Server system, information processing system, data acquisition 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 relates to a server system that processes light intensity data obtained 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 Art]

[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 laser light, and measuring the intensity and / or pattern of fluorescence generated from the excited fluorescent dye. A flow cytometer can be cited as a representative example of a particle analyzer that performs such measurement.

[0003] As a technology related to processing data obtained by a flow cytometer, particularly light intensity data, for example, the following Patent Document 1 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 stored on a non-transitory computer-readable storage medium, and a plurality of processor-executable instructions stored on a non-transitory computer-readable storage medium, wherein the data discovery node data structure includes specific specifications.

[0004] Furthermore, Patent Document 2 discloses a sample analysis system using flow cytometry. This sample analysis system includes a measurement data acquisition unit that acquires measurement data of particles by measuring particles contained in a sample prepared by adding a reagent to a sample, 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 [Overview of the project] [Problems that the invention aims to solve]

[0006] In flow cytometry, the number of fluorescent dyes used in a single measurement is increasing, and consequently, the amount of information processing required in the data analysis process is also increasing. Therefore, in order to perform the data analysis process using these information processing devices, higher specifications are required, but it is often not practical for these devices to have such specifications, and this is undesirable for users.

[0007] Another example of a data analysis system obtained by flow cytometry is a client-server analysis system. However, client-server analysis systems can be costly to operate. This is because, for example, the server is always running even when there are no users, or the analysis processing by the server is always available.

[0008] Furthermore, client-server analysis systems also have challenges related to processing speed. For example, as the number of users performing analyses simultaneously increases, computation resources may be depleted, leading to processing delays. In addition, it may be difficult to achieve high speeds for processes that require a large amount of computation resources at once, such as dimensionality reduction or clustering.

[0009] Therefore, the primary purpose of this disclosure is to provide a technique for solving at least one of these problems. The purpose of this disclosure is not limited to solving any one of the problems described below, for example. [Means for solving the problem]

[0010] This disclosure is, An automated analysis processing unit that generates output data by performing analysis processing on light intensity data or fluorescence label intensity data obtained 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 fluorescence labeling intensity data, An interactive analysis processing unit analyzes the fluorescence label intensity data based on an analysis command for the output data output to the output device and outputs analysis result data. We provide a server system that includes this. The aforementioned automated analysis processing unit may calculate fluorescence label intensity data from the light intensity data. The processing performed by the automated analysis processing unit and the processing performed by the interactive analysis processing unit may be executed on different computation resources. The server system may allocate computation 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 labeling intensity data. The optical data storage unit may include two or more types of storage with different access speeds. The server system may, when certain conditions are met, perform a process to transfer the light intensity data and / or the fluorescence label intensity data stored in storage with a higher access speed to storage with a lower access speed. The output data may include at least one of the following: a two-dimensional plot image, a spectral plot image, a one-dimensional histogram image, a two-dimensional contour plot image, a dimensionality-reduced image, and a clustering result display figure.

[0011] This disclosure relates to a data acquisition client terminal that acquires light intensity data obtained by irradiating a biological sample with light, or acquires fluorescence labeling intensity data by performing calculation processing on said light intensity data; and A light data storage unit that stores the light intensity data or the fluorescence label intensity data transmitted from the data acquisition client terminal, An automated analysis processing unit that calculates fluorescence labeling intensity data from the aforementioned light intensity data, An analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescence labeling intensity data, A server system including: an interactive analysis processing unit that analyzes the fluorescence label intensity data based on analysis commands for the output data output to the output device and outputs analysis result data. We provide an information processing system that includes this. The data acquisition client terminal may transmit the light intensity data or the fluorescent label intensity data to the server system upon acquiring the light intensity data or the fluorescent label intensity data. In response to acquiring said light intensity data or said fluorescent label intensity data, said data acquisition client terminal may perform predetermined processing on said light intensity data or said fluorescent label intensity data, and then transmit the processed light intensity data or said fluorescent label intensity data to said server system. Said server system may preliminarily hold analysis setting data used in processing by said automatic analysis processing unit, Said automatic analysis processing unit may calculate said fluorescent label intensity data from said light intensity data using said analysis setting data. In response to said light intensity data being stored in said optical data storage unit, said automatic analysis processing unit may execute processing for calculating fluorescent label intensity data from said light intensity data. Said information processing system may further include a data analysis client terminal including said output device. Said data analysis client terminal may transmit said analysis command for said output data output to said output device to said server system. Said data analysis client terminal may cause said output device to output a window on which said output data is displayed, and accept input of said analysis command via said window. A plurality of data analysis client terminals may share any one or more of light intensity data, fluorescence intensity data, and analysis setting data in said server system. A plurality of data acquisition client terminals may share analysis setting data in said server system.

