Server system, information processing system, data acquisition client terminal, data analysis client terminal, and information processing method

Abstract

To provide an information processing system that can cope with an increase in an information processing amount in analysis of biological samples.SOLUTION: A server system includes: an automatic analysis processing section that generates data for output by analysis processing on optical intensity data or fluorescence label intensity data acquired by irradiating a biological sample with light; an analysis result data storage section that stores the data for output generated on the basis of the optical intensity data or the fluorescence label intensity data; and an interactive analysis processing section that analyzes the fluorescence label intensity data on the basis of an analysis command for the data for output which is output to an output device and outputs analysis result data. In addition, an information processing system including the server system is provided.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a server system, an information processing system, a data acquisition client terminal, a data analysis client terminal, and an information processing method, and more particularly to a server system that processes light intensity data acquired by irradiating a biological sample with light, an information processing system including the server system, a data acquisition client terminal and a data analysis client terminal included in the information processing system, and an information processing method related to the processing. [Background technology]

[0002] For example, particle characteristics are measured by labeling a particle population, such as cells, microorganisms, and liposomes, with a fluorescent dye, irradiating each particle in the particle population with a laser beam, and measuring the intensity and / or pattern of fluorescence emitted from the excited fluorescent dye. A typical example of a particle analyzer that performs such measurements is a flow cytometer.

[0003] As a technique for processing data acquired by a flow cytometer, particularly light intensity data, for example, Patent Document 1 below discloses a computer program product for processing scientific data according to a model independent of any specific data set. The computer program product includes: a data discovery node data structure residing on a non-transitory computer-readable storage medium; and a plurality of processor-executable instructions residing on the non-transitory computer-readable storage medium, the data discovery node data structure including a specific specification.

[0004] Furthermore, Patent Document 2 below discloses a sample analysis system using flow cytometry. The sample analysis system includes a measurement data acquisition unit that acquires measurement data of particles by measuring particles contained in a measurement sample prepared by adding a reagent to a specimen, an output mode information acquisition unit that acquires output mode information indicating the output mode of the measurement data, and an output unit that outputs the measurement data in an output mode corresponding to the output mode information. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2018-527674 [Patent Document 2] Japanese Patent Publication No. 2020-051838 Summary of the Invention [Problem to be solved by the invention]

[0006] In flow cytometry, the number of fluorescent dyes used in one measurement is on the rise, and as a result, the amount of information processed in the data analysis step is also on the rise. Therefore, in order to execute the data analysis step using these information processing devices, these information processing devices are required to have higher specifications, but providing these information processing devices with such specifications is often not realistic, and is not desirable for users.

[0007] Another example of a system for analyzing data obtained by flow cytometry is a client-server analysis system. However, client-server analysis systems can be costly to operate. This can be due to the fact that the server is always running even when there is no user using it, or that the server is always able to perform analysis processing.

[0008] Client-server analysis systems also face challenges related to processing speed. For example, as the number of users simultaneously performing analysis increases, computational resources may become depleted, resulting in processing delays. It may also be difficult to quickly perform processes that require a large amount of computational resources at once, such as dimensionality reduction or clustering.

[0009] Therefore, a primary object of the present disclosure is to provide a technique for solving at least one of these problems, but the object of the present disclosure is not limited thereto, and may be to solve, for example, any one or more of the problems described below in this specification. [Means for solving the problem]

[0010] The present disclosure provides: an automatic analysis processing unit that generates output data by performing an analysis process on light intensity data or fluorescent label intensity data obtained by irradiating light onto a biological sample; an analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescent label intensity data; an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data; The present invention provides a server system including: The automatic analysis processing unit may calculate fluorescent label intensity data from the light intensity data. The processing by the automatic analysis processing unit and the processing by the interactive analysis processing unit may be executed on different computation resources. The server system may reserve computational resources for processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit in response to receiving an analysis start command. The server system may further include a database in which analysis setting data used in processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit is stored. The server system may further include an optical data storage unit that stores the light intensity data and / or the fluorescent label intensity data. The optical data storage unit may include two or more types of storage with different access speeds, The server system may perform a process of migrating the light intensity data and / or the fluorescent label intensity data stored in storage with a higher access speed to storage with a lower access speed when a predetermined condition is met. The output data may include at least one of a two-dimensional plot image, a spectrum plot image, a one-dimensional histogram image, a two-dimensional contour plot image, a dimensionality reduced image, and a clustering result display diagram.

[0011] The present disclosure provides a data acquisition client terminal that acquires light intensity data obtained by irradiating a biological sample with light or acquires fluorescent label intensity data by performing calculation processing on the light intensity data; and an optical data storage unit that stores the light intensity data or the fluorescent label intensity data transmitted from the data acquisition client terminal; an automatic analysis processing unit that calculates fluorescent label intensity data from the light intensity data; an analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescent label intensity data; an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data. An information processing system including: In response to acquiring the light intensity data or the fluorescent label intensity data, the data acquisition client terminal can transmit the light intensity data or the fluorescent label intensity data to the server system. In response to acquiring the light intensity data or the fluorescent label intensity data, the data acquisition client terminal may perform predetermined processing on the light intensity data or the fluorescent label intensity data, and then transmit the processed light intensity data or the fluorescent label intensity data to the server system. The server system may store in advance analysis setting data to be used in processing by the automatic analysis processing unit, The automatic analysis processing unit may use the analysis setting data to calculate the fluorescent label intensity data from the light intensity data. In response to the light intensity data being stored in the light data storage unit, the automatic analysis processing unit can execute a process of calculating fluorescent label intensity data from the light intensity data. The information processing system may further include a data analysis client terminal including the output device. The data analysis client terminal can transmit the analysis command for the output data output to the output device to the server system. The data analysis client terminal can cause the output device to output a window in which the output data is displayed, and can accept input of the analysis command in the window. A plurality of data analysis client terminals may be able to share any one or more of the light intensity data, the fluorescence intensity data, and the analysis setting data in the server system. A plurality of data acquisition client terminals may be able to share the analysis setting data in the server system.

[0012] The present disclosure also provides a data acquisition unit that acquires light intensity data obtained by irradiating a biological sample with light; a transmitting unit configured to transmit the light intensity data to a server system in response to the light intensity data being acquired; In the server system, fluorescent label intensity data is calculated from the light intensity data. Provide a data acquisition client terminal.

[0013] The present disclosure also provides a communication unit that receives output data created by a server system based on fluorescent label intensity data from the server system; a processing unit that performs processing to output the output data to an output device; A data analysis client terminal including the above is provided. The data analysis client terminal may cause the output device to output a window in which the output data is displayed, and may accept input of an analysis command for the output data in the window.

[0014] The present disclosure also provides an automatic analysis processing step of performing an analysis process on light intensity data acquired by irradiating a biological sample with light or fluorescent label intensity data calculated from the light intensity data to generate output data; an analysis result data storage step of storing the output data generated based on the light intensity data or the fluorescent label intensity data; an interactive analysis processing step of analyzing the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputting analysis result data; The present invention provides an information processing method including the steps of: [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a schematic diagram of the configuration of a flow cytometer. [Figure 2] FIG. 1 is a diagram showing an example of an experimental flow when the present technology is applied to flow cytometry. [Figure 3] FIG. 10 is a diagram illustrating an example of gate settings. [Figure 4] FIG. 1 is a diagram for explaining surface markers. [Figure 5] FIG. 1 illustrates an example of the configuration of an information processing system. [Figure 6] FIG. 2 illustrates an example of a functional configuration of a server system. [Figure 7] FIG. 2 is a diagram illustrating an example of the configuration of metadata. [Figure 8] FIG. 2 is a diagram illustrating an example of the configuration of metadata. [Figure 9] FIG. 2 is a diagram illustrating an example of a hardware configuration of a server device that constitutes the server system. [Figure 10] FIG. 2 is a diagram illustrating an example of a functional configuration of a data analysis client terminal. [Figure 11] FIG. 2 is a diagram illustrating an example of a hardware configuration of a data analysis client terminal. [Figure 12] FIG. 2 illustrates an example of a functional configuration of a data acquisition client terminal. [Figure 13] FIG. 1 is a diagram showing an example of the configuration of a biological sample analyzer. [Figure 14] FIG. 10 is a diagram illustrating an example of a flow of automatic analysis processing by the information processing system. [Figure 15] FIG. 10 is a diagram illustrating an example of a flow of automatic analysis processing by the information processing system. [Figure 16] FIG. 10 is a diagram illustrating an example of a flow of an output data acquisition process performed by the information processing system. [Figure 17] FIG. 10 is a diagram illustrating an example of a flow of interactive analysis processing by the information processing system. [Figure 18] FIG. 10 is a schematic diagram for explaining the interactive analysis processing flow. [Figure 19] FIG. 10 is a schematic diagram for explaining the interactive analysis processing flow. [Figure 20] FIG. 10 is a diagram showing an example of a window that the data analysis client terminal causes the output device to output. [Figure 21] FIG. 10 is a diagram showing an example of a window that the data analysis client terminal causes the output device to output. [Figure 22] FIG. 10 is a diagram showing an example of a window that the data analysis client terminal causes the output device to output. [Figure 23] FIG. 10 is a diagram showing an example of a window that the data analysis client terminal causes the output device to output. [Figure 24] FIG. 10 is a diagram showing an example of a clustering result display diagram. [Figure 25] FIG. 10 is a diagram showing an example of a marker selection window. [Figure 26]FIG. 10 is a diagram showing an example of a marker selection window. [Figure 27] FIG. 10 is a diagram showing an example of a clustering result display diagram. [Figure 28] FIG. 10 is a diagram showing an example of a marker selection window. [Figure 29] FIG. 10 is a diagram showing an example of a clustering result display diagram. [Figure 30] FIG. 10 is a diagram showing an example of a marker selection window. [Figure 31] FIG. 10 is a diagram showing an example of a clustering result display diagram. [Figure 32] FIG. 10 is a diagram showing an example of a clustering result display image. [Figure 33] FIG. 10 is a diagram illustrating an example of a meta cluster chart. [Figure 34] FIG. 10 is a diagram illustrating an example of a meta cluster chart. [Figure 35] 10A and 10B are diagrams showing examples of clustering result displays and corresponding two-dimensional plots. [Figure 36] FIG. 10 is a diagram for explaining a meta-cluster in a clustering result display diagram. [Figure 37] 10A and 10B are diagrams showing examples of user operations on a clustering result display diagram and examples of two-dimensional plots colored by the operations. [Figure 38] FIG. 10 is a diagram for explaining a pie display in a node. [Figure 39] 10A and 10B are diagrams showing examples of two-dimensional plot data and a plot setting window for setting the display of the plot data. DETAILED DESCRIPTION OF THE INVENTION

[0016] Preferred embodiments for carrying out the present disclosure will be described below. Note that the embodiments described below are representative embodiments of the present disclosure, and the scope of the present disclosure is not limited to these embodiments. Note that the present disclosure will be described in the following order. 1. Information Processing System (1) Description of related technology (2) Overview of the information processing system (3) Components included in information processing systems (3-1) Server system (3-2) Data analysis client terminal (3-3) Data acquisition client terminal (3-4) Biological sample analyzer (4) Example of processing flow by information processing system (4-1) Automatic analysis processing (4-2) Example of automatic analysis processing (4-3) Output data acquisition process (4-4) Interactive analysis processing (4-5) Example of interactive analysis processing (5) Region setting (6) Data migration (7) Use of external storage or computing resources (8) Dividing the data to be analyzed (9) Data sharing (10) Unification of output data (11) Example 1 of output control of clustering result display diagram (12) Example 2 of output control of clustering result display diagram (13) Associating metaclusters with two-dimensional plots in the clustering result display (14) Controlling the pie display axis in star charts 2. Information Processing Method

[0017] 1. Information Processing System

[0018] (1) Description of related technology

[0019] Flow cytometers can be broadly classified into filter-type and spectral-type flow cytometers, for example, based on the optical system used for fluorescence measurement. Filter-type flow cytometers can employ a configuration such as that shown in Figure 1 (1) to extract only the desired optical information from the desired fluorochrome. Specifically, light generated by irradiating particles with light is split into multiple beams using a wavelength splitter (DM) such as a dichroic mirror, passes through different filters, and each beam is measured using multiple detectors, such as photomultiplier tubes (PMTs). In other words, filter-type flow cytometers detect multicolor fluorescence by detecting fluorescence in each wavelength band corresponding to each fluorochrome using a detector corresponding to each fluorochrome. When multiple fluorochromes with closely spaced fluorescence wavelengths are used, a fluorescence compensation process can be performed to more accurately calculate the fluorescence intensity.

[0020] Spectral flow cytometers analyze the fluorescence intensity of each particle by deconvoluting (unmixing) the fluorescence data obtained by detecting light emitted from particles irradiated with light using the spectral information of the fluorescent dye used for staining. As shown in Figure 1-2, spectral flow cytometers use a prism spectroscopic optical element P to disperse fluorescence. Furthermore, to detect the dispersed fluorescence, spectral flow cytometers are equipped with an array detector, such as an array photomultiplier tube (PMT), instead of the multiple photodetectors found in filter flow cytometers. Compared to filter flow cytometers, spectral flow cytometers are more easily affected by fluorescence leakage and are therefore more suitable for analysis using multiple fluorescent dyes.

[0021] In recent years, multicolor analysis using multiple fluorescent dyes has become common in flow cytometry to facilitate comprehensive interpretation in both basic medicine and clinical fields. The number of fluorescent dyes used in a single multicolor analysis is on the rise. When multiple fluorescent dyes are used in a single measurement, as described above, in filter-type flow cytometers, fluorescence from fluorescent dyes other than the target fluorescent dye leaks into each detector, reducing analytical accuracy. When a large number of colors are used, the problem of fluorescent leakage can be solved by using a spectral flow cytometer.

[0022] An example of an experimental flow using a flow cytometer will be described below with reference to FIG.

