Pigment data acquisition method, pigment data acquisition device, and pigment data acquisition program

Abstract

To obtain a highly accurate fluorescence spectrum when observation conditions are changed. [Solution] The dye data acquisition method includes a selection step of selecting a desired mixing matrix from a plurality of mixing matrices that are preset based on reference spectral data, which is a distribution of fluorescence intensity values ​​for each of a plurality of detection wavelengths, in order to separate the reference spectral data into a plurality of dye data showing distributions for each of a plurality of fluorescent dyes; a data acquisition step of acquiring observed spectral data, which is a distribution of fluorescence intensity values ​​for each of C (C is an integer of 2 or more) detection wavelengths, for N (N is an integer of 2 or more) analytes; and a data generation step of generating K pieces of dye data showing distributions for K (K is an integer of 2 or more and C or less) fluorescent dyes by unmixing the observed spectral data using the desired mixing matrix.

Description

[Technical Field]

[0001] One aspect of the embodiment relates to a dye data acquisition method, a dye data acquisition device, and a dye data acquisition program. [Background technology]

[0002] Flow cytometry has been known as a technique for counting, selecting, and analyzing the characteristics of samples such as cells using laser light. For example, Patent Document 1 below discloses clustering of cells based on fluorescence data of each color light from the cells output from a flow cytometer. Furthermore, Non-Patent Document 2 below discloses unmixing fluorescence data output from a flow cytometer to generate fluorescence spectra for each fluorescent dye, and then performing clustering processing on the fluorescence spectra. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-87465 [Patent Document 2] Patent Publication No. 2021-36224 Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional methods described above, the processing time required to generate a fluorescence spectrum for each fluorescent dye depending on the type of fluorescent dye being observed tends to be long, which can reduce the throughput when measuring the fluorescence spectrum.

[0005] Therefore, one aspect of the embodiment has been made in consideration of such problems, and aims to provide a dye data acquisition method, a dye data acquisition device, and a dye data acquisition program that can improve throughput when measuring fluorescence spectra. [Means for solving the problem]

[0006] A dye data acquisition method according to a first aspect of the embodiment includes a selection step of selecting desired separation information from a plurality of separation information that is preset based on reference spectral data that is a distribution of fluorescence intensity values ​​for a plurality of detection wavelengths, in order to separate the reference spectral data into a plurality of dye data that indicate distributions for a plurality of fluorescent dyes; a data acquisition step of acquiring observation spectral data that is a distribution of fluorescence intensity values ​​for C (C is an integer of 2 or greater) detection wavelengths for N (N is an integer of 2 or greater) objects; and a data generation step of generating K pieces of dye data that indicate distributions for K (K is an integer of 2 or greater and C or less) fluorescent dyes by unmixing the observation spectral data using the desired separation information.

[0007] Alternatively, a dye data acquisition device according to a second aspect of the embodiment is a dye data acquisition device that processes observation spectral data, which is a distribution of fluorescence intensity values ​​for C (C is an integer of 2 or more) detection wavelengths, for N (N is an integer of 2 or more) objects, and selects desired separation information from the plurality of separation information, which is a plurality of separation information that is preset based on reference spectral data, which is a distribution of fluorescence intensity values ​​for a plurality of detection wavelengths, in order to separate the reference spectral data into a plurality of dye data showing distributions for a plurality of fluorescent dyes, and generates K (K is an integer of 2 or more and C or less) dye data showing distributions for a plurality of fluorescent dyes by unmixing the observation spectral data using the desired separation information.

[0008] Alternatively, a dye data acquisition program according to a third aspect of the embodiment is a dye data acquisition program for generating dye data indicating the distribution of fluorescent dyes in N (N is an integer of 2 or more) objects based on observation spectral data, which is a distribution of fluorescence intensity values ​​for each of C (C is an integer of 2 or more) detection wavelengths, and causes a computer to execute the following steps: selecting desired separation information from the plurality of separation information, which is a plurality of separation information preset based on reference spectral data, which is a distribution of fluorescence intensity values ​​for each of a plurality of detection wavelengths, for separating the reference spectral data into a plurality of dye data indicating the distribution for each of the plurality of fluorescent dyes; and generating K pieces of dye data indicating the distribution for each of K (K is an integer of 2 or more and C or less) fluorescent dyes by unmixing the observation spectral data using the desired separation information.

[0009] According to the first, second, or third aspect, desired separation information is selected from a plurality of pieces of separation information for separating reference spectral data relating to fluorescence for each of a plurality of detection wavelengths into a plurality of dye data, and observation spectral data of the fluorescence distribution at C detection wavelengths is acquired for each of N objects. This observation spectral data is unmixed using the desired separation information, thereby generating K pieces of dye data indicating the distribution of K fluorescent dyes for each object. This eliminates the need to analyze the separation information for separating into dye data each time observation spectral data is acquired, thereby improving throughput when measuring the fluorescence spectrum relating to the distribution of each fluorescent dye.

[0010] In the first aspect, it is preferable that each of the plurality of pieces of separation information is pre-stored in a data storage unit in association with the acquisition conditions of the reference spectral data used to set the separation information, and that in the selection step, separation information associated with the acquisition conditions of the observation spectral data is selected as the desired separation information from the plurality of pieces of separation information stored in the data storage unit. This allows separation information that matches the acquisition conditions of the observation spectral data to be selected as the desired separation information, making it possible to accurately observe the distribution of multiple types of fluorescent dyes for each object.

[0011] In the first aspect, it is also preferable that the acquisition conditions include information about a plurality of detection wavelengths. By using information about a plurality of detection wavelengths as the acquisition conditions, the observation spectrum data can be unmixed using desired separation information that matches the reflection state of fluorescence from the plurality of fluorescent dyes in the observation spectrum data. As a result, the distribution of the plurality of fluorescent dyes for each object can be observed with greater accuracy.

[0012] In addition, the first aspect preferably further comprises a first correction step of correcting the desired separation information based on the observation spectrum data and the desired separation information to obtain corrected separation information. In this case, the observation spectrum data can be unmixed using corrected separation information that matches the reflection state of fluorescence from the multiple fluorescent dyes in the observation spectrum data. As a result, the distribution of multiple types of fluorescent dyes for each object can be observed with greater accuracy.

[0013] In the first aspect, the correction process preferably corrects the plurality of dye data separated from the observation spectral data using the corrected separation information so as to increase the independence of each dye data. In this case, the observation spectral data can be unmixed using the corrected separation information that has been corrected so as to increase the independence of the plurality of dye data separated from the observation spectral data. As a result, the distribution of the plurality of fluorescent dyes for each object can be observed with even greater accuracy.

