scattered light and fluorescence signals that are collected and analyzed by the corresponding detectors. The FSC signal is detected by a photodiode along the direction of the laser; while the SSC and fluorescence signals pass through the filter along the 90° direction of the laser to reach the corresponding channels and are detected by a photomultiplier tube (PMT). Once the light signals are detected, they are converted into electronic voltage pulse signals in proportion. The height, width, and area of each pulse signal are analyzed and then converted into digital signals and stored in a computer. The computer generates flow cytometry graphs, such as histograms, scatter plots, and contour plots, based on these signals for analyzing the test samples.

[0003] However, under the irradiation of the laser, the fluorescent dye that stains the cells is excited to emit fluorescence signals of specific binding, and the cells themselves also emit weak fluorescence under the laser irradiation, that is, background fluorescence; in addition, there will also be fluorescence signals of non-specific binding. The background fluorescence and the fluorescence signals of non-specific binding will cause a large error in the flow cytometry graphs generated by the computer, which is not conducive to accurately analyzing the test samples. Summary of the Invention

[0004] In view of this, the present invention provides a method, medium, and system for processing background fluorescence data graphs of flow cytometers, which can effectively reduce the errors of flow cytometry graphs caused by background fluorescence and fluorescence signals of non-specific binding, and solve the technical problem that it is difficult to accurately analyze test samples in the prior art.

[0005] The present invention is implemented as follows:

[0006] The first aspect of the present invention provides a method for processing background fluorescence data graphs of a flow cytometer, which includes the following steps:

[0007] S10. Obtain the experimental parameters and optical signal data during the flow cytometer experiment. The experimental parameters include the types of sample cells, cell parameters, sample concentration, and sample volume. The optical signal data is the electronic voltage pulse signal detected by the signal detection system of the flow cytometer, including two parameters: pulse width and pulse height. The electronic voltage pulse signal includes the electronic voltage pulse signal sets generated by FSC, SSC, and fluorescence signals, which are respectively denoted as the FSC signal set, the SSC signal set, and the fluorescence signal set. Among them, the cell parameters include cell size and nucleus size.

[0008] S20. In the pre-set experimental optical signal basic database, find the experimental optical signal basic data group with the highest degree of matching of experimental parameters as the reference data group. The experimental optical signal basic database contains multiple experimental optical signal basic data, and each experimental optical signal basic data is the experimental parameters and optical signal data obtained during the basic experiment of the flow cytometer. The basic experiment is a flow cytometer experiment using unstained cells as the experimental sample.

[0009] S30. Use the reference data group to optimize the experimental optical signal data.

[0010] S40. Use the optimized experimental optical signal data as the signal data for generating the flow cytogram.

[0011] Among them, the sample concentration represents the number of cells per unit volume.

[0012] Based on the above technical solution, the method for processing background fluorescence data graphs of a flow cytometer according to the present invention can also be improved as follows:

[0013] Among them, the step of finding the experimental optical signal basic data with the highest degree of matching of experimental parameters as the reference data in the pre-set experimental optical signal basic database is specifically as follows:

[0014] Step 1. If at least one experimental optical signal basic data of the same cell type as the experimental data can be found in the experimental optical signal basic database, go to Step 2; if not, go to Step 4.

[0015] Step 2. Establish a first data set with at least one experimental optical signal basic data of the same cell type as the experimental data found in Step 1.

[0016] Step 3. In the first dataset, select M experimental optical signal basic data with the highest matching degree according to the sample concentration and sample volume of the experiment as the reference data group; 1 ;

[0017] Step 4. Use the cell parameters to find M experimental optical signal basic data with the highest matching degree in the experimental optical signal basic database as the second dataset; 0 ;

[0018] Step 5. In the second dataset, select M experimental optical signal basic data with the highest matching degree according to the sample concentration and sample volume of the experiment as the reference data group; 2 ;

[0019] wherein, M 0 > 2 × M 2 , and M 2 ≥ 2.

[0020] The beneficial effects of adopting the above improvement scheme are as follows: This matching idea ensures that the basic data is preferentially searched according to the cell type, and then the matching of the sample concentration is considered. If the type cannot be matched, it is degraded to match according to the cell parameters. At the same time, it also ensures that the finally output basic data and the experimental parameters achieve the best matching in terms of sample concentration.

[0021] Further, M 1 = 1, M 2 = 5.