[0023] The flow of an experiment using a flow cytometer can be broadly divided into the following steps: experimental planning (Fig. 2, "1: Plan"), in which the cells to be tested and the method for detecting them are considered and fluorescently labeled antibody reagents are prepared; sample preparation (Fig. 2, "2: Preparation"), in which the cells are actually stained and prepared to a state suitable for measurement; FCM measurement (Fig. 2, "3: FCM"), in which the fluorescence intensity of each stained cell is measured using a flow cytometer; and data analysis (Fig. 2, "4: Data Analysis"), in which various data processing steps are performed to obtain the desired analytical results from the data recorded in the FCM measurement. These steps can be repeated as necessary.

[0024] In the experimental planning step, first, it is decided which molecules (e.g., antigens or cytokines) will be used to determine the expression of microparticles (mainly cells) to be detected using a flow cytometer; that is, the markers to be used in detecting microparticles are decided. This decision can be made based on information such as past experimental results and papers. Next, it is considered which fluorescent dyes will be used to detect those markers. Information such as the number of markers to be detected simultaneously, the specifications of the available FCM equipment, available fluorescently labeled reagents, the spectrum and brightness of the fluorescent dyes, price, and delivery time is comprehensively considered, and the combination of fluorescently labeled antibody reagents required for the actual experiment is decided. This process of deciding on reagent combinations is generally called panel design in FCM.

[0025] In the sample preparation process, the experimental subject is first processed to a state suitable for FCM measurement. For example, cell separation and purification may be performed. For example, blood-derived immune cells are extracted by hemolysis and density gradient centrifugation to remove red blood cells and extract white blood cells. The extracted target cell population is then stained using fluorescently labeled antibodies.

[0026] When optically analyzing microparticles in the FCM measurement process, first, excitation light is emitted from the light source in the light irradiation unit of the flow cytometer and irradiates the microparticles flowing through the flow channel. Next, the fluorescence emitted from the microparticles is detected by the detection unit of the flow cytometer. Specifically, a dichroic mirror or bandpass filter is used to separate only light of a specific wavelength (the target fluorescence) from the light emitted from the microparticles, and this is detected by a detector such as a PMT. At this time, the fluorescence is dispersed using a prism or diffraction grating, and light of different wavelengths is detected in each channel using a detector such as a 32-channel PMT. This makes it easy to obtain spectral information about the detected light (fluorescence). A flow cytometer may have a function for recording the fluorescence information of each microparticle obtained by FCM measurement together with scattered light information, time information, and position information other than the fluorescence information. This recording function may be mainly performed by the memory or disk of a computer. In typical cell analysis, thousands to millions of microparticles are analyzed under a single experimental condition, so a large amount of information must be recorded in an organized manner for each experimental condition.

[0027] In the data analysis process, a computer or other device is used to quantify the light intensity data for each wavelength region detected in the FCM measurement process, and the fluorescence amount (intensity) for each fluorescent dye used is determined. This analysis uses a correction method that uses a standard calculated from experimental data. The standard is calculated by statistical processing using two types of data: measurement data for microparticles stained with only one fluorescent dye, and measurement data for unstained microparticles. The calculated fluorescence amount can be recorded in the data recording unit of the computer, along with information such as the name of the fluorescent molecule, the date of measurement, and the type of microparticle. The fluorescence amount (fluorescence spectrum data) of the sample estimated in the data analysis is saved and displayed in a graph depending on the purpose, and the fluorescence amount distribution of the microparticles can be analyzed.

[0028] For example, gates are often used to analyze the fluorescence intensity distribution, allowing the percentage of target cells in a sample to be calculated. For example, as shown in Figure 3, by generating a two-dimensional plot of forward scatter (FSC) and side scatter (SSC) and selecting a specific range from the plot, the percentage of monocytes and lymphocytes among the blood cells contained in PBMCs can be determined. Furthermore, by gating and expanding lymphocytes expressing specific surface markers, the percentage of B cells, T cells, and NK cells among lymphocytes can be calculated. Furthermore, the percentage of memory B cells among B cells, the percentage of killer T cells and helper T cells among T cells, and the percentage of naive T cells and memory T cells can also be determined. It is known that the surface markers expressed by each type of cell vary depending on the cell type, as shown in Figure 4, for example. Therefore, cells in a sample can be examined by appropriately selecting antibodies that bind to surface markers and fluorescent dyes that label each antibody, and then analyzing the sample using a flow cytometer.

[0029] The information processing system according to the present disclosure and the components included in the system can be used for the analysis in the data analysis step.

[0030] (2) Overview of the information processing system

[0031] The information processing in the data analysis step is performed, for example, by an information processing device attached to the flow cytometer or by an information processing device owned by the user of the flow cytometer. However, as mentioned above, the number of fluorescent dyes used in a single measurement in flow cytometry is on the rise, and as a result, the amount of information processed in the data analysis step is also on the rise. Therefore, in order to execute the data analysis step using these information processing devices, these devices are required to have higher specifications. This trend is particularly evident when executing so-called advanced analysis methods, such as dimensionality reduction processing and clustering processing, which require a high computational load. However, providing these information processing devices with such specifications is often impractical and undesirable for users.

[0032] Therefore, it would be desirable for users if the information processing in the data analysis step could be performed by a client-server analysis system, particularly a client-server analysis system that uses the cloud. Furthermore, as mentioned above, client-server analysis systems often require high operational costs, and the analysis systems also have problems with processing speed.

[0033] The present inventors have found that at least one of these problems can be solved by a specific server system. Specifically, the present disclosure provides a server system including an automatic analysis processing unit that generates output data by performing an analysis process on light intensity data or fluorescent label intensity data acquired by irradiating a biological sample with light, an analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescent label intensity data, and an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device, and outputs analysis result data, as well as an information processing system that includes the server system. The server system includes the automatic analysis processing unit and the interactive analysis processing unit, so that the data analysis process can be executed in the server system. Furthermore, the interactive analysis processing unit allows the user to execute the analysis process or adjust the analysis settings while checking the output data output to the output device, making it easier to obtain the desired analysis results. Furthermore, the server system can reduce operational costs by executing the automatic analysis processing unit and the interactive analysis processing unit only when necessary. Furthermore, the configuration described later in this specification can also improve the processing speed of the server system.

[0034] An example configuration of an information processing system according to the present disclosure will be described below with reference to FIG.

[0035] The information processing system 1 shown in FIG. 5 includes a server system 10, a data analysis client terminal 20, a data acquisition client terminal 30, and a biological sample analyzer 40.

[0036] The server system 10 may be connected to the data analysis client terminal 20 and the data acquisition client terminal 30 via a network 50. The network 50 may be a communication network through which data is transmitted and received, such as the Internet, a satellite communication network, a telephone network, or a mobile communication network (e.g., a 4G or 5G network), or a combination thereof.

[0037] The data analysis client terminal 20 and the data acquisition client terminal 30 may be connected, for example, by wire or wirelessly, or may be connected via a network 50 .

[0038] The biological particle analysis device 40 may be connected to the data acquisition client terminal 30, for example, by wire or wirelessly.

[0039] (3) Components included in information processing systems The elements included in the information processing system according to the present disclosure will be described below.

[0040] (3-1) Server system

[0041] The server system 10 will be described with reference to Fig. 6. Fig. 6 is a block diagram showing an example of the functional configuration of the system. The server system 10 may include an automatic analysis processing unit 11, an interactive analysis processing unit 12, an output data generation unit 13, a connection unit 14, an optical data storage unit 15, an analysis result data storage unit 16, and a database 17.

[0042] In one embodiment of the present disclosure, the automatic analysis processing unit 11 may calculate fluorescent label intensity data from light intensity data acquired by irradiating a biological sample with light. The light intensity data may be light intensity data transmitted from the data acquisition client terminal 30 to the server system 10. The light intensity data may be stored in the light data storage unit 15, and the automatic analysis processing unit 11 may acquire the light intensity data from the light data storage unit 15. In another embodiment of the present disclosure, the automatic analysis processing unit 11 may not perform this calculation process. That is, the data acquisition client terminal 30 may perform the process of calculating the fluorescent label intensity data from the light intensity data, and then the data acquisition client terminal 30 may transmit the fluorescent label intensity data to the server system 10.

[0043] The automatic analysis processing unit 11 can execute a process of calculating fluorescent label intensity data from the light intensity data. The automatic analysis processing unit 11 can execute the calculation process using analysis setting data previously stored in the server system 10. The analysis setting data may be, for example, data transmitted in advance (particularly before the calculation process is executed) from the data analysis client terminal 20 or the data acquisition client terminal 30.

[0044] The automatic analysis processing unit 11 calculates fluorescent label intensity data by performing, for example, a fluorescence correction process or an unmixing process on the light intensity data. The unmixing process is also called a fluorescence separation process.

[0045] The automatic analysis processing unit 11 preferably performs the unmixing process using spectral reference data. The spectral reference data used in the unmixing process includes spectral data of fluorescence generated when a predetermined excitation light is irradiated on a fluorescent dye labeling a particle. The spectral reference data used in the unmixing process may include spectral data of fluorescence generated when a light having a predetermined wavelength is irradiated on the fluorescent dye labeling a particle, and spectral data of fluorescence generated when a light having a wavelength different from the predetermined wavelength is irradiated on the fluorescent dye labeling a particle.

[0046] The spectral reference data may be stored in advance in any storage unit or database within the server system 10, for example, in the database 17. In particular, it may be stored as one of the metadata described below. The automatic analysis processing unit 11 may acquire the spectral reference data from, for example, the database 17, and then perform the unmixing process using the acquired spectral reference data.

[0047] The automatic analysis processor 11 can perform the unmixing process using, for example, the least squares method (LSM), more preferably the weighted least squares method (WLSM). The unmixing process using the least squares method can be performed using, for example, the fluorescence intensity correction method described in Japanese Patent No. 5985140. The fluorescence intensity correction method can be performed using, for example, the following WLSM formula (1).

number

[0048] Fluorescent dyes may have a wide fluorescence wavelength distribution. Therefore, for example, a PMT used to detect fluorescence from one fluorescent dye may also detect fluorescence from other fluorescent dyes. In other words, the optical data acquired by each PMT may be data in which fluorescent data from multiple fluorescent dyes are superimposed. Therefore, correction is required to separate the optical data into fluorescent data from each fluorescent dye. The unmixing process is a technique for this correction, and by the unmixing process, data in which fluorescent label intensity data from multiple fluorescent dyes are superimposed is separated into fluorescent label intensity data from each fluorescent dye, thereby obtaining fluorescent label intensity data from each fluorescent dye.

[0049] The automatic analysis processing unit 11 performs an analysis process on the fluorescent label intensity data. The automatic analysis processing unit 11 generates analysis result data through the analysis process. Output data can be generated from the analysis result data. The output data is transmitted to the data analysis client terminal 20, and the data analysis client terminal 20 then causes an output device to output the output data. The output device can be, for example, a display device. The output device can be configured to allow a user to input analysis commands, which will be described later.

[0050] Preferably, the automatic analysis processing unit 11 executes a process of calculating fluorescent label intensity data from the light intensity data in response to the light intensity data being stored in the optical data storage unit 15. The automatic analysis processing unit 11 may then analyze the fluorescent label intensity data to generate analysis result data. Furthermore, the automatic analysis processing unit 11 may generate output data from the analysis result data. For example, the automatic analysis processing unit 11 may start the automatic analysis process in response to the light intensity data being stored in the optical data storage unit 15. In other words, the automatic analysis processing unit 11 may execute an event-driven analysis process triggered by the storage.

[0051] The automatic analysis processing unit 11 secures computation resources for the automatic analysis processing in response to the storage of light intensity data in the optical data storage unit 15. That is, the automatic analysis processing unit 11 treats the storage as receipt of an analysis start command, and in response to this receipt, can secure computation resources within the server system 10. The automatic analysis processing unit 11 can use the secured computation resources to execute the process of calculating fluorescent labeled intensity data, the process of analyzing the fluorescent labeled intensity data, and the process of generating output data from the analysis result data.

[0052] The interactive analysis processing unit 12 can analyze the fluorescent label intensity data based on an analysis command for the output data output to the output device, and generate analysis result data. The output data may be output data generated by the automatic analysis processing unit 11, or may be output data acquired or generated by the output data generation unit 13 described below.

[0053] The interactive analysis processing unit 12 may execute a process of calculating fluorescent label intensity data from the light intensity data. The calculated fluorescent label intensity data may then be analyzed based on the analysis command to generate analysis result data. The interactive analysis processing unit 12 may then generate output data from the generated analysis result data.

[0054] In response to receiving an analysis start command, the interactive analysis processing unit 12 may secure computation resources for processing by the interactive analysis processing unit 12. After securing the resources, the interactive analysis processing unit 12 waits until an analysis command is sent from the data analysis client terminal 20. As described above, the automatic analysis processing unit 11 may secure a computation resource for processing by the automatic analysis processing unit in response to receiving an analysis start command, and the interactive analysis processing unit 12 may also secure a computation resource for processing by the interactive analysis processing unit 12 in response to receiving an analysis start command. Therefore, in the present disclosure, the processing by the automatic analysis processing unit and the processing by the interactive analysis processing unit may be executed on different computation resources.

[0055] In response to receiving an analysis command transmitted from the data analysis client terminal 20, the interactive analysis processing unit 12 may execute a process for calculating fluorescent label intensity data, a process for analyzing the fluorescent label intensity data, and a process for generating output data from the analysis result data. These processes may be event-driven analysis processes triggered by the reception of the analysis command. In other words, the interactive analysis process according to the present technology may be an event-driven analysis process.

[0056] The interactive analysis processing unit 12 may perform the calculation process of the fluorescent label intensity data, the analysis process of the fluorescent label intensity data, and the generation process of output data from the analysis result data in the same manner as these processes performed by the automatic analysis processing unit 11.

[0057] The connection unit 14 is a functional element for executing an RPC (Remote Procedure Call) in interactive processing between the server system 10 (particularly the interactive analysis processing unit 12) and the data analysis client terminal 20. For example, the connection unit 14 causes the server system 10 to execute an analysis command input at the data analysis client terminal 20.