[0014] Furthermore, in the first aspect, it is also preferable that the data generating step performs unmixing using the corrected separation information. In this case, the distribution of multiple types of fluorescent dyes for each object can be observed with higher accuracy.

[0015] Furthermore, in the first aspect, it is also preferable to further include a second correction step of performing a correction process on the K pieces of dye data to generate K pieces of corrected dye data. In this way, by correcting the dye data, it is possible to more accurately observe the distribution of multiple types of fluorescent dyes for each object.

[0016] Furthermore, in the first aspect, it is also preferable that the correction process is a process of correcting the K pieces of dye data so that they are more independent from each other. In this way, by correcting the K pieces of dye data so that they are more independent, the distribution of multiple types of fluorescent dyes for each object can be observed with even greater accuracy.

[0017] The dye data acquisition method of the embodiment is a dye data acquisition method comprising: [1] "a selection step of selecting desired separation information from a plurality of pieces of separation information preset based on reference spectral data which is a distribution of fluorescence intensity values ​​for a plurality of detection wavelengths, in order to separate the reference spectral data into a plurality of dye data indicating distributions for a plurality of fluorescent dyes; a data acquisition step of acquiring observation spectral data which is a distribution of fluorescence intensity values ​​for C (C is an integer of 2 or more) detection wavelengths for N (N is an integer of 2 or more) objects; and a data generation step of generating the K pieces of dye data indicating distributions for K (K is an integer of 2 or more and C or less) fluorescent dyes by unmixing the observation spectral data using the desired separation information."

[0018] The pigment data acquisition method of the embodiment may be [2] "the pigment data acquisition method described in [1] above, in which each of the plurality of separation information is pre-stored in a data storage unit in association with the acquisition conditions of the reference spectral data used to set the separation information, and in the selection step, separation information linked to the acquisition conditions of the observed spectral data is selected as the desired separation information from the plurality of separation information stored in the data storage unit."

[0019] The pigment data acquisition method of the embodiment may be [3] "the pigment data acquisition method according to the above [2], in which the acquisition conditions are information relating to a plurality of the detection wavelengths."

[0020] The pigment data acquisition method of the embodiment may be [4] "a pigment data acquisition method described in any of [1] to [3] above, further comprising a first correction step of performing a correction process on the desired separation information based on the observed spectral data and the desired separation information to obtain corrected separation information."

[0021] The pigment data acquisition method of the embodiment may be [5] "the pigment data acquisition method described in [4] above, in which the correction process is a process of correcting the plurality of pigment data separated from the observed spectral data using the corrected separation information so that each of the pigment data has high independence."

[0022] The pigment data acquisition method of the embodiment may be [6] "The pigment data acquisition method described in [4] or [5] above, wherein in the data generation step, unmixing is performed using the corrected separation information."

[0023] The pigment data acquisition method of the embodiment may be [7] "a pigment data acquisition method described in any of [1] to [3] above, further comprising a second correction step of performing a correction process on the K pieces of pigment data to generate K pieces of corrected pigment data."

[0024] The pigment data acquisition method of the embodiment may be [8] "the pigment data acquisition method described in [7] above, in which the correction process is a process of correcting the K pigment data so that each of them has high independence." [Effects of the Invention]

[0025] According to one aspect of the embodiment, it is possible to improve the throughput when measuring a fluorescence spectrum. [Brief explanation of the drawings]

[0026] [Figure 1] 1 is a schematic configuration diagram of a pigment data acquisition system 1 according to an embodiment. [Figure 2] FIG. 2 is a schematic configuration diagram of the data acquisition device 3 of FIG. [Figure 3] 2 is a block diagram showing an example of a hardware configuration of a data processing device 5 in FIG. 1. FIG. [Figure 4] FIG. 2 is a block diagram showing the functional configuration of a data processing device 5 in FIG. [Figure 5] 5 is a diagram showing an image of matrix data Y' regenerated by the matrix acquisition unit 203 in FIG. 4 and dye matrix data X' derived by the matrix acquisition unit 203 in FIG. [Figure 6] FIG. 6 is a flowchart showing an example of the procedure of the library correction process performed by the matrix correction unit 204 in FIG. [Figure 7] FIG. 10 is a diagram showing an image of dye matrix data. [Figure 8] FIG. 10 is a diagram illustrating mutual information. [Figure 9] 10 is a flowchart showing a procedure for processing related to mutual information. [Figure 10] FIG. 10 is a diagram illustrating updating of mutual information. [Figure 11] FIG. 10 is a diagram illustrating a grid search in the mutual information update process. [Figure 12] 1 is a flowchart showing the procedure of a pigment data acquisition method according to an embodiment. [Figure 13]1 is a graph plotting two dye data showing the distribution of each of two fluorescent dyes output by the dye data acquisition system 1. [Figure 14] FIG. 10 is a schematic diagram of a data acquisition device 3A according to a modified example. [Figure 15] 10 is a flowchart showing the procedure of a pigment data acquisition method according to a modified example. [Figure 16] FIG. 10 is a diagram illustrating an example of independence evaluation using a correlation coefficient, among examples of independence evaluation other than mutual information. DETAILED DESCRIPTION OF THE INVENTION

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description, the same elements or elements having the same functions will be denoted by the same reference numerals, and redundant description will be omitted.

[0028] FIG. 1 is a schematic diagram of a dye data acquisition system 1, which is a dye data acquisition device according to an embodiment. The dye data acquisition system 1 is a device for generating dye data for identifying the amount of dye (fluorescent dye) contained in a sample, such as a cell or particle, that is an analyte. The dye data generated by the dye data acquisition system 1 is used for purposes such as counting, sorting, and characteristic analysis of cells through analysis of the data. Therefore, the dye data acquisition system 1 is required to generate dye data for a large amount of analyte with high throughput. The dye data acquisition system 1 includes a data acquisition device 3 that performs flow cytometry analysis of the analyte, a data processing device 5 that processes the data acquired by the data acquisition device 3, and a DB (data storage unit, memory unit) 6 that stores mixing matrices (separation information) for obtaining multiple dye data indicating the distribution of each fluorescent dye with a different fluorescence wavelength. The data acquisition device 3, the data processing device 5, and the DB 6 may be configured to transmit and receive data between them using wired or wireless communication, or may be configured to input and output data via a recording medium. Furthermore, the data acquisition device 3, the data processing device 5, and the DB 6 may be configured as a device in which all or part of them are integrated.

[0029] Figure 2 is a schematic diagram of the data acquisition device 3 in Figure 1. The data acquisition device 3 is a system for performing flow cytometry, generally called a flow cytometer, and is composed of a fluid system 52, an optical system (optical system) 53, and an electronic system (signal processing device) 54.

