Information processing method, information processing apparatus, and information processing system

Abstract

There is provided an information processing method including: by an information processing apparatus, calculating intensity of light corresponding to each dye from light detected from each of multi-stained particles in a sample, by using a spectral reference of each dye acquired by a single-stained particle; calculating a spillover evaluation index on the basis of a distribution of the intensity of light corresponding to each dye, the spillover evaluation index indicating a degree of spillover of light corresponding to each dye into light corresponding to other dye; and adjusting the spectral reference of each dye on the basis of the spillover evaluation index.

Description

INFORMATION PROCESSING METHOD, INFORMATION PROCESSING APPARATUS, AND INFORMATION PROCESSING SYSTEM

[0001] The present technology relates to an information processing method, an information processing apparatus, and an information processing system.

[0002] <CROSS REFERENCE TO RELATED APPLICATIONS> This application claims the benefit of Japanese Priority Patent Application JP 2024-115755 filed July 19, 2024, the entire contents of which are incorporated herein by reference.

[0003] Conventionally, a technology of generating a two-dimensional plot regarding two desired fluorescent dyes of a plurality of fluorescent dyes used to mark a particle population and adjusting a spectral reference (hereinafter, referred to as SR) of the fluorescent dyes corresponding to fluorescence data in the two-dimensional plot in accordance with a user operation on the two-dimensional plot has been proposed (e.g., see Patent Literature 1).

[0004] WO 2021 / 153192

[0005] However, adjusting the SR by the user operation is a complicated and time-consuming process. Especially in recent years, flow cytometers (FCMs) have become increasingly multicolor, and the SR adjustment process by the user operation is expected to become more complicated and time-consuming.

[0006] The present technology has been made in view of such circumstances to enable easy SR adjustment.

[0007] An information processing system according to a first aspect of the present technology includes: at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method of: acquiring spectral references of a first dye and a second dye; obtaining information regarding light detected from at least one multi-stained particle in a sample; calculating an intensity of light corresponding to the first dye and to the second dye by using the spectral references of the first dye and the second dye; calculating an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and adjusting the spectral reference of the first dye and / or the second dye based on the index.

[0008] An information processing apparatus according to the first aspect of the present technology includes first circuitry configured to: acquire spectral references of a first dye and a second dye; obtain information regarding light detected from at least one multi-stained particle in a sample; and calculate an intensity of light corresponding to the first dye and to the second dye by using the spectral references of the first dye and the second dye; second circuitry configured to: calculate an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and third circuitry configured to: adjust the spectral reference of the first dye and / or the second dye based on the index.

[0009] An information processing method according to the first aspect of the present technology includes acquiring spectral references of a first dye and a second dye; obtaining information regarding light detected from at least one multi-stained particle in a sample; calculating an intensity of light corresponding to the first dye and the second dye by using the spectral references of the first dye and the second dye; calculating an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and adjusting the spectral reference of the first dye and / or second dye based on the index.

[0010] In the first aspect of the present technology, intensity of light corresponding to each dye from light detected from each of multi-stained particles in a sample is calculated by using a spectral reference of each dye acquired by a single-stained particle, a spillover evaluation index is calculated on the basis of a distribution of the intensity of light corresponding to each dye, the spillover evaluation index indicating a degree of spillover of light corresponding to each dye into light corresponding to other dye, and the spectral reference of each dye is adjusted on the basis of the spillover evaluation index.

[0011] In the second aspect of the present technology, light from each of a plurality of multi-stained particles in a sample is detected, intensity of light corresponding to each dye from light detected from each of multi-stained particles in a sample is calculated by using a spectral reference of each dye acquired by a single-stained particle, a spillover evaluation index is calculated on the basis of a distribution of the intensity of light corresponding to each dye, the spillover evaluation index indicating a degree of spillover of light corresponding to each dye into light corresponding to other dye, and the spectral reference of each dye is adjusted on the basis of the spillover evaluation index.

[0012] Fig. 1 is a diagram showing examples of a two-dimensional plot of light intensity.Fig. 2 is a diagram for describing a problem in a case where an SR is used in an application other than typical applications.Fig. 3 is a block diagram showing a configuration example of an information processing system.Fig. 4 is a block diagram showing a configuration example of a biological sample analyzer.Fig. 5 is a block diagram showing a configuration example of an information processing unit.Fig. 6 is a block diagram showing a configuration example of an analysis pre-processing unit.Fig. 7 is a flowchart for describing a flow of an experiment.Fig. 8 is a flowchart for describing a first embodiment of analysis pre-processing.Fig. 9 is a diagram for describing examples of gating.Fig. 10 is a flowchart for describing the details of a first embodiment of SR adjustment processing.Fig. 11 is a diagram for describing relationships between states of unmixing and SIs.Fig. 12 is a diagram for describing an SR adjustment method.Fig. 13 is a diagram for describing the SR adjustment method.Fig. 14 is a diagram showing examples of an orthogonality evaluation index.Fig. 15 is a diagram showing examples of the orthogonality evaluation index.Fig. 16 is a flowchart for describing a second embodiment of the analysis pre-processing.Fig. 17 is a diagram showing an example of a two-dimensional plot of light intensity and correlation coefficients.Fig. 18 is a diagram showing examples of correlation coefficients between dyes.Fig. 19 is a diagram showing an example of the two-dimensional plot of light intensity and the correlation coefficients.Fig. 20 is a diagram showing an example of the two-dimensional plot of light intensity and the correlation coefficients.Fig. 21 is a diagram showing an example of the two-dimensional plot of light intensity and the correlation coefficients.Fig. 22 is a diagram showing an example of the two-dimensional plot of light intensity and the correlation coefficients.Fig. 23 is a flowchart for describing the details of a second embodiment of the SR adjustment processing.Fig. 24 is a diagram showing examples of the correlation coefficients between the dyes.Fig. 25 is a diagram showing examples of the correlation coefficients between the dyes.Fig. 26 is a diagram for describing a method of suppressing excessive deformation of the SR.Fig. 27 is a diagram for describing the method of suppressing excessive deformation of the SR.Fig. 28 is a diagram for describing the method of suppressing excessive deformation of the SR.Fig. 29 is a diagram for describing excessive adjustment of the SR.Fig. 30 is a diagram for describing a method of excluding a dim population using dip test and flow density.Fig. 31 is a diagram for describing the method of excluding a dim population using dip test and flow density.Fig. 32 is a diagram for describing a method of excluding a dim population using a histogram.Fig. 33 is a diagram for describing the method of excluding a dim population using the histogram.Fig. 34 is a diagram for describing the method of excluding a dim population using the histogram.Fig. 35 is a diagram for describing the method of excluding a dim population using display with contours of the two-dimensional plot.Fig. 36 is a diagram for describing the method of excluding a dim population using display with contours of the two-dimensional plot.Fig. 37 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 38 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 39 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 40 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 41 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 42 is a flowchart for describing dependency evaluation calculation processing.Fig. 43 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 44 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 45 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 46 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 47 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 48 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 49 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 50 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 51 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 52 is a diagram for describing the method of excluding a dim population by gating with the other marker.Fig. 53 is a diagram showing an example of the UI in a case of excluding the dim population by gating with the other marker.Fig. 54 is a diagram showing an example of the UI in a case of excluding the dim population by gating with the other marker.Fig. 55 is a diagram showing an example of the UI in a case of excluding the dim population by gating with the other marker.Fig. 56 is a diagram showing an example of the UI in a case of excluding the dim population by gating with the other marker.Fig. 57 is a diagram showing an example of the UI in a case of excluding the dim population by gating with the other marker.Fig. 58 is a diagram showing an example of the UI in a case of excluding the dim population by gating with the other marker.Fig. 59 is a diagram for describing an example in which a p-value for a correlation coefficient is used as an SR adjustment termination condition.Fig. 60 is a diagram for describing an example in which the SR adjustment termination condition is used as mutual information.Fig. 61 is a diagram for describing an example in which the mutual information is used as the SR adjustment termination condition.Fig. 62 is a diagram for describing an example in which a tilt of a linear regression line is used as the SR adjustment termination condition.Fig. 63 is a diagram for describing an example in which the tilt of the dependency evaluation index is used as the SR adjustment termination condition.Fig. 64 is a diagram for describing a modified example of an SR adjustment ratio.Fig. 65 is a diagram for describing a modified example of the SR adjustment ratio.Fig. 66 is a diagram for describing a modified example of the SR adjustment method.Fig. 67 is a diagram for describing a modified example of the SR adjustment method.Fig. 68 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 69 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 70 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 71 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 72 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 73 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 74 is a diagram showing an example of the UI that visualizes the dependency evaluation index.Fig. 75 is a diagram showing a configuration example of a computer.

[0013] Hereinafter, embodiments for carrying out the present technology will be described. Descriptions thereof will be given in the following order. 0. Background of Present Technology 1. Embodiments 2. Modified Examples 3. Others

[0014] <0. Background of Present Technology> First of all, the background of the present technology will be described with reference to Figs. 1 to 2.

[0015] For example, in the technology described in Patent Literature 1 described above, unmixing processing is performed on light data obtained by detecting light generated by light radiation onto bioparticles (hereinafter, referred to as multi-stained particles) labeled (stained) with a plurality of dyes (e.g., fluorescent dyes). Accordingly, light intensity data individually indicating intensity of light corresponding to each dye (e.g., fluorescence) of each multi-stained particle is obtained.

[0016] In this unmixing processing, an SR of each dye is used. The SR is reference data representing a standard wavelength distribution (spectrum) of the light corresponding to each dye that is used for staining the bioparticles. For example, the SR of each dye is obtained by individually detecting light generated by light radiation onto bioparticles (hereinafter, referred to as single-stained particles) labeled with only a target dye.

[0017] In a case where unmixing processing of a sample (hereinafter, referred to as multi-stained particle sample) including a plurality of multi-stained particles is performed by using the SR acquired by the single-stained particles, it is ideal to completely separate the light corresponding to each dye.

[0018] For example, it is ideal that orthogonality of the two-dimensional plot (hereinafter, referred to as two-dimensional plot orthogonality) indicating a distribution of light intensity corresponding to two dyes is retained. For example, it is ideal that in the two-dimensional plot, a distribution of light intensity in a population of bioparticles that show a negative reaction to one dye is orthogonal to an axis indicating light intensity corresponding to that dye. For example, it is ideal that in the two-dimensional plot, a distribution of light intensity in a population of bioparticles that show a positive reaction to the one dye is orthogonal to the axis indicating the light intensity corresponding to that dye.

[0019] Hereinafter, the light intensity corresponding to each dye will be referred to as light intensity to each dye.

[0020] Moreover, for example, it is ideal that the light intensity to each dye changes independently of each other. That is, it is ideal that there is no dependency of the light intensity between the respective dyes (hereinafter, referred to as dependency between the dyes).

[0021] For example, Fig. 1 shows an example of a two-dimensional plot of light intensity to dyes 1 and 2. Specifically, the vertical axis in each of A and B of Fig. 1 is obtained by axis-transforming the light intensity to the dye 1 by a biexponential transformation and the horizontal axis is obtained by axis-transforming the light intensity to the dye 2 by a biexponential transformation. A and B of Fig. 1 show a distribution of light intensity of bioparticles that show a negative reaction to the dye 1.

[0022] In this case, for example, as shown in A of Fig. 1, it is ideal that the distribution of the light intensity is substantially orthogonal to the axis (vertical axis) of the light intensity to the dye 1. In other words, it is ideal that the light intensity to the dye 1 is substantially constant regardless of the light intensity to the dye 2.

[0023] However, when the SR of each dye is used for analyzing a plurality of multi-stained particle samples, the two-dimensional plot orthogonality decreases or the dependency between the dyes increases in some cases. For example, as shown in B of Fig. 1, the higher the light intensity to the dye 2, the higher the light intensity to the dye 1 in some cases.

[0024] It is due to the following causes, for example.

[0025] For example, a computational error due to standardization may cause a decrease in the two-dimensional plot orthogonality or an increase in the dependency between the dyes in some cases. Here, the computational error due to standardization is a computational error in standardization of an SR performed when the SR obtained from a single-stained particle sample is used for unmixing a multi-stained particle sample.

[0026] For example, in a case where a multi-stained particle sample is analyzed using an FCM of a different model than the FCM used to generate the SR, differences in optical properties due to differences in lasers mounted on the respective FCMs or another factor may cause a decrease in the two-dimensional plot orthogonality and an increase in the dependency between the dyes.

[0027] For example, even in a case where a multi-stained particle sample is analyzed using an FCM of the same model as the FCM used to generate the SR, differences in optical properties between the individual FCMs may cause a decrease in the two-dimensional plot orthogonality and an increase in the dependency between the dyes.

[0028] For example, a spectrum notch area is different between individual FCMs in some cases, like notch areas NA1 and NA2 shown in B and C of Fig. 2. In addition, in a case where an SR with no notch areas is used as shown in A of Fig. 2, the notch area difference between the individual FCMs may cause a decrease in the two-dimensional plot orthogonality and an increase in the dependency between the dyes.

[0029] For example, even in a case where a multi-stained particle sample is analyzed using the same FCM as the FCM used to generate the SR, various setting values may differ between the time the SR is generated and the time the multi-stained particle sample is analyzed, resulting in a decrease in the two-dimensional plot orthogonality and an increase in the dependency between the dyes.

[0030] In this regard, the present technology allows the SR of each dye to be easily and suitably adjusted so that the decrease in the two-dimensional plot orthogonality and the increase in the dependency between the dyes can be suppressed.

[0031] <1. Embodiments> Next, embodiments of the present technology will be described with reference to Figs. 3 to 22.

[0032] Configuration Example of Information Processing System Fig. 3 shows a configuration example of an information processing system 1 to which the present technology is applied.

[0033] The information processing system 1 includes biological sample analyzers 11-1 to 11-n and servers 12-1 and 12-2.

[0034] The biological sample analyzers 11-1 to 11-m and the server 12-2 are connected via a network (not shown). The biological sample analyzers 11-1 to 11-m and the server 12-2 are owned by a single organization.

[0035] It should be noted that the unit of organization that owns the biological sample analyzers 11-1 to 11-m and the server 12-2 is not particularly limited. For example, the organization may be a single company, school, organization, or the like or it may be a division of a company, school, organization, or the like.

[0036] The biological sample analyzers 11-m+1 to 11-n and the servers 12-1 and 12-2 are connected via a network (not shown).

[0037] In a case where there is no need to distinguish between the biological sample analyzers 11-1 to 11-n individually, they will be simply referred to as biological sample analyzers 11, hereinafter. In a case where there is no need to distinguish between the servers 12-1 and 12-2 individually, they will be simply referred to as servers 12, hereinafter.

[0038] The biological sample analyzer 11 is constituted by, for example, a flow cytometer and an imaging cytometer.

[0039] The server 12 controls each biological sample analyzer 11, for example. The server 12 manages and processes, for example, data and programs to be used by each biological sample analyzer 11, as well as data and the like obtained from the processing of each biological sample analyzer 11.

[0040] Moreover, server 12-1 prevents, for example, data obtained by the biological sample analyzer 11 in the organization from leaking out of the organization.

[0041] It should be noted that the number of servers 12 and organizations is not limited to the example in this figure. Moreover, for example, one organization may own a plurality of servers 12.

[0042] Configuration Example of Biological Sample Analyzer 11 Fig. 4 shows a configuration example of the biological sample analyzer 11 in Fig. 3.

[0043] The biological sample analyzer 11 includes a light irradiation unit 111, a detection unit 112, and an information processing unit 113. The light irradiation unit 111 radiates light onto a biological sample S flowing in a flow channel C. The detection unit 112 detects light generated by radiating light onto the biological sample S. The information processing unit 113 processes information about the light detected by the detection unit 112. Examples of the biological sample analyzer 11 can include a flow cytometer and an imaging cytometer. The biological sample analyzer 11 may include a sorting unit 114 that sorts particular bioparticles P in the biological sample. Examples of the biological sample analyzer 11 including the sorting unit 114 can include a cell sorter.

[0044] Biological Sample The biological sample S may be a liquid sample containing bioparticles. The bioparticles are for example cells or non-cellular bioparticles. The cells may be living cells, and more specific examples can include blood cells such as red blood cells and white blood cells and reproductive cells such as sperm and fertilized eggs. Moreover, the cells may be directly collected from a specimen, such as whole blood, or may be cultured cells acquired after culture. Examples of the non-cellular bioparticles can include extracellular vesicles, especially exosomes and microvesicles.

[0045] The bioparticles may be labeled with one or more labeling substances (e.g., dyes (especially fluorescent dyes) and fluorescent dye-labeled antibodies or the like). For example, the bioparticles may be labeled (stained) with one or more kinds of fluorescent dyes. The bioparticles with fluorescent dyes may be labeled by known methods. Specifically, in a case where the bioparticles are cells, measurement target cells can be labeled with a fluorescent dye by mixing fluorescent-labeled antibodies, which selectively bind to antigens existing on the surfaces of cells, with the measurement target cells and binding the fluorescent-labeled antibodies to the cell surface antigens. Alternatively, the measurement target cells can be labeled with a fluorescent dye by mixing a fluorescent dye, which is selectively taken up by particular cells, with the measurement target cells.

[0046] The fluorescent-labeled antibodies are antibodies to which a fluorescent dye is bound as labels. The fluorescent-labeled antibodies may be a fluorescent dye directly bound to antibodies. Alternatively, the fluorescent-labeled antibodies may be biotin-labeled antibodies to which a fluorescent dye bound to avidin is bound by an avidin-biotin reaction. It should be noted that both polyclonal and monoclonal antibodies may be used as the antibodies.

[0047] The fluorescent dyes for labeling cells are not particularly limited, and at least one of well-known dyes used for staining cells or the like may be used. For example, phycoerythrin (PE), fluorescein isothiocyanate (FITC), PE-Cy5, PE-Cy7, PE-Texas Red (registered trademark), allophycocyanin (APC), APC-Cy7, ethidium bromide, propidium iodide, Hoechst (registered trademark) 33258, Hoechst (registered trademark) 33342, DAPI (4', 6-diamidino-2-phenylindole), acridine orange, chromomycin, mithramycin, olivomycin, pyronin Y, thiazole orange, rhodamine 101, isothiocyanate, BCECF, BCECF-AM, C.SNARF-1, C.SNARF-1-AMA, aequorin, Indo-1, Indo-1-AM, Fluo-3, Fluo-3-AM, Fura-2, Fura-2-AM, oxonol, Texas Red (registered trademark), Rhodamine 123, 10-N Nonyl acridine orange, fluorescein, fluorescein diacetate, carboxyfluorescein, carboxyfluorescein diacetate, carboxy-dichlorofluorescein, carboxy-dichlorofluorescein diacetate, and the like may be employed as the fluorescent dyes. Moreover, derivatives of the fluorescent dyes and the like may be used.

[0048] Flow Channel The flow channel C is configured to allow the biological sample S to flow. In particular, the flow channel C may be configured so that the bioparticles contained in the biological sample form a flow substantially in line. The flow channel structure including the flow channel C may be designed such that a laminar flow is formed. In particular, the flow channel structure is designed such that a laminar flow is formed in which a biological sample flow (sample flow) is enveloped by a sheath liquid flow. The design of the flow channel structure may be selected as appropriate by those skilled in the art and may be known. The flow channel C may be formed in a flow channel structure, such as a microchip (a chip with flow channels on the order of micrometers) or a flow cell. The width of the flow channel C may be 1 mm or less, especially 10 μm or more and 1 mm or less. The flow channel C and the flow channel structure including it may be formed from a material such as plastic or glass.

