Information processing method, information processing device, and information processing system

Abstract

[Problem] To identify autofluorescence of various biological particles. [Solution] Provided is an information processing method involving: acquiring optical data by irradiating a plurality of biological particles with light; identifying autofluorescence data of the plurality of biological particles by clustering the optical data; and outputting the autofluorescence data.

Description

Information processing method, information processing device, and information processing system

[0001] The present disclosure relates to an information processing method, an information processing device, and an information processing system.

[0002] In recent years, in the fields of medicine and biochemistry, it has become common to use a flow cytometer to rapidly analyze the characteristics of a large number of bioparticles labeled with at least one fluorescent dye. A flow cytometer can rapidly measure the scattered light and fluorescence of each bioparticle by irradiating light onto the bioparticles flowing in a substantially single file.

[0003] However, the fluorescence emitted from bioparticles includes not only the fluorescence from the labeled fluorescent dye but also the autofluorescence emitted from the bioparticles themselves. Therefore, in order to accurately detect the fluorescence from the fluorescent dye labeled on the bioparticles, it is important to identify the autofluorescence of the bioparticles.

[0004] For example, Patent Document 1 listed below discloses a method for separating an autofluorescence image containing an autofluorescence component from an image of a specimen labeled with at least one or more fluorescent dyes, and further correcting the separated autofluorescence image using a reference spectrum of the autofluorescent substance.

[0005] International Publication No. 2022 / 004500

[0006] However, the technology disclosed in Patent Document 1 separates the autofluorescence image using the reference spectrum of the autofluorescent substance contained in the bioparticle, making it difficult to identify the autofluorescence of unknown bioparticles whose autofluorescent substance is unknown.

[0007] Therefore, the present disclosure proposes a new and improved information processing method, information processing device, and information processing system that are capable of identifying the autofluorescence of a wider variety of bioparticles.

[0008] According to the present disclosure, there is provided an information processing method including: acquiring light data by irradiating light onto a plurality of biological particles; identifying autofluorescence data of the plurality of biological particles by clustering the light data; and outputting the autofluorescence data.

[0009] Furthermore, according to the present disclosure, there is provided an information processing device including: an acquisition unit that acquires light data by irradiating light onto a plurality of biological particles; an autofluorescence identification unit that identifies autofluorescence data of the plurality of biological particles by clustering the light data; and an output unit that outputs the autofluorescence data.

[0010] Furthermore, according to the present disclosure, there is provided an information processing system including an information processing device having: a detection device that acquires light data by irradiating light onto a plurality of biological particles; an autofluorescence identification unit that identifies autofluorescence data of the plurality of biological particles by clustering the light data; and an output unit that outputs the autofluorescence data.

[0011] 6 is a diagram showing an outline of the overall configuration of a biological sample analyzer. It is a schematic diagram showing the configuration of an analysis system including the biological sample analyzer. It is a flowchart showing the flow of an analytical experiment using the biological sample analyzer. It is a block diagram showing the functional configuration of an information processing unit. It is a graph showing an example of optical data detected by the detection unit of a spectral flow cytometer. It is a graph showing an example of optical data of each cluster obtained by clustering the optical data shown in FIG. 5 with the number of clusters k=2. It is a graph showing an example of optical data of each cluster obtained by clustering the optical data shown in FIG. 5 with the number of clusters k=2. It is a flowchart showing the flow of operations related to the information processing unit. It is a flowchart showing the flow of a first method for determining the number of clusters. It is a scatter plot of optical data of bioparticles whose fluorescence has been corrected with autofluorescence data identified based on optical data of clusters clustered with the number of clusters 1. It is a scatter plot of optical data of bioparticles whose fluorescence has been corrected with autofluorescence data identified based on optical data of clusters clustered with the number of clusters 2. It is a scatter plot of optical data of bioparticles whose fluorescence has been corrected with autofluorescence data identified based on optical data of clusters clustered with the number of clusters 3. It is a flowchart showing the flow of a second method for determining the number of clusters. 1 is a graph showing the spectrum of each cluster identified based on the optical data of clusters clustered with the number of clusters being 1. FIG. 2 is a graph showing the spectrum of each cluster identified based on the optical data of clusters clustered with the number of clusters being 2. FIG. 3 is a graph showing the spectrum of each cluster identified based on the optical data of clusters clustered with the number of clusters being 3. FIG. 4 is a block diagram showing the functional configuration of an information processing unit according to a first modified example. FIG. 5 is a graph showing an example of the spectrum of each cluster identified by clustering optical data of bioparticles preprocessed with a linear conversion function. FIG. 6 is a graph showing an example of the spectrum of each cluster identified by clustering optical data of bioparticles preprocessed with a bi-exponential conversion function. FIG. 7 is a schematic diagram showing an example of a clustering target of an information processing unit according to a second modified example. FIG. 8 is a flowchart showing the flow of operations related to an information processing unit according to a third modified example.FIG. 2 is a block diagram illustrating an example of a hardware configuration of an information processing device that realizes an information processing unit according to the present embodiment.

[0012] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0013] The explanation will be given in the following order: 1. Biological sample analyzer 1.1. Configuration of biological sample analyzer 1.2. Configuration of analysis system 1.3. Analysis flow 2. Information processing unit 2.1. Configuration of information processing unit 2.2. Operation of information processing unit 2.3. Method for determining the number of clusters 2.4. Modified example 3. Hardware configuration

[0014] <1. Biological Sample Analyzer> (1.1. Configuration of Biological Sample Analyzer) An example configuration of a biological sample analyzer according to the present disclosure is shown in FIG. 1. The biological sample analyzer 100 shown in FIG. 1 includes a light irradiation unit 101 that irradiates light onto a biological sample S flowing through a flow path C, a detection unit 102 that detects light generated by irradiating the biological sample S with light, and an information processing unit 103 that processes information related to the light detected by the detection unit 102. Examples of the biological sample analyzer 100 include a flow cytometer and an imaging cytometer. The biological sample analyzer 100 may also include a fractionation unit 104 that separates specific biological particles P from within the biological sample S. An example of a biological sample analyzer 100 that includes a fractionation unit 104 is a cell sorter.

[0015] (Biological Sample S) The biological sample S may be a liquid sample containing biological particles P. The biological particles P may be, for example, cells or non-cellular biological particles. The cells may be living cells, and more specific examples include blood cells such as red blood cells and white blood cells, and reproductive cells such as sperm and fertilized eggs. The cells may also be directly collected from a specimen such as whole blood, or may be cultured cells obtained after culturing. Examples of non-cellular biological particles include extracellular vesicles (particularly exosomes and microvesicles). The biological particles P may be labeled with one or more labeling substances (e.g., dyes (particularly fluorescent dyes) and antibodies labeled with fluorescent dyes). The biological sample analyzer 100 may also analyze particles other than biological particles P, such as beads for calibration purposes.