[0012] In addition, the present disclosure includes: a data acquisition unit that acquires light intensity data obtained by irradiating a biological sample with light, a transmission unit that transmits said light intensity data to a server system in response to acquiring said light intensity data, wherein in said server system, fluorescent label intensity data is calculated from said light intensity data, provided is a data acquisition client terminal.

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

[0014] Also, the present disclosure provides: an automatic analysis processing step of performing analysis processing 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 comprising the above. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] [Figure 1] It is a schematic diagram of the configuration of a flow cytometer. [Figure 2] It is a diagram showing an example of an experimental flow when the present technology is applied in flow cytometry. [Figure 3] It is a diagram showing an example of gate setting. [Figure 4] It is a diagram for explaining a surface marker. [Figure 5] It is a diagram showing a configuration example of an information processing system. [Figure 6] It is a diagram showing an example of the functional configuration of a server system. [Figure 7] It is a diagram showing a configuration example of metadata. [Figure 8] It is a diagram showing a configuration example of metadata. [Figure 9] This figure shows an example of the hardware configuration of server devices that make up a server system. [Figure 10] This figure shows an example of the functional configuration of a data analysis client terminal. [Figure 11] This figure shows an example of the hardware configuration of a data analysis client terminal. [Figure 12] This figure shows an example of the functional configuration of a data acquisition client terminal. [Figure 13] This figure shows an example of the configuration of a biological sample analysis device. [Figure 14] This figure shows an example of the flow of automated analysis processing by an information processing system. [Figure 15] This figure shows an example of the flow of automated analysis processing by an information processing system. [Figure 16] This figure shows an example of the flow of data acquisition processing for output by an information processing system. [Figure 17] This figure shows an example of the flow of interactive analysis processing by an information processing system. [Figure 18] This is a schematic diagram illustrating the interactive analysis processing flow. [Figure 19] This is a schematic diagram illustrating the interactive analysis processing flow. [Figure 20] This figure shows an example of a window that a data analysis client terminal outputs to an output device. [Figure 21] This figure shows an example of a window that a data analysis client terminal outputs to an output device. [Figure 22] This figure shows an example of a window that a data analysis client terminal outputs to an output device. [Figure 23] This figure shows an example of a window that a data analysis client terminal outputs to an output device. [Figure 24] This figure shows an example of a clustering result display diagram. [Figure 25] This figure shows an example of a marker selection window. [Figure 26]This figure shows an example of a marker selection window. [Figure 27] This figure shows an example of a clustering result display diagram. [Figure 28] This figure shows an example of a marker selection window. [Figure 29] This figure shows an example of a clustering result display diagram. [Figure 30] This figure shows an example of a marker selection window. [Figure 31] This figure shows an example of a clustering result display diagram. [Figure 32] This is an example of a clustering result display image. [Figure 33] This figure shows an example of a metacluster chart. [Figure 34] This figure shows an example of a metacluster chart. [Figure 35] This figure shows an example of a clustering result display diagram and an example of a corresponding two-dimensional plot. [Figure 36] This diagram explains metaclusters in the clustering result display diagram. [Figure 37] This figure shows examples of user operations on a clustering results display and examples of two-dimensional plots colored as a result of those operations. [Figure 38] This is a diagram to explain the representation of pi within a node. [Figure 39] This figure shows an example of a plot settings window for configuring two-dimensional plot data and the display of said plot data. [Modes for carrying out the invention]

[0016] The following describes preferred forms for implementing this disclosure. The embodiments described below are representative of the disclosure, and the scope of this disclosure is not limited to these embodiments. The description of this disclosure will proceed in the following order. 1. Information Processing System (1) Description of related technologies (2) Overview of the Information Processing System (3) Components included in the information processing system (3-1) Server System (3-2) Data analysis client terminal (3-3) Data acquisition client terminal (3-4) Biological sample analyzer (4) Example of a processing flow by an information processing system (4-1) Automated analysis process (4-2) Example of automated analysis process (4-3) Data acquisition process for output (4-4) Interactive analysis processing (4-5) Examples of interactive analysis processing (5) Region settings (6) Data migration (7) Use of external storage or computation resources (8) Splitting of data to be analyzed (9) Data sharing (10) Standardization of output data (11) Example of output control for clustering result display diagram 1 (12) Example of output control for clustering result display diagram 2 (13) Relationship between metaclusters and two-dimensional plots in clustering result display diagrams (14) Control of the pie display axis in the star chart 2. Information Processing Method