[0022] Among them, in the step of selecting M experimental optical signal basic data with the highest matching degree according to the sample concentration and sample volume of the experiment as the reference data group, the matching method is the cosine similarity method. 1 ;

[0023] Among them, the step of optimizing the experimental optical signal data by using the reference data group is specifically as follows:

[0024] Step 31. Arrange the FSC signal set, SSC signal set, and fluorescence signal set in the experimental optical signal data in ascending order of pulse height;

[0025] Step 32. If the number of elements in the reference data group is greater than 1, go to Step 33; if the number of elements in the reference data group is 1, go to Step 34;

[0026] Step 33. Merge multiple elements in the reference data group into one element, and use the optical signal data in the obtained merged element as the reference signal and go to Step 35;

[0027] Step 34. Use the element in the reference group as the reference signal;

[0028] Step 35: Denote the FSC signal set, SSC signal set, and fluorescence signals in the reference signal as the FSC reference signal set, SSC reference signal set, and fluorescence reference signal set respectively, and arrange the FSC reference signal set, SSC reference signal set, and fluorescence reference signal set in ascending order of pulse height;

[0029] Step 36: For the FSC signal set and the FSC reference signal set in the optical signal data of the experiment, establish a mapping from the FSC signal set in the optical signal data of the experiment to the FSC reference signal set;

[0030] Step 37: According to the established mapping relationship, update the pulse height of each signal in the FSC signal set in the optical signal data of the experiment by subtracting the pulse height of the mapped FSC reference signal;

[0031] Step 38: Update the SSC signal set and the fluorescence signal set in the optical signal data of the experiment in the same way as in Steps 35 - 36 to obtain the optimized optical signal data of the experiment.

[0032] Further, the step of merging multiple elements in the reference data group into one element is specifically: merge the optical signal data in multiple elements in the reference data group, that is, merge the FSC signal set, SSC signal set, and fluorescence signals in each element into three signal sets, and use the merged three signal sets as the optical signal data of the merged element.

[0033] Further, the step of establishing a mapping from the FSC signal set in the optical signal data of the experiment to the FSC reference signal set is specifically: by calculating the Euclidean distance between the experimental FSC pulse signal and the reference FSC pulse signal, find the reference FSC pulse signal with the closest distance for each experimental FSC pulse signal, and establish a mapping correspondence between the two.

[0034] Further, according to the established mapping relationship, the method for updating the pulse height of each signal in the FSC signal set in the optical signal data of the experiment is: subtract the pulse height of the mapped FSC reference signal from the pulse height of each signal in the FSC signal set in the optical signal data of the experiment, and use the obtained result as the pulse height of each signal in the FSC signal set in the optical signal data of the experiment.

[0035] The second aspect of the present invention provides a computer - readable storage medium, wherein program instructions are stored in the computer - readable storage medium, and when the program instructions run, they are used to execute the above - mentioned method for processing the background fluorescence data graph of a flow cytometer.

[0036] The third aspect of the present invention provides a system for processing background fluorescence data graphs of a flow cytometer, which includes the above-mentioned computer-readable storage medium.

[0037] Compared with the prior art, the beneficial effects of a method, medium, and system for processing background fluorescence data graphs of a flow cytometer provided by the present invention are as follows:

[0038] 1. The calibration effect is better, improving the accuracy and sensitivity of detection

[0039] The present invention performs calibration by selecting a reference background signal with matching parameters from a pre-established fluorescence background signal database of a flow cytometer, rather than relying on repeated measurements of a single sample. The reference signal can well reflect the optical characteristics of different cell types, and the effect of background matching and calibration is better, effectively reducing and eliminating the influence of non-specific background signals. It can effectively reduce the flow cytometry errors caused by background fluorescence and fluorescent signals of non-specific binding, and solve the technical problem that it is difficult to accurately analyze the test sample in the prior art.

[0040] 2. No need for repeated detection, saving sample volume and detection time

[0041] Existing methods require repeated detection of the same sample to generate background signals, while the present invention only requires a single detection to achieve background calibration, without repeated detection, thus saving sample volume and detection time costs. This makes the present invention more suitable for application scenarios where the sample volume is precious or the detection time is limited.