[0016] (Flow Channel C) The flow channel C is configured to allow the biological sample S to flow. In particular, the flow channel C can be configured to form a flow in which the biological particles P contained in the biological sample S are aligned in a substantially straight line. The flow channel structure including the flow channel C may be designed to form a laminar flow. In particular, the flow channel structure is designed to form a laminar flow in which the flow of the biological sample S (sample flow) is surrounded by the flow of sheath liquid. The design of the flow channel structure may be appropriately selected by those skilled in the art, or a known design may be adopted. The flow channel C may be formed in a flow channel structure such as a microchip (a chip having flow channels on the order of micrometers) or a flow cell. The width of the flow channel C may be 1 mm or less, particularly 10 μm or more and 1 mm or less. The flow channel C and the flow channel structure including the flow channel C may be formed from a material such as plastic or glass.

[0017] The biological sample analyzer 100 is configured so that light from a light irradiation unit 101 is irradiated onto the biological sample S flowing within a flow path C, particularly onto biological particles P within the biological sample S. The biological sample analyzer 100 may be configured so that the interrogation point of light on the biological sample S is within the flow path structure, or so that the interrogation point of light is outside the flow path structure. An example of the former is a cuvette flow cell system in which light is irradiated onto a flow path C within a microchip or flow cell. An example of the latter is a jet-in-air system in which light is irradiated onto biological particles P after they have exited the flow path structure (particularly its nozzle portion).

[0018] (Light Irradiation Unit 101) The light irradiation unit 101 includes a light source unit that emits light and a light-guiding optical system that guides the light to an irradiation point. The light source unit includes one or more light sources. The type of light source is, for example, a laser light source or an LED (Light Emitting Diode) light source. The wavelength of the light emitted from each light source may be any of ultraviolet light, visible light, and infrared light. The light-guiding optical system includes optical components such as a beam splitter group, a mirror group, or an optical fiber. The light-guiding optical system may also include a lens group for focusing light, such as an objective lens. There may be one or more irradiation points where the biological sample S and the light intersect. The light irradiation unit 101 may be configured to focus light irradiated from one or more light sources to one irradiation point.

[0019] (Detection Unit 102) The detection unit 102 includes at least one photodetector that detects light generated by irradiating the bioparticles P with light. The light detected by the detection unit 102 is, for example, fluorescence or scattered light (e.g., one or more of forward scattered light, backscattered light, and side scattered light). Each photodetector includes one or more light-receiving elements, e.g., a photodetector array. Each photodetector may include one or more photomultiplier tubes (PMTs) as the light-receiving elements, or may include photodiodes such as APDs (Avalanche PhotoDiodes) or MPPCs (Multi-Pixel Photon Counters). For example, the photodetector may be a PMT array in which multiple PMTs are arranged in a one-dimensional direction. The detection unit 102 may also include an imaging element such as a CCD image sensor or a CMOS image sensor. The detection unit 102 can acquire images of the bioparticles P (e.g., bright-field images, dark-field images, and fluorescence images) using these imaging elements.

[0020] The detection unit 102 includes a detection optical system that allows light of a predetermined detection wavelength to reach a corresponding photodetector. The detection optical system includes a spectroscopic unit such as a prism or a diffraction grating, or a wavelength separation unit such as a dichroic mirror or an optical filter. The detection optical system is configured, for example, to disperse light generated by irradiating light onto bioparticles P and detect the dispersed light using a plurality of photodetectors, the number of which is greater than the number of fluorescent dyes with which the bioparticles P are labeled. A flow cytometer including such a detection optical system is called a spectral flow cytometer. Furthermore, the detection optical system may be configured, for example, to separate light corresponding to the fluorescent wavelength range of a specific fluorescent dye from the light generated by irradiating light onto the bioparticles P and detect the separated light using a corresponding photodetector.

[0021] The detection unit 102 may also include a signal processing unit that converts the 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 this conversion. The digital signal obtained by the conversion by the signal processing unit may be transmitted to the information processing unit 103. The digital signal may be treated by the information processing unit 103 as data related to light (hereinafter also referred to as "light data"). The light data may be, for example, light data including fluorescence data, or more specifically, light intensity data. The light intensity may be light intensity data of light including fluorescence (which may include feature quantities such as area, height, and width).

[0022] (Information Processing Unit 103) The information processing unit 103 includes, for example, a processing unit that processes various data (e.g., optical data) and a storage unit that stores various data. When the processing unit acquires optical data corresponding to a fluorescent dye from the detection unit 102, the processing unit may perform fluorescence leakage correction (compensation processing) on ​​the light intensity data. Furthermore, in the case of a spectral flow cytometer, the processing unit may perform fluorescence separation processing on the optical data to acquire light intensity data corresponding to the fluorescent dye. The fluorescence separation processing may be performed, for example, according to the unmixing method described in Japanese Patent Application Laid-Open No. 2011-232259. When the detection unit 102 includes an image sensor, the processing unit may acquire morphological information of the bioparticle P based on an image acquired by the image sensor. The storage unit may be configured to store the acquired optical data and may further be configured to store spectral reference data used in the unmixing processing.

[0023] When the biological sample analyzer 100 includes a fractionating unit 104 described below, the information processing unit 103 can determine whether or not to fractionate the biological particles P based on the optical data and / or morphological information of the biological particles P. The information processing unit 103 controls the fractionating unit 104 based on the result of this determination, so that the fractionating unit 104 can fractionate the biological particles P.

[0024] The information processing unit 103 may be configured to output various types of data (e.g., optical data or images). For example, the information processing unit 103 may output various types of data (e.g., two-dimensional plots or spectral plots) generated based on the optical data. The information processing unit 103 may also be configured to accept input of various types of data. For example, the information processing unit 103 may accept gating processing on a plot by a user. The information processing unit 103 may include an output unit (e.g., a display) or an input unit (e.g., a keyboard) for executing output or input.

[0025] The information processing unit 103 may be configured as a general-purpose computer, and may be configured as an information processing device including, for example, a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory). The information processing unit 103 may be provided inside a housing that includes the light irradiation unit 101 and the detection unit 102, or may be provided outside the housing. Furthermore, various processes or functions performed by the information processing unit 103 may be realized by a server computer or a cloud connected via a network.

