Automatic detection method, device, and storage medium for flow cytometer

Abstract

The application relates to an automatic detection method and device for a flow cytometer and a storage medium, and comprises the following steps: detecting microsphere data by the flow cytometer; simplifying the detected microsphere data; automatically gating the simplified microsphere data to obtain at least eight gates; fitting a linear formula by using the microsphere data in all the gates; and calculating the minimum detection limit and fluorescence linearity by using the fitted linear formula. The application provides a solution for automatic analysis of performance test of the flow cytometer, avoids errors caused by manual data analysis, and improves consistency and reliability of data analysis results.

Description

Technical Field

[0001] This invention relates to the field of cell quantitative analysis technology, and in particular to automated detection methods, devices, and storage media for flow cytometers. Background Technology

[0002] The detection limit of a flow cytometer refers to the lowest amount or concentration of an analyte that the instrument can detect. This is achieved under normal instrument operating conditions, excluding drift and fluctuations in the measurement readings caused by the instrument's own noise. Fluorescence linearity refers to the linearity of the measured data when microspheres carrying a linearly increasing number of fluorescent molecules are used in the instrument.

[0003] Currently, flow cytometry requires manual testing of the instrument's detection limit and fluorescence linearity before use, necessitating data analysis, as there is no automated detection method. Traditional manual data analysis has the following drawbacks:

[0004] (i) Manual data analysis requires manual gate setting, making it impossible to standardize the detection and analysis data. The results of analysis by different personnel at different times will have slight differences, leading to deviations in the detection results.

[0005] (ii) Manual data analysis involves many steps, is slow, and takes a long time.

[0006] Chinese invention patent CN104200114B discloses a rapid analysis method for flow cytometry data. The invention includes the following steps: (1) estimating the number of clusters in the flow cytometry data using a nuclear density estimation method to obtain the range of cluster numbers contained in the data; (2) after obtaining the number of clusters, automatically clustering the data using the K-means method to optimize the initial cluster centers; (3) merging and filtering the optimal results using a two-segment linear regression fitting method on the clustered results. While this analysis method can analyze the mean center of parameter samples, it is difficult to automate the analysis, requires a K value for calculation, and requires that different categories of parameter samples not overlap, making it only suitable for high-dimensional data with no obvious patterns. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the purpose of this invention is to provide an automated detection method, apparatus, and storage medium for flow cytometers to solve one or more problems in the prior art.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] The automated detection method for flow cytometry includes the following steps:

[0010] Flow cytometry was used to analyze microsphere data.

[0011] The measured microsphere data were simplified.

[0012] Automatic gate formation was performed on the simplified microsphere data to obtain at least eight gates;

[0013] A linear formula was fitted using microsphere data from all the gates;

[0014] The minimum detection limit is calculated using the fitted linear formula.

[0015] Optionally, in the automated detection method for flow cytometry, the simplified processing of the microsphere data includes the following steps:

[0016] Save the microsphere data as a variable data matrix;

[0017] Normalize the variable data matrix;

[0018] Perform logarithmic processing on the variable data matrix.

[0019] Optionally, in the automated detection method for flow cytometers described above, the microsphere data includes fluorescence intensity and the corresponding number of microspheres.

[0020] Optionally, in the automated detection method for flow cytometers, the step of normalizing the variable data matrix includes: substituting the variable data matrix into a normalization formula, wherein the normalization formula is d = ((c - cmin) / (cmax - cmin)) * 1024, where c is the fluorescence intensity parameter in the variable data matrix, cmin is the minimum value of the fluorescence intensity parameter in the variable data matrix, cmax is the maximum value of the fluorescence intensity parameter, and d is the normalized fluorescence intensity parameter.

[0021] Optionally, in the automated detection method for flow cytometers, the step of performing logarithmic processing on the variable data matrix includes: substituting the variable data matrix into a logarithmic formula, wherein the logarithmic formula is q = log10p, where p is the fluorescence intensity parameter in the variable data matrix, and q is the logarithm of the fluorescence intensity parameter to the base 10.

[0022] Optionally, in the automated detection method for flow cytometry, the step of automatically gating the microsphere data includes:

[0023] Find the optimal hyperplane for bipartite microsphere data;

[0024] Find the optimal hyperplane for the bipartite microsphere data below the optimal hyperplane, and repeat this step at least six times;

[0025] Eight gates are obtained based on the partitioning result of the optimal hyperplane.