[0049] The biological sample analyzer in the present disclosure is configured such that light from the light irradiation unit 111 is radiated onto the biological sample flowing in the flow channel C, and in particular to the bioparticles in the biological sample. The biological sample analyzer in the present disclosure may be configured such that an interrogation point on the biological sample is in the flow channel structure in which the flow channel C is formed or may be configured such that the interrogation point is outside of the flow channel structure. An example of the former can be a configuration in which the light is radiated onto the flow channel C in a microchip or flow cell. In the latter, the light may be radiated onto the bioparticles after they exit the flow channel structure (especially its nozzle part), for example, a jet-in-air flow cytometer.

[0050] Light Irradiation Unit The light irradiation unit 111 includes a light source part that emits light and a light guide optical system that directs the light to an interrogation point. The light source part includes one or more light sources. A type of light source is, for example, a laser light source or an LED. The wavelength of light emitted from each light source may be the wavelength of ultraviolet light, visible light, or infrared light. The light guide optical system includes optical components such as a beam splitter group, a mirror group, and optical fibers. Moreover, the light guide optical system may include a group of lenses for focusing light and includes, for example, an objective lens. There may be one or more interrogation points at which light intersects with the biological sample. The light irradiation unit 111 may be configured to focus light radiated from one or a plurality of different light sources on a single interrogation point.

[0051] Detection Unit The detection unit 112 is equipped with at least one photodetector that detects light generated by light radiation onto the bioparticles. The light to be detected is, for example, fluorescence or scattered light (e.g., at least one of forward scattered light, backward scattered light, or side scattered light). Each photodetector includes one or more light-receiving elements and has, for example, a light-receiving element array. Each photodetector may include one or more photomultiplier tubes (PMTs) and / or photodiodes, such as avalanche photodiodes (APDs) and multi-pixel photon counters (MPPCs), as light-receiving elements. The photodetector includes a PMT array, for example, a plurality of PMTs arranged in a one-dimensional direction. Moreover, the detection unit 112 may include an image sensor such as a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS). The detection unit 112 may acquire images of bioparticles (e.g., bright field images, dark field images, and fluorescence images) with the image sensor.

[0052] The detection unit 112 includes a detection optical system that allows light of a predetermined detection wavelength to reach the corresponding photodetector. The detection optical system includes a spectrometer such as a prism or diffraction grating or a wavelength separation unit such as a dichroic mirror or optical filter. For example, the detection optical system is configured to spectrally analyze the light generated by light radiation onto the bioparticles such that the spectrally analyzed light is detected by a plurality of photodetectors greater in number than the number of fluorescent dyes with which the bioparticles are labeled. A flow cytometer including such a detection optical system is called a spectral flow cytometer. Moreover, for example, the detection optical system is configured to separate the light corresponding to the fluorescent wavelength range of a particular fluorescent dye from the light generated by light radiation onto the bioparticles and to cause the corresponding photodetector to detect the separated light.

[0053] Moreover, the detection unit 112 may include a signal processing unit that transforms an electrical signal obtained by the photodetector into a digital signal. The signal processing unit may include an A / D converter as a device that performs the transformation. The digital signal obtained by the transformation by the signal processing unit may be transmitted to the information processing unit 113. The digital signal may be handled by the information processing unit 113 as data about light (hereinafter, also referred to as "light data"). The light data may be light data including, for example, fluorescence data. More specifically, the light data may be light intensity data, in which the light intensity may be light intensity data (which may include feature amounts such as area, height, width, etc.) of light including fluorescence.

[0054] Information Processing Unit The information processing unit 113 includes, for example, a processing unit that performs processing of various types of data (e.g., light data) and a storage unit that stores the various types of data. The processing unit may perform fluorescence spillover correction (compensation processing) on the light intensity data in a case where the processing unit acquires light data corresponding to a fluorescent dye is acquired from the detection unit 112. Moreover, in a case where the processing unit is a spectral flow cytometer, the processing unit performs fluorescence separation processing on the light data and acquires light intensity data corresponding to the fluorescent dye. The fluorescence separation processing may be performed in accordance with, for example, an unmixing method described in Japanese Patent Application Laid-open No. 2011-232259. In a case where the detection unit 112 includes an image sensor, the processing unit may acquire morphological information of the bioparticles on the basis of an image acquired by the image sensor. The storage unit may be configured to be capable of storing the acquired light data. The storage unit may be further configured to be capable of storing spectral reference data to be used in the unmixing processing.

[0055] In a case where the biological sample analyzer 11 includes the sorting unit 114 described below, the information processing unit 113 may perform a determination on the basis of the light data and / or morphological information as to whether to sort the bioparticles. The information processing unit 113 may control the sorting unit 114 to sort the bioparticles by the sorting unit 114 on the basis of a result of the determination.

[0056] The information processing unit 113 may be configured to be capable of outputting various types of data (e.g., light data and images). For example, the information processing unit 113 may output various types of data (e.g., two-dimensional plots, spectral plots, or the like) generated on the basis of the light data. Moreover, the information processing unit 113 may be configured to be capable of receiving input of various types of data and, for example, receives gating processing on the plot by a user. The information processing unit 113 may include an output unit (e.g., a display, etc.) or an input unit (e.g., a keyboard, etc.) for performing the output or the input.

[0057] The information processing unit 113 may be configured as a general-purpose computer and may be configured as, for example, an information processing apparatus including a CPU, RAM, and ROM. The information processing unit 113 may be contained within an enclosure in which the light irradiation unit 111 and the detection unit 112 are provided or may be located outside of the enclosure. Moreover, various processing or functions by the information processing unit 113 may be realized by a server computer or cloud connected via a network.

[0058] Sorting Unit The sorting unit 114 sorts the bioparticles in accordance with the determined results by the information processing unit 113. The sorting method may be a method of generating droplets containing bioparticles by vibration, applying an electric charge to sorting target droplets, and controlling travelling directions of the droplets by electrodes. The sorting method may be a method of controlling the travelling directions of the bioparticles in the flow channel structure and performing the sorting. For example, a control mechanism using a pressure (jet or suction) or electric charge is provided in the flow channel structure. Examples of the flow channel structure can include a chip (e.g., the chip described in Japanese Patent Application Laid-open No. 2020-76736) having a flow channel structure in which the flow channel C branches into a collection flow channel and a waste liquid flow channel downstream thereof, and particular bioparticles are collected in the collection flow channel.

[0059] Configuration Example of Information Processing Unit 113 Fig. 5 shows a configuration example of the functions of the information processing unit 113 of the biological sample analyzer 11 of Fig. 4.

[0060] The information processing unit 113 includes an input unit 201, a control unit 202, an output unit 203, a communication unit 204, a reference DB 205, and a storage unit 206.

[0061] The input unit 201 includes various input devices for performing input and operation on the biological sample analyzer 11.

[0062] The control unit 202 controls the respective parts of the information processing unit 113 to perform various types of processing thereof. The control unit 202 includes an analysis pre-processing unit 221 and an analysis unit 222.

[0063] The analysis pre-processing unit 221 performs pre-processing before analyzing the light data of the multi-stained particle sample provided by the detection unit 112. For example, the analysis pre-processing unit 221 adjusts the SR of each dye stored in the reference DB 205.

[0064] The analysis unit 222 performs various types of analysis processing on the light data of the multi-stained particle sample provided by the detection unit 112 by using the SR of each dye stored in the reference DB 205.

[0065] The output unit 203 includes output devices capable of outputting various types of information, for example, a display device such as a display and an audio output device such as a loudspeaker. For example, the output unit 203 outputs analysis information indicating analysis results of the light data of the multi-stained particle sample.

[0066] The communication unit 204 communicates with the server 12 or the like via a network (not shown).

[0067] The reference DB 205 stores reference data about the SR of each dye.

[0068] The storage unit 206 stores data and programs necessary for processing by the information processing unit 113.

[0069] Configuration Example of Analysis Pre-Processing Unit 221 Fig. 6 shows a configuration example of the functions of the analysis pre-processing unit 221 of Fig. 5. The analysis pre-processing unit 221 includes an unmixing unit 251, an axis transformation unit 252, a gating unit 253, an evaluation index calculation unit 254, and an SR adjustment unit 255.

[0070] The unmixing unit 251 performs unmixing processing of the light data of the multi-stained particle sample provided by the detection unit 112 by using the SR of each dye stored in the reference DB 205. Accordingly, the light intensity to each dye of each bioparticle (multi-stained particle) contained in the multi-stained particle sample is individually calculated. The unmixing unit 251 provides information individually indicating the light intensity to each dye of each bioparticle to the axis transformation unit 252.

[0071] The axis transformation unit 252 performs an axis transformation on the light intensity to each dye of each bioparticle. For example, the axis transformation unit 252 performs a biexponential transformation on the light intensity to each dye of each bioparticle. The axis transformation unit 252 provides information individually indicating the light intensity to each dye of each bioparticle after the axis transformation to the gating unit 253, the evaluation index calculation unit 254, and the SR adjustment unit 255.

[0072] The gating unit 253 performs gating of the bioparticles in the multi-stained particle sample for each dye on the basis of the distribution of the light intensity to each dye of each bioparticle after the axis transformation. The gating unit 253 provides information indicating a result of the gating for each dye to the evaluation index calculation unit 254 and the SR adjustment unit 255.

[0073] The evaluation index calculation unit 254 calculates a spillover evaluation index of each dye on the basis of the distribution of the light intensity to each dye of each bioparticle after the axis transformation and the result of the gating for each dye. The spillover evaluation index is an index indicating the degree of spillover of the light corresponding to each dye to light corresponding to other dye. The evaluation index calculation unit 254 provides information indicating the spillover evaluation index of each dye to the SR adjustment unit 255.

[0074] The SR adjustment unit 255 adjusts the SR of each dye stored in the reference DB 205 on the basis of the distribution of the light intensity to each dye of each bioparticle after the axis transformation, the result of the gating for each dye, and the spillover evaluation index of each dye. The SR adjustment unit 255 provides the adjusted SR of each dye to the unmixing unit 251 or stores it in the reference DB 205 as necessary. Moreover, the SR adjustment unit 255 selects dyes to be used for the SR adjustment and notifies the axis transformation unit 252 of the selected dye.

[0075] Procedure of Experiment Fig. 7 shows an example of a procedure of the experiment using the biological sample analyzer 11.

[0076] In Step S1, a hypothesis to be tested in the experiment is set.

[0077] In Step S2, a protocol for the experiment is created. At this time, the panel design of the biological sample analyzer 11 is made. That is, an optimal combination of a plurality of labeling substances (e.g., dyes) to be used in the experiment is designed.

[0078] In Step S3, the equipment to be used in the experiment is set up.

[0079] In Step S4, reference data including data about the SR of each dye is acquired. The acquired reference data is stored in the reference DB 205.

[0080] In Step S5, the light generated by light radiation onto the bioparticles contained in the multi-stained particle sample is measured. Accordingly, experiment data including the light data of the multi-stained particle sample is obtained.

[0081] In Step S6, the analysis pre-processing is performed. Accordingly, the SR of each dye included in the reference data is adjusted on the basis of the experiment data.

[0082] In Step S7, the experiment data is analyzed by using the adjusted SR of each dye. For example, analysis by a regular user, analysis by machine learning, or regular analysis by machine learning is supported.

[0083] In Step S8, the experiment data is summarized. For example, an experiment report containing analysis results of the experiment data is compiled.

[0084] In Step S9, the experiment data is shared. For example, the experiment report is published on the server 12 or a presentation of the experiment report is made.

[0085] It should be noted that the present technology mainly targets the processing of Step S6.

[0086] First Embodiment of Analysis Pre-Processing Next, a first embodiment of the analysis pre-processing of Step S6 of Fig. 7 will be described with reference to the flowchart in Fig. 8.

[0087] In the first embodiment of the analysis pre-processing, the SR of each dye is adjusted using the orthogonality evaluation index as the spillover evaluation index. It should be noted that the details of the spillover evaluation index and the orthogonality evaluation index will be described later.

[0088] In Step S101, the unmixing unit 251 performs unmixing processing. Specifically, the unmixing unit 251 performs unmixing processing of the multi-stained particle sample included in the experiment data by using the SR of each dye stored in the reference DB 205. Accordingly, the light intensity to each dye of each bioparticle (multi-stained particle) included in the multi-stained particle sample is calculated. The unmixing unit 251 provides information individually indicating the light intensity to each dye of each bioparticle to the axis transformation unit 252.

[0089] In Step S102, the SR adjustment unit 255 selects dyes to be used for the SR adjustment. For example, the SR adjustment unit 255 selects one of the dyes for which the two-dimensional plot orthogonality has not yet been evaluated as an evaluation target dye. For example, the SR adjustment unit 255 selects one of the dyes for which the SR has not yet been adjusted as an evaluation target dye. The SR adjustment unit 255 notifies the axis transformation unit 252 of the selected evaluation target dye and the selected adjustment target dye.

[0090] In Step S103, the axis transformation unit 252 performs an axis transformation. For example, the axis transformation unit 252 performs a biexponential transformation on the light intensity to the evaluation target dye and the adjustment target dye of each bioparticle. The axis transformation unit 252 provides information individually indicating the light intensity to the evaluation target dye and the adjustment target dye of each bioparticle after the axis transformation to the gating unit 253, the evaluation index calculation unit 254, and the SR adjustment unit 255.

[0091] In Step S104, the gating unit 253 performs gating. Specifically, the gating unit 253 performs gating of the bioparticles in the multi-stained particle sample on the basis of a graph (hereinafter, referred to as light intensity distribution graph) indicating the distribution of the light intensity to the evaluation target dye of each bioparticle after the axis transformation.

[0092] Accordingly, a boundary (hereinafter, referred to as Posi-Nega boundary) that separates a population (cluster) including bioparticles that have shown a positive reaction (hereinafter, referred to as positive particles) from a population (cluster) including bioparticles that have shown a negative reaction (hereinafter, referred to as negative particles) to the evaluation target dye is set. The bioparticles in the multi-stained particle sample are then separated into a population including positive particles (hereinafter, referred to as positive population) and a population including negative particles (hereinafter, referred to as negative population).

[0093] It should be noted that a method of setting the Posi-Nega boundary is not particularly limited, but the Posi-Nega boundary is set so that the dispersion of light intensity between the positive and negative populations is larger and the dispersion of light intensity in each population is smaller.

[0094] For example, the gating unit 253 sets the Posi-Nega boundary by using clustering methods such as Otsu's method, silhouette analysis, and K-means method.

[0095] It should be noted that, for example, the user may manually set the Posi-Nega boundary.

[0096] For example, Fig. 9 shows a setting example of the Posi-Nega boundary. A and B of Fig. 9 show examples of the light intensity distribution graph (histogram) with respect to the evaluation target dye. The horizontal axis in A and B of Fig. 9 indicates the light intensity after the axis transformation and the vertical axis indicates the number of particles.

[0097] For example, in a case where Otsu's method is used, the Posi-Nega boundary may be set to the value shown by the dotted line in A of Fig. 9, contrary to the user's intention. In this case, for example, the user may manually adjust the value of the Posi-Nega boundary, as shown in B of Fig. 9.

[0098] Similarly, the gating unit 253 performs gating of the bioparticles in the multi-stained particle sample on the basis of the light intensity distribution graph with respect to the adjustment target dye of each bioparticle.

[0099] Accordingly, the Posi-Nega boundary with respect to the adjustment target dye is set and the bioparticles in the multi-stained particle sample are separated into the positive population and the negative population for the adjustment target dye.

[0100] The gating unit 253 provides information indicating the results of the gating of the bioparticles in the multi-stained particle sample with respect to the evaluation target dye and the adjustment target dye to the evaluation index calculation unit 254 and the SR adjustment unit 255.

[0101] In Step S105, the evaluation index calculation unit 254 calculates an orthogonality evaluation index. Specifically, the evaluation index calculation unit 254 calculates an orthogonality evaluation index indicating the orthogonality of the light intensity to the evaluation target dye in a two-dimensional plot indicating the light intensity to the evaluation target dye of the bioparticles in the multi-stained particle sample and the distribution of the light intensity to the adjustment target dye. For example, the evaluation index calculation unit 254 calculates a stain index (SI), signal separation (SS), or a spillover spreading matrix (SSM) as the orthogonality evaluation index.

[0102] For example, the SI with respect to the evaluation target dye is calculated by Expression (1) below.

[0103] SI = {median(posi) - median(nega)} / {2 × σ(nega)}... (1)

[0104] It should be noted that median(posi) is a median value of the light intensity of the bioparticles in the positive population for the evaluation target dye. median(nega) is a median value of the light intensity of the bioparticles in the negative population for the evaluation target dye. σ(nega) is a standard deviation of the light intensity of the bioparticles in the negative population for the evaluation target dye.

[0105] For example, SS with respect to the evaluation target dye is calculated by Expression (2) below.

[0106] SS = {mean(posi) - mean(nega)} / {σ(posi) - σ(nega)}... (2)

[0107] mean(posi) is a mean value of the light intensity of the bioparticles in the positive population for the evaluation target dye. mean(nega) is a mean value of the light intensity of the bioparticles in the negative population for the evaluation target dye. σ(posi) is a standard deviation of the light intensity of the bioparticles in the positive population for the evaluation target dye.

[0108] The evaluation index calculation unit 254 provides information indicating the orthogonality evaluation index with respect to the evaluation target dye to the SR adjustment unit 255.

[0109] In Step S106, the analysis pre-processing unit 221 performs SR adjustment processing.

[0110] The details of the SR adjustment processing will be described with reference to the flowchart in Fig. 10.

[0111] In Step S151, the SR adjustment unit 255 adjusts the SR. Specifically, the SR adjustment unit 255 detects a state of unmixing with respect to the evaluation target dye on the basis of the distribution of the light intensity to the evaluation target dye and the adjustment target dye of each bioparticle after the axis transformation and the results of the gating of the bioparticles in the multi-stained particle sample with respect to the evaluation target dye and the adjustment target dye. For example, the SR adjustment unit 255 adjusts the SR of the adjustment target dye by using the SR of the evaluation target dye on the basis of the orthogonality evaluation index with respect to the evaluation target dye and the state of unmixing with respect to the evaluation target dye. The SR adjustment unit 255 provides the SR of the adjustment target dye after the adjustment to the unmixing unit 251.

[0112] It should be noted that the details of the SR adjustment method will be described later.

[0113] In Step S152, unmixing processing is performed as in the processing of Step S101 of Fig. 8. At this time, the adjusted SR provided by the SR adjustment unit 255 is used for the SR of the adjustment target dye, and the SR stored in the reference DB 205 is used for the SR of other dyes.

[0114] In Steps S153 to S155, processing similar to that of Steps S103 to S105 of Fig. 8 is performed. Accordingly, the orthogonality evaluation index with respect to the evaluation target dye is calculated by using the SR of each color including the SR of the adjustment target dye after the adjustment.

[0115] In Step S156, the evaluation index calculation unit 254 determines whether or not the orthogonality evaluation index meets a termination condition. In a case where it is determined that the orthogonality evaluation index does not meet the termination condition, the processing returns to Step S151.