[0026] (Sorting unit 104) The sorting unit 104 sorts the bioparticles P based on the determination result by the information processing unit 103. For example, sorting of the bioparticles P may be performed by a sorting method in which droplets containing the bioparticles P are generated by vibration and the direction of travel of the charged droplets to be sorted is controlled by electrodes. Sorting of the bioparticles P may also be performed by a sorting method in which the direction of travel of the bioparticles P is controlled within the flow channel structure. In such a case, the flow channel structure is provided with, for example, a control mechanism using pressure (spray or suction) or electric charge. An example of a flow channel structure is a chip (for example, the chip described in JP 2020-76736 A) that has a flow channel structure in which a flow channel C branches downstream into a recovery flow channel and a waste flow channel and is capable of recovering specific bioparticles P into the recovery flow channel.

[0027] (1.2. Configuration of the Analysis System) Figure 2 is a schematic diagram showing the configuration of an analysis system 10 including a biological sample analyzer 100. As shown in Figure 2, the analysis system 10 includes a plurality of biological sample analyzers 100 and a server 200 connected to the plurality of biological sample analyzers 100. Note that while Figure 2 shows four biological sample analyzers 100, the number of biological sample analyzers 100 connected to the server 200 is not particularly limited, and may be three or less, or five or more.

[0028] The biological sample analyzer 100 is an analyzer that analyzes the biological sample S containing the above-mentioned biological particles P. The biological sample analyzer 100 may be, for example, a flow cytometer, an imaging cytometer, or a cell sorter.

[0029] Server 200 is an information processing server that stores various information used in each analysis by biological sample analyzer 100. Server 200 may store, for example, reference data for biological sample analyzer 100, information about the fluorescence emitted by the fluorescent dye that labels biological particles P (such as fluorescence spectrum data), or a machine learning model used to analyze biological particles P.

[0030] The server 200 may also accumulate the analysis results of each of the biological sample analyzers 100. The analysis results of each of the biological sample analyzers 100 accumulated in the server 200 can be used, for example, as training data for a machine learning model used in analyzing biological particles P. Furthermore, by accumulating the analysis results of each of the biological sample analyzers 100 in the server 200, they can be shared with other biological sample analyzers 100.

[0031] However, if transmitting information outside the organization Cm is prohibited from the standpoint of protecting privacy or preventing information leaks, the analysis results of each biological sample analyzer 100 may be accumulated on an in-organization server 300 provided within the organization Cm. An example of an organization Cm is a research institute such as a university, a medical institution such as a hospital, or a company. The in-organization server 300 can accumulate the analysis results of each biological sample analyzer 100 within the organization Cm. The in-organization server 300 may also obtain from the server 200 various types of information used in each analysis by the biological sample analyzer 100, and store this information.

[0032] (1.3. Analysis Flow) FIG. 3 is a flow chart showing the flow of an analytical experiment using the biological sample analyzer 100.

[0033] 3 , first, a hypothesis to be verified is set as the purpose of the analytical experiment (S11). Next, a protocol for verifying the set hypothesis is created (S12). The created protocol determines, for example, details of the biological sample S sample used to verify the hypothesis (such as details of the fluorescent dye used to label the biological particles P), details of various controls, settings for the biological sample analyzer 100, and the procedure for the analytical experiment. Next, the light irradiation unit 101 and detection unit 102 of the biological sample analyzer 100 are set based on the created protocol (S13).

[0034] Thereafter, reference data to be used for calibrating the biological sample analyzer 100 is acquired using the biological sample analyzer 100 (S14). After calibration, a sample of the biological sample S is measured using the biological sample analyzer 100, and optical data of the biological particles P contained in the biological sample S is acquired (S15). Next, the measurement results of the sample of the biological sample S are analyzed by the information processing unit 103 of the biological sample analyzer 100 (S16). Specifically, in analyzing the measurement results, fluorescence spillover correction or fluorescence separation processing is performed on the optical data of the biological particles P, and light intensity data corresponding to the fluorescent dye that labels the biological particles P is acquired from the optical data of the biological particles P.

[0035] Furthermore, experimental data for verifying the set hypothesis is compiled based on the acquired light intensity data corresponding to the fluorescent dye (S17). The compiled experimental data is shared with other users of the biological sample analyzer 100, for example, by being sent to the server 200 (S18).

[0036] An analytical experiment using the biological sample analyzer 100 is performed according to the above flow. In analytical experiments using the biological sample analyzer 100, the analysis of the measurement results in step S16 is important. In particular, in multicolor analytical experiments in which biological particles P are labeled with multiple fluorescent dyes, it is important to identify the autofluorescence of the biological particles P from the optical data that is the measurement result and to exclude the identified autofluorescence of the biological particles P from the optical data. By excluding the autofluorescence from the optical data, the information processing unit 103 is able to analyze only the fluorescence emitted from the multiple fluorescent dyes that have labeled the biological particles P. The information processing related to the identification of the autofluorescence of the biological particles P performed by the information processing unit 103 will be described in more detail below.

[0037] 2. Information Processing Unit> (2.1. Configuration of Information Processing Unit) Fig. 4 is a block diagram showing the functional configuration of the information processing unit 103. As shown in Fig. 4, the information processing unit 103 comprises an acquisition unit 110, an autofluorescence identification unit 120, an output unit 140, and a storage unit 130. Note that the information processing unit 103 may be part of the biological sample analyzer 100, or may be an information processing device separate from the biological sample analyzer 100.

[0038] The acquiring unit 110 acquires optical data of the bioparticles P from the detecting unit 102. Specifically, the acquiring unit 110 may acquire optical data of light generated by irradiating light onto bioparticles P that are not labeled with a fluorescent dye from the detecting unit 102. In this way, the autofluorescence identifying unit 120 at the subsequent stage can identify the autofluorescence of the bioparticles P by analyzing the optical data of the bioparticles P that are not labeled with a fluorescent dye.

[0039] For example, the acquiring unit 110 may acquire the optical data shown in Fig. 5 from the detecting unit 102. Fig. 5 is a graph showing an example of optical data detected by the detecting unit 102 of the spectral flow cytometer.

[0040] In the detection unit 102 of the spectral flow cytometer, the fluorescence or scattered light of the bioparticles P irradiated with laser light of multiple wavelengths is dispersed and detected as a spectrum in the detection unit 102. The detection unit 102 expresses the number of occurrences of the light intensity of the fluorescence or scattered light as a heat map by integrating the spectrum of the light intensity of the fluorescence or scattered light emitted from each bioparticle P. For example, in the light data shown in Fig. 5, the vertical axis represents the light intensity of the detected fluorescence or scattered light, and the horizontal axis represents the channel number of the photodetector arrayed in wavelength order, and the number of occurrences of the light intensity of the fluorescence or scattered light from the bioparticles P is expressed as a heat map.