[0058] The output data generation unit 13 can generate output data from the analysis result data stored in the analysis result data storage unit 16. Then, the output data generation unit 13 transmits the output data to the data analysis client terminal 20. The computational resources required for processing by the output data generation unit 13 are small, which makes it possible to reduce the operating costs of the server system.

[0059] The processing by the output data generation unit 13 may be executed by a virtual server that is always running in the server system 10. By having the virtual server always running, the output data acquisition process can be executed at high speed without the waiting time that accompanies server startup. The output data generation unit 13 may also be configured as a serverless architecture.

[0060] The light data storage unit 15 stores light intensity data and / or fluorescent label intensity data. The light data storage unit 15 may include two storage units: a light intensity data storage unit that stores light intensity data, and a fluorescent label intensity data storage unit that stores fluorescent label intensity data.

[0061] The analysis result data storage unit 16 stores analysis result data generated by the automatic analysis processing unit 11 based on the fluorescent label intensity data and / or analysis result data generated by the interactive analysis processing unit 12 based on the fluorescent label intensity data. Furthermore, the analysis result data storage unit 16 stores output data generated from these pieces of analysis result data.

[0062] The database 17 may store various types of metadata. The metadata may include analysis setting data. In particular, the database 17 may store analysis setting data used in processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit. For example, the analysis setting data may include ancillary data that is referenced or used in the analysis process of the light intensity data. The ancillary data may include, for example, data about the biological sample itself and / or data about the analysis setting of the biological sample. The analysis setting data may also include, for example, data used to calculate fluorescent label intensity data from light intensity data (e.g., including spectral reference data), and / or data used in the analysis process of the fluorescent intensity label data (e.g., including analysis commands).

[0063] An example of the structure of metadata will be described with reference to FIGS.

[0064] As shown in FIG. 7, project data (project in FIG. 7) included in the metadata may specify a project unit arbitrarily set by the user, for example.

[0065] The metadata may include one or more of project data, default fluorochrome data, custom fluorochrome data, spectral reference data, autofluorescence data, and instrument setting data. For example, one project data may be associated with one or more of the following data: default fluorescent dye data (Fluorochrome (preset) in FIG. 7), arbitrarily set fluorescent dye data (Fluorochrome (custom)), spectral reference data (Spectral Reference), autofluorescence data (Autofluorescence), and instrument settings data (Instrument Settings). The predetermined fluorescent dye data includes data (such as fluorescent dye name data) relating to one or more fluorescent dyes that are commonly used in data analysis in which the project data is used. The arbitrarily set fluorescent dye data includes data (such as fluorescent dye name data) relating to a fluorescent dye arbitrarily selected by the user for each experiment or sample, for example, in data analysis in which the project data is used. The spectral reference data includes spectral reference data for each of the fluorescent dyes included in the predetermined fluorescent dye data and the arbitrarily set fluorescent dye data. The autofluorescence data includes data regarding the autofluorescence of a biological sample analyzed using the project data. The device setting data includes data related to the analytical settings of the biological sample analyzer that acquired the light intensity data of the biological sample to be analyzed using the project data. The number of these data items associated with one project data item may be one or more. For example, one project data item may be associated with one or more predetermined fluorescent dye data items.

[0066] The metadata may include experiment data. For example, one or more pieces of experiment data (Experiment in FIG. 7) may be associated with one piece of project data. Each piece of experiment data may include, for example, one or more of experiment name data, user name data of the person who created the experiment data, and date and time data of the date and time the experiment data was created.

[0067] The metadata may include plate data. For example, one or more pieces of plate data (plate in FIG. 7) may be associated with one piece of experimental data. Each piece of plate data may include, for example, data that identifies the plate (particularly a well plate or a microtiter plate) to be analyzed by the biological sample analyzer, and may include, for example, one or more of the plate name, data on the name of the user who created the plate data, data on the date and time the plate data was created, and data on the type of plate.

[0068] The metadata may include sample group data. For example, one or more sample group data (Sample Group in FIG. 7) may be associated with one plate data. Each sample group data may include, for example, data identifying a group of biological samples included in each plate data, and may include, for example, one or more of sample group name data, user name data of the person who created the sample group data, and date and time data of the date and time the sample group data was created.

[0069] The metadata may include protocol data. For example, each piece of sample group data may be associated with protocol data (Protocol in FIG. 7). For example, the same protocol data may be associated with multiple pieces of sample group data, or different protocol data may be associated with each piece of sample group data. Each protocol data includes, for example, data specifying an analysis protocol for a biological sample, and may include, for example, one or more of unmixing configuration data (Unmixing Config), fluorescent dye configuration data (Color Palette), device configuration data (Measurement Settings), and common worksheet configuration data (Shared Worksheet). The unmixing setting data may include data specifying how to perform the unmixing process (for example, the setting or calculation method of the unmixing matrix, etc.). The fluorochrome configuration data may include data regarding the assignment of fluorochromes to biomolecules, and may include, for example, the results of a panel design. The device setting data may include data relating to the settings for analysis by the biological sample analyzer. The worksheet setting data may include data related to analysis settings commonly applied to all biological samples included in one sample group data. For example, the worksheet setting data may include gate setting data commonly applied to all biological samples included in one sample group data.

[0070] One or more sample data (Sample in FIG. 7) may be associated with one sample group data. Each sample data may include, for example, one or more of individual worksheet setting data (Individual Worksheet) applied in the analysis of each sample, unmixing setting data (Sample Unmixing Config) applied in the analysis of each sample, a data path to light intensity data measured for each sample (Raw Data), and fluorescent label intensity data (Unmixed Data) calculated from the light intensity data measured for each sample.

[0071] The metadata may include comparison worksheet data. For example, one or more comparison worksheet data (Comparison Worksheet in FIG. 7) may be associated with one experimental data. The comparison worksheet data may be worksheet data used to compare or overlay the analysis results for each sample data.

[0072] Of the data shown in FIG. 7, the worksheet setting data will be described in more detail below with reference to FIG.

[0073] As shown in FIG. 8, the worksheet setting data may include one, two, three, four, five, or all six of the following: region setting data (Region in FIG. 8), gate setting data (Gate), axis parameter setting data (Axis Parameter), plot setting data (Plot), axis tick setting data (Axis Tick setting), and statistics setting data (Statistic).

[0074] The region setting data is data relating to a region set by a gate in a plot displayed on a worksheet, and includes, for example, data relating to the range of fluorescent label intensity and / or the range of wavelength defined by the gate.

[0075] The gate setting data may include, for example, data regarding the type of gate setting in a plot displayed on a worksheet and / or data regarding gate parameters. The data regarding the type of gate setting may include, for example, data regarding the shape of the gate, which may be, for example, rectangular, linear, polygonal, elliptical, or four-quadrant. The data regarding the gate parameters may include, for example, data regarding the position and / or size of the gate.

[0076] The axis parameter setting data may include, for example, data regarding the type of field of a plot displayed on a worksheet and / or data regarding the type of signal employed in the plot. The field type may be, for example, forward scatter (FSC), side scatter (SSC), fluorescence, wavelength, channel (type or number of light detection channel), event count, or light intensity. Any of these field types may be employed as an axis parameter. Furthermore, the data regarding the type of signal may be, for example, data regarding area, height, or width.

[0077] The plot configuration data may include data regarding the type of plot to be displayed on the worksheet, such as a spectral plot, a histogram plot, a dot plot, or a density plot.

[0078] The axis scale setting data may include data regarding the scale of an axis of a plot displayed on a worksheet, such as data regarding the scale scale, data regarding maximum and / or minimum values ​​of the scale, and data regarding whether the setting is associated with a bi-exponential function.

[0079] The statistical setting data may include, for example, data specifying statistical quantities to be output on a worksheet, such as the number of events, the percentage of the number of events in the gate to the number of events in the parent gate (parent%), the percentage of the number of events in the gate to the total number of events in the sample (total%), the mean, the maximum, the minimum, the standard deviation, the coefficient of variation (CV), and the median.

[0080] 9 shows an example of the hardware configuration of a server device that constitutes the server system 10. The server system 10 may include a plurality of such server devices. The plurality of server devices may be located within a single data center, or may be distributed across a plurality of data centers located in different locations or different countries.

[0081] 9 includes a CPU (Central Processing Unit) 1001, a RAM 1002, and a ROM 1003. The CPU 1001, the RAM 1002, and the ROM 1003 are interconnected via a bus 1004. An input / output interface 1005 is further connected to the bus 1004.

[0082] The input / output interface 1005 is connected to a communication device 1006, a storage device 1007, a drive 1008, an output unit 1009, and an input unit 1010.

[0083] The communication device 1006 connects the server device 1000 to the network 1011 by wire or wirelessly. The communication device 1006 enables the server device 1000 to acquire various data (e.g., image data) via the network 1011. The acquired data can be stored in, for example, the storage device 1007. The type of the communication device 1006 may be appropriately selected by one skilled in the art.

[0084] The storage device 1007 can store an operating system, a program for causing a server system to implement the information processing method according to the present disclosure, various other programs, image data, various data used in the information processing method according to the present disclosure, and various other data. The operating system may be, for example, a UNIX (registered trademark)-based OS, particularly LINUX (registered trademark), or a Windows (registered trademark)-based OS.

[0085] The drive 1008 can read data (e.g., light intensity data, fluorescent label intensity data, analysis result data, or output data) or programs recorded on a recording medium and output them to the RAM 1003. The recording medium is, for example, but is not limited to, an HDD, an SSD, a microSD memory card, an SD memory card, or a flash memory.

[0086] An output device, for example, a display device, may be connected to the output unit 1009. The input unit 1010 can receive input for operating the server system itself.

[0087] (3-2) Data analysis client terminal

[0088] The data analysis client terminal 20 will be described with reference to Fig. 10. Fig. 10 is a block diagram showing an example of the functional configuration of the terminal. The data analysis client terminal 20 includes a processing unit 21, an analysis instruction unit 22, a connection unit 23, an output data storage unit 24, and a communication unit 25.

[0089] The processing unit 21 performs processing to output the output data transmitted from the server system 10 to an output device attached to the data analysis client terminal 20. In particular, the output data is displayed in a window on a screen displayed on the output device (particularly a display device). That is, the data analysis client terminal may include an output device to which the output data is output.

[0090] The analysis instruction unit 22 transmits to the server system 10 a request to start the interactive analysis process described above. The analysis instruction unit 22 also accepts input of an analysis command used in the interactive analysis process described above and transmits the analysis command to the server system 10. The analysis command may include the worksheet setting data described above. The interactive analysis processing unit 12 executes the analysis process by referring to the worksheet setting data, thereby generating output data according to the worksheet setting data. In this manner, the data analysis client terminal according to the present disclosure may be configured to transmit the analysis command for the output data output to the output device to the server system. The data analysis client terminal according to the present disclosure may also be configured to cause the output device to output a window displaying the output data and to accept input of the analysis command in the window.

[0091] The connection unit 23 is a functional element for executing an RPC (Remote Procedure Call) in interactive processing between the server system 10 (particularly the interactive analysis processing unit 12) and the data analysis client terminal 20 (particularly the analysis instruction unit 22). For example, a combination of the connection unit 23 of the data analysis client terminal 20 and the connection unit 14 of the server system 10 may enable an analysis command input at the data analysis client terminal 20 to be transmitted to the server system 10 and executed in the server system 10.

[0092] The output data storage unit 24 stores the output data transmitted from the server system 10 .

[0093] The communication unit 25 receives output data generated by the server system 10 based on the fluorescent label intensity data from the server system.

[0094] 11 shows an example of the hardware configuration of the data analysis client terminal 20. Note that this terminal may be, for example, a general-purpose information processing device (particularly, a computer).

[0095] 11 includes a CPU (Central Processing Unit) 1101, a RAM 1102, and a ROM 1103. The CPU 1101, the RAM 1102, and the ROM 1103 are connected to one another via a bus 1104. An input / output interface 1105 is further connected to the bus 1104.

[0096] The input / output interface 1105 is connected to a communication device 1106, a storage device 1107, a drive 1108, an output unit 1109, and an input unit 1110.

[0097] The communication device 1106 connects the information processing device 1100 to a network 1111 via a wired or wireless connection. The communication device 1106 allows the information processing device 1100 to transmit or receive various data via the network 1111. For example, the communication device 1106 transmits various data to the server system 10 or receives various data from the server system 10. The type of the communication device 1106 may be appropriately selected by one skilled in the art.

[0098] The storage device 1107 can store an operating system, a program for causing an output data output unit to output output data, a program for implementing interactive analysis processing, and various other programs, as well as various data used in information processing according to the present disclosure. The operating system may be, for example, a UNIX (registered trademark)-based OS, particularly LINUX (registered trademark), or a WINDOWS (registered trademark)-based OS.

[0099] The drive 1108 can read data (e.g., output data) or programs recorded on a recording medium and output them to the RAM 1103. The recording medium is, for example, an HDD, an SSD, a microSD memory card, an SD memory card, or a flash memory, but is not limited to these.

[0100] The output unit 1109 outputs output data to an output device. The output device may be, for example, a display device. The input unit 1110 accepts input of analysis commands, for example, in interactive analysis processing. An input device such as a keyboard or a mouse may be connected to the input unit 1110, and the analysis commands can be input via these input devices.

[0101] (3-3) Data acquisition client terminal

[0102] The data acquisition client terminal 30 will be described with reference to Fig. 12. Fig. 12 is a block diagram showing an example of the functional configuration of the terminal. The data acquisition client terminal 30 includes a data acquisition unit 31, a transmission unit 32, a data processing unit 33, and a data storage unit 34.

[0103] Data acquisition section 31 acquires measurement data transmitted from biological sample analyzer 40. The measurement data includes light intensity data that is the subject of processing by automatic analysis processing section 11. The light intensity data may be light intensity data acquired by irradiating a biological sample with light.