[0026] Optionally, in the automated detection method for flow cytometry described above, the step of finding the optimal hyperplane for the bipartite microsphere data includes:

[0027] Substitute the variable data matrix into the optimal hyperplane formula, which is w0x. r +b0=0, where x r The fluorescence intensity parameter is a variable in the data matrix.

[0028] Optionally, in the automated detection method for flow cytometry described above, the step of finding the optimal hyperplane for the bipartite microsphere data includes:

[0029] The goal is to place 45%–55% of the microsphere data above the optimal hyperplane and 45%–55% of the microsphere data below the optimal hyperplane.

[0030] Optionally, in the automated detection method for flow cytometry described above, the step of fitting a linear formula using microsphere data from all gates includes:

[0031] The arithmetic mean, standard deviation, and coefficient of variation of fluorescence intensity were calculated using data from microspheres in all gates.

[0032] The arithmetic mean of the microsphere data in all gates except the last one and the set detection limit for each gate are used to fit a linear formula;

[0033] Optionally, in the automated detection method for flow cytometers described above, the least squares method is used to fit the linear formula.

[0034] Optionally, in the automated detection method for flow cytometry described above, the step of calculating the minimum detection limit using a fitted linear formula includes:

[0035] The minimum detection limit is obtained by substituting the microsphere data from the last gate into the linear formula.

[0036] Optionally, in the described automated detection method for flow cytometry, after the step of calculating the minimum detection limit using a fitted linear formula, the method further includes the following step:

[0037] Fluorescence linearity is calculated using the fitted linear formula.

[0038] Optionally, in the automated detection method for flow cytometry described above, the step of calculating fluorescence linearity using a fitted linear formula includes:

[0039] The Pearson correlation coefficient, i.e., fluorescence linearity, is calculated using the fitted linear formula.

[0040] Optionally, in the automated detection method for flow cytometers described above, a support vector machine is used to divide the optimal hyperplane.

[0041] Optionally, in the automated detection method for flow cytometry described above, microspheres with linearly increased fluorescence intensity are used.

[0042] To achieve the above objectives, the present invention also provides a flow cytometer apparatus, comprising:

[0043] Data processing module: used to simplify the measured microsphere data;

[0044] Automatic gate generation module: used to generate eight gates based on the optimal hyperplane partitioning result;

[0045] Fitting module: Fits a linear formula using the arithmetic mean of the microsphere data from all but the last gate;

[0046] Calculation module: Calculates the minimum detection limit and fluorescence linearity using the fitted linear formula.

[0047] Optionally, the device further includes:

[0048] First processing module: used to save microsphere data as a variable data matrix;

[0049] The second processing module is used to normalize the variable data matrix.

[0050] The third processing module is used to perform logarithmic processing on the variable data matrix.

[0051] Optionally, the device further includes:

[0052] The gating module is used to gate the data above and below each optimal hyperplane.

[0053] Naming module: Used to name each door according to the order in which they are set.

[0054] Optionally, the device further includes:

[0055] The first calculation module is used to calculate the arithmetic mean, standard deviation, and coefficient of variation of fluorescence intensity using microsphere data from all gates.

[0056] The second calculation module is used to substitute the microsphere data in the last gate into the linear formula to obtain the minimum detection limit;

[0057] The third calculation module is used to calculate the Pearson correlation coefficient using a linear formula.

[0058] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the automated detection method for flow cytometers described above.

[0059] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0060] (I) The automated detection method, device, and storage medium for flow cytometers provided by this invention detect microsphere data using a flow cytometer; simplify the measured microsphere data; automatically gate the simplified microsphere data to obtain at least eight gates; fit a linear formula using the microsphere data from all gates; and calculate the minimum detection limit and fluorescence linearity using the fitted linear formula. Therefore, this invention provides an automated analysis solution for flow cytometer performance testing, avoiding errors caused by manual data analysis and improving the consistency and reliability of data analysis results.

[0061] (ii) Furthermore, the present invention implements automatic gate-looping based on the support vector machine algorithm, which has a fast processing speed and greatly saves the time required for data analysis.

[0062] (iii) Furthermore, the automatic detection method for flow cytometers of the present invention has good compatibility, supports most common data formats, and is compatible with commonly available instruments. Attached Figure Description

[0063] Figure 1 A flowchart illustrating the automated detection method for flow cytometers provided in Embodiment 1 of the present invention is shown.