[0042] 3. Reduce detection costs and improve economic benefits

[0043] The present invention does not rely on repeated experiments and control experiments. Only by building a basic database of experimental optical signals inside the flow cytometer before leaving the factory can background calibration be achieved, saving the consumption of a large amount of experimental materials such as samples and reagents, and also saving detection time and reducing experimental costs. This significantly improves the economic benefits of using the flow cytometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of a method for processing background fluorescence data graphs of a flow cytometer provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0047] As Figure 1 shown, it is a flowchart of a method for processing background fluorescence data of a flow cytometer provided by the first aspect of the present invention. This method includes the following steps:

[0048] S10. Obtain the experimental parameters and optical signal data during the flow cytometer experiment. Among them, the experimental parameters include the types of sample cells, cell parameters, sample concentration, and sample volume; the optical signal data is the electronic voltage pulse signal detected by the signal detection system of the flow cytometer, including two parameters of pulse width and pulse height. The electronic voltage pulse signal includes the electronic voltage pulse signal sets generated by FSC, SSC, and fluorescence signals, which are respectively denoted as the FSC signal set, the SSC signal set, and the fluorescence signal set. Among them, the cell parameters include cell size and nucleus size;

[0049] S20. In the pre-set experimental optical signal basic database, find the experimental optical signal basic data group with the highest experimental parameter matching degree as the reference data group; the experimental optical signal basic database contains multiple experimental optical signal basic data. Each experimental optical signal basic data is the experimental parameter and optical signal data obtained during the basic experiment of the flow cytometer. The basic experiment is a flow cytometer experiment using unstained cells as the experimental sample;

[0050] S30. Use the reference data group to optimize the experimental optical signal data;

[0051] S40. Use the optimized experimental optical signal data as the signal data for generating the flow cytogram.

[0052] Among them, in the above technical solution, the step of finding the experimental optical signal basic data with the highest experimental parameter matching degree as the reference data in the pre-set experimental optical signal basic database is specifically:

[0053] Step 1. If at least one experimental optical signal basic data of the same cell type as the experimental data can be found in the experimental optical signal basic database, go to Step 2; if not, go to Step 4;

[0054] Step 2. Establish a first data set with at least one experimental optical signal basic data of the same cell type as the experimental data found in Step 1;

[0055] Step 3. In the first data set, according to the highest matching degree of the sample concentration and sample volume of the experiment, M1 The basic data of an experimental optical signal is used as a reference data group;

[0056] Step 4: Use the cell parameters to find M 0 pieces of experimental optical signal basic data with the highest matching degree in the experimental optical signal basic database as the second data set;

[0057] Step 5: In the second data set, M 2 pieces of experimental optical signal basic data with the highest matching degree of the sample concentration and sample volume in the experiment are used as the reference data group;

[0058] where, M 0 > 2×M 2 , and M 2 ≥ 2.

[0059] The following provides a specific implementation manner of step S20:

[0060] First, we define some variables and constants:

[0061] -E represents experimental data

[0062] -E type represents the cell type of the experimental data

[0063] -E conc represents the sample concentration of the experimental data

[0064] -E vol represents the sample volume of the experimental data

[0065] -D represents a preset experimental optical signal basic database

[0066] -D i represents the i-th experimental optical signal basic data in the database D

[0067] -D i,type represents D i 's cell type of the data

[0068] -D i,conc represents D i 's sample concentration of the data

[0069] -D i,vol represents D i 's sample volume of the data

[0070] -M 1 represents the size of the reference data group selected in step 3

[0071] -M 2 represents the size of the reference data group selected in step 5

[0072] -M 0 is a constant, and its value is greater than 2×M 2

[0073] Then step S20 can be specifically implemented as follows:

[0074] Step 1: Search in database D for all data that satisfy D i,type = E type , and put this data into set S 1 ;

[0075] Step 2: If S 1 is not empty, execute step 3; otherwise execute step 4;

[0076] Step 3: In set S 1 , select the M i,conc data of sample concentration D i,vol and sample volume D conc that are closest to the sample concentration E vol and sample volume E 1 of the experimental data E as the reference data group R 1 . The degree of closeness can be calculated using the following formula:

[0077]

[0078] Select the M i data with the largest score score 1 . If multiple data with the same score are obtained, randomly select M 1 data.