[0041] The autofluorescence identifying unit 120 identifies autofluorescence data of the bioparticle P from the optical data of the bioparticle P acquired by the acquiring unit 110, and stores the identified autofluorescence data in the storage unit 130. Specifically, the autofluorescence identifying unit 120 includes a clustering unit 121 and an evaluating unit 122. Note that the functions of the autofluorescence identifying unit 120 may be executed by the information processing unit 103, or may be executed by a cloud server or the like connected to the information processing unit 103 via a network.

[0042] The clustering unit 121 clusters the optical data of the bioparticles P acquired by the acquisition unit 110, thereby dividing the optical data of the bioparticles P into at least one or more independent spectral groups. Specifically, the clustering unit 121 may cluster the optical data of the bioparticles P using a known clustering method such as k-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), or hierarchical clustering.

[0043] For example, when the optical data of the bioparticle P is the optical data shown in Fig. 5, the clustering unit 121 can cluster the optical data of the bioparticle P into spectrum groups as shown in Fig. 6 and Fig. 7 with the number of clusters k = 2. Figs. 6 and 7 are graphs showing examples of optical data of each cluster obtained by clustering the optical data shown in Fig. 5 with the number of clusters k = 2.

[0044] The number of clusters to be clustered by the clustering unit 121 may be a predetermined number, or may be a number appropriately set by a user. Furthermore, the number of clusters to be clustered by the clustering unit 121 may be a number optimized by a method described later.

[0045] Although the above describes an example of clustering spectral optical data acquired by a spectral flow cytometer, the technology according to the present disclosure is not limited to the above example. The clustering unit 121 can also cluster non-spectral optical data obtained by detecting light from bioparticles P separated into specific wavelength ranges. Even in such a case, the clustering unit 121 can extract various trends contained in the optical data of bioparticles P as clusters by clustering groups of light intensity data for each specific wavelength range acquired from each bioparticle P.

[0046] The evaluation unit 122 identifies the autofluorescence data of the bioparticle P based on the optical data of each cluster clustered by the clustering unit 121 .

[0047] Clustering is a machine learning technique that groups data based on the similarity between the data. Therefore, each cluster is considered to be a data group that extracts some tendency contained in the data before clustering. That is, each cluster of the optical data of bioparticles P clustered by the clustering unit 121 is a cluster that extracts various tendencies contained in the optical data of bioparticles P. Because the autofluorescence of bioparticles P occurs based on the internal structure and substances contained in the bioparticles P, each type of bioparticle P has a specific tendency. Therefore, any of the optical data of a cluster obtained by clustering the optical data of bioparticles P that are not labeled with a fluorescent dye is considered to be optical data corresponding to the autofluorescence of the bioparticles P.

[0048] Specifically, the evaluation unit 122 may identify the autofluorescence data of the bioparticle P based on a statistical representative value of the optical data of a cluster selected from each of the clusters obtained by the clustering. The statistical representative value is, for example, the mean value, median value, or mode value of the optical data of the cluster.

[0049] For example, the evaluation unit 122 may identify the autofluorescence data of the bioparticle P based on the optical data of a cluster arbitrarily selected by the user, or may identify the autofluorescence data of the bioparticle P based on the optical data of a cluster with the largest number of cluster data. Furthermore, the evaluation unit 122 may identify the autofluorescence data of the bioparticle P based on the optical data of a cluster with the number of cluster data equal to or greater than a threshold.

[0050] The evaluation unit 122 may identify the autofluorescence data of the bioparticle P based on the optical data of the plurality of clusters. In such a case, the evaluation unit 122 identifies a plurality of spectra as the autofluorescence data of the bioparticle P. For example, if the bioparticle P includes a plurality of particle populations having different autofluorescence, or if the bioparticle P has autofluorescence that varies depending on the state or the like, the evaluation unit 122 can take the plurality of autofluorescences into account when performing fluorescence correction or the like by generating autofluorescence data from the plurality of clusters.

[0051] The storage unit 130 stores the autofluorescence data identified by the evaluation unit 122. The autofluorescence data stored in the storage unit 130 can be used, for example, to remove autofluorescence from the optical data of the bioparticles P labeled with fluorescent dyes. The autofluorescence data stored in the storage unit 130 can also be used, for example, to perform fluorescence correction to extract the fluorescence of each fluorescent dye from the optical data of the bioparticles P labeled with fluorescent dyes.

[0052] The output unit 140 outputs the autofluorescence data stored in the storage unit 130. For example, the output unit 140 may present the autofluorescence data of the bioparticle P to a user by outputting the autofluorescence data stored in the storage unit 130 to a display unit external to the information processing unit 103. Furthermore, the output unit 140 may output the autofluorescence data stored in the storage unit 130 to a calculation unit external to the information processing unit 103, so that the autofluorescence data of the bioparticle P can be used in the analysis of other bioparticles P.

[0053] (2.2. Operation of Information Processing Unit) FIG. 8 is a flowchart showing the flow of operations related to the information processing unit 103.

[0054] 8 , first, optical data of a bioparticle P is acquired by the acquisition unit 110 (S101). For example, the acquisition unit 110 may acquire optical data of a bioparticle P that is not labeled with a fluorescent dye from the detection unit 102.

[0055] Next, the clustering unit 121 determines the number of clusters to be used when clustering the optical data of the bioparticles P (S102). The number of clusters may be determined based on an input from a user, may be determined based on predetermined parameters of the optical data of the bioparticles P, or may be determined by a determination method described later.

[0056] Next, the clustering unit 121 clusters the optical data of the bioparticles P using the number of clusters determined in step S102 (S103). For example, the clustering unit 121 may cluster the optical data of the bioparticles P using a known clustering method such as k-means, DBSCAN, or hierarchical clustering.

[0057] Furthermore, the evaluation unit 122 identifies the autofluorescence data of the bioparticle P based on the optical data of each cluster clustered by the clustering unit 121 (S104). For example, the evaluation unit 122 may extract, for each channel number, the average value, median value, or mode value of the optical data of a cluster selected from each of the clusters clustered, thereby identifying the spectrum corresponding to the autofluorescence data of the bioparticle P. The autofluorescence data of the bioparticle P identified by the evaluation unit 122 is stored in, for example, the storage unit 130.

[0058] Thereafter, the information processing unit 103 performs fluorescence correction on the optical data of the bioparticles P labeled with fluorescent dyes (S105). Fluorescence correction refers to, for example, a process of separating the optical data acquired as a spectrum into fluorescence for each fluorescent dye using an unmixing method, or a process of acquiring fluorescence for each fluorescent dye by correcting for leakage of fluorescence from each fluorescent dye from optical data acquired for each wavelength band. When performing fluorescence correction on the optical data of the bioparticles P, the information processing unit 103 takes into account the autofluorescence of the bioparticles P, thereby enabling separation or correction of the fluorescence of the fluorescent dyes with higher accuracy.