[0104] The transmitting unit 32 transmits the measurement data (including light intensity data) acquired by the data acquiring unit 31 or the measurement data processed by the data processing unit 33 described below to the server system 10 (particularly, the light data storage unit 15 of the server system 10). The transmitting unit 32 may transmit analysis setting data in addition to the measurement data to the server system 10. Preferably, the transmitting unit 32 transmits the light intensity data, or the light intensity data and the analysis setting data, to the server system in response to acquiring the light intensity data. For example, the transmitting unit 32 may automatically start transmitting the measurement data or the processed measurement data (and the analysis setting data) in response to acquiring the measurement data. In this way, a data client terminal according to the present disclosure may transmit the light intensity data to the server system 10. Furthermore, a data acquisition client terminal according to the present disclosure may be configured to transmit the light intensity data (and the analysis setting data) to the server system 10 in response to acquiring the light intensity data. Furthermore, in one embodiment of the present disclosure, the data acquisition client terminal 30 may execute a calculation process to calculate the fluorescent label intensity data from the light intensity data. The calculation process may be executed as described in (3-1) above, and may be, for example, a fluorescence correction process or an unmixing process. In this embodiment, the transmission unit 32 may transmit the fluorescent label intensity data (or the fluorescent label intensity data after processing by the data processing unit 33 described below) to the server system 10 (particularly, the optical data storage unit 15 of the server system 10). In this manner, the data client terminal according to the present disclosure may transmit the fluorescent label intensity data to the server system 10. Furthermore, the data acquisition client terminal according to the present disclosure may be configured to transmit the fluorescent label intensity data to the server system 10 in response to acquiring the fluorescent label intensity data. In the present disclosure, the transmitting unit 32 may transmit both the measurement data and the fluorescent label intensity data to the server system 10.

[0105] The data acquisition client terminal 30 has a functional unit called a transmitter 32 in addition to the data acquisition unit 31, and is therefore able to execute the data acquisition process and the upload process separately. This makes it possible to upload measurement data (including light intensity data), fluorescent label intensity data calculated from the measurement data, or both, to the server system 10 without affecting the data acquisition process from the biological sample analyzer 40.

[0106] The data processing unit 33 can perform predetermined processing on the measurement data and / or fluorescent labeled intensity data acquired by the data acquisition unit 31. This processing can be compression processing or data processing (e.g., format conversion processing). This data processing can convert the measurement data and / or fluorescent labeled intensity data into a format suitable for information processing in the server system 10, thereby improving the efficiency of processing in the server system 10. In this way, the data acquisition client terminal according to the present disclosure can be configured to perform predetermined processing on the light intensity data or the fluorescent labeled intensity data in response to acquiring the light intensity data or the fluorescent labeled intensity data, and then transmit the processed light intensity data or the fluorescent labeled intensity data to the server system.

[0107] The data storage unit 34 can store the measurement data or the processed measurement data. The data storage unit 34 may store the fluorescent label intensity data or the processed fluorescent label intensity data.

[0108] The above description of the data analysis client terminal 20 in (3-2) applies to an example of the hardware configuration of the data acquisition client terminal 30. The data acquisition client terminal 30 may also be, for example, a general-purpose information processing device (particularly, a computer).

[0109] (3-4) Biological sample analyzer

[0110] The biological sample analyzer may be, for example, a flow cytometer as described above, but is not limited to this. An example configuration of a biological sample analyzer is shown in FIG. 13. As shown in FIG. 13, a biological sample analyzer 40 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 the irradiation, and an information processing unit 103 that processes information related to the light detected by the detection unit. Examples of biological sample analyzers 40 include flow cytometers and imaging cytometers. The biological sample analyzer 40 may also include a sorting unit 104 that sorts specific biological particles P from within the biological sample. An example of a biological sample analyzer 40 that includes a sorting unit is a cell sorter.

[0111] (biological samples) The biological sample S may be a liquid sample containing biological particles. The biological particles 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 be directly collected from a specimen such as whole blood, or may be cultured cells obtained after culturing. Examples of the non-cellular biological particles include extracellular vesicles, particularly exosomes and microvesicles. The biological particles may be labeled with one or more labeling substances (e.g., dyes (particularly fluorescent dyes) and fluorescent dye-labeled antibodies). The biological sample analyzer of the present disclosure may also analyze particles other than biological particles, such as beads for calibration purposes.

[0112] (flow path) The flow channel C can be configured to allow the biological sample to flow, particularly to form a flow in which biological particles contained in the biological sample are aligned in a substantially straight line. The flow channel structure including the flow channel C can be designed to form a laminar flow, particularly a laminar flow in which the flow of the biological sample (sample flow) is enveloped by the flow of sheath liquid. The design of the flow channel structure can be appropriately selected by those skilled in the art, and a known design may be adopted. The flow channel C can be formed in a flow channel structure (particularly a flow channel structure in which focusing is performed) 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 can 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 it can be formed from materials such as plastic or glass.

[0113] The device of the present disclosure may be configured so that the light from the light irradiation unit is irradiated onto the biological sample flowing through the flow path C, particularly onto biological particles in the biological sample. The device of the present disclosure may be configured so that the interrogation point of light on the biological sample is within the flow path structure in which the flow path C is formed, or so that the interrogation point of light is outside the flow path structure. An example of the former is a configuration in which the light is irradiated onto the flow path C within a microchip or flow cell. In the latter, the light may be irradiated onto biological particles after they have exited the flow path structure (particularly its nozzle portion), and an example of this is a jet-in-air flow cytometer.

[0114] (Light irradiation part) The light irradiation unit 101 includes a light source unit that emits light and a light-guiding optical system that guides the light to the flow path C. The light source unit includes one or more light sources. The type of light source may be, for example, a laser light source or an LED. 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. The biological sample may be irradiated with light at one or more points. The light irradiation unit 101 may be configured to focus light irradiated from one or more different light sources at one irradiation point.

[0115] (Detection unit) The detection unit 102 includes at least one photodetector that detects light generated by the light irradiation unit irradiating particles with light. The detected light is, for example, fluorescence or scattered light (e.g., one or more of forward scattered light, back scattered light, and side scattered light). Each photodetector includes one or more light-receiving elements, and has, for example, a photodetector array. Each photodetector may include, as the light-receiving element, one or more PMTs (photomultiplier tubes) and / or photodiodes such as APDs and MPPCs. The photodetector includes, for example, a PMT array in which multiple PMTs are arranged in a one-dimensional direction. The detection unit may also include an imaging element such as a CCD or CMOS. The detection unit can acquire images of the bioparticles (e.g., bright-field images, dark-field images, and fluorescence images) using the imaging element.

[0116] The detection unit 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 may be configured, for example, to disperse light from bioparticles and detect light of different wavelength ranges using multiple photodetectors, the number of which is greater than the number of fluorescent dyes. 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 from the light from the bioparticles into light corresponding to the fluorescent wavelength range of the fluorescent dye, and to detect the separated light using the corresponding photodetector.

[0117] The detection unit 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 the conversion. The digital signal obtained by the conversion by the signal processing unit may be transmitted to an information processing unit. The digital signal may be handled by the information processing unit as data related to light (hereinafter also referred to as "light data"). The light data may be light data including, for example, fluorescent light data. More specifically, the light data may be light intensity data, and the light intensity may be light intensity data of light including fluorescent light (which may include feature quantities such as area, height, and width).

[0118] (Information Processing Department) The information processing unit 103 includes, for example, a processing unit that executes processing of various data (for example, optical data) and a storage unit that stores various data.

[0119] If the biological sample analyzer includes a fractionating unit (described below), the information processing unit can determine whether to fractionate bioparticles based on the optical data and / or morphological information. The information processing unit can then control the fractionating unit based on the result of this determination, allowing the fractionating unit to fractionate the bioparticles.

[0120] The information processing unit may be configured as a general-purpose computer, for example, as an information processing device including a CPU, RAM, and ROM. The information processing unit may be included in a housing that includes the light irradiation unit and the detection unit, or may be located outside the housing. The information processing unit may be realized by, for example, the data acquisition client terminal 30.

[0121] (Preparative separation section) The sorting unit 104 can sort the bioparticles, for example, according to the determination result by the information processing unit. The sorting method may be a method of generating droplets containing the bioparticles by vibration, applying an electric charge to the droplets to be sorted, and controlling the direction of movement of the droplets using electrodes. The sorting method may also be a method of controlling the direction of movement of the bioparticles within a flow channel structure to perform sorting. The flow channel structure may be provided with a control mechanism using, for example, pressure (spray or suction) or electric charge. An example of such a flow channel structure is a chip (e.g., the chip described in 2020-76736) having a flow channel structure in which a flow channel C branches downstream into a recovery flow channel and a waste flow channel, and specific bioparticles are recovered into the recovery flow channel.

[0122] The biological sample analyzing device 40 may be, for example, a microscope device, particularly a fluorescence microscope device, for performing multicolor fluorescence imaging. In recent years, the number of fluorophores used in fluorescence imaging has also tended to increase, and the information processing system of the present disclosure may be executed on light intensity data acquired by the microscope device.

[0123] (4) Example of processing flow by information processing system

[0124] The information processing method executed by the information processing system 1 may include an automatic analysis processing step. Furthermore, in addition to the automatic analysis processing step, the information processing method may also include an interactive analysis processing step that uses output data generated by the automatic analysis processing step. In addition, the information processing method executed by the information processing system 1 may include an output data acquisition process for acquiring output data based on analysis result data present in the server system, and an interactive analysis processing process for using the output data obtained by the acquisition process. The automatic analysis process, the output data acquisition process, and the interactive analysis process will be described below.

[0125] (4-1) Automatic analysis processing

[0126] The flow of the automatic analysis process by the information processing system 1 will be described with reference to FIG.

[0127] In step S101, the data acquisition client terminal 30 acquires light intensity data of the biological sample from the biological sample analyzer 40. This light intensity data may be light intensity data acquired by irradiating the biological sample with light.

[0128] In step S101, the data acquisition client terminal 30 may acquire, in addition to light intensity data, additional data to be referenced or used in the analysis of the light intensity data. The additional data may include, for example, data about the biological sample itself and / or data about the analysis settings for the biological sample. Examples of the data about the biological sample itself include attributes of the biological sample (e.g., the type of organism from which the biological sample is derived, the type of body fluid from which the biological sample is derived, the type of organ from which the biological sample is derived, or the type of disease), the fluorescent dye used to label the biological sample, and the creator and date of creation of the biological sample. Examples of data about the analysis settings for the biological sample include, but are not limited to, the settings of the analyzer, unmixing settings, and measurement conditions. In step S101, the data acquisition client terminal 30 may execute a process of calculating fluorescent label intensity data from the light intensity data.

[0129] In step S102, the data acquisition client terminal 30 transmits the light intensity data to the server system 10. In step S102, the data acquisition client terminal 30 may transmit the auxiliary data in addition to the light intensity data. If the process of calculating fluorescent label intensity data is executed in step S101, the data acquisition client terminal 30 can transmit the fluorescent label intensity data to the server system 10 in step S102.

[0130] Preferably, in order to automatically execute the series of steps in (4-1), the data acquisition client terminal 30 may execute the transmission in response to acquiring light intensity data or fluorescent label intensity data from the biological sample analyzer 40 in step S101. More specifically, the data acquisition client terminal 30 may start transmitting the light intensity data to the server system 10 when it starts receiving the light intensity data. This transmission may be performed via the network 50. Note that the data acquisition client terminal 30 may start transmitting the light intensity data to the server system 10 when it finishes receiving the light intensity data, but as described above, using the start of light intensity data transmission as a trigger can speed up the completion of uploading.

[0131] In step S103, the server system 10 receives the light intensity data or fluorescent label intensity data transmitted from the data acquisition client terminal 30 via the network 50. The server system 10 stores the light intensity data or fluorescent label intensity data in the optical data storage unit 15.

[0132] In step S104, the server system 10 executes an automatic analysis process on the light intensity data or fluorescent label intensity data. This automatic analysis process is executed, in particular, by the automatic analysis processing unit 11. In this automatic analysis process, the server system 10 calculates fluorescent label intensity data from the light intensity data, and performs an analysis process on the fluorescent label intensity data to generate output data. Furthermore, when the server system 10 receives fluorescent label intensity data, in this automatic analysis process, the server system 10 may execute an analysis process on the received fluorescent label intensity data without performing a calculation process on the fluorescent label intensity data. The process in step S104 will be described in detail below with reference to FIG.

[0133] 15, the automatic analysis processing unit 11 starts the automatic analysis process. Preferably, the automatic analysis process is started in response to the storage of light intensity data (or fluorescent label intensity data) in the light data storage unit 15 in step S103. In particular, the automatic analysis process may be an event-driven analysis process triggered by the storage.

[0134] The processing by the automatic analysis processing unit 11 may be executed, for example, without a server, and may be configured as a so-called serverless architecture. In this specification, "processing executed serverlessly" does not mean processing that does not use a server, but may mean executing only predetermined information processing in an event-driven format, i.e., executing the information processing on a function-by-function basis, on a pre-built server architecture. The pre-built server architecture may be built in advance by a business that provides analysis services to users of data analysis client terminals 20 via server system 10, or may be built by a so-called cloud business. If the server system 10 is, for example, Amazon Web Services (trademark), the processing by the automatic analysis processing unit 11 may be performed by, for example, AWS Lambda, and in particular, by a system including a combination of AWS Lambda and Amazon EC2.

[0135] In step S152, the automatic analysis processing unit 11 secures computational resources for executing the analysis process of the light intensity data (or fluorescent label intensity data) on the server system 10. The computational resources function as a virtual server for executing the analysis process. In step S152, the virtual server is started.

[0136] In step S153, the automatic analysis processing unit 11 (particularly the virtual server) downloads the light intensity data (or fluorescent label intensity data) stored in the light data storage unit 15.

[0137] In step S154, the automatic analysis processing unit 11 (particularly the virtual server) acquires data necessary for analysis. The data necessary for analysis may include, for example, data used to calculate the fluorescent labeled intensity data and data used in the analysis process of the fluorescent intensity labeled data. These data may be stored in any memory unit or database within the server system 10, for example, in the database 17. The data used to calculate the fluorescent label intensity data may include, for example, spectral reference data, which is used for the unmixing process. The data used in the analysis process of the fluorescence intensity labeled data may further include analysis commands. The analysis commands include, for example, gate setting commands, plot setting commands, axis setting commands, axis display setting commands, statistics setting commands, and region setting commands. The analysis commands are used to obtain the analysis result data desired by the user, such as a two-dimensional plot. The analysis commands may also include a clustering command. The analysis command may be one that has been transmitted in advance from the data analysis client terminal 20 or the data acquisition client terminal 30 to the server system 10.