[0116] Then, the processing of Steps S151 to S156 is repeatedly performed until it is determined that the orthogonality evaluation index meets the termination condition in Step S156. Accordingly, the SR of the adjustment target dye is adjusted so that the orthogonality evaluation index with respect to the evaluation target dye meets the termination condition.

[0117] On the other hand, in a case where it is determined that the orthogonality evaluation index meets the termination condition in Step S156, the processing proceeds to Step S157.

[0118] An example of the SR adjustment method will be described with reference to Figs. 11 to 13.

[0119] Fig. 11 shows examples of two-dimensional plots of light intensity to the dyes 1 and 2 and the SI with respect to the dyes 1 and 2.

[0120] The vertical axis of each two-dimensional plot in Fig. 11 is obtained by axis-transforming the light intensity to the dye 1 and the horizontal axis is obtained by axis-transforming the light intensity to the dye 2.

[0121] The two-dimensional plots arranged in the horizontal direction on the lower side of Fig. 11 show examples of changes in the two-dimensional plots with respect to the state of unmixing with respect to the dye 1 (vertical axis). A horizontal dotted auxiliary line L1 shows, for example, the center of gravity (median value) of the distribution of the light intensity in the vertical axis (dye 1) direction in a population of bioparticles negative for the dyes 1 and 2 (hereinafter, referred to as Nega-Nega population).

[0122] The lower bars in the center of Fig. 11 show examples of changes in the SI with respect to the state of unmixing with respect to the dye 1.

[0123] In a case where the unmixing with respect to the dye 1 is optimal, the light intensity to the dye 1 in the negative population for the dye 1 is substantially the same regardless of the light intensity to the dye 2. In this case, the SI with respect to the dye 1 is maximized.

[0124] On the other hand, in a case of a state of over-unmixing with respect to the dye 1, the light intensity to the dye 1 in the negative population for the dye 1 decreases as the light intensity to the dye 2 increases. That is, the light intensity to the dye 1 in the negative population for the dye 1 changes in a negative direction (- direction) with respect to the auxiliary line L1 as the light intensity to the dye 2 increases. This is a state in which, for example, the light intensity of the bioparticles to the dye 1, which are negative for the dye 1 and positive for the dye 2, spills over in the negative direction. In this case, as the degree of over-unmixing increases, in other words, as the amount of light intensity of the bioparticles to the dye 1, which are negative for the dye 1 and positive for the dye 2, spilling over in the negative direction increases, the SI with respect to the dye 1 becomes smaller.

[0125] Moreover, in a case of a state of under-unmixing with respect to the dye 1, the light intensity to the dye 1 in the negative population for the dye 1 increases as the light intensity to the dye 2 increases. That is, the light intensity to the dye 1 in the negative population for the dye 1 changes in a positive direction (+ direction) with respect to the auxiliary line L1 as the light intensity to the dye 2 increases. This is a state in which, for example, the light intensity of the bioparticles to the dye 1, which are negative for the dye 1 and positive for the dye 2, spills over in the positive direction. In this case, as the degree of under-unmixing increases, in other words, as the amount of light intensity of the bioparticles to the dye 1, which are negative for the dye 1 and positive for the dye 2, spilling over in the positive direction increases, the SI with respect to the dye 1 becomes smaller.

[0126] For example, the spillover evaluation index with respect to the dye 1 is an index for evaluating the degree to which the light corresponding to the dye 2 spills over into the light corresponding to the dye 1. Specifically, for example, the spillover evaluation index with respect to the dye 1 is an index for evaluating the degree to which the distribution of the light intensity in the population of the bioparticles negative for the dye 1 and positive for the dye 2 spills over in the positive or negative direction with respect to the auxiliary line L1.

[0127] For example, the orthogonality evaluation index with respect to the dye 1 is a type of spillover evaluation index and is an index indicating the degree to which the distribution of the light intensity in the population of the bioparticles negative for the dye 1 is orthogonal to the vertical axis (the axis of the dye 1). The SI, SS, or SSM can be used as the orthogonality evaluation index with respect to the dye 1.

[0128] The two-dimensional plots arranged in the vertical direction on the left side of Fig. 11 show examples of changes in the two-dimensional plots with respect to the state of unmixing with respect to the dye 2 (horizontal axis). A vertical dotted auxiliary line L2 shows, for example, the position of the center of gravity of the distribution of the light intensity in the Nega-Nega population in the direction of the horizontal axis (dye 2).

[0129] The upper bars in the center of Fig. 11 shows examples of changes in the SI with respect to the state of unmixing with respect to the dye 2.

[0130] In a case where the unmixing with respect to the dye 2 is optimal, the light intensity to the dye 2 in the negative population for the dye 2 is substantially the same regardless of the light intensity to the dye 1. In this case, the SI with respect to the dye 2 is maximized.

[0131] On the other hand, in a case of a state of over-unmixing with respect to the dye 2, the light intensity to the dye 2 in the negative population for the dye 2 decreases as the light intensity to the dye 1 increases. That is, the light intensity to the dye 2 in the negative population for the dye 2 changes in a negative direction (- direction) with respect to the auxiliary line L2 as the light intensity to the dye 1 increases. This is a state in which, for example, the light intensity of the bioparticles to the dye 2, which are positive for the dye 1 and negative for the dye 2, spills over in the negative direction. In this case, as the degree of over-unmixing increases, in other words, as the amount of light intensity of the bioparticles to the dye 2, which are positive for the dye 1 and negative for the dye 2, spilling over in the negative direction increases, the SI with respect to the dye 2 becomes smaller.

[0132] Moreover, in a case of a state of under-unmixing with respect to the dye 2, the light intensity to the dye 2 in the negative population for the dye 2 increases as the light intensity to the dye 1 increases. That is, the light intensity to the dye 2 in the negative population for the dye 2 changes in a positive direction (+ direction) with respect to the auxiliary line L2 as the light intensity to the dye 1 increases. This is a state in which, for example, the light intensity of the bioparticles to the dye 2, which are positive for the dye 1 and negative for the dye 2, spills over in the positive direction. In this case, as the degree of under-unmixing increases, in other words, as the amount of light intensity of the bioparticles to the dye 2, which are positive for the dye 1 and negative for the dye 2, spilling over in the positive direction increases, the SI with respect to the dye 2 becomes smaller.

[0133] For example, the spillover evaluation index with respect to the dye 2 is an index for evaluating the degree to which the light corresponding to the dye 1 spills over into the light corresponding to the dye 2. Specifically, for example, the spillover evaluation index with respect to the dye 2 is an index for evaluating the degree to which the distribution of the light intensity in the population of bioparticles positive for the dye 1 and negative for the dye 2 spills over in the positive or negative direction with respect to the auxiliary line L2.

[0134] For example, the orthogonality evaluation index with respect to the dye 2 is a type of spillover evaluation index and is an index indicating the degree to which the distribution of the light intensity in the population of bioparticles negative for the dye 2 is orthogonal to the horizontal axis (axis of the dye 2). The SI, SS, or SSM can be used as the orthogonality evaluation index with respect to the dye 2.

[0135] In contrast, the SR adjustment unit 255 adjusts the SR of the adjustment target dye to improve the orthogonality evaluation index with respect to the evaluation target dye.

[0136] For example, A to E of Fig. 12 schematically show two-dimensional plots of light intensity to the evaluation target dye and the adjustment target dye. The vertical axis of the two-dimensional plot is obtained by axis-transforming the light intensity to the evaluation target dye and the horizontal axis is obtained by axis-transforming the light intensity to the adjustment target dye.

[0137] The shaded ellipses in A to E of Fig. 12 schematically show the distribution of the light intensity of the population of bioparticles (Nega-Nega population), which are negative for the evaluation target dye and the adjustment target dye. The dot-pattern ellipses in A to E of Fig. 12 schematically show the distribution of the light intensity of the population of the bioparticles (hereinafter, referred to as Posi-Nega population), which are positive for the evaluation target dye and negative for the adjustment target dye. The white ellipses in A to E of Fig. 12 schematically show the distribution of the light intensity of the population of bioparticles (hereinafter, referred to as Nega-Posi population), which are negative for the evaluation target dye and positive for the adjustment target dye (hereinafter, referred to as the Nega-Posi population).

[0138] It should be noted that the illustration of the distribution of the light intensity of the population of bioparticles (hereinafter, referred to as Posi-Posi population), which are positive for the evaluation target dye and the adjustment target dye, is omitted.

[0139] For example, as shown in A of Fig. 12, in a case of a state of under-unmixing with respect to the evaluation target dye, the SR adjustment unit 255 adjusts SR2 of the current adjustment target dye to SR2r1 in accordance with Expression (3) below.

[0140] SR2r1 = SR2 + αSR1... (3)

[0141] That is, SR1 of the evaluation target dye multiplied by a weight α is added to SR2 of the current adjustment target dye.

[0142] Accordingly, the result of unmixing with respect to the evaluation target dye using SR2r1 after the adjustment is over-unmixing, for example, as shown in B of Fig. 12.

[0143] In contrast, the SR adjustment unit 255 adjusts SR2r1 of the current adjustment target dye to SR2r2 in accordance with Expression (4) below.

[0144] SR2r2 = SR2r1 - βSR1... (4)

[0145] That is, SR1 of the evaluation target dye multiplied by a weight β is subtracted from SR2r1 of the current adjustment target dye.

[0146] Accordingly, the result of unmixing with respect to the evaluation target dye using SR2r2 after the adjustment is under-unmixing, for example, as shown in C of Fig. 12.

[0147] In contrast, the SR adjustment unit 255 adjusts SR2r2 of the current adjustment target dye to SR2r3 in accordance with Expression (5) below.

[0148] SR2r3 = SR2r2 + γSR1... (5)

[0149] That is, SR1 of the evaluation target dye multiplied by a weight γ is added to SR2r2 of the current adjustment target dye.

[0150] Accordingly, the result of unmixing with respect to the evaluation target dye using SR2r3 after the adjustment is over-unmixing, for example, as shown in D of Fig. 12.

[0151] In contrast, the SR adjustment unit 255 adjusts SR2r3 of the current adjustment target dye to SR2r4 in accordance with Expression (6) below.

[0152] SR2r4 = SR2r3 - ΔSR1... (6)

[0153] That is, SR1 of the evaluation target dye multiplied by a weight Δ is subtracted from SR2r3 of the current adjustment target dye.

[0154] Accordingly, the state of unmixing with respect to the evaluation target dye becomes suitable. SR2r4 at this time is then updated as the SR of the adjustment target dye after the adjustment.

[0155] It should be noted that the weights used for the SR adjustment (e.g., the weights α to Δ) are adjusted on the basis of the degree of under-unmixing or over-unmixing with respect to the evaluation target dye. Hereinafter, the weight used for the SR adjustment will be also referred to as a SR adjustment coefficient or adjustment ratio. For example, an SR adjustment coefficient (adjustment ratio) is set on the basis of a difference (hereinafter, center-of-gravity difference) of the center of gravity in the vertical axis (evaluation target dye) direction in the distribution of the light intensity of the Nega-Posi population from the center of gravity in the vertical axis (evaluation target dye) direction in the distribution of the light intensity of the Nega-Nega population.

[0156] For example, the sign of the weight is set to be positive and the absolute value of the weight is increased as the center-of-gravity difference increases in the positive direction, i.e., as the degree of under-unmixing with respect to the evaluation target dye increases. Accordingly, the amount of SR of the evaluation target dye to be added to the SR of the adjustment target dye increases.

[0157] On the other hand, as the center-of-gravity difference increases in the negative direction, i.e., the degree of over-unmixing with respect to the evaluation target dye increases, the sign of the weight is set to be negative and the absolute value of the weight is increased. Accordingly, the amount of SR of the evaluation target dye to be subtracted from the SR of the adjustment target dye increases.

[0158] Fig. 13 shows examples of changes in the SI with respect to the evaluation target dye due to the SR adjustment of the adjustment target dye. The vertical axis in A of Fig. 13 indicates the SI with respect to the evaluation target dye and the horizontal axis indicates the weights (e.g., the weights α, β, etc.) used for the SR adjustment of the adjustment target dye. The vertical axis in B of Fig. 13 indicates the SI with respect to the evaluation target dye and the horizontal axis indicates the number of times the SR of the adjustment target dye is adjusted.

[0159] In this manner, as the SR of the adjustment target dye is adjusted, the SI with respect to the evaluation target dye becomes larger. Then, for example, in a case where the SI with respect to the evaluation target dye reaches near the peak in Step S156, the SR adjustment unit 255 determines that the orthogonality evaluation index meets the termination condition, and the processing proceeds to Step S157.

[0160] It should be noted that in some cases, the SI decreases after the SI with respect to the evaluation target dye reaches near the peak. In these cases, for example, the SR of the adjustment target dye is finally set to the SR just before the SI decreases.

[0161] As described above, the SR of the adjustment target dye is adjusted so that the SI with respect to the evaluation target dye is as large as possible.

[0162] It should be noted that, although a detailed description is omitted, in a case where the SS is used as the orthogonality evaluation index, the SR of the adjustment target dye is adjusted so that the SS with respect to the evaluation target dye is as large as possible.

[0163] In this manner, the SR of the adjustment target dye is adjusted so that the orthogonality of the distribution of the light intensity to the evaluation target dye is as high as possible.

[0164] Referring back to Fig. 10, in Step S157, the SR adjustment unit 255 saves the adjusted SR. For example, the SR adjustment unit 255 updates the SR of the adjustment target dye stored in the reference DB 205 with the adjusted SR.

[0165] Then, the SR adjustment processing is terminated.

[0166] It should be noted that, for example, after the SR of the adjustment target dye is adjusted in the SR adjustment processing, the evaluation target dye and the adjustment target dye may be swapped, so that the SR of the adjustment target dye after the swapping (the evaluation target dye before the swapping) is subsequently adjusted.

[0167] Referring back to Fig. 8, in Step S107, the SR adjustment unit 255 determines whether or not the SRs of all dyes have been adjusted. In a case where it is determined that the SRs of all dyes have not yet been adjusted, the processing returns to Step S101.

[0168] Then, the processing of Steps S101 to S107 is repeatedly performed until it is determined that the SRs of all dyes have been adjusted in Step S107.

[0169] On the other hand, in a case where it is determined in Step S107 that the SRs of all dyes have been adjusted, the analysis pre-processing is terminated.

[0170] Modified Examples of Orthogonality Evaluation Index Next, modified examples of the orthogonality evaluation index will be described with reference to Figs. 14 and 15.

[0171] A and B of Fig. 14 show examples of two-dimensional plots of light intensity to the dyes 1 and 2. The vertical axis in A and B of Fig. 14 is obtained by axis-transforming the light intensity to the dye 1 and the horizontal axis is obtained by axis-transforming the light intensity to the dye 2.

[0172] For example, the slope of the two-dimensional plot may be used as the orthogonality evaluation index.

[0173] Specifically, for example, the slope of the straight line L11 connecting the center of gravity in the vertical axis (dye 1) direction of the distribution of the light intensity in the Nega-Nega population and the center of gravity in the vertical axis (dye 1) direction of the distribution of the light intensity in the Nega-Posi population may be used as the orthogonality evaluation index with respect to the dye 1.

[0174] In this case, for example, the SR of the dye 2 is adjusted so that the slope of the straight line L11 approaches 0.

[0175] For example, the slope of the straight line L12 connecting the center of gravity in the horizontal axis (dye 2) direction of the distribution of the light intensity in the Nega-Nega population and the center of gravity in the horizontal axis (dye 2) direction of the distribution of the light intensity in the Posi-Nega population may be used as the orthogonality evaluation index with respect to the dye 2.

[0176] In this case, for example, the SR of the dye 1 is adjusted so that the slope of the straight line L12 approaches ∞. It should be noted that since it is difficult to make the slope of the straight line L12 approach ∞, the vertical and horizontal axes may be swapped and the SR of the dye 1 may be adjusted so that the slope of the straight line L12 approaches 0.

[0177] It should be noted that A of Fig. 14 shows a case where the state of unmixing with respect to the dye 2 is suitable. B of Fig. 14 shows a state of under-unmixing with respect to the dye 2.

[0178] Moreover, a mean may be used instead of the center of gravity of the distribution of the light intensity in the Nega-Nega, Nega-Posi, and Posi-Nega populations.

[0179] Fig. 15 shows two-dimensional plots of light intensity to the dyes 1 and 2, marked with contours. The vertical axis in A and B of Fig. 15 is obtained by axis-transforming the light intensity to the dye 1 and is obtained by axis-transforming the light intensity to the dye 2.

[0180] For example, approximate straight lines L21 and L22 that follow the ridges of the two-dimensional plots may be calculated by the least squares method and used for the orthogonality evaluation index.

[0181] For example, the approximate straight line L21 is used for the orthogonality evaluation index with respect to the dye 1. In this case, the SR of the dye 2 is adjusted so that the slope of the approximate straight line L22 approaches 0.

[0182] For example, the approximate straight line L22 is used for the orthogonality evaluation index with respect to the dye 2. In this case, the SR of the dye 1 is adjusted so that the slope of the approximate straight line L21 approaches ∞. It should be noted that since it is difficult to make the slope of the approximate straight line L21 approach ∞, the vertical and horizontal axes may be swapped and the SR of the dye 1 may be adjusted so that the slope of the approximate straight line L21 approaches 0.

[0183] In this case, the gating processing with respect to the dyes 1 and 2 is not required.

[0184] It should be noted that A of Fig. 15 shows a case where the state of unmixing with respect to the dye 2 is suitable. B of Fig. 15 shows a state of under-unmixing with respect to the dye 2.

[0185] In a case where the slope of the two-dimensional plot is used as the orthogonality evaluation index as shown in Figs. 14 and 15, it is possible to clearly define the termination condition of the orthogonality evaluation index.

[0186] As described above, the SR of each pixel is easily and suitably adjusted using the orthogonality evaluation index. For example, manual SR adjustment is not required, which reduces the user's time and effort and suppresses the occurrence of variations in SR adjustment results from user to user. As a result, for example, turnaround time (TAT) required to analyze the experiment data is reduced and the quality of the analysis results is improved.

[0187] Moreover, adjusting the SR once generated enables the SR to be used for the same FCM, FCMs of the same model with individual differences, and FCMs of different models, thus expanding the range of application of the SR.

[0188] Second Embodiment of Analysis Pre-Processing Next, a second embodiment of the analysis pre-processing of Step S6 of Fig. 7 will be described with reference to the flowchart in Fig. 16.

[0189] In the second embodiment of the analysis pre-processing, the SR of each dye is adjusted using a dependency evaluation index, which is an index for evaluating the dependency between the dyes, as the spillover evaluation index.

[0190] In Step S201, unmixing processing is performed as in the processing of Step S101 of Fig. 8.

[0191] In Step S202, the axis transformation unit 252 performs an axis transformation. For example, the axis transformation unit 252 performs a biexponential transformation on the light intensity to each dye of each bioparticle by processing similar to that of Step S102 of Fig. 8. The axis transformation unit 252 provides information individually indicating the light intensity to each dye of each bioparticle after the axis transformation to the gating unit 253 and the evaluation index calculation unit 254.