[0059] For example, the information processing unit 103 may perform fluorescence correction on the optical data of the bioparticle P using the following first or second method.

[0060] The first method is a method of using one piece of autofluorescence data to perform fluorescence correction on the optical data of the bioparticle P. Specifically, the information processing unit 103 can perform fluorescence correction on the optical data of the bioparticle P by performing a matrix operation expressed by the following (2) or (3) using a fluorescence correction matrix A expressed by the following (1).

[0061]

[0062] The a included in the fluorescence correction matrix A is a parameter for fluorescence correction. Furthermore, n is the number of the fluorescent dye, and m is the channel number of the photodetector. The optical data x before fluorescence correction is subtracted by the autofluorescence data u, and then subjected to the inverse matrix of the fluorescence correction matrix A, to become the fluorescence data y after fluorescence correction. Note that A Ｔis a matrix that adjusts the mismatch of m≠n to enable matrix operations.

[0063] According to the first method, the information processing unit 103 can perform fluorescence correction on the optical data of the bioparticles P obtained by dividing the autofluorescence data.

[0064] Note that the information processing unit 103 can also perform the following calculation (4) instead of the calculation (3) above to perform fluorescence correction on the optical data of the bioparticle P. This allows the information processing unit 103 to match the scale of the signal intensity of the fluorescence data y after fluorescence correction with the scale of the signal intensity of the optical data x before fluorescence correction.

[0065]

[0066] The second method is a method of using a plurality of autofluorescence data to perform fluorescence correction on the optical data of the bioparticle P. Specifically, the information processing unit 103 can perform fluorescence correction on the optical data of the bioparticle P by performing the calculation expressed by (6) or (7) below using a fluorescence correction matrix A expressed by (5) below.

[0067]

[0068] The a in the fluorescence correction matrix A is a parameter for fluorescence correction, and u is the autofluorescence data. Furthermore, n is the number of the fluorescent dye, m is the channel number of the photodetector, and l is the identification number of the autofluorescence data. By applying the inverse matrix of the fluorescence correction matrix A to the optical data x before fluorescence correction, the fluorescence data y after fluorescence correction and the autofluorescence data af after fluorescence correction are obtained. Note that A Ｔ is a matrix that adjusts the mismatch of m≠n to enable matrix operations.

[0069] According to the second method, the information processing unit 103 can perform fluorescence correction on the optical data of the bioparticle P by taking into account the l pieces of autofluorescence data.

[0070] (2.3. Method for Determining the Number of Clusters) (First Determination Method) A first method for determining the number of clusters by the clustering unit 121 will be described with reference to Figs. 9 to 12. The first method for determining the number of clusters is a method for determining the number of clusters based on the degree of variability in optical data after fluorescence correction of optical data of bioparticles P not labeled with a fluorescent dye. Fig. 9 is a flowchart showing the flow of the first method for determining the number of clusters. Figs. 10 to 12 are scatter plots showing the degree of variability in optical data after fluorescence correction using identified autofluorescence data after clustering with different numbers of clusters.

[0071] 9 , first, the acquisition unit 110 acquires optical data of the bioparticle P (S211). For example, the acquisition unit 110 may acquire optical data of the bioparticle P that is not labeled with a fluorescent dye from the detection unit 102.

[0072] Next, the clustering unit 121 determines a search range for the number of clusters when clustering the optical data of the bioparticles P (S212). The determined search range for the number of clusters may be, for example, 1 to 10. Next, the clustering unit 121 clusters the optical data of the bioparticles P with any number of clusters within the search range determined in step S212 (S213).

[0073] Furthermore, the evaluation unit 122 identifies the autofluorescence data of the bioparticle P based on the optical data of each cluster clustered by the clustering unit 121 (S214). For example, the evaluation unit 122 may identify the spectrum corresponding to the autofluorescence data of the bioparticle P based on a statistical representative value of the optical data of a cluster selected from each of the clusters clustered.

[0074] Next, the information processing unit 103 performs fluorescence correction on the optical data of the bioparticles P that are not labeled with a fluorescent dye using the autofluorescence data of the bioparticles P identified in step S214 (S215). Next, the information processing unit 103 calculates the degree of variability in the fluorescence-corrected optical data of the bioparticles P (S216). Specifically, the information processing unit 103 calculates the degree of variability in the optical data of the bioparticles P by plotting the light intensity of the fluorescence-corrected optical data of the bioparticles P for each wavelength corresponding to the color of the fluorescent dye that labels the bioparticles P during the actual analytical experiment.

[0075] For example, Fig. 10 is a scatter plot of optical data of a bioparticle P after fluorescence correction using autofluorescence data of the bioparticle P identified based on optical data of clusters clustered with the cluster number 1. Fig. 11 is a scatter plot of optical data of a bioparticle P after fluorescence correction using autofluorescence data of the bioparticle P identified based on optical data of clusters clustered with the cluster number 2. Fig. 12 is a scatter plot of optical data of a bioparticle P after fluorescence correction using autofluorescence data of the bioparticle P identified based on optical data of clusters clustered with the cluster number 3.

[0076] For example, the information processing unit 103 first plots the fluorescence-corrected optical data of the bioparticles P on a scatter diagram using the light intensities of wavelengths corresponding to the colors of all the fluorescent dyes used in the analytical experiment (FITC_A and BV785_A in FIGS. 10 to 12). Next, the information processing unit 103 calculates the degree of variability in the optical data of the bioparticles P for each cluster number by averaging the degrees of variability in the plots of the bioparticles P calculated at wavelengths corresponding to the colors of all the fluorescent dyes used in the analytical experiment. Examples of the degree of variability in the optical data of the bioparticles P include variance and standard deviation.

[0077] The information processing unit 103 then determines whether the degree of variation has been calculated for all the numbers of clusters in the search range determined in step S212 (S217). If the degree of variation has not been calculated for all the numbers of clusters in the search range (S217 / NO), the process returns to step S213, where clustering and autofluorescence data identification are performed using a different number of clusters.

[0078] On the other hand, if the degree of variation has been calculated for all cluster numbers in the search range (S217 / YES), the information processing unit 103 adopts the number of clusters with the lowest calculated degree of variation as the number of clusters to be used for actual clustering (S218). For example, in the data shown in Figures 10 to 12, the number of clusters shown in Figure 12, which has the smallest variation in the optical data of the bioparticles P, is adopted as the number of clusters to be used for actual clustering.

[0079] According to the first method for determining the number of clusters described above, the information processing unit 103 can adopt, as the number of clusters for clustering, the number of clusters that minimizes the variation in the optical data after fluorescence correction at wavelengths corresponding to each color of the fluorescent dye used in the analytical experiment.