[0079] Step 4: In database D, find the M 0 data that are closest to the cell parameters of the experimental data E according to the cell parameters, and put them into set S 2 . The degree of closeness can be obtained by calculating the Euclidean distance between the cell parameters;

[0080] Step 5: In set S 2 , select the M i,conc data of sample concentration D i,vol and sample volume D conc that are closest to the sample concentration E vol and sample volume E 2 of the experimental data E as the reference data group R 2 . The calculation method of the degree of closeness is the same as that in step 3;

[0081] Step 6: Merge the reference data group R 1 and R 2 , and use it as the output of step S20;

[0082] If R 1 is empty, the output is R 2 ; if R 2 is empty, the output is R 1 . If R 1 and R 2 are both empty, the output is the empty set.

[0083] The above method realizes searching for the reference data group that best matches the experimental data in the database by first finding the candidate data according to the cell type and then finding the sub-optimal data according to the sample parameters. At the same time, the value of M 0 is set to be greater than 2×M 2 , ensuring that the amount of data obtained by matching the cell parameters is greater than the amount of data obtained by only matching the sample parameters, making the matching of the cell parameters play a dominant role.

[0084] The key lies in using combinations of multiple conditions such as cell type, cell parameters, sample concentration, and sample volume for matching, which not only ensures the matching of cell parameters but also considers the proximity of sample parameters, and selects the reference data group that is closest to the experimental data. The scoring formula makes full use of the quantitative information of the sample parameters to make the matching more accurate. The definition of variables is also very clear, and each variable and constant has a unique meaning.

[0085] Furthermore, in the above technical solution, M 1 = 1, M 2 = 5.

[0086] Among them, in the above technical solution, in the step of using the M 1 experimental optical signal basic data with the highest matching degree of sample concentration and sample volume as the reference data group, the matching method is the cosine similarity method.

[0087] Among them, in the above technical solution, the step of using the reference data group to optimize the experimental optical signal data is specifically as follows:

[0088] Step 31: Arrange the FSC signal set, SSC signal set, and fluorescence signal set in the experimental optical signal data in ascending order of pulse height;

[0089] Step 32: If the number of elements in the reference data group is greater than 1, go to Step 33; if the number of elements in the reference data group is 1, go to Step 34;

[0090] Step 33: Combine multiple elements in the reference data group into one element, and use the optical signal data in the obtained combined element as the reference signal and go to Step 35;

[0091] Step 34: Use the element in the reference group as the reference signal;

[0092] Step 35: Denote the FSC signal set, SSC signal set, and fluorescence signals in the reference signal as the FSC reference signal set, SSC reference signal set, and fluorescence reference signal set respectively, and arrange the FSC reference signal set, SSC reference signal set, and fluorescence reference signal set in ascending order of pulse height;

[0093] Step 36: For the FSC signal set in the optical signal data of the experiment and the FSC reference signal set, establish a mapping from the FSC signal set in the optical signal data of the experiment to the FSC reference signal set;

[0094] Step 37: According to the established mapping relationship, update the pulse height of each signal in the FSC signal set in the optical signal data of the experiment, and update it by subtracting the pulse height of the mapped FSC reference signal;

[0095] Step 38: Update the SSC signal set and the fluorescence signal set in the optical signal data of the experiment in the manner of Steps 35 - 36 to obtain the optimized optical signal data of the experiment.

[0096] Further, in the above technical solution, the step of merging multiple elements in the reference data group into one element is specifically: merge the optical signal data in multiple elements in the reference data group, that is, merge the FSC signal set, SSC signal set, and fluorescence signals in each element into three signal sets, and use the merged three signal sets as the optical signal data of the merged element.

[0097] Step 33: The specific implementation manner of merging the optical signal data in multiple elements in the reference data group is as follows:

[0098] 1) Define that the reference data group contains N reference data elements:

[0099] R = {R 1 , R 2 , …, R N}, N > 1;

[0100] 2) Each reference data element contains optical signal data:

[0101] R i = {R i,FSC , R i,SSC , R i,FL};

[0102] 3) Merge the optical signal data of each reference data element:

[0103]

[0104] 4) Obtain the merged reference signal:

[0105] R′ = {R′ FSC , R′ SSC , R′ FL};

[0106] 5) Use R′ as the output of step 33

[0107] Furthermore, in the above technical solution, for the FSC signal set and the FSC reference signal set in the optical signal data of the experiment, the steps to establish the mapping from the FSC signal set in the optical signal data of the experiment to the FSC reference signal set are specifically as follows: By calculating the Euclidean distance between the experimental FSC pulse signal and the reference FSC pulse signal, find the reference FSC pulse signal with the closest distance for each experimental FSC pulse signal, and establish the mapping correspondence between the two.