[0192] In Step S203, the gating unit 253 performs gating. Specifically, the gating unit 253 performs gating of the bioparticles in the multi-stained particle sample for each dye on the basis of the light intensity distribution graph indicating the distribution of the light intensity to each dye of each bioparticle after the axis transformation by processing similar to that of Step S103 of Fig. 8. Accordingly, the Posi-Nega boundary is set for each dye, and the bioparticles in the multi-stained particle sample are separated into the positive and negative populations for each dye. The gating unit 253 provides information indicating the results of the gating of the bioparticles in the multi-stained particle sample with respect to each dye to the evaluation index calculation unit 254.

[0193] In Step S204, the evaluation index calculation unit 254 calculates a dependency evaluation index. For example, the evaluation index calculation unit 254 calculates, for each dye combination, a correlation coefficient that quantifies the dependency of the light intensity between the two dyes as the dependency evaluation index.

[0194] For example, Fig. 17 shows an example of a two-dimensional plot of light intensity between the two dyes. For example, the evaluation index calculation unit 254 calculates a correlation coefficient between the two dyes in the population of bioparticles in the frame. The population of bioparticles in the frame is a Nega-Posi population including bioparticles negative for the dye on the vertical axis (hereinafter, referred to as vertical-axis dye) and positive for the dye on the horizontal axis (hereinafter, referred to as horizontal-axis dye). That is, the evaluation index calculation unit 254 calculates a correlation coefficient between the light intensity to the vertical-axis dye and the light intensity to the horizontal-axis dye in the Nega-Posi population.

[0195] For example, the evaluation index calculation unit 254 calculates the correlation coefficient of the light intensity of bioparticles in the Nega-Posi population as to combinations of all dyes.

[0196] Hereinafter, for example, the correlation coefficient between the light intensity to the dye 1 and the light intensity to the dye 2 in the Nega-Posi population including bioparticles negative for the dye 1 (vertical-axis dye) and positive for the dye 2 (horizontal-axis dye) will be simply referred to as a correlation coefficient between the dyes 1 and 2.

[0197] Fig. 18 shows examples of correlation coefficients between the dyes 1 to 5. The vertical axis show the vertical-axis dyes of the two-dimensional plot and the horizontal axis show the horizontal-axis dyes of the two-dimensional plot.

[0198] For example, the correlation coefficient between the dye 1 (vertical-axis dye) and the dye 2 (horizontal-axis dye) is -0.1101. For example, the correlation coefficient between the dyes 1 and 3 is -0.1517. For example, the correlation coefficient between the dyes 1 and 4 is -0.1836. For example, the correlation coefficient between the dyes 1 and 5 is -0.9477.

[0199] It should be noted that Fig. 17 described above shows an example of the two-dimensional plot used to calculate the correlation coefficient between the dyes 3 and 5 in Fig. 18. In this example, the state of unmixing with respect to the dye 3 (vertical-axis dye) is suitable. Moreover, the correlation coefficient between the dyes 3 and 5 is 0.02931.

[0200] In this manner, in a case where the unmixing is suitable for the vertical-axis dye, the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population is smaller.

[0201] Fig. 19 shows an example of the two-dimensional plot used to calculate the correlation coefficient between the dyes 4 and 5 in Fig. 18. In this example, the unmixing with respect to the dye 4 is under-unmixing. Moreover, the correlation coefficient between the dyes 4 and 5 is 0.9851.

[0202] In this manner, in a case of a state of under-unmixing with respect to the vertical-axis dye, the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population is typically a positive value. Moreover, the stronger the positive correlation between the light intensity to the vertical-axis dye and the light intensity to the horizontal-axis dye, the larger the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population.

[0203] Fig. 20 shows an example of the two-dimensional plot used to calculate the correlation coefficient between the dyes 4 and 2 in Fig. 18. In this example, the unmixing with respect to the dye 4 is under-unmixing. Moreover, the correlation coefficient between the dyes 4 and 2 is 0.1148.

[0204] In this manner, even if the state of under-unmixing with respect to the vertical-axis dye is significant, the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population becomes smaller if the positive correlation between the light intensity to the vertical-axis dye and the light intensity to the horizontal-axis dye is weak.

[0205] Fig. 21 shows an example of the two-dimensional plot used to calculate the correlation coefficient between the dyes 1 and 5 in Fig. 18. In this example, the unmixing with respect to the dye 1 is over-unmixing. Moreover, the correlation coefficient between the dyes 1 and 5 is -0.9477.

[0206] In this manner, the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population is typically a negative value in a case of a state of over-unmixing with respect to the vertical-axis dye. Moreover, the stronger the negative correlation between the light intensity to the vertical-axis dye and the light intensity to the horizontal-axis dye, the larger the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population.

[0207] Fig. 22 shows an example of the two-dimensional plot used to calculate the correlation coefficient between the dyes 1 and 2 in Fig. 18. In this example, the unmixing with respect to the dye 1 is over-unmixing. Moreover, the correlation coefficient between the dyes 1 and 2 is -0.1101.

[0208] In this manner, even if the state of over-unmixing with respect to the vertical-axis dye is significant, the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population becomes smaller if the negative correlation between the light intensity to the vertical-axis dye and the light intensity to the horizontal-axis dye is weak.

[0209] In Step S205, the evaluation index calculation unit 254 determines whether or not all dependency evaluation indices meet the termination condition. For example, the evaluation index calculation unit 254 compares the absolute value of the correlation coefficient between the respective dyes with a predetermined threshold value (e.g., 0.1). In a case where the absolute value of at least one correlation coefficient is equal to or larger than the threshold value, the evaluation index calculation unit 254 determines that all dependency evaluation indices have not yet met the termination condition, and the processing proceeds to Step S206.

[0210] In Step S206, the SR adjustment unit 255 selects dyes to be used for the SR adjustment. Specifically, the evaluation index calculation unit 254 provides information indicating the correlation coefficients between the respective dyes to the SR adjustment unit 255. For example, the SR adjustment unit 255 selects a combination of dyes with the largest absolute value of the correlation coefficient as the dyes to be used for the SR adjustment. Moreover, out of the selected combination of the dyes, the SR adjustment unit 255 sets the horizontal-axis dye as an adjustment target dye and the vertical-axis dye as an addition / subtraction target dye.

[0211] The adjustment target dye is a target dye whose SR is to be adjusted and the addition / subtraction target dye is a target dye whose SR is to be multiplied by a weight and added or subtracted to / from the SR of the adjustment target dye.

[0212] For example, in a case of the example of Fig. 18, the absolute value of the correlation coefficient between the dyes 4 and 5 is the largest. Therefore, the combination of the dyes 4 and 5 is selected, the dye 5 is set as the adjustment target dye, and the dye 4 is set as the addition / subtraction target dye.

[0213] It should be noted that in a case where there is a combination of two dyes between which a positive or negative correlation is confirmed due to a biological factor rather than the spillover, the SR adjustment unit 255 is designed not to select such a combination of dyes. For example, in a case where the addition / subtraction target dye is a marker for CD3 and the adjustment target dye is a marker for a T-cell receptor that exists with CD3, a positive correlation is originally confirmed between these two markers, regardless of the spillover. In contrast, the SR adjustment unit 255 is designed not to select the combination of the marker for CD3 and the marker for a T-cell receptor that exists with CD3.

[0214] In Step S207, the analysis pre-processing unit 221 performs SR adjustment processing.

[0215] The details of the SR adjustment processing will be described with reference to the flowchart in Fig. 23.

[0216] In Step S251, the SR adjustment unit 255 adjusts the SR. Specifically, the SR adjustment unit 255 adjusts the SR of the adjustment target dye by adding or subtracting the SR of the addition / subtraction target dye (vertical-axis dye) multiplied by a weight to / from the SR of the adjustment target dye (horizontal-axis dye) by processing similar to that of Step S151 of Fig. 10.

[0217] Specifically, in a case where the correlation coefficient between the addition / subtraction target dye and the adjustment target dye is a positive value, the SR adjustment unit 255 determines that it is an under-unmixing state with respect to the addition / subtraction target dye, and adds the SR of the addition / subtraction target dye multiplied by a weight to the SR of the adjustment target dye. At this time, the SR adjustment unit 255 increases the weight as the absolute value of the correlation coefficient increases.

[0218] On the other hand, in a case where the correlation coefficient between the addition / subtraction target dye and the adjustment target dye is a negative value, the SR adjustment unit 255 determines that it is an over-unmixing state with respect to the addition / subtraction target dye and subtracts the SR of the addition / subtraction target dye multiplied by a weight from the SR of the adjustment target dye. At this time, the SR adjustment unit 255 increases the weight as the absolute value of the correlation coefficient increases.

[0219] In Step S252, unmixing processing is performed as in the processing of Step S152 of Fig. 10. That is, the unmixing processing is performed by using the SR of each dye including the SR of the adjustment target dye after the adjustment.

[0220] In Step S253, an axis transformation is performed as in the processing of Step S202 of Fig. 16. At this time, for example, the axis transformation is performed only on the adjustment target dye and the addition / subtraction target dye.

[0221] In Step S254, gating is performed as in the processing of Step S203 of Fig. 16. At this time, for example, the gating is performed only on the adjustment target dye and the addition / subtraction target dye.

[0222] In Step S255, a dependency evaluation index is calculated as in the processing of Step S204 of Fig. 16. At this time, for example, only the correlation coefficient between the addition / subtraction target dye and the adjustment target dye is calculated. That is, the correlation coefficient between the addition / subtraction target dye and the adjustment target dye in the Nega-Posi population of the two-dimensional plot where the addition / subtraction target dye is set as the vertical-axis dye and the adjustment target dye is set as the horizontal-axis dye is calculated.

[0223] In Step S256, the evaluation index calculation unit 254 determines whether or not the dependency evaluation index meets the termination condition. For example, the evaluation index calculation unit 254 compares the absolute value of the correlation coefficient calculated in the processing of Step S255 with a predetermined threshold value (e.g., 0.1). In a case where the absolute value of the correlation coefficient is equal to or larger than the threshold value, the evaluation index calculation unit 254 determines that the dependency evaluation index does not meet the termination condition, and the processing returns to Step S251.

[0224] Then, the processing of Steps S251 to S256 is repeatedly performed until it is determined that the dependency evaluation index meets the termination condition in Step S256. Accordingly, the SR of the adjustment target dye is adjusted until the correlation coefficient between the adjustment target dye and the addition / subtraction target dye becomes smaller than the predetermined threshold value.

[0225] On the other hand, in a case where the correlation coefficient becomes smaller than the threshold value in Step S256, the evaluation index calculation unit 254 determines that the dependency evaluation index meets the termination condition, and the processing proceeds to Step S257.

[0226] It should be noted that, for example, in a case where the state of under-unmixing and the state of over-unmixing are repeated in spite of a sufficiently small SR adjustment range, the SR adjustment may be terminated even if the termination condition, "absolute value of correlation coefficient < threshold value," is not met. That is, the loop processing of Steps S251 to S256 may be terminated and the processing may proceed to Step S257.

[0227] In Step S257, the adjusted SR is saved as in the processing of Step S157 of Fig. 10.

[0228] Then, the SR adjustment processing is terminated.

[0229] The processing then returns to Step S201 of Fig. 16, and the processing of Steps S201 to S207 is repeatedly performed until it is determined that all dependency evaluation indices meet the termination condition in Step S205.

[0230] Accordingly, the SR of the horizontal-axis dye is adjusted in the order of the dye combinations with the highest absolute values of the correlation coefficients.

[0231] For example, in the example in Fig. 18, the SR of the dye 5 is adjusted in the combination of the dyes 4 and 5, then the SR of the dye 5 is adjusted in the combination of the dyes 1 and 5, and then the SR of the dye 5 is adjusted in the combination of the dyes 2 and 5.

[0232] It should be noted that the SR adjustment is not necessarily performed in this order because the SR adjustment of the dye 5 changes the correlation coefficient of each dye combination.

[0233] Fig. 24 shows examples of correlation coefficients between the respective dyes after adjusting the SR of the dye 5. In this example, the absolute values of the correlation coefficients between the dyes 1 and 3, between the dyes 2 and 3, and between the dyes 4 and 3 are larger. Thus, the SR of the dye 3 is adjusted.

[0234] For example, the SR of the dye 3 is adjusted in the combination of the dyes 1 and 3, then the SR of the dye 3 is adjusted in the combination of the dyes 4 and 3, and then the SR of the dye 3 is adjusted in the combination of the dyes 2 and 3.

[0235] It should be noted that the SR adjustment is not necessarily performed in this order because the SR adjustment of the dye 3 changes the correlation coefficient of each dye combination.

[0236] Fig. 25 shows examples of correlation coefficients between the respective dyes after adjusting the SR of the dye 3. In this example, the correlation coefficients between the respective dyes are all below the threshold value (smaller than 0.1).

[0237] Accordingly, in Step S205 of Fig. 16, it is determined that all dependency evaluation indices meet the termination condition, and the analysis pre-processing is terminated.

[0238] It should be noted that in a case where a dye combination that once met the termination condition for the dependency evaluation index no longer meets the termination condition after the SR of the other dye is adjusted, the SR can be adjusted again.

[0239] As described above, the SR of each dye is easily and suitably adjusted using the dependency evaluation index as in a case where the orthogonality evaluation index is used.

[0240] Moreover, it is unnecessary to adjust all SRs as long as the dependency evaluation index (correlation coefficient) meets the termination condition, thus reducing the load and time required for the processing.

[0241] In addition, while the distributions of light intensity of the Nega-Nega and Nega-Posi populations of the two-dimensional plots are used to calculate the orthogonality evaluation index, only the distribution of the light intensity of the Nega-Posi population of the two-dimensional plot is used to calculate the dependency evaluation index. Therefore, the amount of calculation required to calculate the evaluation index is reduced.

[0242] Method of Suppressing Excessive Deformation of SR Next, a method of suppressing excessive deformation of the SR will be described with reference to Figs. 26 to 28.

[0243] For example, in a case of adjusting the SR of the adjustment target dye by subtracting the SR of the addition / subtraction target dye multiplied by a weight from the SR of the adjustment target dye (case of correcting over-unmixing), the SR of the adjustment target dye after the adjustment can have a negative value when there is a channel where the value of the SR of the addition / subtraction target dye is larger than the value of the SR of the adjustment target dye. However, the SR spectrum does not normally contain negative values with large absolute values.

[0244] In contrast, for example, a predetermined threshold may be set and the SR adjustment may be terminated when the SR of the adjustment target dye after the adjustment have a negative value smaller than the threshold.

[0245] Fig. 26 shows an example of the SR of the adjustment target dye. The horizontal axis indicates a channel and the vertical axis indicates a signal value standardized with a maximum peak.

[0246] For example, every time the SR is adjusted, the SR adjustment unit 255 calculates a value obtained by standardizing the SR after the adjustment with the maximum peak as shown in Fig. 26. For example, when a channel that exhibits a negative value below 5% of 1 as a maximum value, i.e., -0.05, is generated in the standardized SR, the SR adjustment unit 255 may determine that the SR shape has been excessively deformed and terminate the SR adjustment of the adjustment target dye.

[0247] Moreover, for example, when a pair of dyes with substantially no overlap between the SRs is selected as an adjustment target and addition or subtraction of the SR is performed, a peak may appear in a channel where no peak existed in the SR before the adjustment.

[0248] In contrast, for example, the SR adjustment unit 255 may determine whether or not there is an overlap between the SR of the adjustment target dye and the SR of the addition / subtraction target dye. In a case where that the SR adjustment unit 255 determines that the overlap between the SRs is small, the SR adjustment unit 255 may determine them as a pair of dyes with a small spillover and exclude the pair from the SR adjustment targets.

[0249] Figs. 27 and 28 each show an example of a part of the SR of the adjustment target dye and the SR of the addition / subtraction target dye before the adjustment. The dotted line indicates the SR of the adjustment target dye and the solid line indicates the SR of the addition / subtraction target dye. The horizontal axis indicates the channel and the vertical axis indicates a signal value standardized with a maximum peak.

[0250] For example, the SR adjustment unit 255 detects all channels that show a peak in the SR of the addition / subtraction target dye and determines whether or not the value of the SR of the adjustment target dye in each of the detected channels is smaller than a predetermined threshold (e.g., 0.1). Moreover, the SR adjustment unit 255 detects all channels that show a peak in the SR of the adjustment target dye and determines whether or not the value of the SR of the addition / subtraction target dye in each of the detected channels is smaller than the predetermined threshold (e.g., 0.1).

[0251] Then, in a case where channels where one SR has a peak do not include any channel where the signal value of the other SR is equal to or larger than the threshold, the SR adjustment unit 255 determines that there is substantially no overlap between the SR of the adjustment target dye and the SR of the addition / subtraction target dye and excludes that pair of dyes from the adjustment targets. On the other hand, in a case where the channels where one SR has a peak include at least one channel where the signal value of the other SR is equal to or larger than the threshold, the SR adjustment unit 255 determines that there is an overlap between the SR of the adjustment target dye and the SR of the addition / subtraction target dye and executes the SR adjustment as normal.

[0252] For example, in the example of Fig. 27, the SR of the addition / subtraction target dye has a peak in the 10th channel. On the other hand, the signal value of the SR of the adjustment target dye in the 10th channel is 0.00119, which is smaller than 0.1. Also, as for a peak of the other SR, in a case where the signal value of the other SR is smaller than the threshold, it is determined that there is substantially no overlap between the SR of the adjustment target dye and the SR of the addition / subtraction target dye and this pair of dyes is excluded from the adjustment targets.

[0253] For example, in the example of Fig. 28, the SR of the addition / subtraction target dye has a peak in the 32nd channel. On the other hand, the signal value of the adjustment target dye in the 32nd channel is 0.174, which is equal to or larger than 0.1. In this case, the channels where one SR has a peak include a channel where the signal value of the other SR is equal to or larger than the threshold, so it is determined that there is an overlap between the SR of the adjustment target dye and the SR of the addition / subtraction target dye, and the SR adjustment is executed as normal.

[0254] It should be noted that for example, in a case of determining that there is substantially no overlap between the SR of the adjustment target dye and the SR of the addition / subtraction target dye, the SR adjustment unit 255 may execute the SR adjustment until the adjustment ratio reaches a predetermined threshold (e.g., 0.005) and terminate the SR adjustment of the adjustment target dye when the adjustment ratio exceeds the threshold.

[0255] Method of Suppressing Excessive Adjustment of SR Next, a method of suppressing excessive adjustment of the SR will be described with reference to Figs. 29 to 58.

[0256] For example, in a case where the Nega-Posi population of the two-dimensional plot includes a population of bioparticles (hereinafter, referred to as dim population) weakly positive for the vertical-axis dye, a dependency evaluation index different from the appearance of the two-dimensional plot is calculated in some cases. As a result, the SR may be adjusted too much.

[0257] For example, the Nega-Posi population of the two-dimensional plot in A of Fig. 29 includes the dim population in a region enclosed by the dotted-line circle. Therefore, the distribution of the light intensity of the Nega-Posi population appears to be orthogonal to the vertical-axis dye on the two-dimensional plot, but the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population in the Nega-Posi population becomes larger due to the presence of the dim population.

[0258] In contrast, when the SR of the horizontal-axis dye (adjustment target dye) is adjusted so that the absolute value of the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population in the Nega-Posi population decreases, the apparent orthogonality of the distribution of the light intensity of the Nega-Posi population to the vertical-axis dye may decrease in the two-dimensional plot. That is, the SR may be adjusted too much.