[0080] (Second Determination Method) A second method for determining the number of clusters by the clustering unit 121 will be described with reference to Figs. 13 to 16. The second method for determining the number of clusters is a method for determining the number of clusters based on the similarity of spectra identified from each clustered cluster. Fig. 13 is a flowchart showing the flow of the second method for determining the number of clusters. Figs. 14 to 16 are graphs comparing the similarity of spectra identified from each clustered cluster after clustering with different numbers of clusters.

[0081] 13 , first, the acquisition unit 110 acquires optical data of the bioparticle P (S221). For example, the acquisition unit 110 may acquire optical data of the bioparticle P that is not labeled with a fluorescent dye from the detection unit 102.

[0082] Next, the clustering unit 121 determines a search range for the number of clusters when clustering the optical data of the bioparticles P (S222). The determined search range for the number of clusters may be, for example, 1 to 10. Next, the clustering unit 121 clusters the optical data of the bioparticles P with any number of clusters within the search range determined in step S222 (S223).

[0083] Furthermore, the evaluation unit 122 calculates the similarity of the optical data of each cluster clustered by the clustering unit 121 (S226). Specifically, the evaluation unit 122 identifies the spectrum of each cluster based on the optical data of each cluster clustered, and calculates the similarity of the spectra of each cluster.

[0084] For example, Fig. 14 shows the spectra of each cluster identified based on the optical data of clusters clustered with the cluster number 1. Fig. 15 shows the spectra of each cluster identified based on the optical data of clusters clustered with the cluster number 2. Fig. 16 shows the spectra of each cluster identified based on the optical data of clusters clustered with the cluster number 3.

[0085] For example, the information processing unit 103 can calculate the similarity for each number of clusters by calculating the similarity between the spectra of each cluster. The information processing unit 103 may determine the average value of the similarities between the spectra of each cluster as the similarity for the number of clusters, or may determine the maximum value of the similarities between the spectra of each cluster as the similarity for the number of clusters. For example, cosine similarity or spectral similarity index can be used as the similarity between the spectra of each cluster.

[0086] In fluorescence correction, if multiple spectra with similar shapes are used, fluorescence correction may not be performed correctly. This is because, when multiple spectra with similar shapes are used, some of the simultaneous equations used to perform fluorescence correction may not be independent of each other and may have multiple solutions. Furthermore, if the spectra of each cluster are similar to each other, there is a possibility that groups that should not be divided into different clusters may be divided into multiple clusters. Therefore, the information processing unit 103 can perform more appropriate fluorescence correction by selecting a number of clusters that reduces the similarity between each cluster.

[0087] Thereafter, the information processing unit 103 determines whether or not similarities have been calculated for all the cluster numbers in the search range determined in step S222 (S227). If similarities have not been calculated for all the cluster numbers in the search range (S227 / NO), the process returns to step S223, where clustering and similarity calculation are performed for a different number of clusters.

[0088] On the other hand, if similarities have been calculated for all cluster numbers in the search range (S227 / YES), the information processing unit 103 adopts the number of clusters with the lowest calculated similarity as the number of clusters to be used for actual clustering (S228). For example, in the data shown in Figures 14 to 16, the number of clusters to be used for actual clustering is 2, as shown in Figure 15, which has the lowest spectral similarity between each cluster.

[0089] According to the second method for determining the number of clusters described above, the information processing unit 103 can adopt, as the number of clusters for clustering, the number of clusters that results from clustering and that tend to be independent of each other.

[0090] (2.4. Modifications) (First Modification) Fig. 17 is a block diagram showing the functional configuration of an information processing unit 103A according to a first modification of this embodiment. The information processing unit 103A according to the first modification differs from the information processing unit 103 shown in Fig. 4 in that it preprocesses the optical data of the bioparticles P before clustering.

[0091] 17, the autofluorescence identifying unit 120A includes a preprocessing unit 123, a clustering unit 121, and an evaluation unit 122. The functions and operations of the clustering unit 121 and the evaluation unit 122 are the same as those described with reference to FIG. 4, and therefore will not be described here.

[0092] The preprocessing unit 123 performs preprocessing by applying a conversion function to the optical data of the bioparticles P acquired by the acquisition unit 110. Specifically, the preprocessing unit 123 performs preprocessing by applying a conversion function that performs scale conversion on the optical data of the bioparticles P. The conversion function that performs scale conversion is, for example, a conversion function such as Linear, Log, HyperLog, or Bi-exponential, and can convert the scale of the light intensity data of the optical data of the bioparticles P.

[0093] The conversion function used in the preprocessing by the preprocessing unit 123 is appropriately selected depending on the characteristics of the optical data of the bioparticles P. For example, the conversion function used in the preprocessing by the preprocessing unit 123 may be a HyperLog or Bi-exponential conversion function. This allows the preprocessing unit 123 to further emphasize the difference between, for example, bioparticles P labeled with a fluorescent dye and bioparticles P not labeled with a fluorescent dye in the optical data of the bioparticles P.

[0094] 18 and 19 are graphs showing an example in which identical optical data of bioparticles P is clustered with a cluster number of 3 and the spectrum of each cluster is identified. FIG. 18 shows clustering of optical data of bioparticles P preprocessed with a linear conversion function, and FIG. 19 shows clustering of optical data of bioparticles P preprocessed with a bi-exponential conversion function. As shown in FIGS. 18 and 19 , by preprocessing with the bi-exponential conversion function, the clustering unit 121 can cluster the optical data of bioparticles P into clusters that are more independent from each other, compared to when preprocessing is performed with a linear conversion function.

[0095] Therefore, the information processing unit 103A according to the first modified example performs preprocessing of scale conversion on the optical data of the biological particles P, thereby making it possible to cluster the optical data of the biological particles P into clusters that are more independent from each other.

[0096] 20 is a schematic diagram showing an example of a clustering target of the information processing unit 103 according to a second modified example of this embodiment. The clustering target by the information processing unit 103 according to the second modified example is not limited to the optical data of bioparticles P measured by a flow cytometer, and may be other image data groups including autofluorescence.

[0097] 20 , the clustering target by the information processing unit 103 may be a biological image group G captured by an imaging flow cytometer or a fluorescence microscope. The biological image group G is, for example, an image capturing fluorescence including autofluorescence or scattered light of a biological particle P, or a group of multiple images capturing fluorescence including autofluorescence from a cell or tissue.

[0098] The information processing unit 103 similarly performs clustering using the biological image group G as an explanatory variable, thereby being able to identify the autofluorescence of biological particles P, cells, or tissues included in the biological image group G. For example, when images G1, G2, ... of each cluster are obtained by clustering the biological image group G, the information processing unit 103 is able to identify the autofluorescence images of biological particles P, cells, or tissues included in the biological image group G from the images G1, G2, ... of each cluster.