[0138] In step S155, the automatic analysis processing unit 11 (particularly the virtual server) may execute a process of calculating fluorescent labeled intensity data from the light intensity data. Then, the automatic analysis processing unit 11 executes a process of generating analysis result data using the calculated fluorescent labeled intensity data. Note that if the server system 10 receives the fluorescent labeled intensity data in step S103, the calculation process may be omitted. The calculation process of the fluorescent label intensity data may be performed using the data used for calculating the fluorescent label intensity data mentioned in relation to step S154. The calculation process may include, for example, using the data to perform an unmixing process on the light intensity data to obtain the fluorescent label intensity data. Furthermore, the automatic analysis processing unit 11 may execute advanced analysis processes such as clustering processing and dimensionality reduction processing. The process of generating the analysis result data may be executed using the analysis command mentioned in relation to step S154. The generation process may include, for example, using the analysis command to generate analysis result data including one or more of a plot image, a clustering result display diagram, and statistics based on the fluorescent label intensity data.

[0139] The automatic analysis processing unit 11 (particularly the virtual server) stores the analysis result data generated in step S155, for example, in the analysis result data storage unit 16. Furthermore, the automatic analysis processing unit 11 stores the fluorescent label intensity data generated in step S155, for example, in the optical data storage unit 15. These storage processes may be performed after the processing of step S155, or after the processing of step S156 or step S157, for example.

[0140] In step S156, the automatic analysis processing unit 11 (particularly the virtual server) generates output data to be output to the data analysis client terminal 20. For example, the automatic analysis processing unit 11 may identify or extract output data from the analysis result data generated in step S155. The output data may be a portion of the analysis result data. The output data may include, for example, one or more of a two-dimensional plot image, a spectral plot image, and a clustering result display diagram. The output data may include data for generating these images, but may not include the fluorescent label intensity data used in the analysis process. The output data may be configured to allow the data analysis client terminal 20 to edit these images. This allows the data analysis client terminal 20 to perform editing processing on the output data, which is easy to use in the interactive analysis process described below. The output data may also include numerical data, such as statistical analysis result data.

[0141] In this specification, "analysis result data" refers to data generated by the analysis process in the server system 10. "Output data" is a part of the analysis result data, and is particularly data used to output the analysis results in the data analysis client terminal 20.

[0142] In step S157, the automatic analysis processing unit 11 (particularly the virtual server) ends the analysis process, and the process proceeds to step S105.

[0143] In step S105, the automatic analysis processing unit 11 (particularly the virtual server) sends an automatic analysis completion notification to the data analysis client terminal 20. The automatic analysis completion notification may be sent by email or by a notification method using server-side push technology. In response to the completion of sending the automatic analysis completion notification, the automatic analysis processing unit 11 stops the virtual server. This prevents the virtual server from operating for an excessively long time, reducing the cost of using the server.

[0144] In step S106, the data analysis client terminal 20 receives the automatic analysis completion notification. The data analysis client terminal 20 outputs the automatic analysis completion notification to an output device. In addition, in response to receiving the automatic analysis completion notification, the data analysis client terminal 20 may display, for example, a screen inquiring the user about whether to send an output data request to the server system 10. For example, the screen may include a button for causing the data analysis client terminal 20 to send the output data request to the server system 10.

[0145] In step S107, the data analysis client terminal 20 transmits an output data request to the server system 10. For example, the data analysis client terminal 20 can execute the transmission in response to a button on the screen being clicked or selected by the user of the data analysis client terminal 20.

[0146] In step S108, the server system 10 receives the output data request.

[0147] In step S109, the server system 10 transmits the output data generated in step S104 to the data analysis client terminal 20.

[0148] In step S110, the data analysis client terminal 20 receives the output data transmitted from the server system 10.

[0149] In step S111, the data analysis client terminal 20 (particularly the processing unit 21) outputs the output data to the output device. An example of the output data output to the output device is shown in FIG. 23. As shown in FIG. 23, a window displaying the output data is displayed on the output device. Seven plot images are displayed in the lower left of the window. Furthermore, one clustering result display diagram is displayed to the right of the window. Furthermore, statistical data (number of events, Parent%, and Total%) corresponding to each gate is displayed in the upper left of the window. In this way, image data and / or statistical data based on the output data can be displayed in the window. The image data may include one or more plot images and / or one or more clustering result display diagrams as described above. Furthermore, the statistical data may include one or more statistics.

[0150] The automatic analysis process described above allows virtual servers to be run only when necessary, which significantly reduces running costs compared to servers that are always running. It also ensures scalability.

[0151] (4-2) Example of automatic analysis processing

[0152] An example of the automatic analysis process flow when the server system 10 is Amazon Web Services (trademark) will be described below with reference to FIGS.

[0153] In step S101, data acquisition client terminal 30 acquires light intensity data of the biological sample from biological sample analyzer 40.

[0154] In step S102, the data acquisition client terminal 30 transmits the light intensity data to the server system 10. Preferably, the data acquisition client terminal 30 may perform the transmission in response to acquiring the light intensity data from the biological sample analyzer 40 in step S101.

[0155] In step S103, the server system 10 receives the light intensity data transmitted from the data acquisition client terminal 30 via the network 50. The light intensity data is uploaded to a bucket of Amazon S3 that functions as the light data storage unit 15.

[0156] In step S104, the server system 10 performs an automatic analysis process on the light intensity data. Details of the process in step S104 will be described below with reference to FIG.

[0157] In step S151, when an object including, for example, light intensity data is uploaded to an Amazon S3 bucket functioning as the light data storage unit 15, AWS Lambda functioning as the automatic analysis processing unit 11 starts the automatic analysis processing in step S151.

[0158] In step S152, AWS Lambda secures computational resources on the server system 10 for executing the analysis process described below. In particular, an Amazon EC2 instance is launched as a virtual server for executing the analysis process in step S155 described below. Once the Amazon EC2 instance is launched, AWS Lambda may be terminated.

[0159] In step S153, the instance launched in step S152 downloads the object containing the light intensity data stored in the bucket.

[0160] In step S154, the instance launched in step S152 obtains data required for the analysis that is pre-stored in Amazon Aurora. The data required for the analysis may be transmitted simultaneously with the light intensity data in step S102 and pre-stored in Amazon Aurora.

[0161] In step S155, the instance calculates fluorescent label intensity data from the light intensity data, for example using the spectral reference data, and the fluorescent label intensity data can be stored in the bucket. The instance also generates analysis result data using the calculated fluorescent label intensity data.

[0162] The instance stores the analysis result data generated in step S155 in the bucket. The instance may also store the fluorescent label intensity data generated in step S155 in the bucket.

[0163] In step S156, the instance generates output data from the analysis result data, for example, by identifying or extracting the output data from the analysis result data.

[0164] In step S157, the instance ends the analysis process. Upon completion of the analysis process, the instance proceeds to step S105.

[0165] Note that the light intensity data acquired in step S101 may be one or more. When there are multiple light intensity data, the multiple light intensity data may be, for example, multiple light intensity data acquired by performing measurements on multiple biological samples, respectively. In this case, in step S102, the data acquisition client terminal 30 transmits the plurality of light intensity data to the server system 10. The data acquisition client terminal 30 may also transmit to the server system 10 data required for analysis. Next, in step S103, the server system 10 may record the plurality of light intensity data and / or data required for the analysis in a database such as Amazon DynamoDB. Next, in step S104, the instance may execute the automatic analysis process for each of the plurality of light intensity data. Upon completion of the automatic analysis process, the instance may update the database. Upon completion of the analysis process for all of the plurality of light intensity data, the instance may proceed to step S105.

[0166] In step S105, the instance transmits an automatic analysis completion notification to the data analysis client terminal 20. When the transmission of the automatic analysis completion notification has finished, the instance may stop.

[0167] In step S106, the data analysis client terminal 20 receives the automatic analysis completion notification. The data analysis client terminal 20 outputs the automatic analysis completion notification to an output device. In addition, in response to receiving the automatic analysis completion notification, the data analysis client terminal 20 may display, for example, a screen inquiring the user about whether to send an output data request to the server system 10. For example, the screen may include a button for causing the data analysis client terminal 20 to send the output data request to the server system 10.

[0168] In step S107, the data analysis client terminal 20 transmits an output data request to the server system 10. For example, the data analysis client terminal 20 can execute the transmission in response to a button on the screen being clicked or selected by the user of the data analysis client terminal 20.

[0169] In step S108, the server system 10 receives the output data request.

[0170] In step S109, the server system 10 transmits the output data generated in step S104 to the data analysis client terminal 20.

[0171] In step S110, the data analysis client terminal 20 receives the output data transmitted from the server system 10.

[0172] In step S111, the data analysis client terminal 20 causes the output device to output the output data.

[0173] (4-3) Output data acquisition process

[0174] The output data acquisition process performed by the information processing system 1 will be described below with reference to FIG.

[0175] In step S201, the data analysis client terminal 20 transmits an output data request to the server system 10. The request may include information used by the server system 10 to acquire or generate output data. For example, the request includes information used to identify analysis result data and / or information used to generate output data from the analysis result data. In step S201, the information transmitted may include user authentication information of the data analysis client terminal 20 in addition to the request. The transmission in step S201 may be performed via the network 50.

[0176] In step S202, the server system 10 receives the output data request transmitted from the data analysis client terminal 20 via the network 50.

[0177] In step S203, the server system 10 (particularly the output data generation unit 13) may acquire output data from the analysis result data storage unit 16, or may generate output data from the analysis result data stored in the analysis result data storage unit 16. The output data may be a part of the analysis result data. The output data may include, for example, one or more of a two-dimensional plot image, a spectral plot image, and a clustering result display diagram. The output data may include data for generating these images, but may not include the fluorescent label intensity data used in the analysis process. The output data may be configured to allow these images to be edited. This allows, for example, a data analysis client terminal to perform editing processing on the output data. The output data may also include numerical data such as statistical analysis result data.

[0178] The processing by the output data generation unit 13 may be executed by a virtual server that is constantly running in the server system 10. By having the virtual server constantly running, the output data acquisition process can be executed at high speed. The output data generation unit 13 may also be configured as a serverless architecture.

[0179] When the server system 10 is, for example, Amazon Web Services (trademark), the process of acquiring or generating output data by the output data generation unit 13 may be executed in a container generated or managed by, for example, Amazon ECS. In the container, for example, AWS Fargate may execute the process. For example, AWS Fargate may acquire output data stored in Amazon S3 or generate output data from analysis result data stored in Amazon S3.

[0180] In step S204, the server system 10 transmits the output data to the data analysis client terminal 20 via the network 50.

[0181] In step S205, the data analysis client terminal 20 receives the output data via the network 50.

[0182] In step S206, the data analysis client terminal 20 causes the output device to output the output data.

[0183] (4-4) Interactive analysis processing

[0184] The flow of the interactive analysis process by the information processing system 1 will be described below with reference to Fig. 17. The analysis process may be executed following the automatic analysis process described in (4-1) above, or following the output data acquisition process described in (4-3) above.

[0185] In step S301, the data analysis client terminal 20 transmits an interactive analysis processing start request to the server system 10 via the network 50. Prior to this transmission, the data analysis client terminal 20 may cause an output device to output data to be output. For example, step S301 may be executed after step S107 described in (4-1) above, or after step S206 described in (4-3) above.

[0186] For example, when the output data acquisition process described in (4-3) above is performed, the data analysis client terminal 20 causes the output device to output a window such as that shown in Fig. 20. Then, in step S206, the acquired output data (plot data) is displayed in the window, as shown in Fig. 21.

[0187] The request includes, for example, identification data used to identify the analysis target data to be subjected to the interactive analysis process, such as information identifying the biological sample and information identifying the analysis performed on the biological sample.

[0188] In step S302, the server system 10 receives the request via the network 50.

[0189] In step S303, in response to receiving the request, the server system 10 (particularly the dialogue analysis processing unit 12) secures a computation resource for executing the dialogue analysis process on the server system 10. The computation resource functions as a virtual server that executes the dialogue analysis process.

[0190] In step S304, the interactive analysis processing unit 12 (particularly the virtual server) acquires the analysis target data. The interactive analysis processing unit 12 may refer to the identification data to identify the analysis target data to be acquired.

[0191] The analysis target data may include analysis result data from which the output data is derived, and may also include light intensity data and / or fluorescent label intensity data from which the analysis result data is derived. After acquiring the analysis target data in step S304, the interactive analysis processing unit 12 monitors whether an analysis command (described later) has been received, that is, waits until the analysis command is received.

[0192] After execution of step S304 and before step S305, a session for executing an RPC (Remote Procedure Call) may be established between the server system 10 and the data analysis client terminal 20. For example, to establish the session, the server system 10 may send a notification to the data analysis client terminal 20, notifying the data analysis client terminal 20 that a computational resource has been secured. The session may be established in response to receipt of the notification. The notification may be, for example, a notification using a server-side push technology. Alternatively, the session may be established by obtaining a status by polling the data analysis client terminal 20 to the server system 10. By establishing the RPC, from step S305 onwards, the server system 10 can execute the analysis process in accordance with the analysis command sent from the data analysis client terminal 20. Both the server system 10 and the data analysis client terminal 20 may include a connection unit, which is a functional element for executing the RPC. The RPC may be executed by a combination of the connection unit 14 of the server system 10 and the connection unit 23 of the data analysis client terminal 20. An example of such a connection unit is MagicOnion, but is not limited to this.

[0193] In step S304, the interactive analysis processing unit 12 (particularly the virtual server) may acquire data necessary for analysis. The data necessary for analysis may include, for example, data used to calculate the fluorescence-labeled intensity data and data used in the analysis process of the fluorescence intensity-labeled data. The data used to calculate the fluorescent label intensity data may include, for example, spectral reference data, which is used for the unmixing process. The data used in the analysis process for the fluorescence intensity label data may further include the analysis command.