[0017] 1. Information Processing System

[0018] (1) Description of related technologies

[0019] Flow cytometers can be broadly classified into filter type and spectral type, for example, from the viewpoint of the optical system for fluorescence measurement. Filter-type flow cytometers can employ a configuration as shown in Figure 1, 1, in order to extract only the desired optical information from the target fluorescent dye. Specifically, the light generated by irradiating particles with light is split into multiple streams by a wavelength separation means DM, such as a dichroic mirror, passed through different filters, and then each of the split streams is measured by multiple detectors, such as photomultiplier tubes (PMTs). In other words, filter-type flow cytometers perform multicolor fluorescence detection by detecting fluorescence in each wavelength band corresponding to each fluorescent dye using a detector corresponding to each fluorescent dye. In this case, if multiple fluorescent dyes with closely spaced fluorescence wavelengths are used, fluorescence correction processing may be performed to calculate a more accurate fluorescence amount.

[0020] A spectral flow cytometer analyzes the fluorescence intensity of each particle by deconvolving (unmixing) the fluorescence data obtained by detecting the light generated when particles are irradiated with light, using the spectral information of the fluorescent dye used for staining. As shown in Figure 1-2, a spectral flow cytometer uses a prism spectroscopic optical element P to spectrally analyze the fluorescence. Furthermore, to detect the spectrally analyzed fluorescence, a spectral flow cytometer is equipped with an array-type detector, such as an array-type photomultiplier tube (PMT), instead of the numerous photodetectors found in a filter-type flow cytometer. Compared to a filter-type flow cytometer, a spectral flow cytometer is better suited to analysis using multiple fluorescent dyes because it is more effective at avoiding the effects of fluorescence leakage.

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

[0022] An example of an experimental flow using a flow cytometer is described below with reference to Figure 2.

[0023] The flow cytometry experiment can be broadly classified into three stages: the experimental planning stage (Figure 2, "1: Plan"), which involves considering the cells to be tested and the method for detecting them, and preparing antibody reagents with fluorescent indicators; the sample preparation stage (Figure 2, "2: Preparation"), which involves staining and preparing the cells to be measured; the FCM measurement stage (Figure 2, "3: FCM"), which involves measuring the fluorescence intensity of each stained cell using a flow cytometer; and the data analysis stage (Figure 2, "4: Data Analysis"), which involves performing various data processing steps to obtain the desired analytical results from the data recorded by the FCM measurement. These stages can be repeated as needed.

[0024] In the experimental design step, the first step is to determine which molecule (e.g., antigen or cytokine) expression will be used to identify the microparticles (mainly cells) to be detected using a flow cytometer; that is, to determine the marker to be used for detecting the microparticles. This decision can be made based on information such as past experimental results or published papers. Next, the choice of fluorescent dye to detect that marker is considered. Simultaneously, the number of markers to be detected, the specifications of the available FCM instrument, available fluorescently labeled reagents, the spectrum and brightness of the fluorescent dyes, price, and delivery time are all considered in an integrated manner to determine the combination of fluorescently labeled antibody reagents required for the actual experiment. This process of determining the reagent combination is generally called panel design in FCM.

[0025] In the sample preparation process, the experimental subjects are first treated to prepare them for FCM measurement. For example, cell separation and purification may be performed. For example, for immune cells derived from blood, red blood cells are removed from the blood by hemolysis and density gradient centrifugation, and white blood cells are extracted. The extracted target cell population is then stained using a fluorescently labeled antibody.

[0026] In the FCM measurement process, when optically analyzing minute particles, first, excitation light is emitted from the light source of the flow cytometer's light irradiation unit and irradiated onto the minute particles flowing through the channel. Next, the fluorescence emitted from the minute particles is detected by the flow cytometer's detection unit. Specifically, a dichroic mirror or bandpass filter is used to separate only the light of a specific wavelength (the desired fluorescence) from the light emitted from the minute particles, and this is detected by a detector such as a PMT. At this time, the fluorescence is spectrally separated using a prism or diffraction grating, and a detector such as a 32-channel PMT is used to detect light of different wavelengths in each channel. This makes it easy to obtain spectral information of the detected light (fluorescence). Flow cytometers may have the function of recording fluorescence information of each microparticle obtained by FCM measurement, along with scattered light information, temporal information, and positional information other than fluorescence information. This recording function can mainly be performed using computer memory or disk. In typical cell analysis, thousands to millions of microparticles are analyzed under a single experimental condition, so it is necessary to record a large amount of information in an organized manner for each experimental condition.