[0108] Step 36 is to establish the mapping relationship between the experimental FSC signal set and the FSC reference signal set, and its specific implementation method is as follows:

[0109] 1) Define the experimental FSC signal set: E FSC = {e FSC1 , e FSC2 , …, e FSCm}, which contains m pulse signals;

[0110] 2) Define the FSC reference signal set: R FSC = {r FSC1 , r FSC2 , …, r FSCn}, which contains n pulse signals;

[0111] 3) Calculate the distance between each experimental FSC pulse signal e FSCi and each reference FSC pulse signal r FSCj :

[0112] d ij = distance(e FSCi , r FSCj );

[0113] where the distance() function calculates the Euclidean distance between two pulses.

[0114] 4) For each experimental FSC pulse signal e FSCi , find the reference FSC pulse signal r FSCj in the FSC pulse signal set that is closest to it in sorting:

[0115] 5) Establish the mapping relationship:

[0116]

[0117] That is, the experimental pulse e FSCi is mapped to its closest reference pulse r FSCj .

[0118] 6) Use the mapping relationship f as the output of step 36.

[0119] Furthermore, in the above technical solution, according to the established mapping relationship, the method for updating the pulse height of each signal in the FSC signal set of the experimental optical signal data is as follows: Subtract the pulse height of the mapped FSC reference signal from the pulse height of each signal in the FSC signal set of the experimental optical signal data, and use the obtained result as the pulse height of each signal in the FSC signal set of the experimental optical signal data.

[0120] Step 37 is to update the height of each pulse in the experimental FSC signal set according to the established mapping relationship. The specific implementation method is as follows:

[0121] 1) Define the experimental FSC signal set: E FSC ={e FSC1 , e FSC2 , …, e FSCm}, which contains m pulse signals;

[0122] 2) Define the established mapping relationship:

[0123] 3) For each pulse signal e FSC in E FSCi :

[0124] - Find its corresponding reference pulse signal r FSCj according to the mapping relationship f;

[0125] - Subtract the pulse height h FSCi of r eFSCi from the pulse height h FSCj of e rFSCj :

[0126] h′ eFSCi = h eFSCi - h rFSCj ;

[0127] - Update the pulse height of e FSCi to h′ eFSCi ;

[0128] 4) Obtain the updated experimental FSC signal set: E′ FSC , where the height of each pulse signal has been updated;

[0129] 5) Take E′ FSC as the output of step 37.

[0130] Step S40 uses the optical signal data of the optimized processed experiment as the signal data for generating the flow cytometry graph, which is a functional step of the software built into the flow cytometer itself.

[0131] The second aspect of the present invention provides a computer-readable storage medium, wherein program instructions are stored in the computer-readable storage medium, and when the program instructions run, they are used to execute the above-mentioned method for processing the background fluorescence data graph of a flow cytometer.

[0132] The third aspect of the present invention provides a system for processing the background fluorescence data graph of a flow cytometer, which includes the above-mentioned computer-readable storage medium.

[0133] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for processing background fluorescence data graphs of a flow cytometer, characterized in that, it comprises the following steps: S10. Obtain the experimental parameters and optical signal data during the flow cytometer experiment. Among them, the experimental parameters include the types of sample cells, cell parameters, sample concentration, and sample volume; the optical signal data is the electronic voltage pulse signal detected by the signal detection system of the flow cytometer, including two parameters: pulse width and pulse height. The electronic voltage pulse signal includes the electronic voltage pulse signal sets generated by FSC, SSC, and fluorescence signals, which are respectively denoted as the FSC signal set, the SSC signal set, and the fluorescence signal set. Among them, the cell parameters include cell size and nucleus size; S20. In the pre-set experimental optical signal basic database, find the experimental optical signal basic data group with the highest experimental parameter matching degree as the reference data group; the experimental optical signal basic database contains multiple experimental optical signal basic data, and each experimental optical signal basic data is the experimental parameters and optical signal data obtained during the basic experiment of the flow cytometer. The basic experiment is a flow cytometer experiment using unstained cells as the experimental sample; S30. Use the reference data group to optimize the experimental optical signal data; S40. Use the optimized experimental optical signal data as the signal data for generating the flow cytogram; Among them, the step of finding the experimental optical signal basic data with the highest experimental parameter matching degree in the pre-set experimental optical signal basic database as the reference data is specifically: Step 1. If at least one experimental optical signal basic data of the same cell type as the experimental data can be found in the experimental optical signal basic database, go to Step 2; if not, go to Step 4; Step 2. Establish a first data set with at least one experimental optical signal basic data of the same cell type as the experimental data found in Step 1; Step 3: In the first dataset, match the M 1 experimental optical signal basic data with the highest sample concentration and sample volume matching degree in the experiment as the reference data group; Step 4: Use the cell parameters to find M in the experimental optical signal basic database 0 experimental optical signal basic data with the highest matching degree as the second data set; Step 5. In the second dataset, match the M 2 experimental optical signal basic data with the highest sample concentration and sample volume matching degree in the experiment as the reference data group; Among them, M 0 > 2×M 2 , and M 2 ≥2 2. The method for processing background fluorescence data graphs of a flow cytometer according to claim 1, characterized in that, M 1 = 1, M 2 = 5.