[0194] In step S305, the data analysis client terminal 20 accepts input of an analysis command for the output data. In step S305, the data analysis client terminal 20 displays a window on the output device for accepting input of the analysis command. The user inputs the analysis command via the window. The input analysis command may be, for example, an analysis command in which one or more of the various setting commands included in the analysis command used to acquire the output data output in step S301 have been changed. For example, the input analysis command may include at least one or more of a changed gate setting command, a changed plot setting command, a changed axis setting command, a changed axis display setting command, a changed statistics setting command, and a changed region setting command. The analysis command may also include a changed clustering command.

[0195] In step S306, the data analysis client terminal 20 transmits the analysis command input in step S305 to the server system 10.

[0196] In step S307, the server system 10 receives the analysis command transmitted from the data analysis client terminal 20. In response to receiving the analysis command, the processes of steps S308 to S310 are executed. These processes may be event-driven analysis processes triggered by the reception of the analysis command. In other words, the interactive analysis process according to the present technology may be event-driven analysis process.

[0197] The processing by the interactive analysis processing unit 12 may be executed, for example, without a server, and may be configured as a so-called serverless architecture. If the server system 10 is, for example, Amazon Web Services (trademark), the processing by the interactive analysis processing unit 12 may be executed, for example, by AWS Lambda, particularly by a system including a combination of AWS Lambda and Amazon EC2.

[0198] In step S308, the interactive analysis processing unit 12 (particularly the virtual server) executes a process of calculating fluorescent labeled intensity data from the light intensity data, and then executes a process of generating analysis result data using the calculated fluorescent labeled intensity data. In addition, if no changes occur to the fluorescent label intensity data, the interactive analysis processing unit 12 may omit the calculation process of the fluorescent label intensity data, i.e., may only perform the process of generating analysis result data using the existing fluorescent label intensity data. The interactive analysis processing unit 12 stores the analysis result data generated in step S308, for example, in the analysis result data storage unit 16. The interactive analysis processing unit 12 also stores the fluorescent label intensity data generated in step S308, for example, in an optical data storage unit. These storage processes may be performed after the processing of step S308, for example, or after the processing of step S310.

[0199] In step S309, the interactive analysis processing unit 12 generates output data to be output at the data analysis client terminal. For example, the interactive analysis processing unit 12 may identify or extract output data from the analysis result data generated in step S309. The output data may be a part of the analysis result data. The output data may include, for example, one or more of a two-dimensional plot image, a spectral plot image, and a clustering result display diagram. The output data may include data for generating these images, but may not include the fluorescent label intensity data used in the analysis process. The output data may be configured to allow these images to be edited. This allows, for example, the data analysis client terminal to perform editing processing on the output data. The output data may also include numerical data such as statistical analysis result data.

[0200] In step S309, the server system 10 transmits the generated output data to the data analysis client terminal 20. The output data may be transmitted for each data unit constituting the output data, or the entire output data may be transmitted all at once. Regarding the former, for example, a case is assumed in which the output data includes both statistical data and image data (one or more of a two-dimensional plot image, a spectral plot image, and a clustering result display diagram). For example, when the output statistical data is generated, the server system 10 may transmit the statistical data, and then when the output image data is generated, the server system 10 may transmit the image data. Alternatively, the opposite may be true, in which the image data is transmitted first, and then the statistical data is transmitted. In this way, the server system 10 may transmit the output data for each data unit.

[0201] In step S310, the data analysis client terminal 20 receives the output data.

[0202] In step S311, the data analysis client terminal 20 causes the output device to output the output data.

[0203] For example, as shown in FIG. 22, a plot image in which a part of the plot image in FIG. 21 is changed is displayed in the window.

[0204] The processes of steps S305 to S312 may be repeated for the output data output in step S312. Each time these processes are repeated, the output data to be output to the output device may be updated and redrawn. As described above, the input of analysis commands in the data analysis client terminal 20 and the interactive analysis process by the server system 10 are repeated, and in this manner, the interactive analysis process by the server system 10 and the data analysis client terminal 20 is repeated.

[0205] After the interactive analysis process is completed, the interactive analysis processing unit 12 stops the virtual server, which prevents the virtual server from operating for an excessively long time, thereby reducing the cost of using the server. The interactive analysis process may be terminated, for example, by the data analysis client terminal 20 receiving an instruction to terminate the interactive analysis process. In response to receiving the termination instruction, the data analysis client terminal 20 transmits the termination instruction to the server system 10. In response to receiving the termination instruction, the interactive analysis processing unit 12 stops the virtual server. This prevents the virtual server from operating for an excessively long time, thereby reducing the cost of using the server. Alternatively, when a predetermined time has elapsed without the server system 10 receiving an analysis command from the data analysis client terminal 20, the interactive analysis processing unit 12 may stop the virtual server.

[0206] The interactive analysis process described above allows virtual servers to be run only when necessary, which significantly reduces running costs compared to servers that are always running. It also ensures scalability.

[0207] (4-5) Example of interactive analysis processing

[0208] An example of the interactive analysis process flow of FIG. 17 in the case where the server system 10 is Amazon Web Services (trademark) will be described below with further reference to FIGS.

[0209] As shown in FIG. 18, in step S301, the data analysis client terminal 20 transmits an interactive analysis processing start request to the server system 10 via the network 50.

[0210] In step S302, the server system 10 receives the request via the network 50.

[0211] In step S303, in response to receiving the request, AWS Lambda, functioning as the interactive analysis processing unit 12, secures computational resources for executing the interactive analysis process on the server system 10. In particular, AWS Lambda causes Amazon ECS to execute a container (particularly a Docker container) in Amazon EC2. In the container, processing of a virtual server that executes the analysis process described below is executed. Alternatively, the processing of the virtual server may be executed without constructing the container.

[0212] In step S304, the instance acquires the data to be analyzed, which is stored in, for example, Amazon S3. In step S304, the instance may acquire data necessary for the analysis. For example, the instance may acquire data necessary for the analysis (e.g., data used to calculate fluorescent label intensity data) that is pre-stored in Amazon Aurora. After execution of step S304 and before step S305, a session for executing an RPC (Remote Procedure Call) may be established between the server system 10 and the data analysis client terminal 20. For example, the server system 10 may send a notification to the data analysis client terminal 20 informing that computational resources have been reserved in Amazon EC2. The establishment may be performed in response to receipt of the notification. Both the server system 10 and the data analysis client terminal 20 may include MagicOnion as a connection unit, which is a functional element for executing an RPC. After acquiring the analysis target data in step S304, the instance monitors whether an analysis command, which will be described later, has been received, that is, waits until the analysis command is received.

[0213] As shown in FIG. 19, in step S305, the data analysis client terminal 20 accepts input of an analysis command for the output data.

[0214] In step S306, the data analysis client terminal 20 transmits the analysis command input in step S305 to the server system 10.

[0215] In step S307, the server system 10 receives the analysis command transmitted from the data analysis client terminal 20. In response to receiving the analysis command, the processes of steps S308 to S309 are executed. These processes may be event-driven analysis processes triggered by the reception of the analysis command. In other words, the interactive analysis process according to the present technology may be event-driven analysis process.

[0216] In step S308, the instance executes a process of calculating fluorescent label intensity data from the light intensity data, and then executes a process of generating analysis result data using the calculated fluorescent label intensity data. If no changes occur to the fluorescent label intensity data, the instance may omit the fluorescent label intensity data, that is, may execute only the process of generating analysis result data using the existing fluorescent label intensity data. The instance stores the analysis result data generated in step S308 in, for example, a bucket S3. The interactive analysis processing unit 12 also stores the fluorescent label intensity data generated in step S308 in the bucket.

[0217] In step S309, the instance generates output data from the analysis result data, and transmits the generated output data to the data analysis client terminal 20.

[0218] In step S310, the data analysis client terminal 20 receives the output data.

[0219] In step S311, the data analysis client terminal 20 causes the output device to output the output data.

[0220] The processing of steps S305 to S311 may be repeated for the output data output in step S311. That is, the input of analysis commands in the data analysis client terminal 20 and the interactive analysis processing by the server system 10 are repeated, and in this way, the interactive analysis processing by the server system 10 and the data analysis client terminal 20 is repeated.

[0221] (5) Region setting

[0222] The server system 10 may be configured with a group of servers located in multiple geographically distributed data centers. The multiple data centers may be located in multiple countries, for example. Each data center may be called a region.

[0223] In the present disclosure, the server system 10 identifies a data center from among the plurality of data centers that will perform the automatic analysis process and / or the interactive analysis process according to the present disclosure, depending on the location of the data analysis client terminal 20 and / or the data acquisition client terminal 30, and a server in the identified data center can perform these processes. Preferably, the server system 10 can identify a data center from among the plurality of data centers that is closest to the location as the data center that will perform the processes.

[0224] In the present disclosure, the server system 10 identifies a data center from among the plurality of data centers to which data used in the processing of the present disclosure is uploaded or stored, based on pre-set information such as contract information or information regarding the location of the data analysis client terminal 20 and / or the data acquisition client terminal 30, and the data can be uploaded or stored in a server in the identified data center. Preferably, the server system 10 can identify a data center from among the plurality of data centers that is closest to the location as the data center where the uploading or storage will be performed.

[0225] Particularly preferably, the server that performs the processing and the server where the uploading or storage occurs may be located in the same data center.

[0226] By selecting a data center as described above, it is possible to speed up processing according to the present disclosure. For example, by configuring the data center where data is stored to be configurable on a per-user or per-contract basis, a data center geographically close to the user can be used, enabling faster data uploads and faster responses during interactive analysis.

[0227] (6) Data migration

[0228] In the present disclosure, the optical data storage unit may include two or more types of storage with different access speeds. Furthermore, the server system may execute a migration process to migrate the light intensity data and / or the fluorescent label intensity data stored in storage with a higher access speed (hereinafter also referred to as "hot storage") to storage with a lower access speed (hereinafter also referred to as "cold storage") when a predetermined condition is satisfied.

[0229] Generally, cloud platforms often offer several types of storage services, such as so-called hot storage, which allows immediate access to data but has a high unit price, and so-called cold storage, which has a slower response time to data access but a correspondingly lower unit price. In the present disclosure, as described above, by performing the migration process, data can be migrated from hot storage to cold storage, thereby reducing costs. The predetermined condition for performing the migration process may be set in advance by a user. For example, the predetermined condition may be a condition for performing the migration process when a predetermined number of days have passed since the last access to the data without any access to the data.

[0230] Preferably, the server system compresses the data to be migrated before executing the migration process, thereby further reducing storage costs.

[0231] To access data stored in the cold storage, the server system 10 may copy the data from the cold storage to the hot storage. In addition, to access compressed data, the server system 10 may also perform decompression processing during the data copy.

[0232] The server system 10 may include a database containing information about the storage in which the data resides (whether in which cold storage or hot storage). The server system 10 or the data analysis client terminal 20 can easily access the data in the cold storage by referring to the database.

[0233] (7) Use of external storage or computing resources

[0234] As described in (4-1) above, the automatic analysis processing unit included in the information processing system according to the present disclosure may start the automatic analysis process in response to light intensity data being stored in the light data storage unit 15. In the embodiment described in (4-1) above, the light data storage unit 15 exists within the server system 10, but in the present disclosure, the light data storage unit 15 may exist outside the server system 10. For example, an external storage outside the server system 10 may be used as the light data storage unit 15 in which the light intensity data is stored.

[0235] The external storage located outside the server system 10 may be, for example, online storage owned by a user of the information processing system according to the present disclosure. The server system 10 (particularly the automatic analysis processing unit 11) may start the automatic analysis process described in (4-1) above in response to light intensity data being stored in the external storage. For example, the external storage may notify the server system 10 that the light intensity data has been stored. Receipt of the notification may trigger the automatic analysis processing unit 11 to execute the event-driven analysis process described in (4-1) above.

[0236] In this case, steps S152 and S154 to S157 may be executed as described in (4-1) above. In step S153, the automatic analysis processing unit 11 downloads the light intensity data stored in the external storage used as the light data storage unit 15.

[0237] Furthermore, in the above (4-1), it is explained that the automatic analysis processing unit 11 secures computational resources for executing the analysis processing on the server system 10, but the computational resources may be computational resources outside the server system 10. For example, computational resources for executing the analysis processing may be secured in an information processing device that exists outside the server system 10.

[0238] In recent years, research institutions such as universities and corporate research laboratories have been holding accounts for online storage and cloud platforms, storing data in the cloud, and performing analysis using computational resources on the cloud. By using external online storage as a data storage destination or external computational resources to perform analysis processing as described above, the information processing system according to the present disclosure allows users to further reduce the operating costs of their server systems.

[0239] (8) Dividing the data to be analyzed

[0240] In step S155 described in (4-2) above and step S309 described in (4-4) above, processing is performed to calculate fluorescent label intensity data from light intensity data. This processing includes, for example, fluorescence correction processing or unmixing processing, and these processing are performed particularly when the biological sample analyzer is a biological particle analyzer such as a flow cytometer. Data acquired by the biological particle analyzer can be divided into event units (i.e., for each measurement result of each biological particle).

[0241] Therefore, an information processing system (particularly a server system) according to the present disclosure may, in a calculation process for calculating fluorescent label intensity data from light intensity data, first divide the light intensity data into event units and then perform the calculation process for each of the divided light intensity data. The calculation process for each of the divided light intensity data may be performed simultaneously in parallel, for example. For example, the server system (particularly an automatic analysis processing unit) launches multiple AWS Lambdas and assigns each divided light intensity data to each virtual server. Then, each virtual server performs the calculation process for each divided light intensity data assigned to it.

[0242] By performing the division process as described above, it is possible to speed up the process and also to improve the efficiency of the process.