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

[0028] For example, gate settings are often used to analyze fluorescence distribution, which can then be used to calculate the proportion of target cells in a sample. For instance, as shown in Figure 3, a two-dimensional plot of forward scatter (FSC) and side scatter (SSC) can be generated, and by selecting a predetermined range from this plot, the proportion of monocytes and lymphocytes among the blood cells contained in PBMCs can be identified. Furthermore, by setting and expanding gates for lymphocytes expressing a predetermined surface marker, the proportion of B cells, T cells, and NK cells among the lymphocytes can be calculated. In addition, the proportion of memory B cells among B cells, the proportion of killer T cells and helper T cells among T cells, and the proportion of naive T cells and memory T cells can also be identified. It is known that the surface markers expressed by each type of cell differ depending on the cell type, as shown in Figure 4, for example. Therefore, by appropriately selecting antibodies that bind to surface markers and fluorescent dyes that label each antibody, and then analyzing them using a flow cytometer, the cells in a sample can be examined.

[0029] Information processing systems and components included in said systems pursuant to this disclosure may be used for analysis in the data analysis process.

[0030] (2) Overview of the Information Processing System

[0031] The information processing in the aforementioned data analysis process is performed, for example, by an information processing device attached to the flow cytometer or by an information processing device used 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 increasing, and consequently, the amount of information processing required in the data analysis process is also increasing. Therefore, in order to perform the data analysis process using these information processing devices, higher specifications are required. This trend is particularly pronounced when performing so-called advanced analysis methods such as dimensionality reduction and clustering, which have a large computational load. However, it is often not practical to equip these information processing devices with such specifications, and this is also undesirable for the user.

[0032] Therefore, it would be desirable for users if the information processing in the aforementioned data analysis process could be performed using a client-server analysis system, particularly a cloud-based client-server analysis system. Furthermore, as mentioned above, client-server analysis systems often incur high operating costs. In addition, these analysis systems also have challenges related to processing speed.

[0033] The inventors have found that at least one of these problems can be solved by a specific server system. Specifically, this disclosure provides a server system including an automatic analysis processing unit that generates output data by analyzing light intensity data or fluorescence label intensity data obtained 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 fluorescence label intensity data; and an interactive analysis processing unit that analyzes the fluorescence label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data, and an information processing system including the server system. The server system includes the automatic analysis processing unit and the interactive analysis processing unit, enabling the server system to perform data analysis. Furthermore, the interactive analysis processing unit allows the user to perform analysis processing or adjust analysis settings while confirming the output data output to the output device, making it easier to obtain the desired analysis results. Furthermore, the server system can reduce operating costs by executing the processing performed by the automatic analysis processing unit and the interactive analysis processing unit only when necessary. Additionally, the processing speed of the server system can be improved by the configuration described below in this specification.

[0034] Below, an example of the configuration of an information processing system in accordance with this disclosure will be described with reference to Figure 5.

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

[0036] The server system 10 may be connected to the data analysis client terminal 20 and the data acquisition client terminal 30 via the network 50. The network 50 may be a communication network through which data is transmitted and received, and may be, for example, the Internet, a satellite communication network, a telephone line network, or a mobile communication network (such as 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 wired or wireless connection, or they may be connected via a network 50.

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

[0039] (3) Components included in the information processing system The elements included in the information processing system pursuant to this disclosure are described below.

[0040] (3-1) Server System

[0041] The server system 10 will be described with reference to Figure 6. Figure 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 this disclosure, the automated analysis processing unit 11 may calculate fluorescence label intensity data from light intensity data obtained by irradiating a biological sample with light. The light intensity data may be light intensity data transmitted from a data acquisition client terminal 30 to a server system 10. The light intensity data may be stored in an optical data storage unit 15, and the automated analysis processing unit 11 may obtain the light intensity data from the optical data storage unit 15. In other embodiments of this disclosure, the automatic analysis processing unit 11 does not need to perform the calculation process. That is, the data acquisition client terminal 30 may perform a process to calculate fluorescence label intensity data from light intensity data, and the data acquisition client terminal 30 may transmit the fluorescence label intensity data to the server system 10.