[0064] Figure 2 The diagram shows a simplified processing step for the measured microsphere data in the automated detection method for flow cytometer provided in Embodiment 1 of the present invention.

[0065] Figure 3 The diagram shows a flowchart of the automatic gating step for simplified microsphere data in the automatic detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0066] Figure 4 The diagram shows a flowchart illustrating the step of fitting a linear formula using microsphere data from all gates in the automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0067] Figure 5 The diagram illustrates the process of calculating the minimum detection limit and fluorescence linearity using a fitted linear formula in the automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0068] Figure 6 A connection diagram of the flow cytometer apparatus provided in Embodiment 1 of the present invention is shown.

[0069] Figure 7 This diagram illustrates the selection of a flow cytometer file in an automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0070] Figure 8This diagram illustrates the automatic detection method for flow cytometers provided in Embodiment 1 of the present invention for reading flow cytometer data.

[0071] Figure 9 This diagram illustrates the calculation of the minimum detection limit and fluorescence linearity in the automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0072] Figure 10 The diagram shows the calculation results of the automated detection method for flow cytometer provided in Embodiment 1 of the present invention.

[0073] Figure 11 This diagram illustrates the variable data matrix in the automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0074] Figure 12 This diagram illustrates the normalization result of the variable data matrix in the automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0075] Figure 13 This diagram illustrates the logarithmic processing results of the variable data matrix in the automated detection method for flow cytometers provided in Embodiment 1 of the present invention.

[0076] Figure 14 A schematic diagram of the microsphere instruction manual for the automated detection method for flow cytometers provided in Embodiment 1 of the present invention is shown. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0078] The following detailed description, with reference to the accompanying drawings, of the automated detection method, apparatus, and storage medium for flow cytometers for which protection is claimed in this invention, using an example, is provided for reference only, though it will be understood by those skilled in the art.

[0079] Example 1

[0080] Flow cytometry uses automated detection methods, such as Figure 1 As shown, it includes the following steps:

[0081] Step S1: Flow cytometry is used to detect microsphere data.

[0082] Specifically, in the automated detection method for flow cytometers, the microsphere data includes fluorescence intensity and the corresponding number of microspheres.

[0083] In the automated detection method for flow cytometers described in Embodiment 1 of this invention, the SE309 flow cytometer from Veran Technology is used. The detection method of this invention supports FSC standard data from any manufacturer's flow cytometer (FCS files must conform to the FCS file standard, including standards 2.0, 3.0, and 3.1). Eight-peak microspheres from Spherotech RCP-30-5A are used. The measured values ​​for the FITC, PE, ECD, PE-cy5, and APC channels are the actual values. The fluorescence molecular weights of the remaining channels are replaced by the fluorescence molecular weights of the bandpass filters used for those channels whose wavelengths are closest to those of the FITC, PE, ECD, PE-cy5, and APC channels. Therefore, the corresponding detection limits are the detection limits of the FITC, PE, ECD, PE-cy5, and APC dyes in their respective channels.

[0084] Step S2 involves simplifying the measured microsphere data.

[0085] For details, please refer to Figure 2 The simplified processing of the microsphere data includes the following steps:

[0086] Step S201: Save the microsphere data as a variable data matrix.

[0087] For details, please refer to Figure 7 and Figure 8 Select and read the raw data FCS or LMD file generated by the flow cytometer. After reading, save the raw data FCS or LMD file as a variable data matrix in memory. Please refer to [link / reference]. Figure 11 The number of rows in the matrix represents the number of data rows obtained, which is usually no less than 500 rows. The number of columns represents the number of instrument channels. In the automatic detection method for flow cytometer described in this embodiment, the variable data matrix has 2881 rows and 24 columns.

[0088] Step S202: Normalize the variable data matrix.

[0089] Please refer to Figure 12 Specifically, the parameters in the variable data matrix are substituted into the normalization formula, which is d = ((c-cmin) / (cmax-cmin))*1024, where c is the fluorescence intensity parameter in the variable data matrix, cmin is the minimum value of the fluorescence intensity parameter in the variable data matrix, cmax is the maximum value of the fluorescence intensity parameter, and d is the normalized fluorescence intensity parameter, which facilitates subsequent logarithmic processing.

[0090] Step S203: Perform logarithmic processing on the variable data matrix.