[0243] (9) Data sharing

[0244] The metadata (particularly the analysis setting data), light intensity data, and fluorescent label intensity data described in (4-1) above may be stored in a storage or memory unit included in the server system 10 in the present disclosure. In the present disclosure, these pieces of data may be stored in the server system 10 in a state associated with each other. Associating these pieces of data with each other makes it easier to reproduce measurements and / or analyses of measurement results. Furthermore, the auxiliary data described in (4-1) above, the data used to calculate the fluorescent labeled intensity data, and the data used in the analysis process of the fluorescent intensity labeled data may also be stored in a mutually associated state in the server system 10. Associating these data with each other makes it easier to reproduce measurements and / or analyses of measurement results.

[0245] Furthermore, these data (especially those data associated with each other) may be available only to one data analysis client terminal and / or data acquisition client terminal, or may be available to two or more data analysis client terminals and / or data acquisition client terminals, i.e., these data may be shared by two or more data analysis client terminals and / or data analysis client terminals. For example, in the present disclosure, a plurality of data analysis client terminals can share one or more of the light intensity data, the fluorescent label intensity data, and the analysis setting data in the server system. Particularly preferably, a plurality of data analysis client terminals can share the analysis setting data in the server system. In the present disclosure, a plurality of data acquisition client terminals can share one or more of the light intensity data, the fluorescent label intensity data, and the analysis setting data in the server system. It is particularly preferable that a plurality of data acquisition client terminals can share the analysis setting data in the server system. In the present disclosure, by configuring the information processing system (or the server system, data analysis client terminal, or data acquisition client terminal included in the system) in this manner, information regarding measurements and / or analyses by a user can be reused by other users, making it easier to reproduce measurements and / or analyses of measurement results.

[0246] (10) Unification of output data

[0247] The output data generated by the automatic analysis process described in (4-1) above, the output data acquired or generated by the output data acquisition process described in (4-3) above, and the output data generated by the interactive analysis process described in (4-4) above may all be configured to be output to a window (particularly a worksheet) having the same user interface. For example, these three pieces of output data may have the same metadata used to cause an output device to output them. This makes it easier to use the output data generated by the automatic analysis process or the output data acquired or generated by the output data acquisition process in the interactive analysis process. Furthermore, the analysis command entered in step S305 of the interactive analysis process can also be used to execute the automatic analysis process on newly acquired light intensity data.

[0248] In this way, output data is displayed on a worksheet with a similar user interface, allowing users to view and execute automatic analysis processing, output data generation processing, and interactive analysis processing on a consistent user interface. In addition, various analysis setting data used in measurements are saved along with the analysis results, making it possible to easily reproduce measurements and analyses using similar settings. These mechanisms, along with the function for standardizing data between devices, allow for easy data standardization. By combining these functions, it becomes possible to easily reproduce the same experiment across multiple different devices.

[0249] (11) Example 1 of output control of clustering result display diagram

[0250] As described in (4-1) above, in the present disclosure, the data analysis client terminal 20 causes an output device to output output data including a clustering result display diagram. The clustering result display diagram may be, for example, a clustering result display diagram as shown in Fig. 23, which is an output result obtained when a clustering algorithm called FlowSOM is executed, and is also called a star chart.

[0251] When a large number of markers are used in the analysis, the number of markers included in the output data also increases. In such cases, if data related to all markers is displayed in the clustering result display diagram, the contents of the clustering result display diagram become complicated, making it difficult to grasp the expression level of each marker. For example, in the case of a star chart or population pie chart in FlowSOM, if the number of types of markers displayed in each cluster increases, it may become difficult to grasp the expression level of each cluster.

[0252] In a preferred embodiment of the present disclosure, the data analysis client terminal 20 may be configured to enable the number of markers displayed in the clustering result display diagram to be changed, and more specifically, to enable the selection of markers to be included in each cluster in the clustering result display diagram. The ability to select the markers included in each cluster allows the user to tailor the clustering result display to suit their needs. Furthermore, the ability to adjust the number of markers included in the clustering result display makes it easier to understand the expression levels of markers.

[0253] For example, in response to the selection of one or more markers from all markers included in the output data, the data analysis client terminal 20 may generate a clustering result display diagram for the selected one or more markers. The generated clustering result display diagram may not include data for one or more surface markers that were not selected. That is, in response to the selection, the data analysis client terminal 20 can form a clustering result display diagram based on the data of the one or more selected markers. The clustering result display diagram may be, but is not limited to, a star chart or a pie chart.

[0254] An example in which the clustering result display diagram is a star chart will be further described below with reference to the drawings.

[0255] Assume that in step S111 described in (4-1) above, data analysis client terminal 20 is displaying, on the screen of the output device, a star chart 500 as shown in Fig. 24 as a clustering result display diagram based on the output data. Furthermore, assume that the user wishes to reduce the number of markers displayed in the star chart.

[0256] In this case, in response to the user selecting (clicking or touching) the star chart, for example, or in response to the user selecting a predetermined button, data analysis client terminal 20 displays on the output device a marker selection window 501 as shown in Fig. 25. The window includes a color-coded display element 502 indicating the markers displayed in the star chart and the colors corresponding to each marker, and a marker list 503 displayed in the star chart. Here, the marker list is configured so that it is possible to select whether or not each marker is to be displayed on the star chart. Furthermore, although the color-coded display elements are shown as pie displays in the same figure, the color-coded display elements may be displayed in other display formats as long as they are shown so that markers and colors correspond to each other.

[0257] Next, the user selects from the marker list the markers they wish to display in the star chart. For example, the marker list 512 shown in Figure 26 shows a state in which the user has selected five markers (shaded in gray).

[0258] In response to the selection of the five markers, the data analysis client terminal 20 displays only the colors of the selected markers in the color-coded display element, as shown in color-coded display element 513 in the same figure, allowing the user to understand the display content of the star chart after marker selection.

[0259] In response to the selection of the five markers, the data analysis client terminal 20 also changes the star chart shown in Fig. 24. For example, in response to the selection of the five markers, the data analysis client terminal 20 changes star chart 500 to star chart 510 shown in Fig. 27.

[0260] As can be seen from a comparison between the enlarged view 504 on the left side of FIG. 24 and the enlarged view 514 on the left side of FIG. 27, the data analysis client terminal 20 changes the data elements (number of colors) displayed in each cluster.

[0261] As described above, the data analysis client terminal 20 may be configured to change the clustering result display diagram in response to the selection of a marker in the marker list. The clustering result display diagram may be changed so as to display only the data of the marker selected in the marker list (data based on expression levels and / or colors corresponding to each marker, etc.).

[0262] As described above, the data analysis client terminal 20 may change the clustering result display diagram to be displayed in conjunction with the selection operation of a marker in the marker list 512. Alternatively, the data analysis client terminal 20 may change the displayed star chart in response to the selection of a predetermined button (e.g., a predetermined button in the marker selection window, such as the Close button shown in the same figure).

[0263] Although the above example shows a case where the number of markers is decreased, the number of markers may be increased. Furthermore, in conjunction with the selection of a marker, the data analysis client terminal 20 may change the color-coded display elements in the marker selection window. Furthermore, in conjunction with the selection of a marker, the data analysis client terminal 20 may also change the clustering result display diagram. 28 shows a state in which 10 markers are selected in a marker list 522 in a marker selection window 521. In response to the selection, the data analysis client terminal 20 displays a color-coded display element 523 that is color-coded to 10. In response to the selection, the data analysis client terminal 20 may then display a clustering result display diagram 520 as shown in FIG. 30 also shows a state in which three markers are selected in a marker list 532 in a marker selection window 531. In response to the selection, the data analysis client terminal 20 displays a color-coded display element 533 that is color-coded into three different colors. In response to the selection, the data analysis client terminal 20 may display a clustering result display diagram 530 as shown in FIG. The display order of markers in the star chart may also be changeable as desired. That is, data analysis client terminal 20 may be configured to be able to change the display order of markers in the star chart. The change in the display order may be performed, for example, in response to a user operation on the marker list or color-coded display elements in the marker selection window, or in response to a user operation on the star chart itself. For example, when the user changes the positional relationship between two or more selected markers in the marker list by a predetermined operation (for example, a drag operation), the data analysis client terminal 20 can change the display order. Alternatively, the data analysis client terminal 20 may change the display order in response to a user changing the relative positions of two or more pies in the color-coded display element by a predetermined operation (for example, a drag operation).The data analysis client terminal 20 may also change the display order by a similar operation on nodes in the star chart.

[0264] Furthermore, in the above description, the data analysis client terminal 20 changes the clustering result display diagram, but the clustering result display diagram may be changed by the server system 10. For example, in response to a user selecting a marker in the marker selection window 501, the data analysis client terminal 20 transmits data related to the selected marker to the server system 10. Then, the server system 10 generates a modified clustering result display diagram based on the data related to the selected marker, and transmits the modified clustering result display diagram to the data analysis client terminal 20. Then, the data analysis client terminal 20 may display the modified clustering result display diagram on an output device.

[0265] (12) Example 2 of output control of clustering result display diagram

[0266] Regarding the clustering result display diagram described in (11) above, the number of clusters (e.g., nodes in FlowSOM) often increases. For example, the number of clusters increases as the number of cell types to be analyzed increases. FlowSOM has a function called metacluster, which groups clusters with similar marker expression trends, but star charts and population pie charts are displayed on a cluster-by-cluster basis. In such cases, if all clusters are displayed in the clustering result display diagram, it may be difficult to grasp the overall trend or overview of the marker expression levels of the measured samples from the clustering result display diagram (e.g., the trend in expression levels per metacluster).

[0267] FIG. 32 shows an example of a clustering result display diagram (star chart) containing many nodes. Because many clusters are displayed in the clustering result display diagram 600 in the figure, many clusters are displayed overlapping each other in, for example, the area 601 surrounded by a dotted line, making it difficult to confirm the trends in expression levels in these clusters. Even if the area is enlarged, for example, as shown on the right side of the figure, it is still difficult to confirm the trends in expression levels. In addition, it is even more difficult to grasp the trends and characteristics of expression levels in meta-clusters, which are groups of multiple clusters. As a result, it is difficult to grasp an overview of the expression trends in the sample as a whole.

[0268] In a preferred embodiment of the present disclosure, the data analysis client terminal 20 may be configured to be able to output star charts or population pie charts in units of metaclusters, which are groups of one or more clusters with similar expression trends. The metaclusters may be formed by an algorithm automatically grouping clusters with similar marker expression trends, or may be formed in response to a user manually selecting one or more clusters. Such a clustering result output diagram in meta-cluster units, i.e., a meta-cluster chart, makes it easier to understand the trends in expression levels.

[0269] An example of a metacluster chart formed from the clustering result display diagram is shown in FIG. 33. The clustering result display diagram 600 described above is displayed on the left side of the figure. The data analysis client terminal 20 generates a metacluster chart 602 as shown on the right side of the figure from the clustering result display diagram 600 in response to, for example, receiving a predetermined operation by the user. Here, the predetermined operation may be, for example, an operation such as clicking or touching a predetermined operation button.

[0270] The clusters in the clustering result display diagram 600 can be classified into seven cluster groups (called metaclusters 611 to 617) as shown in the diagram. Each metacluster has a common feature (for example, a common feature related to the expression status of a marker). Cluster groups belonging to the same metacluster are assigned the same color. Furthermore, metaclusters are assigned different colors. The data analysis client terminal 20 generates a metacluster node for each of these seven metaclusters based on the data of one or more clusters belonging to each metacluster. A metacluster chart 602 including the generated seven metacluster nodes 621 to 627 is shown on the right side of the figure. The color of the metacluster node 621 is the same as the color of the metacluster 611. Similarly, the colors of the metacluster nodes 622 to 627 are the same as the colors of the metaclusters 612 to 617, respectively. In this way, by assigning the same color to the metaclusters and metacluster nodes before and after the formation of the metacluster chart, the relationship between the metaclusters and the metacluster nodes can be easily understood.

[0271] The data analysis client terminal 20 may set the size of the metacluster node (the diameter of the circle in the figure) according to quantitative data such as the number of events or the number of cells belonging to the metacluster node. In the figure, a metacluster node that includes more events has a circle with a larger diameter.

[0272] Furthermore, the data analysis client terminal 20 may place each metacluster node in a position in the metacluster chart that is determined based on the positions of one or more clusters belonging to each metacluster node. For example, the data analysis client terminal 20 may determine the position of each metacluster node in the metacluster chart based on the number of events included in each of the one or more clusters belonging to each metacluster node and the positions of each of the one or more clusters. In one example, the position of each metacluster node may be the position of the center of gravity of the positions of one or more clusters integrated into the metacluster node.

[0273] Note that the position of the metacluster nodes does not have to be specified in the metacluster chart as described above. For example, the data analysis client terminal 20 may arrange the generated one or more metacluster nodes to form a predetermined number of rows and / or columns, or may arrange the generated one or more metacluster nodes in a grid pattern, as shown in the metacluster chart 603 of FIG.

[0274] In the above description, the data analysis client terminal 20 forms a meta-cluster chart from the clustering result display diagram, but the formation of the meta-cluster chart may be executed by the server system 10. For example, a user may perform a predetermined operation to generate a metacluster chart from a clustering result display diagram. In response to receiving the predetermined operation, the data analysis client terminal 20 transmits instruction data for generating a metacluster chart from the clustering result display diagram to the server system 10. In response to receiving the instruction data, the server system 10 generates a metacluster chart from the clustering result display diagram and transmits the metacluster chart to the data analysis client terminal 20. The data analysis client terminal 20 may then display the metacluster chart on an output device.

[0275] (13) Associating metaclusters with two-dimensional plots in the clustering result display

[0276] As mentioned in (12) above, multiple clusters in the clustering result display diagram can be classified into metaclusters consisting of one or more clusters with common characteristics. Here, if the metaclusters in the clustering result display diagram can be associated with the events in the two-dimensional plot, the events belonging to the metaclusters can be intuitively grasped.