[0030] The fluid system 52 is configured to include a flow cell 56 into which a sample fluid containing analytes such as cells or particles is injected, and which allows the analytes contained in the sample fluid to be aligned and passed through a narrow channel 55. The flow cell 56 is also provided with a function (not shown) for sorting (classifying and distributing) the gated analytes by controlling an electric field or the like.

[0031] The optical system 53 optically analyzes an analyte passing through a flow cell 56 by flow cytometry. The optical system 53 includes light sources 7a, 7b, 7c, and 7d, dichroic mirrors 57a, 57b, and 57c, a mirror 57d, a lens 8, filters 9a, 9b, 9c, and 9d, dichroic mirrors 10b and 10c, and photodetectors 11a, 11b, 11c, and 11d. The light sources 7a, 7b, 7c, and 7d are light source devices that generate light (excitation light) having different center wavelengths (excitation wavelengths). The light sources 7a, 7b, 7c, and 7d are, for example, laser light sources, light-emitting diodes, or superluminescent diodes. The dichroic mirror 57a transmits light emitted from the light source 7a toward the lens 8 and reflects light emitted from the other light sources 7b, 7c, and 7d toward the lens 8. The dichroic mirror 57b reflects light emitted from the light source 7b toward the dichroic mirror 57a and transmits light emitted from the light sources 7c and 7d toward the dichroic mirror 57a. The dichroic mirror 57c reflects light emitted from the light source 7c toward the dichroic mirror 57b and transmits light emitted from the light source 7d toward the dichroic mirror 57b. The mirror 57d reflects light emitted from the light source 7d toward the dichroic mirror 57c. The lens 8 focuses the light emitted from the light sources 7a, 7b, 7c, and 7d onto the channel 55 in the flow cell 56. Filter 9a transmits forward scattered light generated from the sample fluid by irradiation with light (i.e., at least one of the lights from light sources 7a, 7b, 7c, and 7d). Dichroic mirror 10b reflects fluorescence (first fluorescence) in a first wavelength band (first detection wavelength) generated from the sample fluid by irradiation with light emitted from light sources 7a, 7b, 7c, and 7d, and transmits the remaining fluorescence generated from the sample fluid.Dichroic mirror 10c reflects fluorescence (second fluorescence) of a second wavelength band (second detection wavelength) from the fluorescence that has passed through dichroic mirror 10b, and transmits fluorescence (third fluorescence) of the remaining wavelength band (third detection wavelength) from the fluorescence that has passed through dichroic mirror 10b. Filter 9b transmits the first fluorescence of the first wavelength band reflected by dichroic mirror 10b, and filter 9c transmits the second fluorescence of the second wavelength band reflected by dichroic mirror 10c. Filter 9d transmits the third fluorescence of a third wavelength band from the fluorescence that has passed through dichroic mirror 10c. Photodetectors 11a, 11b, 11c, and 11d are provided on the optical axes of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence, respectively, and measure the intensities of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence, respectively. The photodetectors 11a, 11b, 11c, and 11d are, for example, photomultiplier tubes, avalanche photodiodes, HPDs (Hybrid Photo Detectors), or SiPMs (Silicon Photomultipliers). Alternatively, the photodetectors 11a, 11b, 11c, and 11d may be configured as a single photodetector having multiple detection ports. An example of such a single photodetector is a multi-anode photomultiplier tube. The optical system 53 can measure the intensity of each of the fluorescence at C (C is an integer equal to or greater than 2) detection wavelengths, depending on the number of combinations of dichroic mirrors, filters, and photodetectors.

[0032] The optical system 53 is capable of measuring the intensities of the forward scattered light, the first fluorescent light, the second fluorescent light, and the third fluorescent light while switching between irradiating light of multiple wavelength bands from the light sources 7a, 7b, 7c, and 7d. This allows for efficient measurement of the intensities of fluorescence of multiple wavelength bands. The optical system 53 may also be capable of measuring the intensities of the forward scattered light, the first fluorescent light, the second fluorescent light, and the third fluorescent light while continuously irradiating light of multiple wavelength bands from the light sources 7a, 7b, 7c, and 7d. The optical system 53 may also have a configuration similar to that described above that allows for observation of side scattered light.

[0033] The electronic system 54 is a device for collecting data on the light intensity measured by the optical system 53. Specifically, the electronic system 54 is electrically connected to the multiple photodetectors 11a, 11b, 11c, and 11d, and performs A / D conversion on intensity signals indicating the intensity detected by each channel of the multiple photodetectors 11a, 11b, 11c, and 11d to generate array data in which the A / D-converted intensity values ​​for each analyte are linearly arrayed, and transmits the array data to an external device. Here, the electronic system 54 may generate the array data by directly A / D-converting the intensity signals detected by the multiple photodetectors 11a, 11b, 11c, and 11d, or may correct the intensity signals or the A / D-converted intensity values ​​using various parameters and generate array data based on the corrected intensity signals or the corrected intensity values.

[0034] Next, the configuration of the data processing device 5 will be described with reference to Fig. 3 and Fig. 4. Fig. 3 is a block diagram showing an example of the hardware configuration of the data processing device 5, and Fig. 4 is a block diagram showing the functional configuration of the data processing device 5.

[0035] As shown in Fig. 3, the data processing device 5 is physically a computer or the like including a processor such as a CPU (Central Processing Unit) 101, a recording medium such as a RAM (Random Access Memory) 102 or a ROM (Read Only Memory) 103, a communication module 104, and an input / output module 106, all of which are electrically connected to one another. The data processing device 5 may include input / output devices such as a display, a keyboard, a mouse, a touch panel display, or a data recording device such as a hard disk drive or semiconductor memory. The data processing device 5 may also be composed of multiple computers. The DB 6 may also have a similar hardware configuration.

[0036] As shown in FIG. 4 , the data processing device 5 includes, as functional components, an information search unit (selection unit) 201, a data acquisition unit 202, a matrix acquisition unit 203, a matrix correction unit 204, and a data generation unit 205. The functional units of the data processing device 5 shown in FIG. 4 are realized by loading a program (a dye data acquisition program according to an embodiment) onto hardware such as a CPU 101 and RAM 102, thereby operating a communication module 104, an input / output module 106, and reading and writing data from and to the RAM 102 under the control of the CPU 101. The CPU 101 of the data processing device 5 executes the computer program to cause the functional units of FIG. 4 to function and sequentially execute processes corresponding to the dye data acquisition method described below. The CPU 101 may be a standalone piece of hardware or may be implemented in a programmable logic device such as an FPGA, like a software processor. The RAM and ROM may also be standalone pieces of hardware or may be incorporated into a programmable logic device such as an FPGA. All of the various data required for executing this computer program and the various data generated by executing this computer program are stored in built-in memories such as ROM 103 and RAM 102, or in storage media such as a hard disk drive. The functions of the functional components of the data processing device 5 will be described in detail below.