[0099] Therefore, the information processing unit 103 according to the second modified example can identify autofluorescence images by clustering even from a group of fluorescence image data that includes autofluorescence.

[0100] 21 is a flowchart showing the flow of operations related to the information processing unit 103 according to a third modification of this embodiment. The information processing unit 103 according to the third modification generates a learning model that uses machine learning to learn the relationship between the identified autofluorescence data and the bioparticles P, thereby making it possible to estimate the type of the bioparticles P from the autofluorescence data.

[0101] 21 , first, the acquisition unit 110 acquires optical data of the bioparticle P (S301). For example, the acquisition unit 110 may acquire optical data of the bioparticle P that is not labeled with a fluorescent dye from the detection unit 102.

[0102] Next, the clustering unit 121 clusters the optical data of the bioparticles P using the number of clusters determined in step S102 (S302). For example, the clustering unit 121 may cluster the optical data of the bioparticles P using a known clustering method such as k-means, DBSCAN, or hierarchical clustering. The number of clusters used in clustering may be determined based on an input from a user, may be determined based on a predetermined parameter of the optical data of the bioparticles P, or may be determined by the first or second determination method described above.

[0103] Next, the evaluation unit 122 identifies the autofluorescence data of the bioparticle P based on the optical data of each cluster clustered by the clustering unit 121 (S303). For example, the evaluation unit 122 may extract, for each channel number, the average value, median value, or mode value of the optical data of a cluster selected from each of the clusters obtained by clustering, thereby identifying the spectrum corresponding to the autofluorescence data of the bioparticle P. The autofluorescence data of the bioparticle P identified by the evaluation unit 122 is stored in, for example, the storage unit 130.

[0104] Thereafter, the information processing unit 103 trains the learning model to learn the relationship between the autofluorescence data of the identified bioparticles P and the types of bioparticles P (S304). The relationship between the autofluorescence data of the bioparticles P and the types of bioparticles P may be set, for example, by the user. By machine learning the relationship between the autofluorescence data of a large number of bioparticles P and the types of bioparticles P, a learning model that estimates the relationship between the autofluorescence data of the bioparticles P and the types of bioparticles P is generated.

[0105] According to this, the information processing unit 103 can estimate the type of bioparticle P corresponding to the input autofluorescence data by inputting the autofluorescence data identified separately in the processing of steps S301 to S303 into the generated learning model (S305).

[0106] Therefore, the information processing unit 103 according to the third modified example can estimate the type of bioparticle P from other autofluorescence data by generating a learning model that uses machine learning to understand the relationship between the identified autofluorescence and the bioparticle P.

[0107] 3. Hardware Configuration A hardware configuration for realizing the information processing unit 103 according to this embodiment will be described with reference to Fig. 22. Fig. 22 is a block diagram showing an example of the hardware configuration of an information processing device 900 that realizes the information processing unit 103 according to this embodiment.

[0108] The functions of the information processing unit 103 according to this embodiment may be realized by cooperation between software and hardware described below. The functions of the autofluorescence identifying unit 120 may be performed by, for example, the CPU 901. The functions of the acquiring unit 110 may be performed by, for example, the connection port 910 or the communication device 911. The functions of the memory unit 130 may be performed by, for example, the storage device 908. The functions of the output unit 140 may be performed by, for example, the output device 907, the drive 909, the connection port 910, or the communication device 911.

[0109] As shown in FIG. 22, the information processing device 900 includes a CPU (Central Processing Unit) 901 , a ROM (Read Only Memory) 902 , and a RAM (Random Access Memory) 903 .

[0110] The information processing device 900 may further include a host bus 904a, a bridge 904, an external bus 904b, an interface 905, an input device 906, an output device 907, a storage device 908, a drive 909, a connection port 910, or a communication device 911. The information processing device 900 may include a processing circuit such as a DSP (Digital Signal Processor) or an ASIC (Application Specific Integrated Circuit) instead of or in addition to the CPU 901.

[0111] The CPU 901 functions as an arithmetic processing device or a control device, and controls the operations within the information processing device 900 in accordance with various programs recorded in the ROM 902, the RAM 903, the storage device 908, or a removable recording medium attached to the drive 909. The ROM 902 stores programs used by the CPU 901, calculation parameters, etc. The RAM 903 temporarily stores programs used in the execution of the CPU 901, and parameters used during the execution of the programs.

[0112] The CPU 901, ROM 902, and RAM 903 are interconnected by a host bus 904a capable of high-speed data transmission. The host bus 904a is connected to an external bus 904b, such as a PCI (Peripheral Component Interconnect / Interface) bus, via a bridge 904. The external bus 904b is connected to various components via an interface 905.

[0113] The input device 906 is a device that accepts input from a user, such as a mouse, keyboard, touch panel, button, switch, or lever. The input device 906 may also be a microphone that detects the user's voice. The input device 906 may also be, for example, a remote control device that uses infrared rays or other radio waves, or may be an externally connected device that supports operation of the information processing device 900.

[0114] The input device 906 further includes an input control circuit that outputs an input signal generated based on information input by the user to the CPU 901. By operating the input device 906, the user can input various data to the information processing device 900 or instruct the information processing device 900 to perform processing operations.

[0115] The output device 907 is a device capable of visually or audibly presenting information acquired or generated by the information processing device 900 to a user. The output device 907 may be, for example, a display device such as an LCD (Liquid Crystal Display), a PDP (Plasma Display Panel), an OLED (Organic Light Emitting Diode) display, a hologram, or a projector, or may be a sound output device such as a speaker or headphones, or a printing device such as a printer. The output device 907 can output information acquired by processing by the information processing device 900 as video such as text or an image, or sound such as voice or audio.

[0116] The storage device 908 is a data storage device configured as an example of a storage unit of the information processing device 900. The storage device 908 may be configured, for example, by a magnetic storage device such as a hard disk drive (HDD), a semiconductor storage device, an optical storage device, or a magneto-optical storage device. The storage device 908 can store programs executed by the CPU 901, various data, various data acquired from the outside, and the like.

[0117] The drive 909 is a device for reading or writing data from or to a removable recording medium such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, and is built into or externally attached to the information processing device 900. For example, the drive 909 can read information recorded on an attached removable recording medium and output the information to the RAM 903. The drive 909 can also write data to an attached removable recording medium.

[0118] The connection port 910 is a port for directly connecting an external device to the information processing device 900. The connection port 910 may be, for example, a Universal Serial Bus (USB) port, an IEEE 1394 port, or a Small Computer System Interface (SCSI) port. The connection port 910 may also be an RS-232C port, an optical audio terminal, or a High-Definition Multimedia Interface (HDMI) (registered trademark) port. By connecting the connection port 910 to an external device, various types of data can be transmitted and received between the information processing device 900 and the external device.