[0259] Hereinafter, the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population of the Nega-Posi population will be simply referred to as the correlation coefficient of the Nega-Posi population. Hereinafter, the orthogonality of the distribution of the light intensity of the Nega-Posi population to the vertical-axis dye will be simply referred to as the orthogonality of the Nega-Posi population. Hereinafter, the apparent orthogonality of the distribution of the light intensity of the Nega-Posi population to the vertical-axis dye will be simply referred to as the apparent orthogonality of the Nega-Posi population.

[0260] Hereinafter, a marker corresponding to the vertical-axis dye, for example, a marker (antigen) that binds to an antibody labeled with the vertical-axis dye, will be referred to as a vertical-axis marker. Hereinafter, a marker corresponding to the horizontal-axis dye, for example, a marker (antigen) that binds to an antibody labeled with the horizontal-axis dye, will be referred to as a horizontal-axis marker.

[0261] For example, the vertical axis in A of Fig. 29 indicates light intensity (expression intensity) after the axis transformation of the vertical-axis marker and the horizontal axis indicates light intensity (expression intensity) after the axis transformation of the horizontal-axis marker. The scale on the right side of the two-dimensional plot in A of Fig. 29 is a scale showing a color change according to the number of bioparticles of each pixel. In practice, the respective pixels and scale in the two-dimensional plot are represented by colors.

[0262] It should be noted that in the following example of the two-dimensional plot, unless otherwise specified, the vertical axis indicates light intensity on the biexponential axis after the axis transformation of the vertical-axis marker and the horizontal axis indicates light intensity on the biexponential axis after the axis transformation of the horizontal-axis marker.

[0263] For example, B of Fig. 29 shows an example of the two-dimensional plot after the SR of the horizontal-axis dye is adjusted with respect to the two-dimensional plot in A of Fig. 29. In this example, the correlation coefficient of the Nega-Posi population decreases from 0.32 to 0.007. On the other hand, the apparent orthogonality of the Nega-Posi population decreases. That is, the SR of the horizontal-axis dye has been adjusted too much.

[0264] In order to suppress such excessive adjustment of the SR, it is effective to exclude a dim population from the Nega-Posi population and calculate a dependency evaluation index (correlation coefficient). Hereinafter, a method of excluding a dim population from the Nega-Posi population will be described.

[0265] Hereinafter, unless otherwise specified, adjusting the SR refers to adjusting the SR of the horizontal-axis dye (adjustment target dye).

[0266] Method of Excluding Dim Population Using Dip Test and Flow Density First of all, the method of excluding a dim population from the Nega-Posi population by using dip test and flow density will be described with reference to Figs. 30 and 31.

[0267] Fig. 30 shows an example of the two-dimensional plot of the light intensity between the two markers. The Nega-Posi population of the two-dimensional plot includes many dim populations.

[0268] For example, the evaluation index calculation unit 254 performs principal component analysis on the Nega-Posi population.

[0269] A of Fig. 31 is a diagram extracting the Nega-Posi population of the two-dimensional plot in Fig. 30. The figure shows axes of a first principal component (PC1) and a second principal component (PC2) resulting from the principal component analysis.

[0270] Next, the evaluation index calculation unit 254 generates a histogram of the Nega-Posi population based on the second principal component. B of Fig. 31 shows an example of a histogram of the Nega-Posi population based on the second principal component in A of Fig. 31. The horizontal axis in B of Fig. 31 shows the second principal component.

[0271] Next, the evaluation index calculation unit 254 applies a dip test to a histogram of the Nega-Posi population based on the second principal component, thereby making a determination as to a multimodal distribution of the histogram.

[0272] In a case where it is determined that the histogram has a multimodal distribution, i.e., the histogram has a plurality of peaks, the evaluation index calculation unit 254 applies flow density to the histogram. Accordingly, as shown in B of Fig. 31, a threshold for separating a negative population (Nega population) and a dim population with respect to the second principal component (≒ the vertical-axis dye) is calculated.

[0273] The evaluation index calculation unit 254 sets a population of bioparticles obtained by excluding the dim population from the separated negative population, i.e., the Nega-Posi population, as a population that is a target for which the dependency evaluation index is calculated (hereinafter, referred to as evaluation index target population). For example, the evaluation index target population is a population of bioparticles below the dotted line in the Nega-Posi population in Fig. 30.

[0274] The evaluation index calculation unit 254 calculates a correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population in the evaluation index target population as a dependency evaluation index. The use of this dependency evaluation index suppresses the excessive adjustment of the SR of the horizontal-axis dye.

[0275] Hereinafter, the correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population as the evaluation index target population will be simply referred to as the correlation coefficient of the evaluation index target population. It should be noted that in a case where the dim population is not excluded from the Nega-Posi population, the Nega-Posi population becomes the evaluation index target population.

[0276] Method of Excluding Dim Population Using Histogram Next, a method of setting a population in a region where events (bioparticles) are sparse as a dim population and excluding the dim population from the Nega-Posi population by using the histogram will be described with reference to Figs. 32 to 34.

[0277] A of Fig. 32 shows an example of the two-dimensional plot of the light intensity between the two markers. B of Fig. 32 shows a histogram of the Nega-Posi population in A of Fig. 32, which is based on the vertical-axis marker. The horizontal axis of the histogram in B of Fig. 32 indicates light intensity with respect to the vertical-axis marker.

[0278] For example, the evaluation index calculation unit 254 sets a population consisting of bioparticles belonging to bins in the histogram whose frequency is less than a predetermined threshold TH1 (e.g., 10) as a dim population and excludes the dim population from the Nega-Posi population.

[0279] C of Fig. 32 shows an example of the two-dimensional plot excluding the dim population from the two-dimensional plot in A of Fig. 32. As shown in the dotted-line circle, the dim population is excluded from the Nega-Posi population.

[0280] The evaluation index calculation unit 254 sets the population of the bioparticles excluding the dim population from the Nega-Posi population, i.e., a population of bioparticles in a region where the events are concentrated in the Nega-Posi population as an evaluation index target population and calculates a correlation coefficient of the evaluation index target population as a dependency evaluation index. The use of this dependency evaluation index suppresses the excessive adjustment of the SR of the horizontal-axis dye.

[0281] For example, Fig. 33 shows an example in which the SR is adjusted without excluding the dim population. Specifically, A of Fig. 33 shows an example of the two-dimensional plot of the light intensity between the two markers before the SR is adjusted. B of Fig. 33 shows an example of the two-dimensional plot of the light intensity between the two markers after the SR is adjusted without excluding the dim population.

[0282] As shown in this example, the SR is adjusted too much by adjusting the SR without excluding the dim population, and the apparent orthogonality of the Nega-Posi population decreases.

[0283] For example, Fig. 34 shows an example in which the SR is adjusted after excluding the dim population. Specifically, A of Fig. 34 shows an example of the two-dimensional plot of the light intensity between the two markers before the SR is adjusted as in A of Fig. 33. B of Fig. 34 shows an example of the two-dimensional plot after excluding the dim population from the Nega-Posi population of the two-dimensional plot in A of Fig. 34 by a method using the above-mentioned histogram. C of Fig. 34 shows an example of the two-dimensional plot of the light intensity between the two markers after the SR of the horizontal-axis dye is adjusted by using the evaluation index target population excluding the dim population from the Nega-Posi population in B of Fig. 34.

[0284] As shown in this example, the SR is suitably adjusted by excluding the dim population and adjusting the SR, and the apparent orthogonality of the Nega-Posi population is enhanced.

[0285] Method of Excluding Dim Population Using Display with Contours of Two-Dimensional Plot Next, a method of setting a population of a region where events (bioparticles) are sparse as a dim population and excluding the dim population from the Nega-Posi population by using display with contours of the two-dimensional plot will be described with reference to Figs. 35 to 36.

[0286] A of Fig. 35 shows an example of the two-dimensional plot of the light intensity between the two markers. B of Fig. 35 shows an example in which contours are superimposed on the two-dimensional plot in A of Fig. 35.

[0287] For example, in a case where the respective pixels of the two-dimensional plot are arranged in order of the number of events (≒ the number of bioparticles) in sequence, the contours are displayed so that a predetermined percentage (e.g., 80%) of pixels from the top are arranged in the contours. Then, bioparticles of the Nega-Posi population, which are outside the contours, are excluded as a dim population.

[0288] Accordingly, for example, the dim population in the region shown in the dotted-line circle in B of Fig. 35 is excluded from the Nega-Posi population.

[0289] A of Fig. 36 shows an example of the two-dimensional plot of the light intensity between the two markers. In this example, even though the SR adjustment is unnecessary, the correlation coefficient of the Nega-Posi population is -0.109, that is, the absolute value becomes larger.

[0290] In contrast, B of Fig. 36 shows an example in which the contours are superimposed on the two-dimensional plot in A of Fig. 36. The correlation coefficient of only the bioparticles in the contours of the Nega-Posi population is -0.081, and it is unnecessary to adjust the SR.

[0291] Accordingly, the SR is adjusted unnecessarily and the excessive adjustment is suppressed.

[0292] Method of Excluding Dim Population by Gating with Other Marker Next, a method of excluding a dim population by gating with the other marker will be described with reference to Figs. 37 to 52.

[0293] A of Fig. 37 shows an example of the two-dimensional plot of the light intensity between the two markers. In this example, the vertical-axis marker is CD123 and the horizontal-axis marker is CD45RA.

[0294] B of Fig. 37 is an enlarged diagram of the Nega-Posi population of the two-dimensional plot in A of Fig. 37.

[0295] Fig. 38 is a list of two-dimensional plots each showing a result obtained by gating the Nega-Posi population in B of Fig. 37 with a marker (hereinafter, referred to as gate marker) other than the vertical-axis marker (CD123) and the horizontal-axis marker (CD45RA). That is, Fig. 38 is a list of the two-dimensional plots each showing a distribution of light intensity of a population of bioparticles (hereinafter, referred to as a gating population) extracted by gating the Nega-Posi population in B of Fig. 37 with the gate marker.

[0296] For example, for each gate marker, two types of gating populations, a population (hereinafter, referred to as positive gating population) extracting bioparticles positive for the gate marker and a population (hereinafter, referred to as negative gating population) extracting bioparticles negative for the gate marker, are extracted.

[0297] Hereinafter, in a case where the positive gating population is extracted with the gate marker, it may be referred to as gating with the "gate marker +," adding ”+” after the name of the gate marker. For example, in a case of extracting the positive gating population with CCR7, it may be referred to as gating with CCR7+. Hereinafter, in a case where the negative gating population is extracted with the gate marker, it may be referred to as gating with the "gate marker -," adding ”-” after the name of the gate marker. For example, in a case of extracting the negative gating population with CCR7, it may be referred to as gating with CCR7-.

[0298] Next, a gate marker (hereinafter, referred to as an optimal marker) and a gating population (hereinafter, referred to as optimal gating population) that are capable of excluding the most suitable dim population from the Nega-Posi population are selected from a plurality of gating populations extracted. For example, in the example in Fig. 38, HLA-DR- is selected as an optimal marker and a gating population extracted with HLA-DR- is selected as an optimal gating population. Fig. 39 shows an example of the two-dimensional plot of the optimal gating population extracted with HLA-DR-.

[0299] Then, the dependency evaluation index is calculated by using the optimal gating population as the evaluation index target population.

[0300] For example, Figs. 40 and 41 compare a case where the SR is adjusted without excluding the dim population with a case where the SR is adjusted after excluding the dim population.

[0301] Specifically, A of Fig. 40 shows a two-dimensional plot similar to A of Fig. 37. B of Fig. 40 shows the Nega-Posi population of the two-dimensional plot in A of Fig. 40 as in B of Fig. 37. C of Fig. 40 shows the two-dimensional plot after the SR is adjusted by using the dependency evaluation index (correlation coefficient) of the Nega-Posi population in B of Fig. 40.

[0302] In this manner, the SR is adjusted too much and the apparent orthogonality of the Nega-Posi population after the SR adjustment decreases.

[0303] A of Fig. 41 shows a two-dimensional plot similar to A of Fig. 37. B of Fig. 41 shows a two-dimensional plot of the gating population after excluding the dim population as in Fig. 39. C of Fig. 41 shows the two-dimensional plot after the SR is adjusted by using the dependency evaluation index (correlation coefficient) of the gating population in B of Fig. 41.

[0304] In this manner, as compared to the example in Fig. 40, the SR is suitably adjusted, and the apparent orthogonality of the Nega-Posi population after the SR adjustment is enhanced.

[0305] Here, a specific example of processing of excluding the dim population by gating with the other marker and calculating a dependency evaluation index in Step S255 of Fig. 23 will be described with reference to the flowchart in Fig. 42.

[0306] In Step S301, the evaluation index calculation unit 254 performs gating on the Nega-Posi population with all other markers. Specifically, the evaluation index calculation unit 254 performs gating on the Nega-Posi population of the two-dimensional plot with the vertical-axis marker and the horizontal-axis marker with markers other than the vertical-axis marker and the horizontal-axis marker. Accordingly, the evaluation index calculation unit 254 extracts two types of gating populations, the positive gating population and the negative gating population, from the Nega-Posi population for each marker.

[0307] Fig. 43 shows an example of the two-dimensional plot of the light intensity in a case where the vertical-axis marker is CD45RA and the horizontal-axis marker is CD123.

[0308] For example, in a case where 23 types of markers including CD45RA and CD123 are performed, as shown in Fig. 44, the gating is performed by using 21 types of markers other than CD45RA and CD123. That is, the positive gating population and the negative gating population are extracted with each of the 21 types of markers other than CD45RA and CD123.

[0309] In Step S302, the evaluation index calculation unit 254 narrows down candidates for the evaluation index target population on the basis of the number of bioparticles (number of events). For example, the evaluation index calculation unit 254 sets a gating population for which the number of bioparticles is a predetermined percentage or more (e.g., 10% or more) of the Nega-Posi population before the gating and the number of bioparticles is a predetermined number or more (e.g., 100 or more), as a candidate for the evaluation index target population.

[0310] In Step S303, the evaluation index calculation unit 254 selects an optimal marker and an optimal gating population on the basis of a difference in standard deviation between a gating population and a non-gating population.

[0311] Here, the non-gating population is a population of bioparticles of the Nega-Posi population, which are not included in the corresponding gating population. That is, the non-gating population is a population of bioparticles extracted by reversing polarity (±) of the marker used in the gating of the corresponding gating population.

[0312] For example, Fig. 45 shows a result obtained by gating the Nega-Posi population of the two-dimensional plot in Fig. 43 with HLA-DR-. Specifically, A of Fig. 45 is a two-dimensional plot of the Nega-Posi population of the two-dimensional plot in Fig. 43. B of Fig. 45 is a two-dimensional plot of the negative gating population extracted from the Nega-Posi population in A of Fig. 45 with HLA-DR-. C of Fig. 45 is a two-dimensional plot of the non-gating population with respect to the gating population in B of Fig. 45. That is, C of Fig. 45 is a two-dimensional plot of the positive gating population extracted from the Nega-Posi population in A of Fig. 45 with HLA-DR+.

[0313] The evaluation index calculation unit 254 calculates a standard deviation of each gating population and a standard deviation of the non-gating population corresponding to each gating population. For example, the evaluation index calculation unit 254 selects an optimal gate and an optimal gating population on the basis of the standard deviation with respect to the vertical-axis marker of each gating population and the standard deviation with respect to the vertical-axis marker of the non-gating population corresponding to each gating population. Specifically, for example, the evaluation index calculation unit 254 selects a gate for which the standard deviation with respect to the vertical-axis marker of the gating population is smaller and the standard deviation with respect to the vertical-axis marker of the non-gating population is larger so that a difference thereof is maximum, as an optimal gate, and selects a gating population extracted by the optimal gate as an optimal gating population.

[0314] For example, A of Fig. 46 is a two-dimensional plot of the Nega-Posi population of the two-dimensional plot where the vertical-axis marker is CD27 and the horizontal-axis marker is CCR7. B of Fig. 46 is a two-dimensional plot of the positive gating population extracted from the Nega-Posi population in A of Fig. 46 with CD45RA+. C of Fig. 46 is a two-dimensional plot of the non-gating population with respect to the gating population in B of Fig. 46. That is, C of Fig. 46 is a two-dimensional plot of the negative gating population extracted from the Nega-Posi population in A of Fig. 46 with CD45RA-.

[0315] For example, A of Fig. 47 is a two-dimensional plot of the Nega-Posi population of the two-dimensional plot where the vertical-axis marker is CD27 and the horizontal-axis marker is CCR7 as in A of Fig. 46. B of Fig. 47 is a two-dimensional plot of the positive gating population extracted from the Nega-Posi population in A of Fig. 47 with IgD+. C of Fig. 47 is a two-dimensional plot of the non-gating population with respect to the gating population in B of Fig. 47. That is, C of Fig. 47 is a two-dimensional plot of the negative gating population extracted from the Nega-Posi population in A of Fig. 47 with IgD-.

[0316] For example, in a case where the gating is performed with CD45RA+, a difference between the standard deviation with respect to the vertical-axis marker of the gating population and the standard deviation with respect to the vertical-axis marker of the non-gating population is 6.354. On the other hand, in a case where the gating is performed with IgD+, a difference between the standard deviation with respect to the vertical-axis marker of the gating population and the standard deviation with respect to the vertical-axis marker of the non-gating population is 13.03.

[0317] In this case, it is determined that IgD+ can suitably exclude the dim population rather than CD45RA+. In practice, in a case where the gating population in B of Fig. 46 is compared with the gating population in B of Fig. 47, it can be seen that the dim population can be excluded more suitably than in the gating population in B of Fig. 47. Then, for example, IgD+ is selected as an optimal gate and the gating population in B of Fig. 47 is selected as an optimal gating population.

[0318] In Step S304, the evaluation index calculation unit 254 determines whether or not the optimal gating population meets an SR adjustment target condition.

[0319] For example, the evaluation index calculation unit 254 calculates a standard deviation with respect to the vertical-axis marker in the two-dimensional plot of the optimal gating population subjected to a biexponential transformation.

[0320] Moreover, for example, the evaluation index calculation unit 254 performs a linear regression analysis on the two-dimensional plot of the optimal gating population on a linear axis to determine a linear regression line. The evaluation index calculation unit 254 calculates a standard deviation of the two-dimensional plot of the optimal gating population with respect to the axis orthogonal to the linear regression line.

[0321] In a case where the standard deviation with respect to the vertical-axis marker is smaller than a predetermined threshold (e.g., 10) or the standard deviation with respect to the axis orthogonal to the linear regression line is smaller than a predetermined threshold (e.g., 0.2), the evaluation index calculation unit 254 determines that the gating population meets the SR adjustment target condition, and the processing proceeds to Step S305.

[0322] For example, A of Fig. 48 is a two-dimensional plot showing, on the biexponential axis, a distribution of light intensity of a gating population extracted with CCR7- from a Nega-Posi population where the vertical-axis marker is CD123 and the horizontal-axis marker is CD24. The standard deviation with respect to the vertical-axis marker of this gating population is 16.99, which is equal to or larger than the threshold.