[0037] The information search unit 201 searches for (selects) a desired mixing matrix from the multiple mixing matrices stored in the DB 6 based on the data acquisition conditions when acquiring spectral data (C pieces of sequence data) related to the intensity values ​​of fluorescence at each of C (C is an integer of 2 or more) detection wavelengths of the analyte, information (estimation processing information) related to the estimation processing of a mixing matrix (separation information) based on the spectral data, and storage setting information when saving the mixing matrix to the DB 6. The information search unit 201 may set the data acquisition conditions, estimation processing information, storage setting information, etc. used for the search based on information input by a user via the input / output module 106 of the data processing device 5, based on information transmitted from an external device such as the data acquisition device 3, or based on information referenced from the RAM 102, ROM 103, etc. of the data processing device 5.

[0038] The data acquisition conditions include the type of analyte, the type of fluorescent dye contained in the analyte, the date and time of staining the analyte, the date and time of measurement of the analyte, the type of excitation light, the intensity of each excitation light, the type or number of filters and dichroic mirrors, information specifying the wavelength bands of the C detection wavelengths, the type of photodetector, the type or magnification of the lens, the exposure time of each excitation light, the environmental temperature, and the acquired spectral data itself.The estimation processing information includes version information of the software used in the estimation processing, the algorithm, parameters, or options used in the estimation processing, the time required for the estimation processing, etc.The saved setting information includes the saved user name, project name, assay name, mixing matrix name, tag information (user-selected keywords, social media hashtags), etc.

[0039] The DB 6, which is the search destination of the information search unit 201, stores a plurality of mixing matrices previously set by the matrix acquisition unit 203 (described later), each associated with the data acquisition conditions used when acquiring the reference spectrum data (e.g., a plurality of sequence data) on which the respective mixing matrices are based, estimation process information related to the estimation process used when acquiring each mixing matrix, and storage setting information used when saving each mixing matrix. The information search unit 201 searches (selects) a mixing matrix associated with information that matches or corresponds (similar to) a search key including the data acquisition conditions, estimation process information, or storage setting information set by a user, an external device, or the like, from among the plurality of mixing matrices stored in the DB 6. The information search unit 201 then transfers the search results of the mixing matrices to the matrix acquisition unit 203. At this time, the information search unit 201 searches for a mixing matrix using at least one item of the data acquisition conditions, estimation process information, and storage setting information as a search key. The information search unit 201 searches for a mixing matrix using a search key that includes at least one item of the data acquisition condition. In particular, it is preferable that the information search unit 201 functions to search for a mixing matrix using a search key that includes information that specifies at least C wavelength bands of detection wavelengths as the data acquisition condition.

[0040] The data acquisition unit 202 acquires array data relating to fluorescence in pre-specified C (C is an integer of 2 or more) wavelength bands for N (N is an integer of 2 or more) analytes from the data acquisition device 3. These C array data are observed spectral data obtained by measuring the distribution of fluorescence intensity values ​​for each of the C wavelength bands from the N analytes while switching between irradiating the N analytes with light in multiple wavelength bands in the data acquisition device 3. At this time, the number C of array data to be acquired (the number C of wavelength bands of observed fluorescence) is designated in advance to be equal to or greater than the maximum number of dyes that can be contained in the analytes.

[0041] If the desired mixing matrix is ​​not found in a search based on the data acquisition conditions used by the information search unit 201 when acquiring the above-mentioned observed spectrum data, the matrix acquisition unit 203 acquires a mixing matrix using the C pieces of array data acquired by the data acquisition unit as reference spectrum data.

[0042] The mixing matrix acquisition function of the matrix acquisition unit 203 will be described below.

[0043] The matrix acquisition unit 203 reads the C pieces of sequence data acquired by the data acquisition unit 202, and performs clustering on the N analytes constituting the C pieces of sequence data based on the intensity values ​​of each analyte for each of the C wavelength bands. Prior to the clustering process, the matrix acquisition unit 203 generates matrix data Y in which the intensity values ​​of the N analytes constituting each of the C pieces of sequence data are arranged in parallel in one dimension.

[0044] Then, the matrix acquisition unit 203 clusters the N analytes into L object groups (L is an integer between 2 and N-1) based on the distribution information of the intensity values ​​of each fluorescence wavelength band. For example, the matrix acquisition unit 203 selects a wavelength band in which intensity values ​​tend to differ between dyes, creates a histogram of the intensity values ​​of that wavelength band, and sets a threshold between two peaks based on the histogram. Furthermore, the matrix acquisition unit 203 can cluster the analytes into two object groups by comparing the intensity values ​​of the sequence data with the set threshold. The matrix acquisition unit 203 can classify the analytes into three or more object groups using a similar function. Here, the number L of object groups to be clustered by the matrix acquisition unit 203 is set in advance as a parameter stored in the data processing device 5, corresponding to the number of types of dyes that can be present in the analyte. The matrix acquisition unit 203 then regenerates matrix data Y, in which the intensity values ​​of the analytes of the C sequence data are arranged in parallel in one dimension, by dividing the matrix data Y into cluster matrices for each of the L object groups. The number L of object groups to be clustered by the matrix acquisition unit 203 may be set in advance depending on the type of detection wavelength band and the number (C) of detection wavelength bands. Also, the number L of object groups to be clustered by the matrix acquisition unit 203 may be set independently of these.

[0045] The matrix acquisition unit 203 obtains a mixing matrix A for generating K pieces of dye data indicating the distribution of each of K dyes (K is an integer between 2 and C) from C pieces of sequence data, based on L cluster matrices obtained for the object to be analyzed. Generally, according to the non-negative matrix factorization (NMF) calculation method, the relationship between matrix data Y, which is an observation matrix, and dye matrix data X, in which K pieces of dye data are arranged in parallel in one dimension for each object to be analyzed, is expressed by the following formula using the mixing matrix A: Y=AX Here, Y is matrix data with C rows and N columns, A is matrix data with C rows and K columns, and X is matrix data with K rows and N columns. Conversely, once the value of mixing matrix A is determined, the dye matrix data X can be calculated by the inverse matrix A of mixing matrix A. -1 and matrix data Y, the following equation (1): X=A -1Y...(1) (This process is called unmixing.)

[0046] Here, the matrix acquisition unit 203 regenerates the matrix data Y' by compressing the generated matrix data Y in units of clustered object groups. In particular, the matrix acquisition unit 203 calculates a statistical value for each object group of the clustered cluster matrix for the intensity values ​​of each row of the matrix data Y, and compresses the object group of each row into one object having the calculated statistical value. In this way, the matrix acquisition unit 203 regenerates the matrix data Y', which is matrix data with C rows and L columns. As the statistical value, the matrix acquisition unit 203 may calculate an average value based on the integrated value of the intensity values, may calculate the most frequent value of the intensity values, or may calculate the median value of the intensity values.