[0277] In a preferred embodiment of the present disclosure, the data analysis client terminal 20 may display a two-dimensional plot in response to a selection of a metacluster in the clustering result display diagram so that events belonging to the selected metacluster can be identified from among the events included in the two-dimensional plot. For example, the data analysis client terminal 20 may apply a color commonly applied to the metaclusters to the events belonging to the selected metacluster in the two-dimensional plot. Such a two-dimensional plot display allows intuitive understanding of events belonging to meta-clusters, and also enables backgating.

[0278] The above-mentioned two-dimensional plot display control method will be further explained below. In step S111 described in (4-1) above, it is assumed that the data analysis client terminal 20 is displaying a clustering result display diagram 600 and two-dimensional plots 630 and 631 on the screen of the output device based on the output data, as shown in Fig. 35. The clustering result display diagram 600 is the one described in (12) above, and the multiple clusters in the clustering result display diagram can be classified into seven meta-clusters as shown in Fig. 36.

[0279] 37, when the user drags the mouse cursor from the position indicated by the reference numeral 632 to the position indicated by the reference numeral 633, an area 634 surrounded by a dashed line is selected. By selecting the area 634, the metaclusters 611 to 617 that overlap with the area are also selected. In response to the selection of the metaclusters 611 to 617, the data analysis client terminal 20 applies the color assigned to each metacluster to the events that belong to each metacluster in the two-dimensional plots 630 and 631, as shown in the same figure. This color-applying display method allows the events that belong to the metaclusters to be intuitively grasped and also enables backgating.

[0280] Although the figure shows an example in which multiple metaclusters are selected by a drag operation, one or more metaclusters may be selected by, for example, a click operation. In this case, the color assigned to each metacluster may be applied to the events added to the selected metacluster.

[0281] (14) Controlling the pie display axis in star charts

[0282] As shown in the drawings described in (11) to (13) above, the expression level data of a group of markers may be displayed as a pie at each node (cluster) of a star chart. For example, the higher the expression level of a marker, the longer the radial length of the area (pie) corresponding to that marker is displayed. For example, node 700 shown in FIG. 38 shows the expression levels of multiple markers as a pie. The expression level of each marker corresponds to a radial length; for example, the expression level of the marker corresponding to the area indicated by reference numeral 701 corresponds to radial length 702. Note that although length 702 is indicated by an arrow in the drawing, such arrows do not need to be displayed in a star chart.

[0283] In the present disclosure, the data analysis client terminal 20 may be configured to be able to change the axis settings of the pie display in each node. For example, the data analysis client terminal 20 may change the axis settings of the pie display so that the axis scale of the pie display in a certain node corresponds to the axis scale of the two-dimensional plot corresponding to the node. Here, each node, which is a group of events, does not have to be a cluster formed by performing clustering. For example, when a gate is created on a two-dimensional plot, each node may be formed based on a group of events present within the gate. The ability to change the axis settings in this way makes it easier to understand the level of expression, and also makes it possible to more accurately understand the relationship with the two-dimensional plot.

[0284] Examples of axis scales for two-dimensional plots include a linear scale, a logarithmic scale, and a biexponential scale. Examples of axis scales for the display also include a linear scale, a logarithmic scale, and a biexponential scale. In the present disclosure, the data analysis client terminal 20 may adopt any of these as the axis scale of the pie display in each node. For example, when the biexponential scale is adopted as the axis scale of a two-dimensional plot, the data analysis client terminal 20 may adopt the biexponential scale as the axis scale of the pie display of one or more nodes corresponding to the two-dimensional plot.

[0285] In the present disclosure, in order to change the axis settings, the data analysis client terminal 20 may be configured to be able to output a setting window for a two-dimensional plot. Assume that the data analysis client terminal 20 is displaying, as output data, for example, two-dimensional plot data 710 shown in Fig. 39 on an output device.

[0286] In this case, for example, in response to a user performing a predetermined operation, the data analysis client terminal 20 displays a plot setting window 711 on the output device. As shown in the figure, the plot setting window has areas 712 and 713 for adjusting the settings of the X-axis and Y-axis of the two-dimensional plot data 710. As shown in the figure, the area 712 for setting the X-axis has a list box 714 for selecting the axis scale of the X-axis. Although "Biexponential" is displayed in the figure, the list box is configured to allow selection of "Linear" and "Log" in addition to "Biexponential." The area 713 for setting the Y-axis also has a list box 715 for selecting the axis scale of the Y-axis. Similarly, the list box is configured to allow selection of any of the three axis scales.

[0287] When a user selects an axis scale for the X-axis and / or Y-axis in the plot setting window, the data analysis client terminal 20 may change the axis scale of the pie display of one or more nodes corresponding to the two-dimensional plot controlled by the plot setting window in response to the selected axis scale, so that the axis scale of the pie display becomes the same as the axis scale selected in the plot setting.

[0288] The present disclosure provides the above-described information processing system, as well as a server system, a data acquisition client terminal, and a data analysis client terminal included in the information processing system, the details of which are as described above.

[0289] 2. Information Processing Method

[0290] The present disclosure also provides an information processing method. The information processing method may include one or more of the processes described in (4) above. For example, the information processing method may include an automatic analysis processing step of performing an analysis process on light intensity data obtained by irradiating a biological sample with light or fluorescent label intensity data calculated from the light intensity data to generate output data; an analysis result data storage step of storing the output data generated based on the light intensity data or the fluorescent label intensity data; and an interactive analysis processing step of analyzing the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputting analysis result data. The description in (4) above applies to each of these steps.

[0291] The present disclosure may also be configured as follows. [1] an automatic analysis processing unit that generates output data by performing an analysis process on light intensity data or fluorescent label intensity data obtained by irradiating light onto a biological sample; an analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescent label intensity data; an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data; A server system including: [2] The server system according to [1], wherein the automatic analysis processing unit calculates fluorescent label intensity data from the light intensity data. [3] The server system according to [1] or [2], wherein the processing by the automatic analysis processing unit and the processing by the interactive analysis processing unit are executed on different computation resources. [4] The server system described in any one of [1] to [3], wherein the server system secures computational resources for processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit in response to receiving an analysis start command. [5] The server system described in any one of [1] to [4] further includes a database in which analysis setting data used in processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit is stored. [6] The server system according to any one of [1] to [5], further comprising an optical data storage unit that stores the light intensity data and / or the fluorescent label intensity data. [7] the optical data storage unit includes two or more types of storage with different access speeds, The server system according to [6], wherein the server system executes a process of migrating the light intensity data and / or the fluorescent label intensity data stored in storage with a higher access speed to storage with a lower access speed when a predetermined condition is met. [8] The server system according to any one of [1] to [7], wherein the output data includes at least one of a two-dimensional plot image, a spectral plot image, a one-dimensional histogram image, a two-dimensional contour plot image, a dimensionally compressed image, and a clustering result display diagram. [9] a data acquisition client terminal that acquires light intensity data obtained by irradiating a biological sample with light or acquires fluorescent label intensity data by performing calculation processing on the light intensity data; and an optical data storage unit that stores the light intensity data or the fluorescent label intensity data transmitted from the data acquisition client terminal; an automatic analysis processing unit that generates output data by performing an analysis process on the light intensity data or the fluorescent label intensity data; an analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescent label intensity data; an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data. An information processing system including:

[10] The information processing system described in [9], wherein the data acquisition client terminal transmits the light intensity data or the fluorescent label intensity data to the server system in response to acquiring the light intensity data or the fluorescent label intensity data.

[11] The information processing system according to [9] or

[10] , wherein the data acquisition client terminal, in response to acquiring the light intensity data or the fluorescent label intensity data, performs predetermined processing on the light intensity data or the fluorescent label intensity data, and transmits the processed light intensity data or the fluorescent label intensity data to the server system.

[12] the server system previously stores analysis setting data to be used in processing by the automatic analysis processing unit, the automatic analysis processing unit calculates the fluorescent label intensity data from the light intensity data using the analysis setting data; [9] to

[11] . An information processing system according to any one of [9] to

[11] .

[13] The information processing system according to any one of [9] to

[12] , wherein the automatic analysis processing unit executes a process of calculating fluorescent label intensity data from the light intensity data in response to the light intensity data being stored in the light data storage unit.

[14] The information processing system according to any one of [9] to

[13] , further comprising a data analysis client terminal including the output device.

[15] The information processing system according to

[14] , wherein the data analysis client terminal transmits the analysis command for the output data output to the output device to the server system.

[16] The information processing system according to

[14] or

[15] , wherein the data analysis client terminal outputs a window in which the output data is displayed to the output device, and accepts input of the analysis command in the window.

[17] An information processing system described in any one of [9] to

[16] , wherein a plurality of data analysis client terminals can share one or more of light intensity data, fluorescence intensity data, and analysis setting data in the server system.

[18] The information processing system according to any one of [9] to

[17] , wherein a plurality of data acquisition client terminals can share the analysis setting data in the server system.

[19] a data acquisition unit that acquires light intensity data obtained by irradiating a biological sample with light; a transmitting unit configured to transmit the light intensity data to a server system in response to the light intensity data being acquired; In the server system, fluorescent label intensity data is calculated from the light intensity data. Data acquisition client terminal.

[20] a communication unit that receives output data created by the server system based on the fluorescent label intensity data from the server system; a processing unit that performs processing to output the output data to an output device; A data analysis client terminal including: 〔twenty one〕 The data analysis client terminal according to

[20] , wherein the data analysis client terminal outputs a window in which the output data is displayed to the output device, and accepts input of analysis commands for the output data in the window. 〔twenty two〕 an automatic analysis processing step of performing an analysis process on light intensity data obtained by irradiating a biological sample with light or fluorescent label intensity data calculated from the light intensity data to generate output data; an analysis result data storage step of storing the output data generated based on the light intensity data or the fluorescent label intensity data; an interactive analysis processing step of analyzing the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputting analysis result data; An information processing method including: [Explanation of symbols]

[0292] 1. Information Processing Systems 10 Server Systems 20 Data analysis client terminals 30 Data Acquisition Client Terminal 40 Biological sample analyzer

Claims

1. an automatic analysis processing unit that generates output data by analyzing fluorescent label intensity data calculated from light intensity data obtained by irradiating a biological sample with light; an analysis result data storage unit that stores the output data; an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data; A server system including:

2. The server system according to claim 1 , wherein the processing by the automatic analysis processing unit and the processing by the interactive analysis processing unit are executed on different computation resources.

3. The server system according to claim 1 , wherein the server system secures computational resources for processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit in response to receiving an analysis start command.

4. The server system according to claim 1 , further comprising a database for storing analysis setting data used in processing by the automatic analysis processing unit and / or processing by the interactive analysis processing unit.

5. The server system according to claim 1 , further comprising an optical data storage unit that stores the light intensity data and / or the fluorescent label intensity data.

6. the optical data storage unit includes two or more types of storage with different access speeds, 6. The server system according to claim 5, wherein the server system executes a process of migrating the light intensity data and / or the fluorescent label intensity data stored in a storage device with a higher access speed to a storage device with a lower access speed when a predetermined condition is satisfied.

7. The server system according to claim 1 , wherein the output data includes at least one of a two-dimensional plot image, a spectrum plot image, a one-dimensional histogram image, a two-dimensional contour plot image, a dimensionality reduced image, and a clustering result display diagram.

8. A data acquisition client terminal that acquires light intensity data obtained by irradiating a biological sample with light or acquires fluorescent label intensity data by performing calculation processing on the light intensity data; and an optical data storage unit that stores the light intensity data or the fluorescent label intensity data transmitted from the data acquisition client terminal; an automatic analysis processing unit that generates output data by performing an analysis process on the fluorescent label intensity data calculated from the light intensity data; an analysis result data storage unit that stores the output data; an interactive analysis processing unit that analyzes the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data. An information processing system including:

9. The information processing system according to claim 8 , wherein the data acquisition client terminal transmits the light intensity data or the fluorescent label intensity data to the server system in response to acquiring the light intensity data or the fluorescent label intensity data.

10. 9. The information processing system according to claim 8, wherein the data acquisition client terminal, in response to acquiring the light intensity data or the fluorescent label intensity data, performs predetermined processing on the light intensity data or the fluorescent label intensity data, and transmits the processed light intensity data or the fluorescent label intensity data to the server system.

11. the server system previously stores analysis setting data to be used in processing by the automatic analysis processing unit, the automatic analysis processing unit calculates the fluorescent label intensity data from the light intensity data using the analysis setting data; The information processing system according to claim 8 .

12. The information processing system according to claim 8 , wherein the automatic analysis processing unit executes a process of calculating fluorescent label intensity data from the light intensity data in response to the light intensity data being stored in the light data storage unit.

13. The information processing system according to claim 8 , further comprising a data analysis client terminal including the output device.

14. 14. The information processing system according to claim 13, wherein the data analysis client terminal transmits the analysis command for the output data output to the output device to the server system.

15. 14. The information processing system according to claim 13, wherein the data analysis client terminal causes the output device to output a window in which the output data is displayed, and accepts input of the analysis command in the window.

16. 9. The information processing system according to claim 8, wherein a plurality of data analysis client terminals can share one or more of light intensity data, fluorescent label intensity data, and analysis setting data in the server system.

17. 9. The information processing system according to claim 8, wherein a plurality of data acquisition client terminals can share the analysis setting data in the server system.

18. a data acquisition unit that acquires light intensity data obtained by irradiating a biological sample with light; a transmitting unit that transmits the light intensity data to a server system in response to acquiring the light intensity data; In the server system, fluorescent label intensity data is calculated from the light intensity data. Data acquisition client terminal.

19. a communication unit that receives output data created by the server system based on the fluorescent label intensity data from the server system; a processing unit that performs processing to output the output data to an output device; A data analysis client terminal including:

20. 20. The data analysis client terminal according to claim 19, wherein the data analysis client terminal causes the output device to output a window in which the output data is displayed, and accepts input of an analysis command for the output data in the window.

21. an automatic analysis processing step of performing an analysis process on fluorescent label intensity data calculated from light intensity data obtained by irradiating light onto a biological sample to generate output data; an analysis result data storage step of storing the output data; an interactive analysis processing step of analyzing the fluorescent label intensity data based on an analysis command for the output data output to an output device and outputting analysis result data; An information processing method including:

Images