3. The method for processing background fluorescence data graphs of a flow cytometer according to claim 1, characterized in that, In the step of matching the M 1 experimental optical signal basic data with the highest sample concentration and sample volume matching degree in the said experiment as the reference data group, the matching method is the cosine similarity method.

4. The method for processing background fluorescence data graphs of a flow cytometer according to claim 1, characterized in that, The step of using the reference data group to optimize the experimental optical signal data is specifically: Step 31. Arrange the FSC signal set, SSC signal set, and fluorescence signal set in the experimental optical signal data in ascending order of pulse height; Step 32. If the number of elements in the reference data group is greater than 1, go to Step 33; if the number of elements in the reference data group is 1, go to Step 34; Step 33. Merge multiple elements in the reference data group into one element, and use the optical signal data in the obtained merged element as the reference signal and go to Step 35; Step 34. Use the element in the reference group as the reference signal; Step 35: Denote the FSC signal set, SSC signal set, and fluorescence signals in the reference signal as the FSC reference signal set, SSC reference signal set, and fluorescence reference signal set respectively, and arrange the FSC reference signal set, SSC reference signal set, and fluorescence reference signal set in ascending order of pulse height; Step 36: For the FSC signal set and FSC reference signal set in the optical signal data of the experiment, establish a mapping from the FSC signal set in the optical signal data of the experiment to the FSC reference signal set; Step 37: According to the established mapping relationship, update the pulse height of each signal in the FSC signal set in the optical signal data of the experiment by subtracting the pulse height of the mapped FSC reference signal; Step 38: Update the SSC signal set and fluorescence signal set in the optical signal data of the experiment in the manner of Steps 35 - 36 to obtain the optimized optical signal data of the experiment.

5. A method for processing a background fluorescence data graph of a flow cytometer according to claim 4, wherein, The step of merging multiple elements in the reference data group into one element is specifically: merge the optical signal data in the multiple elements in the reference data group, that is, merge the FSC signal set, SSC signal set, and fluorescence signals in each element into three signal sets, and use the merged three signal sets as the optical signal data of the merged element.

6. A method for processing a background fluorescence data graph of a flow cytometer according to claim 5, wherein, The step of establishing a mapping from the FSC signal set in the optical signal data of the experiment to the FSC reference signal set is specifically: by calculating the Euclidean distance between the experimental FSC pulse signal and the reference FSC pulse signal, find the reference FSC pulse signal with the closest distance for each experimental FSC pulse signal, and establish a mapping correspondence between the two.

7. A method for processing a background fluorescence data graph of a flow cytometer according to claim 6, wherein, According to the established mapping relationship, the method for updating the pulse height of each signal in the FSC signal set in the optical signal data of the experiment is: subtract the pulse height of the mapped FSC reference signal from the pulse height of each signal in the FSC signal set in the optical signal data of the experiment, and use the obtained result as the pulse height of each signal in the FSC signal set in the optical signal data of the experiment.

8. A computer-readable storage medium, wherein, The computer-readable storage medium stores program instructions that, when running, are used to execute a method for processing a background fluorescence data graph of a flow cytometer according to any one of claims 1 - 7.

9. A system for processing a background fluorescence data graph of a flow cytometer, wherein, It includes the computer-readable storage medium according to claim 8.

Images