[0047] The matrix acquisition unit 203 also calculates the following formula including the mixing matrix A in the reproduced matrix data Y' and the dye matrix data X' compressed in the same manner from the dye matrix data X: Y'=AX' Using the property that the following holds, the mixing matrix A is derived based on the matrix data Y'. FIG. 5 shows an image of the matrix data Y' regenerated by the matrix acquisition unit 203 and the corresponding dye matrix data X'. One square shown in FIG. 5 represents one element of the matrix data. For example, if three object groups PGr 01 ~PGr 03 The pigment matrix data X and matrix data Y are divided into the object group PGr 01 ~PGr 03 The statistical values ​​for each pixel are used as representative values ​​and compressed into three columns of dye matrix data X' and matrix data Y'.

[0048] The matrix obtaining unit 203 derives the mixing matrix A based on the matrix data Y' as follows. That is, the matrix obtaining unit 203 sets an initial value to the mixing matrix A, calculates the following loss function (loss value) Loss while sequentially changing the value of the mixing matrix A, and derives the mixing matrix A that reduces the value of the loss function Loss. Note that a regularization term such as the L1 norm λ|A| (λ is a coefficient indicating the degree to which importance is attached to the regularization term) may be added to this loss function.

number

[0049] As described above, the matrix acquisition unit 203 calculates a loss function for each of the L divided cluster matrices by referring to the statistical values ​​of the C pieces of matrix data Y', calculates the loss function Los based on the sum of the L loss functions, and obtains the mixing matrix A based on the loss function Los. In this case, the matrix acquisition unit 203 corrects the loss function calculated for each of the L cluster matrices by dividing it by the average values ​​a, b, and c of the statistical values ​​of the C pieces of matrix data Y', and then obtains the loss function Los by calculating the sum of the corrected loss functions. Note that the matrix acquisition unit 203 may calculate the loss function for each of the L cluster matrices by correcting the row components of the difference value Y'-AX' for each fluorescence wavelength band by the C statistical values ​​corresponding to each fluorescence wavelength band.

[0050] The above equation can also be generalized as follows. That is, the matrix acquisition unit 203 derives the mixing matrix A and the dye matrix data X' based on the matrix data Y' as follows. That is, the matrix acquisition unit 203 sets initial values ​​for the mixing matrix A and the dye matrix data X', calculates the loss function (loss value) Loss using the equation below while sequentially changing the values ​​of the mixing matrix A and the dye matrix data X', and derives the mixing matrix A and the dye matrix data X' that reduce the value of the loss function Loss. Note that a regularization term such as the L1 norm λ|A| (λ is a coefficient indicating the degree to which the regularization term is emphasized) may be added to this loss function. Furthermore, the calculation may be performed with a constraint that the mixing matrix A and the dye matrix data X' become non-negative values.

number

[0051] As described above, the matrix acquisition unit 203 calculates a loss function for each of the L divided cluster matrices by referring to the statistical values ​​of the C matrix data Y′, and obtains the L loss functions Los i The matrix acquisition unit 203 calculates the loss function Los based on the sum of the L cluster matrices, and obtains the mixing matrix A based on the loss function Los. i may be calculated by correcting the row components of the difference values ​​Y'-AX' for each wavelength band of fluorescence by dividing them by the C statistical values ​​corresponding to each wavelength band of fluorescence.

[0052] The matrix acquisition unit 203 associates the mixing matrix A (separation information for obtaining dye data obtained by separating fluorescence) acquired using the above-described clustering function and statistical value calculation function with the data acquisition conditions, estimation processing information, and storage setting information, etc., and stores the matrix A in the DB 6. The data acquisition conditions, estimation processing information, and storage setting information associated with the mixing matrix are information corresponding to the time when the observed spectral data was acquired, information corresponding to the time when the mixing matrix was estimated, or information corresponding to the time when the mixing matrix was saved.

[0053] When the information search unit 201 finds a desired mixing matrix, the matrix correction unit 204 acquires a corrected mixing matrix (corrected separation information), which is a corrected mixing matrix, based on the desired mixing matrix A and the C pieces of sequence data acquired by the data acquisition unit 202. That is, the matrix correction unit 204 generates matrix data Y2 in which the intensity values ​​of the C pieces of sequence data are arranged in parallel in one dimension.

[0054] For example, the acquisition of a corrected mixing matrix is ​​performed as follows. The matrix correction unit 204 regenerates matrix data Y2' by clustering the matrix data Y2 using a clustering function and compressing it in units of object groups. This clustering is performed in the same manner as the processing in the matrix acquisition unit 203, for example, based on the distribution of fluorescence intensity for each of C wavelength bands. Then, the matrix correction unit 204 uses the mixing matrix A as an initial value and calculates the following equation: L=||Y2'-A2X'|| 2 Here, instead of the above equation, an equation with a regularization term added, such as the following equation, may be used. L=||Y2'-A2X'|| 2 +λ||A2-A|| 2 By adding a regularization term, it is possible to control deviation from the initial value so that it does not become too large. In addition to the above regularization term, a term such as L1 norm ||A2||1 may be added to impose a sparsity constraint on matrix A2. Furthermore, values ​​that are expected to be correct among the elements of mixing matrix A may be fixed, and the values ​​of the other elements may be corrected. Furthermore, instead of Euclidean distance, a loss function such as Kullback-Leibler divergence or Itakura-Saito divergence may be used.

[0055] The matrix correction unit 204 may acquire a corrected mixing matrix by library correction processing based on the desired mixing matrix A and the C pieces of sequence data acquired by the data acquisition unit 202. Hereinafter, the library correction processing by the matrix correction unit 204 will be described in detail with reference to FIGS.

[0056] Fig. 6 is a flowchart showing an example of the procedure for library correction processing. As shown in Fig. 6, pre-correction dye matrix data X is acquired from mixing matrix A and matrix data Y based on the above formula (1) (step S1). Now, as shown in Fig. 7, assume that dye matrix data X is represented by a size K × N (K is the number of dyes, and N is the number of data points (analytes)), and is a matrix in which K dye data are each arranged horizontally in one dimension. In Fig. 7, K = 3, and data for N dyes are arranged in each of three dye matrix data Xs.

[0057] Next, the separation matrix P k is initialized with an identity matrix (step S2). Here, the separation matrix P k is the identity matrix. Among the K data (columns), the pigment matrix data X i ,X j The two are selected (step S3). Figure 6 shows the separation matrix P k is shown, where i and j are selected in the range of i=1, 2, 3 and j=1, 2, 3 (where i≠j).