[0043] The automatic analysis processing unit 11 may perform a process to calculate fluorescence label intensity data from the light intensity data. The automatic analysis processing unit 11 may perform this calculation process using analysis setting data that the server system 10 has in advance. This analysis setting data may be transmitted in advance (especially before the calculation process is performed) from, for example, the data analysis client terminal 20 or the data acquisition client terminal 30.

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

[0045] The automated 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 onto the fluorescent dye labeling the particles. The spectral reference data used in the unmixing process may include spectral data of fluorescence generated when light having a predetermined wavelength is irradiated onto the fluorescent dye labeling the particles, and spectral data of fluorescence generated when light having a wavelength different from the predetermined wavelength is irradiated onto the fluorescent dye labeling the particles.

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

[0047] The automatic analysis processing unit 11 may 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 may be performed using, for example, the fluorescence intensity correction method described in Japanese Patent Publication No. 5985140. This fluorescence intensity correction method may be performed using, for example, the following WLSM formula (1).

number

[0048] The fluorescence wavelength distribution of fluorescent dyes can be broad. Therefore, 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 superimposed data of fluorescence from multiple fluorescent dyes. Therefore, correction is necessary to separate this optical data into fluorescence data from each fluorescent dye. The unmixing process is a method for this correction, and through this unmixing process, the superimposed data of fluorescence label intensity from multiple fluorescent dyes is separated into fluorescence label intensity data from each fluorescent dye, thereby obtaining fluorescence label intensity data from each fluorescent dye.

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

[0050] Preferably, the automatic analysis processing unit 11 performs a process to calculate fluorescence label intensity data from the light intensity data in response to the light intensity data being stored in the light data storage unit 15. The automatic analysis processing unit 11 can then analyze the fluorescence label intensity data to generate analysis result data. Furthermore, the automatic analysis processing unit 11 can 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 light data storage unit 15. That is, the automatic analysis processing unit 11 may perform an event-driven analysis process triggered by the storage.

[0051] The automatic analysis processing unit 11 secures computation resources for 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 the reception of an analysis start command and can secure computation within the server system 10 in response to the reception. Using the secured computation resources, the automatic analysis processing unit 11 can perform the calculation of fluorescence label intensity data, the analysis of fluorescence label intensity data, and the generation of output data from the analysis result data.

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

[0053] The interactive analysis processing unit 12 may perform a process to calculate fluorescence label intensity data from light intensity data. It may then analyze the calculated fluorescence label intensity data 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] The interactive analysis processing unit 12 may allocate computation resources for processing by the interactive analysis processing unit 12 in response to receiving an analysis start command. After such allocation, the interactive analysis processing unit 12 waits until an analysis command is sent from the data analysis client terminal 20. As described above, the automated analysis processing unit 11 reserves computation resources for processing by the automated analysis processing unit in response to receiving an analysis start command, and the interactive analysis processing unit 12 may also reserve computation resources for processing by the interactive analysis processing unit 12 in response to receiving an analysis start command. Therefore, in this disclosure, the processing by the automated analysis processing unit and the processing by the interactive analysis processing unit may be executed on different computation resources.

[0055] The interactive analysis processing unit 12 may perform the following processes in response to receiving an analysis command from the data analysis client terminal 20: calculation of fluorescence label intensity data, analysis of fluorescence label intensity data, and generation of output data from the analysis result data. These processes may be event-driven analysis processes triggered by the receipt of the analysis command. In other words, the interactive analysis process according to this technology may be an event-driven analysis process.

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

[0057] The connection unit 14 is a functional element for executing 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 analysis commands entered on 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. The output data generation unit 13 then transmits this output data to the data analysis client terminal 20. The computation resources required for processing by the output data generation unit 13 are small. Therefore, it is possible to reduce the operating costs of the server system.

[0059] The processing by the output data generation unit 13 may be performed by a continuously running virtual server within the server system 10. Because the virtual server is continuously running, the output data acquisition process can be executed at high speed without any waiting time associated with server startup. Furthermore, the output data generation unit 13 may be configured as a serverless architecture.

[0060] The optical data storage unit 15 stores light intensity data and / or fluorescence label intensity data. The optical data storage unit 15 may include two units: a light intensity data storage unit for storing light intensity data and a fluorescence label intensity data storage unit for storing fluorescence 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 fluorescence label intensity data and / or analysis result data generated by the interactive analysis processing unit 12 based on the fluorescence label intensity data. Furthermore, the analysis result data storage unit 16 stores output data generated from these analysis result data.