[0323] B of Fig. 48 is a two-dimensional plot showing, on the linear axis, a distribution of light intensity of a gating population similar to A of Fig. 48. The standard deviation with respect to the axis orthogonal to the linear regression line in this gating population is 0.057, which is smaller than the threshold.

[0324] Therefore, it is determined that this gating population meets the SR adjustment target condition.

[0325] For example, A of Fig. 49 is a two-dimensional plot showing, on the biexponential axis, a distribution of light intensity of a gating population extracted with CCR7+ from a Nega-Posi population where the vertical-axis marker is CD3 and the horizontal-axis marker is CD24. The standard deviation with respect to the vertical-axis marker of this gating population is 14.82, which is equal to or larger than the threshold.

[0326] B of Fig. 49 is a two-dimensional plot showing, on the linear axis, a distribution of light intensity of a gating population similar to A of Fig. 49. The standard deviation with respect to the axis orthogonal to the linear regression line in this gating population is 0.086, which is smaller than the threshold.

[0327] Therefore, it is determined that this gating population meets the SR adjustment target condition.

[0328] In Step S305, the evaluation index calculation unit 254 extracts an evaluation index target population from the optimal gating population on the basis of the linear regression line.

[0329] For example, A of Fig. 50 is a two-dimensional plot showing a distribution of light intensity of a gating population extracted with CCR7+ from a Nega-Posi population where the vertical-axis marker is CD56 and the horizontal-axis marker is CD8. B of Fig. 50 is a two-dimensional plot showing a distribution of light intensity of the gating population after the SR is adjusted on the basis of a dependency evaluation index calculated by using the gating population in A of Fig. 50.

[0330] As shown in A of Fig. 50, even though the gating is performed with the other marker, the events (bioparticles) distributed sparsely remain in this gating population. Thus, the absolute value of the correlation coefficient of the gating population becomes larger. As a result, the SR is adjusted too much, and the orthogonality of the Nega-Posi population after the SR adjustment decreases as shown in B of Fig. 50.

[0331] In contrast, in a case where the distribution of the light intensity of the gating population is shown on the linear axis, the evaluation index calculation unit 254 excludes the events (bioparticles) distributed away from the dense population, from the gating population.

[0332] For example, A of Fig. 51 is a two-dimensional plot showing, on the linear axis, a distribution of light intensity of the gating population in A of Fig. 50.

[0333] For example, the evaluation index calculation unit 254 performs a linear regression analysis on the distribution of the light intensity of the gating population in A of Fig. 51 to determine a linear regression line. The evaluation index calculation unit 254 extracts only bioparticles in a predetermined range (e.g., in the range of 2σ) from the linear regression line and excludes the bioparticles outside the range.

[0334] B of Fig. 51 is a secondary plot showing, on the biexponential axis, a distribution of light intensity of a gating population obtained by excluding the bioparticles outside the predetermined range from the linear regression line. As compared to the two-dimensional plot in A of Fig. 50, the bioparticles in the region enclosed by the dotted-line circle in the figure are excluded.

[0335] For example, C of Fig. 51 is a two-dimensional plot showing, on the biexponential axis, a distribution of light intensity of a gating population after the SR is adjusted on the basis of a dependency evaluation index calculated by using the gating population in B of Fig. 51. As shown in this two-dimensional plot, the excessive adjustment of the SR is suppressed, and the orthogonality of the Nega-Posi population after the SR adjustment increases.

[0336] In Step S306, the evaluation index calculation unit 254 calculates a dependency evaluation index by using the evaluation index target population. That is, the evaluation index calculation unit 254 calculates a correlation coefficient between the vertical-axis dye and the horizontal-axis dye in the evaluation index target population as the evaluation index target population, which is extracted in the processing of Step S305, as a dependency evaluation index.

[0337] Then, the dependency evaluation index calculation processing ends.

[0338] On the other hand, in Step S304, in a case where the standard deviation with respect to the vertical-axis marker is equal to or larger than a predetermined threshold and the standard deviation with respect to the axis orthogonal to the linear regression line is equal to or larger than the predetermined threshold, the evaluation index calculation unit 254 determines that it does not meet the SR adjustment target condition, and the processing proceeds to Step S307.

[0339] For example, A of Fig. 52 is a two-dimensional plot showing, on the biexponential axis, a distribution of light intensity of a gating population extracted with CD45RO+ from a Nega-Posi population in a case where the vertical-axis marker is CD27 and the horizontal-axis marker is CD3. The standard deviation with respect to the vertical-axis marker of this gating population is 20.378, which is equal to or larger than the threshold.

[0340] B of Fig. 52 is a two-dimensional plot showing, on the linear axis, a distribution of light intensity of a gating population similar to A of Fig. 52. The standard deviation with respect to the axis orthogonal to the linear regression line in this gating population is 0.274, which is equal to or larger than the threshold.

[0341] Therefore, it is determined that this gating population does not meet the SR adjustment target condition.

[0342] In Step S307, the evaluation index calculation unit 254 excludes the current dye combination from the SR adjustment target. That is, the evaluation index calculation unit 254 excludes a dye combination in which the vertical-axis dye corresponding to the current vertical-axis marker is an addition / subtraction target dye and the horizontal-axis dye corresponding to the horizontal-axis marker is an adjustment target dye, from the SR adjustment target.

[0343] Then, the dependency evaluation index calculation processing ends.

[0344] In the above-mentioned manner, the dependency evaluation index is calculated by using the evaluation index target population excluding the dim population from the Nega-Posi population. The use of the calculated dependency evaluation index suppresses the excessive adjustment of the SR, and the SR is suitably adjusted.

[0345] Next, an example of a user interface (UI) in a case of excluding the dim population by using the gating with the other marker will be described with reference to Figs. 53 to 58.

[0346] For example, the output unit 203 displays the UI shown in Figs. 53 to 58 under the control of the SR adjustment unit 255.

[0347] For example, an SR adjustment result display screen shown in Fig. 53 is displayed. For example, two-dimensional plots of light intensity of a pair of markers where the selected marker is the vertical-axis marker and the other marker is the horizontal-axis marker are displayed in a list on the SR adjustment result display screen. In this example, CD56 is selected as the vertical-axis marker.

[0348] The name of the vertical-axis marker is displayed on the vertical axis of each two-dimensional plot. In addition, the name of the vertical-axis dye corresponding to the vertical-axis marker may be displayed. The name of the horizontal-axis marker is displayed on the horizontal axis of each two-dimensional plot. In addition, the name of the horizontal-axis dye corresponding to the horizontal-axis marker may be displayed. For example, a correlation coefficient, a p-value, and mutual information of the Nega-Posi population are displayed above each two-dimensional plot.

[0349] Moreover, for example, an other marker gating result display screen shown in Fig. 54 is displayed.

[0350] On the other marker gating result display screen, two-dimensional plots of light intensity of an evaluation index target population corresponding to the two-dimensional plot of the light intensity of the pair of markers on the SR adjustment result display screen in Fig. 53 are displayed in a list. Each evaluation index target population is a population of bioparticles excluding the dim population by gating the Nega-Posi population of the corresponding pair of markers with an optimal marker selected with respect to the pair of markers.

[0351] For example, the name of the optimal marker used in the gating is displayed above each two-dimensional plot. Moreover, the mean value and the standard deviation of the light intensity of the vertical-axis marker in the evaluation index target population and the correlation coefficient of the evaluation index target population are displayed above each two-dimensional plot.

[0352] It should be noted that for example, the SR adjustment result display screen in Fig. 53 and the other marker gating result display screen in Fig. 54 may be displayed on a single screen or may be displayed on discrete screens.

[0353] Figs. 55 and 56 show display examples in a case where a pair of gates for which the number of bioparticles is smaller than a predetermined percentage (hereinafter, referred to as bioparticle insufficient pair) is present due to gating with the optimal marker. Fig. 55 shows an example of a part of the SR adjustment result display screen before the gating. Fig. 56 shows an example of a part of an other marker gating result screen after the gating.

[0354] For example, Figs. 55 and 56 show examples in a case where the bioparticle insufficient pair is a pair of the vertical-axis marker of CD56 and the horizontal-axis marker of CD25. In this case, on both the SR adjustment result display screen and the other marker gating result screen, the optimal marker indicates that there is a need for paying attention to the number of events (≒ the number of bioparticles) after the gating above the two-dimensional plot of the pair of the vertical-axis marker of CD56 and the horizontal-axis marker of CD25.

[0355] Accordingly, the user is notified of that the SR adjustment result has low reliability because the dependency evaluation index is calculated with a small number of bioparticles, and for example, the user is encouraged to check whether there is a problem in the SR adjustment result or the gating result with the other marker.

[0356] It should be noted that the SR adjustment result display screen in Fig. 55 and the other marker gating result screen in Fig. 56 may be displayed on a single screen or may be displayed on discrete screens.

[0357] Figs. 57 and 58 show display examples in a case where the pair of the markers that is not the SR adjustment target (e.g., referred to as non-adjustment-target pair) is present. Fig. 57 shows an example of a part of the SR adjustment result display screen before the gating. Fig. 58 shows an example of a part of the other marker gating result screen after the gating.

[0358] For example, Figs. 57 and 58 show examples in a case where the pair of the vertical-axis marker of CD27 and the horizontal-axis marker of CD3 is the non-adjustment-target pair. In this case, on both the SR adjustment result display screen and the other marker gating result screen, it is indicated that it is not the SR adjustment target, above the two-dimensional plot of the pair of the vertical-axis marker of CD27 and the horizontal-axis marker of CD3.

[0359] Accordingly, for example, the user is encouraged to check whether the SR adjustment is unnecessary for sure.

[0360] It should be noted that the SR adjustment result display screen in Fig. 57 and the other marker gating result screen in Fig. 58 may be displayed on a single screen or may be displayed on discrete screens.

[0361] <2. Modified Examples > Hereinafter, modified examples of the above-mentioned embodiments of the present technology will be described.

[0362] Modified Examples Regarding Sharing Processing For example, some of the processes of the above-mentioned information processing unit 113 may be designed to be performed by the server 12.

[0363] For example, the analysis pre-processing unit 221 may be provided in the server 12 and the server 12 may adjust the SR of each dye.

[0364] Modified Example Related to Orthogonality Evaluation Index For example, a secondary stain index (SSI) may be used as the orthogonality evaluation index. The SSI is calculated in accordance with Expression (7) below.

[0365] SSI = {MFI(nega-posi) - MFI(nega-nega)} / {2 × σ(nega-nega)} … (7)

[0366] It should be noted that MFI(nega-posi) is a mean value of the light intensity of the bioparticles in the Nega-Posi population negative for the evaluation target dye and positive for the adjustment target dye. MFI(nega-nega) is a mean value of the light intensity of the bioparticles in Nega-Nega population negative for the evaluation target dye and negative for the adjustment target dye. σ(nega-nega) is a standard deviation of the light intensity of the bioparticles in Nega-Nega population.

[0367] For example, in a case where the SSI enters the range of -1.0 to +1.0 in Step S156 of Fig. 10, it may be determined that the orthogonality evaluation index meets the termination condition and the SR adjustment may be terminated.

[0368] Modified Example Related to SR Adjustment Termination Condition Next, a modified example related to the SR adjustment termination condition will be described with reference to Figs. 59 to 65.

[0369] Method Using P Value of Correlation Coefficient For example, in a case where the correlation coefficient of the Nega-Posi population is used for the dependency evaluation index, the evaluation index calculation unit 254 may calculate a p-value for the correlation coefficient of the Nega-Posi population every time the SR is adjusted, and may determine whether or not to terminate the SR adjustment on the basis of the p-value for the correlation coefficient. For example, in Step S256 of Fig. 23, in a case where the p-value for the correlation coefficient is equal to or larger than a predetermined threshold (e.g., 0.01 or more), the SR adjustment may be terminated.

[0370] It should be noted that for example, as shown in an example of the two-dimensional plot between the two markers in Fig. 59, for example, in a case where the number of bioparticles in the Nega-Posi population is extremely small, the p-value for the correlation coefficient of the Nega-Posi population becomes larger. In contrast, in a case where the number of bioparticles in the Nega-Posi population is smaller than the predetermined threshold, the horizontal-axis dye in the pair of the vertical-axis dye and the horizontal-axis dye that are targets may be excluded from the SR adjustment target.

[0371] Method Using Mutual Information Next, a method using the mutual information of the Nega-Posi population as the SR adjustment termination condition will be described with reference to Figs. 60 and 61.

[0372] For example, the evaluation index calculation unit 254 may calculate the mutual information of the vertical-axis dye and the horizontal-axis dye in the Nega-Posi population every time the SR is adjusted, and whether or not to terminate the SR adjustment is determined on the basis of the mutual information.

[0373] For example, Fig. 60 shows an example of the two-dimensional plot of the light intensity between the two markers before / after the SR adjustment. Specifically, A of Fig. 60 shows an example of the two-dimensional plot of the light intensity between the two markers before the SR adjustment. B of Fig. 60 shows an example of the two-dimensional plot of the light intensity between the two markers after the SR adjustment.

[0374] In this example, the Nega-Posi population includes a large number of dim populations. Therefore, the SR is adjusted too much and the apparent orthogonality of the Nega-Posi population after the SR adjustment decreases.

[0375] Fig. 61 shows changes in the correlation coefficient and the mutual information of the Nega-Posi population along with the SR adjustment shown in the example of Fig. 60. The horizontal axis in Fig. 61 indicates a cumulative value of the SR adjustment ratio. The vertical axis on the left side in Fig. 61 indicates an absolute value of the correlation coefficient. The vertical axis on the right side in Fig. 61 indicates mutual information. The line L51 indicates a change in the absolute value of the correlation coefficient with respect to the cumulative value of the SR adjustment ratio. The line L52 indicates a change in the mutual information with respect to the cumulative value of the SR adjustment ratio.

[0376] In this example, the absolute value of the correlation coefficient decreases as the SR adjustment progresses. On the other hand, the mutual information decreases as the SR adjustment progresses, and then increases in turn. In this manner, when the SR adjustment progresses too much, the mutual information increases in turn.

[0377] In contrast, the evaluation index calculation unit 254 terminates the SR adjustment in a case where the mutual information becomes larger than at the point of time at which the SR is adjusted one step earlier. Accordingly, the excessive adjustment of the SR is suppressed.

[0378] Method Using Tilt of Linear Regression Line Next, a method using a tilt of the linear regression line of the Nega-Posi population as the SR adjustment termination condition will be described with reference to Fig. 62.

[0379] For example, the evaluation index calculation unit 254 performs a linear regression analysis on the Nega-Posi population every time the SR is adjusted and calculates a tilt of the linear regression line. The evaluation index calculation unit 254 terminates the SR adjustment if the tilt of the linear regression line meets a predetermined desired value (e.g., smaller than 0.01).

[0380] It should be noted that as in the Nega-Posi population shown in Fig. 62, the magnitude of the correlation coefficient is not correlated to the magnitude of the tilt of the linear regression line, depending on how the distribution of the light intensity of the Nega-Posi population spreads, in some cases. For example, in the example in Fig. 62, the correlation coefficient is -0.356, which is a large absolute value, and the tilt of the linear regression line is -0.073, which is small.

[0381] In contrast, for example, combining the correlation coefficient with the tilt of the linear regression line, the SR adjustment termination condition may be used. For example, the evaluation index calculation unit 254 may terminate the SR adjustment in a case where the correlation coefficient is smaller than a predetermined threshold (e.g., smaller than 0.1) or the tilt of the linear regression line is smaller than a predetermined threshold (e.g., smaller than 0.01). Accordingly, the excessive adjustment of the SR is suppressed.

[0382] Method Using Change Rate of Dependency Evaluation Index Next, a method using a change rate of the dependency evaluation index as the SR adjustment termination condition will be described with reference to Fig. 63.

[0383] For example, the evaluation index calculation unit 254 calculates a change rate of the correlation coefficient that is the dependency evaluation index with respect to the cumulative value of the SR adjustment ratio during the SR adjustment. The evaluation index calculation unit 254 terminates the SR adjustment in a case where the change rate of the correlation coefficient is smaller than a predetermined threshold (e.g., smaller than 1).

[0384] For example, Fig. 63 shows a specific example of the change rate of the correlation coefficient. Specifically, A of Fig. 63 shows an example of the two-dimensional plot of the light intensity between the two markers. B of Fig. 63 shows a change rate of the correlation coefficient of the Nega-Posi population in A of Fig. 63. The horizontal axis in B of Fig. 63 indicates the cumulative value of the SR adjustment ratio and the vertical axis indicates the correlation coefficient.

[0385] For example, the evaluation index calculation unit 254 plots the correlation coefficient with respect to the cumulative value of the SR adjustment ratio in the graph in B of Fig. 63 every time the SR is adjusted. The evaluation index calculation unit 254 calculates a tilt of the straight line connecting plotted points as the change rate of the correlation coefficient.

[0386] For example, as shown in a portion enclosed by the dotted-line circle of the two-dimensional plot in A of Fig. 63, in a case where two markers are correlated due to a biological factor or the like rather than the spillover, the correlation coefficient of the Nega-Posi region may increase. In a case of this example, for example, the correlation coefficient is 0.574.

[0387] In contrast, as shown in B of Fig. 63, the change rate of the correlation coefficient decreases. For example, the change rate of the correlation coefficient is 0.32646, which is smaller than 1 that is a threshold. Accordingly, the SR adjustment ends and the excessive adjustment of the SR is suppressed.

[0388] It should be noted that the plurality of SR adjustment termination conditions described above may be used in combination.

[0389] Modified Example Related to Method of Setting SR Adjustment Ratio Next, a modified example related to a method of setting the SR adjustment ratio (weight used for the SR adjustment) will be described with reference to Figs. 64 and 65.

[0390] Fig. 64 shows an example of changes in the cumulative value of the adjustment ratio in the example of the SR adjustment method described with reference to Figs. 12 and 13. It should be noted that this example shows an example in a case of using the dependency evaluation index, more specifically, the correlation coefficient of the Nega-Posi population as the spillover evaluation index. The horizontal axis in Fig. 64 indicates the cumulative value of the SR adjustment ratio and the vertical axis indicates the correlation coefficient of the Nega-Posi population.

[0391] As shown in the figure, the correlation coefficient gradually converges to a desired value (e.g., 0) along with an increase / decrease in the cumulative value of the SR adjustment ratio.

[0392] In contrast, for example, the evaluation index calculation unit 254 may set a desired value of the spillover evaluation index, predict the cumulative value of the SR adjustment ratio that reaches a desired value of the spillover evaluation index, and set the SR adjustment ratio on the basis of the predicted result.

[0393] Specifically, for example, the evaluation index calculation unit 254 generates a graph of the spillover evaluation index with respect to the cumulative value of the SR adjustment ratio during the SR adjustment. Next, the evaluation index calculation unit 254 predicts the cumulative value of the SR adjustment ratio at which the spillover evaluation index reaches the desired value on the basis of the generated graph. Next, the evaluation index calculation unit 254 sets the SR adjustment ratio to be the predicted cumulative value and adjusts the SR. Then, the evaluation index calculation unit 254 terminates the SR adjustment in a case where the SR adjustment termination condition is met.