[0119] The communication device 911 is, for example, a communication interface configured with a communication device for connecting to the communication network 920. The communication device 911 may be, for example, a communication card for a wired or wireless LAN (Local Area Network), Wi-Fi (registered trademark), Bluetooth (registered trademark), or WUSB (Wireless USB). The communication device 911 may also be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), or a modem for various types of communication.

[0120] The communication device 911 can transmit and receive signals, for example, via the Internet or other communication devices using a predetermined protocol such as TCP / IP. The communication network 920 connected to the communication device 911 is a wired or wireless network, and may be, for example, an Internet communication network, a home LAN, an infrared communication network, a radio wave communication network, or a satellite communication network.

[0121] It is also possible to create a program that causes hardware such as the CPU 901, ROM 902, and RAM 903 built into a computer to perform functions equivalent to those of the information processing device 900. It is also possible to provide a computer-readable recording medium on which the program is recorded.

[0122] Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is clear that a person skilled in the art of the present disclosure can conceive of various modified or altered examples within the scope of the technical idea described in the claims, and it is understood that these also naturally fall within the technical scope of the present disclosure.

[0123] Furthermore, the effects described herein are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that will be apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects.

[0124] The following configurations also fall within the technical scope of the present disclosure: (1) An information processing method comprising: acquiring light data by irradiating light onto a plurality of bioparticles; identifying autofluorescence data of the plurality of bioparticles by clustering the light data; and outputting the autofluorescence data. (2) The information processing method according to (1), wherein the light data is unlabeled data acquired from the plurality of bioparticles that are not labeled with a fluorescent dye. (3) The information processing method according to (2), further comprising performing fluorescence correction to acquire fluorescence data corresponding to the colors of at least one or more fluorescent dyes using the autofluorescence data from labeled data acquired from the plurality of bioparticles labeled with at least one or more fluorescent dyes. (4) The information processing method according to (3), wherein a representative value of the autofluorescence data is used for the fluorescence correction. (5) The information processing method according to (3) or (4), further comprising determining the number of clusters for the clustering, wherein the number of clusters is determined based on the degree of variability of fluorescence data corresponding to the colors of the fluorescent dyes when the fluorescence correction is performed on the unlabeled data. (6) The information processing method according to (5), wherein the degree of variation is calculated for each of the number of clusters within a predetermined range, and the number of clusters with the lowest calculated degree of variation is adopted. (7) The information processing method according to any one of (2) to (4), further comprising determining the number of clusters for the clustering, wherein the number of clusters is determined based on the similarity between the optical data of each cluster obtained by clustering the label-free data. (8) The information processing method according to (7), wherein the degree of similarity is calculated for each of the number of clusters within a predetermined range, and the number of clusters with the lowest calculated similarity is adopted. (9) The information processing method according to any one of (2) to (8), wherein one or more of the optical data of each cluster obtained by clustering the label-free data is identified as the autofluorescence data. (10) The information processing method according to any one of (2) to (9), further comprising applying a transformation function that performs scale conversion to the acquired label-free data before the clustering.(11) The information processing method according to any one of (1) to (10), further comprising generating a machine learning model using a combination of the autofluorescence data and the types of bioparticles corresponding to the autofluorescence data. (12) An information processing device comprising: an acquisition unit that acquires light data by irradiating light on a plurality of bioparticles, an autofluorescence identification unit that identifies the autofluorescence data of the plurality of bioparticles by clustering the light data, and an output unit that outputs the autofluorescence data. (13) An information processing system comprising: a detection device that acquires light data by irradiating light on a plurality of bioparticles, and an information processing device that comprises: an autofluorescence identification unit that identifies the autofluorescence data of the plurality of bioparticles by clustering the light data, and an output unit that outputs the autofluorescence data.

[0125] REFERENCE SIGNS LIST 100 Biological sample analyzer 101 Light irradiation unit 102 Detection unit 103, 103A Information processing unit 104 Sorting unit 110 Acquisition unit 120, 120A Autofluorescence identification unit 121 Clustering unit 122 Evaluation unit 123 Preprocessing unit 130 Storage unit 140 Output unit 10 Analysis system 200, 300 Server C Flow path P Biological particle S Biological sample

Claims

1. An information processing method comprising: acquiring light data by irradiating light onto a plurality of biological particles; identifying autofluorescence data of the plurality of biological particles by clustering the light data; and outputting the autofluorescence data.

2. The information processing method according to claim 1, wherein the optical data is unlabeled data acquired from the plurality of biological particles that are not labeled with a fluorescent dye.

3. The information processing method according to claim 2, further comprising performing fluorescence correction to obtain fluorescence data corresponding to the colors of at least one or more of the fluorescent dyes using the autofluorescence data from labeled data obtained from the plurality of biological particles labeled with at least one or more of the fluorescent dyes.

4. The information processing method according to claim 3, wherein a representative value of the autofluorescence data is used for the fluorescence correction.

5. The information processing method according to claim 3, further comprising determining the number of clusters for the clustering, wherein the number of clusters is determined based on the degree of variability in the fluorescence data corresponding to the color of the fluorescent dye when the fluorescence correction is performed on the unlabeled data.

6. The information processing method according to claim 5, wherein the degree of variation is calculated for each of the numbers of clusters within a predetermined range, and the number of clusters with the lowest calculated degree of variation is adopted.

7. The information processing method according to claim 2, further comprising determining the number of clusters for the clustering, wherein the number of clusters is determined based on the similarity between the optical data of each cluster obtained by clustering the label-free data.

8. The information processing method according to claim 7, wherein the similarity is calculated for each of the cluster numbers within a predetermined range, and the number of clusters with the lowest calculated similarity is adopted.

9. The information processing method according to claim 2, wherein one or more of the optical data in each cluster obtained by clustering the label-free data are identified as the autofluorescence data.

10. The information processing method according to claim 2, further comprising applying a transformation function to scale the acquired unlabeled data prior to the clustering.

11. The information processing method of claim 1, further comprising generating a machine learning model using a combination of the autofluorescence data and the type of bioparticle corresponding to the autofluorescence data.

12. An information processing device comprising: an acquisition unit that acquires light data by irradiating light onto a plurality of biological particles; an autofluorescence identification unit that identifies autofluorescence data of the plurality of biological particles by clustering the light data; and an output unit that outputs the autofluorescence data.

13. An information processing system including: a detection device that acquires light data by irradiating light onto a plurality of biological particles; and an information processing device that includes: an autofluorescence identification unit that identifies autofluorescence data of the plurality of biological particles by clustering the light data; and an output unit that outputs the autofluorescence data.

Images