[0091] Please refer to Figure 13Specifically, in the automated detection method for flow cytometers, the step of performing logarithmic processing on the variable data matrix includes: substituting the variable data matrix into a logarithmic formula, wherein the logarithmic formula is q = log10p, where p is the fluorescence intensity parameter in the variable data matrix, and q is the logarithm of the fluorescence intensity parameter with base 10. After logarithmic processing, the final fitted formula has linear characteristics.

[0092] Step S3: Automatic gate formation is performed on the simplified microsphere data to obtain at least eight gates.

[0093] For details, please refer to Figure 3 The steps for automatically gateding microsphere data include:

[0094] Step S301: Find the optimal hyperplane for the bipartite microsphere data.

[0095] Specifically, we use support vector machines to find the optimal hyperplane for the bipartite sphere data. Let the formula for the optimal hyperplane be w0x. r +b0=0, where x r The fluorescence intensity parameter is located in the variable data matrix. The microsphere data from the variable data matrix are substituted into the optimal hyperplane formula for calculation, ensuring that 45%–55% of the microsphere data are above the optimal hyperplane and 45%–55% are below it.

[0096] Step S302: Find the optimal hyperplane for the bipartite microsphere data below the optimal hyperplane, and repeat this step six times.

[0097] Specifically, the data above and below the optimal hyperplane are named Data_p0_up and Data_p0_down, respectively. Step S301 is repeated in the microsphere data of Data_p0_down to find the hyperplane w1x+b1=0.

[0098] Name the data above and below the optimal hyperplane as Data_p1_up and Data_p1_down, respectively. Repeat step S301 in the microsphere data of Data_p1_down to find the hyperplane w2x. r +b2=0.

[0099] This process continues until w0x is found. r +b0=0 to w6x r +b6 = 0, for a total of 7 hyperplanes.

[0100] Since eight-peak microspheres with linearly increasing fluorescence intensity were selected, the data of the optimal hyperplane division can necessarily be divided into eight categories.

[0101] Step S303: Obtain eight gates based on the partitioning result of the optimal hyperplane.

[0102] Specifically, the microsphere data in Data_p0_up, Data_p1_up, Data_p2_up, Data_p3_up, Data_p4_up, Data_p5_up, Data_p6_up, and Data_p6_down, which are divided by the seven hyperplanes, are used for subsequent calculations.

[0103] Step S4: Fit a linear formula using the microsphere data from all the gates.

[0104] For details, please refer to Figure 4 The steps for fitting a linear formula using microsphere data from all doors include:

[0105] Step S401: Calculate the arithmetic mean, standard deviation, and coefficient of variation using the microsphere data from all the gates.

[0106] For details, please refer to Figure 9 Click Calculation to calculate the arithmetic mean, standard deviation, and coefficient of variation. The calculation results are as follows: Figure 10 As shown, the coefficient of variation reflects the resolution of the flow cytometer; the closer the coefficient of variation is to 0, the higher the resolution.

[0107] Arithmetic mean: M = (x1 + x2 + ... + xn) / n

[0108] Standard deviation: SD = sqrt(((x1-M)^2+(x2-M)^2+......(xn-M)^2) / n)

[0109] Coefficient of variation: CV = SD / M

[0110] Where x1 to xn are fluorescence intensity parameters within the same phylum, and n is the number of microspheres within the same phylum.

[0111] Step S402: Use the arithmetic mean of the microsphere data in all gates except the last gate and the set detection limit for each gate to fit a linear formula.

[0112] Specifically, in the automated detection method for flow cytometers described above, the least squares method is used to fit the linear formula.

[0113] The method for fitting linear formulas is as follows:

[0114] Please refer to Figure 14 Given that the arithmetic mean of the eight groups of fluorescence intensities and the linear distribution of the MESF value of this channel as stated in the microsphere manual, substitute the arithmetic mean of the seven types of microsphere data excluding Data_p6_down into x. i Substitute the MESF value into y i Fitting the linear formula yi =kx i +a, let the least squares method

[0115]

[0116] Find the values ​​of k and a when J is minimized.

[0117] In the above formula, N is x i Number of samples, unknowns a and k ， Therefore, J can be viewed as a multivariate function of a and k. Based on the fundamental conditions for finding the extrema of a multivariate function, we can deduce:

[0118]

[0119]

[0120] By solving the equation, we can obtain the linear formula parameters a and k, which can then be substituted into the fitted linear formula.