[0058] Next, multiple pigment matrix data X i ,X j The updated separation matrix P k A coefficient α related to the derivation of is calculated (step S4).

[0059] FIG. 8 is a diagram illustrating mutual information. FIG. 8(a) is a diagram illustrating the intensity value distribution when the mutual information between two dye matrix data ("Data 1" and "Data 2") is large, and FIG. 8(b) is a diagram illustrating the intensity value distribution when the mutual information between two dye matrix data ("Data 1" and "Data 2") is small. Mutual information indicates a measure of the interdependence (correlation) between two information sources. Here, mutual information is an index that measures the dependency between two dye matrix data, for example, using a probability distribution (histogram). A large mutual information between dye matrix data indicates low independence of each data. A small mutual information between dye matrix data indicates high independence of each data. A mutual information of 0 indicates that each data is completely independent.

[0060] In the examples shown in Figures 8(a) and 8(b), histograms of "Data 1" and "Data 2" (horizontal axis: intensity value, vertical axis: number of data points) are shown, and the distribution of the intensity values ​​of "Data 1" and "Data 2" is shown in the lower left. In the graph showing the distribution of intensity values ​​(lower left graph), the horizontal axis represents the intensity value of "Data 1" and the vertical axis represents the intensity value of "Data 2," showing the distribution of intensity values ​​in "Data 1" and "Data 2" for each analyte. In the example shown in Figure 8(a), the correlation between the intensity values ​​of each analyte in "Data 1" and "Data 2" is extremely high, indicating that the mutual information between "Data 1" and "Data 2" is large. On the other hand, in the example shown in Figure 8(b), the correlation between the intensity values ​​of each analyte in "Data 1" and "Data 2" is low, indicating that the mutual information between "Data 1" and "Data 2" is small.

[0061] 9 is a flowchart showing the procedure of the process related to the mutual information. i ,X j It is assumed that the dye matrix data X is input (step S11). i ,X j Minimizing the mutual information of X i and X j -αX i This means minimizing the mutual information of the intensity values ​​between α and α. First, an initial value α0 is set for α. Here, the initial value α0 is set in advance to an appropriate value within the search range of α.

[0062] Next, for the intensity value of each analyte, X j -αX i is calculated (step S13). Since the initial value α0 is set as the value of α, X j -α0X i Then, a histogram is created (step S14). Specifically, as shown in FIG. 10(a), the horizontal axis is X j The intensity value of each analyte is the vertical axis, X j -αX i A two-dimensional histogram is created of the intensity values ​​of each analyte.

[0063] Next, the mutual information I(α) is calculated (step S15). The mutual information I(α) is calculated based on the following equation (2): j The center coordinates of each bin in the two-dimensional histogram are x bin , X j -αX i The center coordinates of each bin in the two-dimensional histogram are y bin Also, X j and X j -αX i The joint probability of p(x bin ,y bin ) and X j The marginal probability of p(x bin ), X j -αX i The marginal probability of p(y bin )

[0064]

number

[0065] Next, the value of α is updated (step S16), and X in step S13 is executed again. j -αX i For example, in the example shown in FIG. 10, the mutual information I(α=α0=0)=0.66 is calculated first (see FIG. 10(a)), and then the value of α is updated and I(α=0.2)=0.49 is calculated (see FIG. 10(b)). Thereafter, the process of steps S13 to S16 is repeated while updating the value of α until the search for α is completed, and the α that minimizes the mutual information I(α) is calculated. min is determined (step S17). The value of α may be updated randomly (random search), or may be searched for sequentially while shifting the value at equal intervals (grid search). In the case of grid search, as shown in FIG. 11, the value of α is shifted by a predetermined value (here, 0.05) to find the α that minimizes the mutual information I(α). min (Here, α min =0.15) is determined. Finally, the α that minimizes the mutual information I(α) is determined. min is output (step S18).

[0066] Returning to FIG. 6, in step S4, the mutual information I(α) is minimized. min Once calculated, the calculated α(α=α min ) to obtain the dye matrix data X i ,X j The separation matrix P k (j, i) is updated (step S5). Specifically, the separation matrix P k (j,i) is updated. In the following equation (3), γ is the separation matrix P k Coefficient for updating (j,i), α is the calculated α min is. Separation matrix P k (j,i)=-γα (3)

[0067] The processes in steps S3 to S5 are repeated for all possible combinations of i and j. Then, the updated separating matrix P k The dye matrix data is updated using the above formula, and corrected dye matrix data X' is obtained based on the following formula (4) (step S6). X´=P k X···(4)

[0068] The acquisition of the corrected pigment matrix data X' is repeated until the value of the corrected pigment matrix data X' converges within a predetermined range or until a set number of times has been reached.

[0069] Finally, the correction matrix B is output (step S7). The correction matrix B is, for example, the updated separation matrix P k or separation matrix P k For example, P=P k P k-1 ...P2P1X, the correction matrix B may be calculated by the following equation (5) or (6). -1 is the correction mixing matrix A'. B=P -1 ···(5) B=AP -1 -A···(6) Although it has been explained here that the correction matrix B is calculated, if the correction matrix B is not calculated and the separation matrix P k Alternatively, only the separation matrix P k The corrected mixing matrix A' may be calculated based on the following:

[0070] Furthermore, the matrix correction unit 204 stores the corrected corrected mixing matrix A' or the combination data of the mixing matrix A before correction and the correction matrix B in the DB 6 in association with the data acquisition conditions, estimation processing information, and storage setting information.

[0071] If the information search unit 201 does not find a desired mixing matrix, the data generation unit 205 acquires K pieces of dye data by unmixing C pieces of sequence data obtained for the analysis target using the mixing matrix A derived by the matrix acquisition unit 203. Specifically, the data generation unit 205 acquires K pieces of dye data by applying the inverse matrix A of the mixing matrix A to matrix data Y generated based on the C pieces of sequence data. -1 The dye matrix data X is calculated by applying the formula (1) to the data generation unit 205. On the other hand, if the desired mixing matrix A is found by the information search unit 201, the data generation unit 205 similarly performs unmixing using the corrected mixing matrix acquired by the matrix correction unit 204 to acquire K pieces of dye data. However, the data generation unit 205 may generate the dye matrix data X by unmixing using the desired mixing matrix A (above formula (1)), and then use the correction matrix B to generate corrected dye matrix data X' as K pieces of dye data.

[0072] Then, the data generation unit 205 reproduces K pieces of pigment data from the pigment matrix data X or the corrected pigment matrix data X', and outputs the reproduced K pieces of pigment data. The output destination at this time may be an output device of the data processing device 5, such as a display or a touch panel display, or may be an external device connected to the data processing device 5 so as to be able to communicate data. Furthermore, the pigment data generated by the data generation unit 205 may be used in data analysis for purposes such as counting, selecting, and analyzing the characteristics of the analyte.