[0394] For example, in a case of the example in Fig. 64, the adjustment ratio is set to α during the first SR adjustment and the adjustment ratio is set to β during the second SR adjustment. At this point of time, the cumulative value of the SR adjustment ratio (e.g., α - β + γ - Δ) at which the correlation coefficient reaches the desired value is predicted on the basis of a graph of the correlation coefficient with respect to the cumulative value of the SR adjustment ratio. Then, the SR adjustment ratio is set to the predicted cumulative value.

[0395] Accordingly, the SR adjustment ratio is rapidly set to a suitable value, the number of times of SR adjustment decreases, and high-speed SR adjustment processing is realized.

[0396] It should be noted that in a case where the SR adjustment termination condition is not met after the SR is adjusted by setting the adjustment ratio to the predicted cumulative value, the evaluation index calculation unit 254 repeats fine SR adjustment until the adjustment termination condition is met by, for example, setting the adjustment ratio to a smaller value, e.g., 0.001, when the spillover evaluation index is closer to the desired value than before the adjustment. On the other hand, when the spillover evaluation index is farther from the desired value than before the adjustment, the evaluation index calculation unit 254 returns the SR to the state before the adjustment by adding or subtracting the SR at the same adjustment ratio, for example and adjusts the SR in accordance with the above-mentioned method, referring to Fig. 23.

[0397] Moreover, for example, the evaluation index calculation unit 254 may perform a linear regression analysis on the population of the bioparticles that is the SR adjustment target and set the resulting tilt of the linear regression line as the SR adjustment ratio.

[0398] Specifically, for example, the evaluation index calculation unit 254 performs a linear regression analysis on the Nega-Posi population and calculates a tilt of the linear regression line.

[0399] For example, A of Fig. 65 shows an example of the two-dimensional plot of the Nega-Posi population on the linear axis. The straight line in the figure indicates the linear regression line.

[0400] The evaluation index calculation unit 254 sets the tilt of the linear regression line to the SR adjustment ratio and adjusts the SR. In the example in A of Fig. 65, the tilt of the linear regression line is -0.0470, and its value is set as the SR adjustment ratio. Then, the evaluation index calculation unit 254 terminates the SR adjustment in a case where the SR adjustment termination condition is met.

[0401] It should be noted that B of Fig. 65 shows an example of a change in the correlation coefficient along with the SR adjustment in a case where the SR is adjusted by the above-mentioned method with reference to Fig. 23. The horizontal axis indicates the cumulative value of the SR adjustment ratio and the vertical axis indicates the correlation coefficient. For example, the correlation coefficient at the start of the SR adjustment is -0.913 and the correlation coefficient gradually approaches 0 due to the SR adjustment.

[0402] On the other hand, the SR adjustment ratio is set to -0.00470, which is the tilt of the linear regression line, such that the correlation coefficient is -0.0513 and the correlation coefficient sufficiently approaches 0. Thus, the dependency between the two markers is overcome.

[0403] In this manner, the SR adjustment ratio is rapidly set to a suitable value, the number of times of SR adjustment decreases, and high-speed SR adjustment processing is realized.

[0404] It should be noted that in a case where the SR adjustment termination condition is met after the SR is adjusted by setting the tilt of the linear regression line to the adjustment ratio, the evaluation index calculation unit 254 repeats fine SR adjustment until the adjustment termination condition is met by, for example, setting the adjustment ratio to a small value, e.g., 0.001, when the spillover evaluation index is closer to the desired value than before the adjustment. On the other hand, when the spillover evaluation index is farther from the desired value than before the adjustment, the evaluation index calculation unit 254 returns the SR to the state before the adjustment by adding or subtracting the SR at the same adjustment ratio, for example and adjusts the SR in accordance with the above-mentioned method, referring to Fig. 23.

[0405] It should be noted that an upper limit may be set on the cumulative value of the SR adjustment ratio. For example, in a case of adjusting the SR by using a pair of certain markers (dyes), the cumulative value of the adjustment ratio may be calculated and held every time the SR is adjusted, and the SR adjustment may be terminated when the cumulative value of the adjustment ratio exceeds a predetermined threshold (e.g., 0.1).

[0406] Modified Example Related to SR Adjustment Method Next, a modified example related to the SR adjustment method will be described with reference to Figs. 66 and 67.

[0407] For example, in a case where the adjustment target dye is a tandem dye combining two fluorescent dye molecules, donor dye molecule and acceptor dye molecule, the SR adjustment unit 255 may adjust the SR of the adjustment target dye by a method different from the above-mentioned addition / subtraction of the SR of the addition / subtraction target dye.

[0408] The tandem dye is a dye that uses a phenomenon called fluorescence resonance energy transfer (FRET). The FRET is a phenomenon where the donor dye molecule absorbs excitation light and transmits the energy to the acceptor dye molecule, causing the acceptor dye molecule to emit fluorescence. It is generally known that the tandem dye tends to degrade easily, and the efficiency of the FRET decreases due to the influence of exposure to light, long-term preservation, or the like. When the efficiency of the FRET decreases, the level of the donor dye increases and the level of the acceptor dye decreases, causing a change in the shape of the SR.

[0409] Fig. 66 shows an example of a spectrum shape before degradation of the tandem dye. Fig. 67 shows an example of a spectrum shape after degradation of the same tandem dye. The horizontal axis indicates the channel and the vertical axis indicates the signal value.

[0410] In a case where the adjustment target dye is a tandem dye, such a spectrum shape can principally change.

[0411] In contrast, for example, the SR adjustment unit 255 may adjust the SR of the adjustment target dye by changing the intensity ratio of a spectral part mainly constituted by the donor dye of the SR of the adjustment target dye to a spectral part mainly constituted by the acceptor dye. Accordingly, more reasonable SR adjustment can be realized.

[0412] Specifically, for example, in a case where the adjustment target dye selected by using the spillover evaluation index is a tandem dye, the SR adjustment unit 255 may adjust the SR of the adjustment target dye by changing the ratio of the donor dye to the acceptor dye in the SR of the adjustment target dye. Specifically, for example, the SR adjustment unit 255 calculates the area of a region where the SR of the adjustment target dye and the SR of the addition / subtraction target dye overlap. In a case of under-unmixing, the SR adjustment unit 255 changes the shape ratio of the donor dye to the acceptor dye in a direction in which the area of the region increases. On the other hand, in a case of over-unmixing, the SR adjustment unit 255 changes the shape ratio of the donor dye to the acceptor dye in a direction in which the area of the region decreases. Accordingly, the SR of the adjustment target dye is adjusted.

[0413] It should be noted that in this case, the SR adjustment direction may be limited to the direction in which the tandem dye degrades, i.e., the direction in which the ratio of the acceptor dye decreases. Moreover, for example, after the SR of the adjustment target dye is adjusted by using the shape ratio of the donor dye to the acceptor dye, the SR of the adjustment target dye may be further adjusted by the addition / subtraction of the SR of the addition / subtraction target dye.

[0414] It should be noted that information used for determining whether or not the adjustment target dye is a tandem dye is not particularly limited. For example, information input by the user in advance may be used. For example, information based on an external dye database may be used.

[0415] Alternatively, for example, since it is generally possible to determine whether or not it is a tandem dye on the basis of a dye name, a rule to determine whether or not it is a tandem dye on the basis of a dye name may be created in advance and used. In this case, a step of the user checking a determination result based on the rule may be added.

[0416] Moreover, a method of determining a donor dye part and an acceptor dye part in the spectrum of the tandem dye is not particularly limited. For example, the user may specify the donor dye part and the acceptor dye part. For example, the donor dye part and the acceptor dye part may be determined on the basis of the external dye database.

[0417] Alternatively, for example, two peaks in the spectrum of the tandem dye may be detected and the donor dye part and the acceptor dye part may be determined on the basis of the detected peaks. In this case, principally, a spectrum including a peak on a short-wavelength side is assigned to the donor dye part and a spectrum including a peak on a long-wavelength side is assigned to the acceptor dye part. Moreover, in this case, for example, a step of the user checking the assignment of the donor dye part and the acceptor dye part may be added.

[0418] Example of UI That Visualizes Dependency Evaluation Index Next, an example of a UI that visualizes the dependency evaluation index of (dye corresponding to) each marker will be described with reference to Figs. 68 to 74.

[0419] For example, the output unit 203 displays the UI shown in Figs. 68 to 74 under the control of the SR adjustment unit 255.

[0420] For example, as shown in Fig. 68, in a case where the two-dimensional plot of the light intensity between (two dyes corresponding to) the two markers is displayed, the display mode of the region (hereinafter, referred to as the Nega-Posi region) corresponding to the Nega-Posi population may be changed on the basis of the dependency evaluation index.

[0421] Specifically, the name of the vertical-axis marker is displayed on the vertical axis of the two-dimensional plot and the name of the horizontal-axis marker is displayed on the horizontal axis. It should be noted that the name of the vertical-axis dye corresponding to the vertical-axis marker and the name of the horizontal-axis dye corresponding to the horizontal-axis marker may be further displayed. The value of the correlation coefficient of the Nega-Posi population that is the dependency evaluation index is displayed above the two-dimensional plot. The scale indicating a color change according to the number of bioparticles of each pixel is displayed on the right side of the two-dimensional plot.

[0422] For example, in a case where the absolute value of the correlation coefficient of the Nega-Posi population that is the dependency evaluation index is smaller than the predetermined threshold, i.e., in a case where the two-dimensional plot is orthogonal to the vertical-axis marker, the color of the Nega-Posi region is set to be white.

[0423] For example, in a case where the absolute value of the correlation coefficient of the Nega-Posi population that is the dependency evaluation index is equal to or larger than the predetermined threshold and the correlation coefficient is a positive value, i.e., a state of under-unmixing with respect to the vertical-axis dye corresponding to the vertical-axis marker, the color of the Nega-Posi region is set to red. It should be noted that as the larger the absolute value of the correlation coefficient, i.e., larger the degree of the under-unmixing, the red of the Nega-Posi region may become darker.

[0424] For example, in a case where the absolute value of the correlation coefficient of the Nega-Posi population that is the dependency evaluation index is equal to or larger than the predetermined threshold and the correlation coefficient is a negative value, i.e., it is a state of over-unmixing with respect to the vertical-axis dye corresponding to the vertical-axis marker, the color of the Nega-Posi region is set to blue. It should be noted that the larger the absolute value of the correlation coefficient, i.e., the larger the degree of over-unmixing, blue of the Nega-Posi region may become darker.

[0425] For example, as shown in Figs. 69 and 70, a dependency evaluation index matrix indicating the values of the dependency evaluation indices for each pair of markers as a matrix may be displayed. Specifically, Fig. 69 shows the dependency evaluation index matrix before the SR adjustment and Fig. 70 shows the dependency evaluation index matrix after the SR adjustment.

[0426] Each row of the dependency evaluation index matrix indicates the vertical-axis marker corresponding to the vertical-axis dye (addition / subtraction target dye), each column indicates the horizontal-axis marker corresponding to the horizontal-axis dye (adjustment target dye). Each field (each element) of the dependency evaluation index matrix shows the dependency evaluation index with respect to the pair of the corresponding vertical-axis marker and the horizontal-axis marker. That is, each field of the dependency evaluation index matrix indicates the correlation coefficient of the Nega-Posi population of the corresponding vertical-axis marker and the horizontal-axis marker.

[0427] The display mode of each field of the dependency evaluation index matrix changes in accordance with the value of the correlation coefficient that is the dependency evaluation index. For example, the color and darkness of each field of the dependency evaluation index matrix change in accordance with the value of the correlation coefficient as in an example of Fig. 68. Accordingly, it can be seen that the necessity of the SR adjustment of the horizontal-axis dye corresponding to the corresponding horizontal-axis marker is higher as the field has darker color.

[0428] It should be noted that the fields of the pair of the markers excluded from the SR adjustment target become blank.

[0429] Moreover, when each field of the dependency evaluation index matrix is specified by the user, the two-dimensional plot of the light intensity of the pair of the markers of the specified fields are displayed as a pop-up. The two-dimensional plot in the pop-up is displayed as in the example of Fig. 68, for example. Moreover, for example, the arrow indicating the adjustment direction of the two-dimensional plot is displayed in the region showing the Nega-Posi population of the two-dimensional plot. In a case where it is the state of the over-unmixing with respect to the vertical-axis dye, the upward arrow is displayed. In a case where it is a state of the under-unmixing with respect to the vertical-axis dye, the downward arrow is displayed.

[0430] For example, the user can easily evaluate effects of the SR adjustment by comparing the color darkness of each field of the dependency evaluation index matrix before the SR adjustment and after the adjustment. For example, the user can determine that the SR adjustment is effective when the color of each field of the dependency evaluation index matrix is lighter, and can determine that the SR adjustment is not effective when the color of each field of the dependency evaluation index matrix does not significantly change or becomes darker.

[0431] Moreover, for example, the user may be enabled to adjust the SR by manually moving the distribution of the Nega-Posi population of the two-dimensional plot in the pop-up.

[0432] Moreover, for example, corresponding to the dependency evaluation index matrix in Figs. 69 and 70, the two-dimensional plot of the pair of markers may be displayed in a list.

[0433] For example, B of Fig. 71 shows an example in which the list of the two-dimensional plots of the pair of the markers corresponding to the row of CD56 of the dependency evaluation index matrix in A of Fig. 71 is displayed. In this case, for example, the two-dimensional plot of the pair of the markers requiring the SR adjustment may be clearly indicated. In this example, the two-dimensional plot of a pair of the vertical-axis marker of CD56 and the horizontal-axis marker of CD14, which requires the SR adjustment, is enclosed by a frame.

[0434] It should be noted that the dependency evaluation index matrix in A of Fig. 71 and the list of the two-dimensional plots in B of Fig. 71 may be displayed on the same screen or may be displayed on different screens.

[0435] Moreover, for example, corresponding to indication of the pair of the markers in the dependency evaluation index matrix, the two-dimensional plot of the indicated pair of the markers may be clearly indicated in the list of the two-dimensional plots.

[0436] For example, A of Fig. 72 shows an example in which the pair of the vertical-axis marker of CD56 and the horizontal-axis marker of CD14 is indicated in the dependency evaluation index matrix. Correspondingly, in the list of the two-dimensional plots in B of Fig. 72, the two-dimensional plot of the indicated pair of CD56 and CD14 as the horizontal-axis marker is enclosed by a frame.

[0437] It should be noted that the dependency evaluation index matrix in A of Fig. 72 and the list of the two-dimensional plots in B of Fig. 72 may be displayed on the same screen or may be displayed on different screens.

[0438] Moreover, for example, in contrast to the example of Fig. 72, in the list of the two-dimensional plots, corresponding to indication of the two-dimensional plot, the pair of the markers corresponding to the two-dimensional plot indicated may be clearly indicated in the dependency evaluation index matrix.

[0439] For example, A of Fig. 73 shows an example in which the two-dimensional plot of a pair of the vertical-axis marker of CD56 and the horizontal-axis marker of CD14 is indicated in the list of the two-dimensional plots. Correspondingly, in the dependency evaluation index matrix in B of Fig. 73, the field of the pair of the vertical-axis marker of CD56 and the horizontal-axis marker of CD14 is enclosed by a frame.

[0440] It should be noted that the list of the two-dimensional plots in A of Fig. 73 and the dependency evaluation index matrix in B of Fig. 73 may be displayed on the same screen or may be displayed on different screens.

[0441] Moreover, for example, the array of the pair of the markers of the list of the two-dimensional plots and the array of the pair of the markers of the dependency evaluation index matrix may be associated with each other.

[0442] For example, A of Fig. 74 shows an example of the list of the two-dimensional plots where the vertical-axis marker is CD127. B of Fig. 74 shows an example of the dependency evaluation index matrix where the vertical-axis marker is CD127.

[0443] Here, the array of the horizontal-axis marker of the list of the two-dimensional plots in A of Fig. 74 and the array of the horizontal-axis marker of the dependency evaluation index matrix in B of Fig. 74 are associated with each other. That is, in the list of the two-dimensional plots and the dependency evaluation index matrix, the two-dimensional plot and the dependency evaluation index of the same pair of the markers are displayed at the same position in the array.

[0444] It should be noted that the list of the two-dimensional plots in A of Fig. 74 and the dependency evaluation index matrix in B of Fig. 74 may be displayed on the same screen or may be displayed on different screens.

[0445] In the above-mentioned manner, the dependency evaluation index of each dye corresponding to each marker is visualized in an easy-to-understand manner.

[0446] Application Examples of Present Technology The present technology may be applied in a case where particles other than bioparticles are labeled and analyzed by a plurality of dyes. For example, beads or the like may be analyzed for calibration or the like. For example, the particles to be analyzed may be industrially synthesized particles such as latex particles, gel particles, industrial particles, or the like. For example, the industrially synthesized particles may be particles synthesized from organic resin materials such as polystyrene and polymethyl methacrylate, inorganic materials such as glass, silica, and magnetic materials, or metals such as colloidal gold and aluminum. Each particle may be spherical or non-spherical in shape, and is not particularly limited in size and mass.

[0447] <3. Others> Configuration Example of Computer The above-mentioned series of processing may be performed by hardware or may be performed by software. In a case where the series of processing is performed by software, a program that constitutes the software is installed in a computer. Here, the computer includes a computer built into dedicated hardware, a general-purpose personal computer, for example, which can perform various functions by installing various programs, and the like.

[0448] Fig. 75 is a block diagram showing a configuration example of hardware of a computer that performs the above-mentioned series of processing by means of a program.

[0449] In a computer 1000, a central processing unit (CPU) 1001, a read only memory (ROM) 1002, and a random access memory (RAM) 1003 are mutually connected via a bus 1004.

[0450] An input / output interface 1005 is further connected to the bus 1004. An input unit 1006, an output unit 1007, a storage unit 1008, a communication unit 1009, and a drive 1010 are connected to the input / output interface 1005.

[0451] The input unit 1006 includes input switches, buttons, a microphone, an image sensor, and the like. The output unit 1007 includes a display, a loudspeaker, and the like. The storage unit 1008 includes a hard disk, a nonvolatile memory, and the like. The communication unit 1009 includes a network interface or the like. The drive 1010 drives a removable medium 1011 such as a magnetic disk, an optical disc, a magneto-optical disk, or a semiconductor memory.

[0452] In the computer 1000 configured in the above-mentioned manner, the above-mentioned series of processing is performed by the CPU 1001, for example, loading and executing a program recorded in the storage unit 1008 into the RAM 1003 via the input / output interface 1005 and the bus 1004.

[0453] The program to be executed by the computer 1000 (CPU 1011) can be provided recorded on the removable medium 1011, for example, as a packaged medium. Moreover, the program can be provided via a wired or wireless transmission medium, such as a local area network, the Internet, or digital satellite broadcasting.

[0454] In the computer 1000, the program can be installed in the storage unit 1008 via the input / output interface 1005 by mounting the removable medium 1011 on the drive 1010. Moreover, the program can be received by the communication unit 1009 via a wired or wireless transmission medium and installed in the storage unit 1008. Otherwise, the program can be installed in advance in the ROM 1002 or the storage unit 1008.

[0455] It should be noted that the program executed by the computer may be a program in which the processing is performed chronologically in accordance with the order described in the present specification or may be a program in which the processing is performed in parallel or at necessary timing such as when a call is made.

[0456] Moreover, in the present specification, the system means a set of components (apparatuses, modules (parts), etc.), regardless of whether or not all the components are in the same enclosure. Therefore, a plurality of apparatuses housed in separate enclosures and connected via a network, and a single apparatus containing a plurality of modules in a single enclosure are both systems.