[0121] Step S5: Calculate the minimum detection limit using the fitted linear formula.

[0122] For details, please refer to Figure 5 The steps for calculating the detection limit using the fitted linear formula include:

[0123] Step S501: Substitute the microsphere data from the last gate into the linear formula to obtain the minimum detection limit.

[0124] Substitute the microsphere data from Data_p6_down into the fitting formula to calculate the MESF value, which is the minimum detection limit.

[0125] Step S6: Calculate the fluorescence linearity using the fitted linear formula.

[0126] For details, please refer to Figure 5 The steps for calculating fluorescence linearity using the fitted linear formula include:

[0127] Step S601: Calculate the Pearson correlation coefficient r, i.e., fluorescence linearity, using the linear formula.

[0128] Pearson correlation coefficient Where σx is x i The standard deviation of the sample, σy, is y i The standard deviation of the sample, n = x i Number of samples.

[0129] Please refer to Figure 6 Corresponding to the above-mentioned automated detection method for flow cytometers, the present invention also provides a flow cytometer apparatus, comprising:

[0130] Data processing module: used to simplify the measured microsphere data;

[0131] Automatic gate generation module: used to generate eight gates based on the optimal hyperplane partitioning result;

[0132] Fitting module: Fits a linear formula using the arithmetic mean of the microsphere data from all but the last gate;

[0133] Calculation module: Calculates the minimum detection limit and fluorescence linearity using the fitted linear formula.

[0134] Preferably, the device further includes:

[0135] First processing module: used to save microsphere data as a variable data matrix;

[0136] The second processing module is used to normalize the variable data matrix.

[0137] The third processing module is used to perform logarithmic processing on the variable data matrix.

[0138] Preferably, the device further includes:

[0139] The gating module is used to gate the data above and below each optimal hyperplane.

[0140] Naming module: Used to name each door according to the order in which they are set.

[0141] Preferably, the device further includes:

[0142] The first calculation module is used to calculate the arithmetic mean, standard deviation, and coefficient of variation of fluorescence intensity using microsphere data from all gates.

[0143] The second calculation module is used to substitute the microsphere data in the last gate into the linear formula to obtain the minimum detection limit;

[0144] The third calculation module is used to calculate the Pearson correlation coefficient using a linear formula.

[0145] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the automated detection method for flow cytometers described above.

[0146] The readable storage medium of embodiments of the present invention can be any combination of one or more computer-readable media. The readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer hard disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device.

[0147] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0148] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

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2. The automatic detection method for flow cytometry according to claim 1, characterized by: The method comprises the following steps:

3. The automatic detection method for flow cytometry according to claim 1, characterized by: The method comprises the following steps:

4. The automatic detection method for flow cytometry according to claim 1, wherein: The method comprises the following steps:

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10. The automatic detection method for flow cytometry according to claim 1, wherein: The method comprises the following steps:

11. The automatic detection method for flow cytometry according to claim 1, wherein: The method comprises the following steps:

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limit corresponding to each gate; The calculating module calculates the minimum detection limit and the fluorescence linear using the fitted linear formula; the step of calculating the minimum detection limit using the fitted linear formula includes: substituting the microsphere data in the last gate into the linear formula to obtain the minimum detection limit.

13. The apparatus for use in a flow cytometer of claim 12, wherein: The device further comprises: The first processing module is used for saving the microsphere data as a variable data matrix; The second processing module is used for normalizing the variable data matrix; The third processing module is used for logarithm processing the variable data matrix.

14. The apparatus for use in a flow cytometer of claim 12, wherein: The device further comprises: The gate setting module is used for setting gates for the data above and below each optimal hyperplane respectively; The naming module is used for naming each gate according to the gate setting order.

15. The apparatus for use in a flow cytometer of claim 12, wherein: The device further comprises: The first calculating module is used for calculating the arithmetic mean, standard deviation, coefficient of variation of the fluorescence intensity using the microsphere data in all gates; The second calculating module is used for substituting the microsphere data in the last gate into the linear formula to obtain the minimum detection limit; The third calculating module is used for calculating the Pearson correlation coefficient using the linear formula.

16. A computer readable storage medium, said readable storage medium having stored therein a computer program, characterized in that: The computer program is executed by the processor to realize the method in any one of claims 1-11.

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