[0073] Next, the procedure of the observation process for an analysis target object using the pigment data acquisition system 1 according to this embodiment, i.e., the flow of the pigment data acquisition method according to this embodiment, will be described. Fig. 12 is a flowchart showing the procedure of the observation process by the pigment data acquisition system 1.

[0074] First, the data processing device 5 selects a desired mixing matrix A from DB6, which stores a plurality of mixing matrices before correction for obtaining a plurality of dye data showing the distribution of each fluorescent dye having a different fluorescence wavelength (step S21; selection step). Then, the data acquisition device 3 executes an observation process for a plurality of analytes, and as a result, sequence data is generated and transmitted (step S22). Next, the data acquisition unit 202 of the data processing device 5 acquires sequence data for C fluorescence wavelength bands from the data acquisition device 3 (step S23; data acquisition step).

[0075] Furthermore, the matrix correction unit 204 of the data processing device 5 determines whether or not a desired mixing matrix has been found in step S21 (step S24). If the determination result shows that a desired mixing matrix has been found (step S24; Yes), the matrix correction unit 204 derives a corrected mixing matrix A' or a correction matrix B based on the desired mixing matrix and C pieces of array data (step S26; first correction step).

[0076] On the other hand, if the desired mixing matrix is ​​not found (step S24; No), the matrix acquisition unit 203 of the data processing device 5 derives a mixing matrix A using C pieces of sequence data (step S25; data generation step). Finally, the data generation unit 205 of the data processing device 5 unmixes the matrix data Y generated based on the C pieces of sequence data for the analyte using the mixing matrix A or the corrected mixing matrix A', thereby acquiring and outputting K pieces of dye data (step S27; data generation step). This completes the observation process for multiple analytes.

[0077] According to the dye data acquisition system 1 described above, a desired mixing matrix is ​​selected from a plurality of mixing matrices for separating reference spectral data relating to fluorescence in each of a plurality of wavelength bands into a plurality of dye data, and observation spectral data of the fluorescence distribution in C wavelength bands is acquired for each of N analytes. This observation spectral data is unmixed using the desired mixing matrix, thereby generating K dye data indicating the distribution of K fluorescent dyes for each of the objects. This eliminates the need to analyze the mixing matrix for separating the dye data each time observation spectral data is acquired. As a result, the throughput during measurement of the fluorescence spectrum relating to the distribution of each fluorescent dye can be improved.

[0078] In this embodiment, each of the multiple mixing matrices is linked to the acquisition conditions of the reference spectral data used to set the mixing matrix and stored in advance in DB6, and in the selection step, a mixing matrix linked to the acquisition conditions of the observation spectral data is selected as the desired mixing matrix from the multiple mixing matrices stored in DB6. This allows the desired mixing matrix to be selected as the mixing matrix that matches the acquisition conditions of the observation spectral data, making it possible to accurately observe the distribution of multiple types of fluorescent dyes for each analyte.

[0079] In this embodiment, the acquisition conditions are information about multiple detection wavelengths when detecting fluorescence. By setting the acquisition conditions in this manner, the observation spectrum data can be unmixed using a desired mixing matrix that matches the reflection state of fluorescence from multiple fluorescent dyes in the observation spectrum data. As a result, the distribution of multiple types of fluorescent dyes for each analyte can be observed with greater accuracy.

[0080] In this embodiment, a first correction step is performed to obtain a corrected mixing matrix by performing a correction process on the desired mixing matrix based on the observation spectral data and the desired mixing matrix. In this case, the observation spectral data can be unmixed using a correction mixing matrix that matches the reflection state of fluorescence from the multiple fluorescent dyes in the observation spectral data. As a result, the distribution of multiple types of fluorescent dyes for each analyte can be observed with greater accuracy.

[0081] Furthermore, in this embodiment, the correction process is a process of correcting the plurality of dye data separated from the observation spectral data using a correction mixing matrix so that each data item is more independent. In this case, the observation spectral data can be unmixed using a correction mixing matrix corrected so that the plurality of dye data separated from the observation spectral data is more independent. As a result, the distribution of the plurality of fluorescent dyes for each analyte can be observed with even greater accuracy.

[0082] In this embodiment, the data generation step unmixes the observed spectral data using a corrected mixing matrix, which allows for more accurate observation of the distribution of multiple types of fluorescent dyes for each analyte.

[0083] Flow cytometry is a technique that enables the identification or quantification of cell populations by staining cells, cell surface proteins, and intracellular proteins, and can be used for cell sorting, immunophenotyping, etc. According to this embodiment, by analyzing the expression combination of antibodies used on blood cells, for example, based on the acquired dye data, it is possible to accurately identify which cells have become tumorigenic.

[0084] FIG. 13 shows a graph plotting two sets of dye data showing the distribution of each of the two fluorescent dyes output by the dye data acquisition system 1. FIGS. 13(a) and 13(b) are graphs plotting the relationship between the expression levels of two fluorescent dyes for each analyte, using two sets of dye data obtained for a single-color analyte containing either one of two fluorescent dyes, “dye A” or “dye B.” FIG. 13(a) shows the results based on two sets of dye data obtained by unmixing using the mixing matrix retrieved by the information retrieval unit 201. FIG. 13(b) shows the results based on two sets of dye data obtained by unmixing using a corrected mixing matrix corrected by the matrix correction unit 204. In both cases, dye data enabling population identification was obtained. It can be seen that the use of a corrected mixing matrix allows for the acquisition of dye data that accurately captures the quantitative properties of the analyte.

[0085] Various embodiments of the present invention have been described above, but the present invention is not limited to the above embodiments, and may be modified or applied to other things within the scope that does not change the gist of the claims.

[0086] The data acquisition device 3 of the above embodiment may be modified to the configuration shown in FIG. 14. The data acquisition device 3A according to the modified example shown in FIG. 14 differs from the data acquisition device 3A in that it includes a spectrometer 58 and a detector 59 as the optical system 53. The spectrometer 58 separates the fluorescence emitted from the sample fluid into multiple wavelength bands (e.g., 1024 wavelength bands). The detector 59 is a one-dimensional or two-dimensional detector having multiple pixels (e.g., 1024 pixels), and measures the intensity of the fluorescence of each wavelength band incident on each pixel from the spectrometer 58 and outputs the measured intensity to the electronic system 54. Such a data acquisition device 3A can generate array data of multiple fluorescence wavelength bands. For example, the 1024-channel array data generated by the data acquisition device 3A is divided into 512 cluster matrices by the data processing device 5, with two adjacent channels being clustered into one wavelength group.