[0457] In addition, the embodiments of the present technology are not limited to the above-mentioned embodiments, and various modifications can be made without departing from the gist of the present technology.

[0458] For example, the present technology can take the configuration of cloud computing, in which one function is shared and processed jointly by a plurality of apparatuses via a network.

[0459] Moreover, each of the steps described in the above-mentioned flowcharts can be performed by a single apparatus or shared by a plurality of apparatuses.

[0460] Furthermore, in a case where a single step includes a plurality of processes, the plurality of processes in the single step can be performed by a single apparatus or shared by a plurality of apparatuses.

[0461] Combination Examples of Configurations The present technology can also take the following configurations.

[0462] (1) An information processing system, comprising: at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method of: acquiring spectral references of a first dye and a second dye; obtaining information regarding light detected from at least one multi-stained particle in a sample; calculating an intensity of light corresponding to the first dye and to the second dye by using the spectral references of the first dye and the second dye; calculating an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and adjusting the spectral reference of the first dye and / or the second dye based on the index. (2) The information processing system of (1), wherein the method further comprises: using the adjusted spectral reference to recalculate the intensity of light corresponding to the first dye and the second dye; and using the adjusted spectral reference to output data about the sample in a format suitable for display on an electronic device. (3) The information processing system of (1), further comprising circuitry configured to electronically communicate the information regarding light detected from the at least one multi-stained particle in the sample with at least one detector. (4) The information processing system of (1), wherein adjusting the spectral reference of the first dye and / or the second dye comprises: multiplying the spectral reference of the second dye by a weight; and adding the spectral reference of the second dye to, or subtracting the spectral reference of the second dye from, the spectral reference of the first dye. (5) The information processing system of (4), wherein adjusting the spectral reference of the first dye and / or the second dye comprises: adding the spectral reference of the second dye multiplied by the weight to the spectral reference of the first dye in a case of a state of under-unmixing; and subtracting the spectral reference of the second dye multiplied by the weight from the spectral reference of the first dye in a case of a state of over-unmixing. (6) The information processing system of (1), wherein the method further comprises repeating at least a portion of the method until the index meets a predetermined condition. (7) The information processing system of (1), wherein the index is an orthogonality evaluation index. (8) The information processing system of (7), wherein the orthogonality evaluation index is a stain index, a signal separation, or a spillover spreading matrix. (9) The information processing system of (7), wherein the orthogonality evaluation index represents a slope in a two-dimensional plot of the distribution of intensity of light corresponding to the first dye and the second dye in a population of multi-stained particles in the sample that have shown a negative reaction to the second dye. (10) The information processing system of (1), wherein the index is a dependency evaluation index indicating dependencies of an intensity of light corresponding to the first dye and an intensity of light corresponding to the second dye. (11) The information processing system of (10), wherein the dependency evaluation index indicates dependency of the intensity of light corresponding to the first dye and the intensity of light corresponding to the second dye in a population of multi-stained particles in the sample that have shown a positive reaction to the first dye and have shown a negative reaction to the second dye. (12) The information processing system of (11), wherein the dependency evaluation index is a correlation coefficient indicating a correlation between the intensity of light corresponding to the first dye and the intensity of light corresponding to the second dye in the population of multi-stained particles. (13) The information processing system of (1), wherein the method further comprises: gating multi-stained particles in the sample based on a distribution of an intensity of light corresponding to the first dye and an intensity of light corresponding to the second dye; and calculating the index based on a result of gating the multi-stained particles. (14) The information processing system of (13), wherein the method further comprises: separating the multi-stained particles in the sample into: multi-stained particles that have shown a positive reaction to the first dye; and multi-stained particles that have shown a negative reaction to the first dye; and separating the multi-stained particles in the sample into: multi-stained particles that have shown a positive reaction to the second dye; and multi-stained particles that have shown a negative reaction to the second dye. (15) The information processing system of (14), wherein the method further comprises calculating the index in a population of multi-stained particles included in both: the multi-stained particles that have shown a positive reaction to the first dye; and the multi-stained particles that have shown a negative reaction to the second dye. (16) The information processing system of (14), wherein the method further comprises: calculating the index in a population of multi-stained particles included in both: the multi-stained particles that have shown a positive reaction to the first dye; and the multi-stained particles have shown a negative reaction to the second dye; and calculating the index in a population of the multi-stained particles included in both: the multi-stained particles that have shown a negative reaction to the first dye; and the multi-stained particles that have shown a negative reaction to the second dye. (17) The information processing system of (14), wherein the method further comprises calculating the index in: a population of multi-stained particles included in the multi-stained particles that have shown a positive reaction to the second dye; and a population of multi-stained particles included in the multi-stained particles that have shown a negative reaction to the second dye. (18) The information processing system of (1), wherein the method further comprises performing analysis processing of the information regarding the light detected from the at least one multi-stained particle in the sample by using the adjusted spectral reference of the first dye and / or the second dye. (19) An information processing apparatus, comprising: first circuitry configured to: acquire spectral references of a first dye and a second dye; obtain information regarding light detected from at least one multi-stained particle in a sample; and calculate an intensity of light corresponding to the first dye and to the second dye by using the spectral references of the first dye and the second dye; second circuitry configured to: calculate an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and third circuitry configured to: adjust the spectral reference of the first dye and / or the second dye based on the index. (20) An information processing method, comprising: acquiring spectral references of a first dye and a second dye; obtaining information regarding light detected from at least one multi-stained particle in a sample; calculating an intensity of light corresponding to the first dye and the second dye by using the spectral references of the first dye and the second dye; calculating an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and adjusting the spectral reference of the first dye and / or second dye based on the index. (21) An information processing method, including: by an information processing apparatus, calculating intensity of light corresponding to each dye from light detected from each of multi-stained particles in a sample, by using a spectral reference of each dye acquired by a single-stained particle; calculating a spillover evaluation index on the basis of a distribution of the intensity of light corresponding to each dye, the spillover evaluation index indicating a degree of spillover of light corresponding to each dye into light corresponding to other dye; and adjusting the spectral reference of each dye on the basis of the spillover evaluation index. (22) The information processing method according to (21), further including by the information processing apparatus, adjusting a first spectral reference that is the spectral reference of the first dye on the basis of the spillover evaluation index of first light corresponding to a first dye to second light corresponding to a second dye. (23) The information processing method according to (22), further including by the information processing apparatus, adjusting the first spectral reference by multiplying a second spectral reference that is the spectral reference of the second dye by a weight and adding or subtracting the second spectral reference to / from the first spectral reference on the basis of the spillover evaluation index. (24) The information processing method according to (23), further including by the information processing apparatus, adding the second spectral reference multiplied by a weight to the first spectral reference in a case of a state of under-unmixing with respect to the second dye and subtracting the second spectral reference multiplied by a weight from the first spectral reference in a case of a state of over-unmixing with respect to the second dye. (25) The information processing method according to (23) or (24), in which the spillover evaluation index is a dependency evaluation index indicating dependency of intensity of the first light and intensity of the second light, and the information processing apparatus predicts a weight by which the second spectral reference is multiplied to be added or subtracted to / from the first spectral reference when the dependency evaluation index reaches a desired value, and adds or subtracts the second spectral reference multiplied by the predicted weight to / from the first spectral reference. (26) The information processing method according to (23) or (24), in which the spillover evaluation index is a dependency evaluation index indicating dependency of intensity of the first light and intensity of the second light, and the information processing apparatus sets a weight by which the second spectral reference is multiplied to be added or subtracted to / from the first spectral reference, as a value of the dependency evaluation index. (27) The information processing method according to any of (22) to (26), further including by the information processing apparatus, repeating, until the spillover evaluation index meets a predetermined condition, calculating intensity of light corresponding to each dye from light detected from the multi-stained particles, by using the spectral reference of each dye including the adjusted first spectral reference, calculating the spillover evaluation index of the first light to the second light, and adjusting the first spectral reference on the basis of the spillover evaluation index. (28) The information processing method according to any of (22) to (27), further including by the information processing apparatus, calculating the spillover evaluation index on the basis of a distribution of the intensity of the first light and the intensity of the second light in the sample. (29) The information processing method according to (28), in which the spillover evaluation index is an orthogonality evaluation index in a two-dimensional plot, the orthogonality evaluation index indicating orthogonality of a distribution of the intensity of the second light, the two-dimensional plot indicating a distribution of the intensity of the first light and the intensity of the second light. (30) The information processing method according to (29), in which the orthogonality evaluation index is a stain index, signal separation, or a spillover spreading matrix. (31) The information processing method according to (29), in which the orthogonality evaluation index is a secondary stain index. (32) The information processing method according to any of (29) and (31), in which the orthogonality evaluation index is a slope of a distribution of intensity of light in a population of the multi-stained particles that have shown a negative reaction to the second dye, in the two-dimensional plot. (33) The information processing method according to (28), in which the spillover evaluation index is a dependency evaluation index indicating dependency of intensity of the first light and intensity of the second light. (34) The information processing method according to (33), in which the dependency evaluation index indicates dependency of the intensity of the first light and the intensity of the second light in a population of the multi-stained particles that have shown a positive reaction to the first dye and have shown a negative reaction to the second dye. (35) The information processing method according to (34), in which the dependency evaluation index is a correlation coefficient indicating a correlation between the intensity of the first light and the intensity of the second light in the population of the multi-stained particles. (36) The information processing method according to (35), further including: by the information processing apparatus, excluding a population of bioparticles weakly positive for the second dye from the population of the multi-stained particles; and calculating the correlation coefficient as the dependency evaluation index in a population excluding the population of the weakly positive bioparticles from the population of the multi-stained particles. (37) The information processing method according to (36), in which the information processing apparatus excludes the population of the weakly positive bioparticles by gating the population of the multi-stained particles with a marker corresponding to the first dye and another marker different from a marker corresponding to the second dye. (38) The information processing method according to (37), in which the information processing apparatus controls display of a distribution of light intensity in a plurality of populations of bioparticles obtained by gating the population of the multi-stained particles with each of a plurality of the other markers. (39) The information processing method according to any of (35) to (38), further including by the information processing apparatus, repeating, until a termination condition based on at least one of a p-value for the correlation coefficient, mutual information of the first dye and the second dye in the population of the multi-stained particles, a tilt of a linear regression line in the population of the multi-stained particles, or a change rate of the dependency evaluation index is met, calculating light intensity corresponding to each dye from light detected from the multi-stained particles by using the spectral reference of each dye including the adjusted first spectral reference, calculating the dependency evaluation index, and adjusting the first spectral reference on a basis of the dependency evaluation index. (40) The information processing method according to (34), in which the dependency evaluation index is mutual information of the first dye and the second dye in the population of the multi-stained particles or a tilt of a linear regression line in the population of the multi-stained particles. (41) The information processing method according to any of (33) to (40), in which the information processing apparatus controls display of the dependency evaluation index for each combination of a marker corresponding to the first dye and a marker corresponding to the second dye. (42) The information processing method according to any of (22) to (41), further including: by the information processing apparatus, performing gating on the multi-stained particles in the sample on the basis of a distribution of the intensity of the first light and a distribution of the intensity of the second light in the sample; and calculating the spillover evaluation index on the basis of a result of the gating of the multi-stained particles. (43) The information processing method according to (42), further including by the information processing apparatus, separating the multi-stained particles in the sample into the multi-stained particles that have shown a positive reaction to the first dye and the multi-stained particles that have shown a negative reaction to the first dye and separates the multi-stained particles in the sample into the multi-stained particles that have shown a positive reaction to the second dye and the multi-stained particles that have shown a negative reaction to the second dye, by the gating. (44) The information processing method according to (43), further including by the information processing apparatus, calculating the spillover evaluation index in a population of the multi-stained particles that have shown a positive reaction to the first dye and have shown a negative reaction to the second dye. (45) The information processing method according to (43), further including by the information processing apparatus, calculating the spillover evaluation index in a population of the multi-stained particles that have shown a positive reaction to the first dye and have shown a negative reaction to the second dye and in a population of the multi-stained particles that have shown a negative reaction to the first dye and have shown a negative reaction to the second dye. (46) The information processing method according to (43), further including by the information processing apparatus, calculating the spillover evaluation index in a population of the multi-stained particles that have shown a positive reaction to the second dye and in a population of the multi-stained particles that have shown a negative reaction to the second dye. (47) The information processing method according to any of (21) to (46), further including by the information processing apparatus, performing analysis processing of data about the light detected from each of the multi-stained particles in the sample, by using the adjusted spectral reference of each dye. (48) An information processing apparatus, including: an unmixing unit that calculates intensity of light corresponding to each dye from light detected from each of multi-stained particles in a sample, by using a spectral reference of each dye acquired by a single-stained particle; an evaluation calculation unit that calculates a spillover evaluation index on the basis of a distribution of the intensity of light corresponding to each dye, the spillover evaluation index indicating a degree of spillover of light corresponding to each dye into light corresponding to other dye; and a spectral reference adjustment unit that adjusts the spectral reference of each dye on the basis of the spillover evaluation index. (49) An information processing system, including: a detection unit that detects light from each of a plurality of multi-stained particles in a sample; and an information processing unit including an unmixing unit that calculates intensity of light corresponding to each dye from light detected from each of multi-stained particles in a sample, by using a spectral reference of each dye acquired by a single-stained particle, an evaluation calculation unit that calculates a spillover evaluation index on the basis of a distribution of the intensity of light corresponding to each dye, the spillover evaluation index indicating a degree of spillover of light corresponding to each dye into light corresponding to other dye, and a spectral reference adjustment unit that adjusts the spectral reference of each dye on the basis of the spillover evaluation index.

[0463] It should be noted that the effects described in the present specification are merely exemplary and not limitative, and other effects may be provided.

[0464] It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.

[0465] 1 information processing system 11-1to 11-n biological sample analyzer 12-1, 12-2 server 111 light irradiation unit 112 detection unit 113 information processing unit 114 sorting unit 202 control unit 203 output unit 205 reference DB 221 analysis pre-processing unit 222 analysis unit 251 unmixing unit 252 axis transformation unit 253 gating unit 253 evaluation index calculation unit 255 SR adjustment unit

Claims

1. An information processing system, comprising: at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method of: acquiring spectral references of a first dye and a second dye; obtaining information regarding light detected from at least one multi-stained particle in a sample; calculating an intensity of light corresponding to the first dye and to the second dye by using the spectral references of the first dye and the second dye; calculating an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and adjusting the spectral reference of the first dye and / or the second dye based on the index.

2. The information processing system of claim 1, wherein the method further comprises: using the adjusted spectral reference to recalculate the intensity of light corresponding to the first dye and the second dye; and using the adjusted spectral reference to output data about the sample in a format suitable for display on an electronic device.

3. The information processing system of claim 1, further comprising circuitry configured to electronically communicate the information regarding light detected from the at least one multi-stained particle in the sample with at least one detector.

4. The information processing system of claim 1, wherein adjusting the spectral reference of the first dye and / or the second dye comprises: multiplying the spectral reference of the second dye by a weight; and adding the spectral reference of the second dye to, or subtracting the spectral reference of the second dye from, the spectral reference of the first dye.

5. The information processing system of claim 4, wherein adjusting the spectral reference of the first dye and / or the second dye comprises: adding the spectral reference of the second dye multiplied by the weight to the spectral reference of the first dye in a case of a state of under-unmixing; and subtracting the spectral reference of the second dye multiplied by the weight from the spectral reference of the first dye in a case of a state of over-unmixing.

6. The information processing system of claim 1, wherein the method further comprises repeating at least a portion of the method until the index meets a predetermined condition.

7. The information processing system of claim 1, wherein the index is an orthogonality evaluation index.

8. The information processing system of claim 7, wherein the orthogonality evaluation index is a stain index, a signal separation, or a spillover spreading matrix.

9. The information processing system of claim 7, wherein the orthogonality evaluation index represents a slope in a two-dimensional plot of the distribution of intensity of light corresponding to the first dye and the second dye in a population of multi-stained particles in the sample that have shown a negative reaction to the second dye.

10. The information processing system of claim 1, wherein the index is a dependency evaluation index indicating dependencies of an intensity of light corresponding to the first dye and an intensity of light corresponding to the second dye.

11. The information processing system of claim 10, wherein the dependency evaluation index indicates dependency of the intensity of light corresponding to the first dye and the intensity of light corresponding to the second dye in a population of multi-stained particles in the sample that have shown a positive reaction to the first dye and have shown a negative reaction to the second dye.

12. The information processing system of claim 11, wherein the dependency evaluation index is a correlation coefficient indicating a correlation between the intensity of light corresponding to the first dye and the intensity of light corresponding to the second dye in the population of multi-stained particles.

13. The information processing system of claim 1, wherein the method further comprises: gating multi-stained particles in the sample based on a distribution of an intensity of light corresponding to the first dye and an intensity of light corresponding to the second dye; and calculating the index based on a result of gating the multi-stained particles.

14. The information processing system of claim 13, wherein the method further comprises: separating the multi-stained particles in the sample into: multi-stained particles that have shown a positive reaction to the first dye; and multi-stained particles that have shown a negative reaction to the first dye; and separating the multi-stained particles in the sample into: multi-stained particles that have shown a positive reaction to the second dye; and multi-stained particles that have shown a negative reaction to the second dye.

15. The information processing system of claim 14, wherein the method further comprises calculating the index in a population of multi-stained particles included in both: the multi-stained particles that have shown a positive reaction to the first dye; and the multi-stained particles that have shown a negative reaction to the second dye.

16. The information processing system of claim 14, wherein the method further comprises: calculating the index in a population of multi-stained particles included in both: the multi-stained particles that have shown a positive reaction to the first dye; and the multi-stained particles have shown a negative reaction to the second dye; and calculating the index in a population of the multi-stained particles included in both: the multi-stained particles that have shown a negative reaction to the first dye; and the multi-stained particles that have shown a negative reaction to the second dye.

17. The information processing system of claim 14, wherein the method further comprises calculating the index in: a population of multi-stained particles included in the multi-stained particles that have shown a positive reaction to the second dye; and a population of multi-stained particles included in the multi-stained particles that have shown a negative reaction to the second dye.

18. The information processing system of claim 1, wherein the method further comprises performing analysis processing of the information regarding the light detected from the at least one multi-stained particle in the sample by using the adjusted spectral reference of the first dye and / or the second dye.

19. An information processing apparatus, comprising: first circuitry configured to: acquire spectral references of a first dye and a second dye; obtain information regarding light detected from at least one multi-stained particle in a sample; and calculate an intensity of light corresponding to the first dye and to the second dye by using the spectral references of the first dye and the second dye; second circuitry configured to: calculate an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and third circuitry configured to: adjust the spectral reference of the first dye and / or the second dye based on the index.

20. An information processing method, comprising: acquiring spectral references of a first dye and a second dye; obtaining information regarding light detected from at least one multi-stained particle in a sample; calculating an intensity of light corresponding to the first dye and the second dye by using the spectral references of the first dye and the second dye; calculating an index using a distribution of the intensity of light corresponding to the first dye and the second dye, wherein the index indicates a degree of spillover of light corresponding to the first dye into light attributed to the second dye; and adjusting the spectral reference of the first dye and / or second dye based on the index.

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