[0087] In the above embodiment, the C array data used to correct the mixing matrix shown in step S26 of Fig. 12 may be data that has been reduced by, for example, selecting data to be used by the user, thinning, binning, clustering, etc. In this case, the C array data only needs to have enough data points to perform independence evaluation. With this configuration, the processing load can be reduced.

[0088] In the above embodiment, the procedure of the observation process shown in Fig. 12 may be modified so that the mixing matrix is ​​corrected and corrected dye matrix data is generated after the unmixing process, as in the procedure of a modified example shown in Fig. 15. The processes of steps S31 to S35 in Fig. 15 are the same as the processes of steps S21 to S25 in Fig. 12.

[0089] In this modified example, when the desired mixing matrix is ​​found (step S34; Yes), the matrix correction unit 204 generates K pieces of pre-correction pigment data based on the desired mixing matrix and C pieces of array data (step S36). Thereafter, the matrix correction unit 204 derives a corrected mixing matrix A' or a correction matrix B based on the K pieces of pre-correction pigment data, and performs a correction process on the K pieces of pre-correction pigment data in the same manner as in the above embodiment to generate corrected pigment matrix data X' (step S37; second correction step). Thereafter, the data generation unit 205 of the data processing device 5 acquires and outputs the K pieces of pigment data based on the corrected pigment matrix data X' (step S38; data generation step).

[0090] This modification also makes it possible to observe the distribution of multiple types of fluorescent dyes for each object with higher accuracy by correcting K pieces of dye data.

[0091] In the above embodiment, in the first correction step, the separation matrix P after update is calculated so as to minimize the mutual information between the plurality of pre-correction dye matrix data X. kHowever, the present invention is not limited to this, and an index of independence evaluation other than mutual information may be used. Specifically, a correlation coefficient, HSIC (Hilbert-Schmidt Independence Criteria), or independent component analysis may be used as an index of independence evaluation of a plurality of pre-correction dye matrix data X.

[0092] The correlation coefficient is, for example, a statistic related to second-order correlation (correlation between two variables), and may be, for example, Pearson's product-moment correlation coefficient. In the example shown in FIG. 16(a), for the correlation between two variables x and y, it can be determined that the closer the absolute value of the correlation coefficient is to 1, the lower the independence, and that the closer the absolute value is to 0, the higher the independence. However, even if the correlation coefficient is 0, it does not necessarily mean that the independence is high. For example, if two variables x and y have a correlation as shown in FIG. 16(b), even if the absolute value of the correlation coefficient is 0, it does not mean that the independence is high. Independence evaluation using the correlation coefficient allows for high-speed processing.

[0093] HSIC is a statistic related to higher-order correlation. When HSIC=0, it can be determined that the data are completely independent. However, (number of samples) 2 This requires a large amount of memory and increases the amount of calculation. Independent component analysis allows for high-speed processing because it performs optimization calculations for all dye images simultaneously, rather than for each pair of dye images. [Explanation of symbols]

[0094] 1...pigment data acquisition system, 3, 3A...data acquisition device, 5...data processing device, 201...information search unit, 202...data acquisition unit, 203...matrix acquisition unit, 204...matrix correction unit, 205...data generation unit, 6...DB.

Claims

1. a selection step of selecting desired separation information from a plurality of pieces of separation information preset based on reference spectral data which is a distribution of fluorescence intensity values ​​for each of a plurality of detection wavelengths, in order to separate the reference spectral data into a plurality of dye data which indicate distributions for each of a plurality of fluorescent dyes; a data acquisition step of acquiring observation spectrum data, which is a distribution of fluorescence intensity values ​​for C (C is an integer of 2 or more) detection wavelengths, for N (N is an integer of 2 or more) objects; a data generating step of generating K dye data items (K is an integer of 2 to C) showing distributions for each of the K fluorescent dyes by unmixing the observed spectral data items using the desired separation information; A dye data acquisition method comprising:

2. each of the plurality of pieces of separation information is associated with an acquisition condition of the reference spectrum data used to set the separation information and is stored in advance in a data storage unit; In the selection step, selecting, as the desired separation information, separation information linked to the acquisition conditions of the observed spectrum data from the plurality of separation information stored in the data storage unit; The method for obtaining dye data according to claim 1 .

3. The acquisition condition is information regarding a plurality of the detection wavelengths. The method for obtaining dye data according to claim 2 .

4. a first correction step of performing a correction process on the desired separation information based on the observed spectrum data and the desired separation information to obtain corrected separation information; The dye data acquisition method according to any one of claims 1 to 3.

5. the correction processing is a processing for correcting the plurality of pigment data separated from the observed spectrum data using the corrected separation information so that each of the pigment data is highly independent. The method for obtaining dye data according to claim 4 .

6. In the data generating step, unmixing is performed using the corrected separation information. The method for obtaining dye data according to claim 4 .

7. further comprising a second correction step of performing a correction process on the K pieces of dye data to generate K pieces of corrected dye data. The dye data acquisition method according to any one of claims 1 to 3.

8. The correction process is a process of correcting the K pieces of pigment data so that the respective pieces of pigment data are highly independent of each other. The method for obtaining dye data according to claim 7 .

9. A dye data acquisition device that processes observed spectrum data, which is a distribution of fluorescence intensity values ​​for C (C is an integer of 2 or more) detection wavelengths, for N (N is an integer of 2 or more) objects, comprising: a plurality of pieces of separation information preset based on reference spectral data which is a distribution of fluorescence intensity values ​​for each of a plurality of detection wavelengths, wherein desired separation information is selected from the plurality of pieces of separation information in order to separate the reference spectral data into a plurality of dye data which indicate distributions for each of a plurality of fluorescent dyes; generating K dye data items (K is an integer of 2 or more and C or less) indicating distributions for each of the K fluorescent dyes by unmixing the observed spectral data items using the desired separation information; Dye data acquisition device.

10. 1. A dye data acquisition program for generating dye data indicating a distribution of fluorescent dyes in N (N is an integer of 2 or more) objects based on observation spectrum data that is a distribution of fluorescence intensity values ​​for C (C is an integer of 2 or more) detection wavelengths for the N (N is an integer of 2 or more) objects, On the computer, a step of selecting desired separation information from a plurality of pieces of separation information preset based on reference spectral data which is a distribution of fluorescence intensity values ​​for each of a plurality of detection wavelengths, for separating the reference spectral data into a plurality of dye data which indicate distributions for each of a plurality of fluorescent dyes; and generating K dye data items (K is an integer between 2 and C) showing distributions for each of the K fluorescent dyes by unmixing the observed spectral data items using the desired separation information; Execute the dye data acquisition program.

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