[0062] Database 17 may store various metadata. The metadata may include analysis setting data. In particular, 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 settings data may include supplementary data that is referenced or used in the analysis processing of light intensity data. Such supplementary data may include, for example, data relating to the biological sample itself and / or data relating to the analysis settings of the biological sample. Furthermore, the analysis setting data may include, for example, data used to calculate fluorescence labeling intensity data from light intensity data (including, for example, spectral reference data), and / or data used in the analysis process for fluorescence labeling data (including, for example, analysis commands).

[0063] Examples of metadata structure are explained with reference to Figures 7 and 8.

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

[0065] Metadata may include one or more of the following: project data, default fluorescent dye data, optional fluorescent dye data, spectral reference data, autofluorescence data, and instrument configuration data. For example, a single project data set may be associated with one or more of the following data: default fluorescent dye data (Fluorochrome(preset) in Figure 7), arbitrarily set fluorescent dye data (Fluorochrome(custom)), spectral reference data, autofluorescence data, and instrument settings data. The aforementioned default fluorescent dye data includes data (e.g., 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 (e.g., fluorescent dye name data) relating to fluorescent dyes that are arbitrarily selected by the user, for example, for each experiment or sample, in the data analysis in which the project data is used. The spectral reference data includes spectral reference data for each fluorescent dye included in the default fluorescent dye data and the arbitrarily set fluorescent dye data. The autofluorescence data includes data relating to the autofluorescence of biological samples analyzed using the project data. The aforementioned device setting data includes data relating to the settings of a biological sample analyzer that acquired light intensity data of a biological sample analyzed using the aforementioned project data. The number of these data associated with a single project data may be one or more. For example, a single project data may have one or more default fluorescent dye data associated with it.

[0066] Metadata may include experimental data. For example, one or more experimental data (Experiment in Figure 7) may be associated with a single project data. Each experimental data may include one or more of the following: experimental name data, username data of the user who created the experimental data, and date and time data of the experimental data.

[0067] Metadata may include plate data. For example, one or more plate data (plate in Figure 7) may be associated with one experimental data set. Each plate data set may include data that identifies the plate (particularly a well plate or microtiter plate) to be analyzed by a biological sample analyzer, and may include one or more of the following: plate name, user name data that created the plate data, date and time data that created the plate data, and plate type data.

[0068] Metadata may include sample group data. For example, one or more sample group data (Sample Group in Figure 7) may be associated with a single plate data. Each sample group data may include, for example, data that identifies the group of biological samples contained in each plate data, and may include one or more of the following: sample group name data, username data that created the sample group data, and date and time data that created the sample group data.

[0069] Metadata may include protocol data. For example, each sample group of data may be associated with protocol data (Protocol in Figure 7). For example, multiple sample group data may be associated with the same protocol data, or they may be associated with different protocol data. Each protocol data set may include, for example, data that specifies the analysis protocol for a biological sample, and may include one or more of the following: Unmixing Config, Color Palette, Measurement Settings, and Shared Worksheet. The aforementioned unmixing setting data may include data that specifies how the unmixing process is performed (for example, the setting or calculation method of the unmixing matrix). The aforementioned fluorescent dye setting data may include data relating to the assignment of fluorescent dyes to biomolecules, and may include, for example, the results of panel design. The aforementioned device setting data may include data relating to the settings for analysis by the biological sample analyzer. The worksheet settings data may include data relating to analysis settings that are applied in common to all biological samples included in a single sample group data. For example, the worksheet settings data may include gate setting data that is applied in common to all biological samples included in a single sample group data.

[0070] A single sample group of data may be associated with one or more sample data (Sample in Figure 7). Each sample data may include, for example, one or more of the following: Individual Worksheet settings applied in the analysis of each sample, Sample Unmixing Config applied in the analysis of each sample, Raw Data to the light intensity data measured for each sample, and Unmixed Data calculated from the light intensity data measured for each sample.

[0071] Metadata may include comparison worksheet data. For example, one or more comparison worksheet data (Comparison Worksheet in Figure 7) may be associated with a single experimental data set. The comparison worksheet data may be worksheet data used to display the analysis results for each sample data set side by side or overlaid on top of each other.

[0072] The worksheet settings data shown in Figure 7 will be explained in more detail below with reference to Figure 8.

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

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

[0075] The gate setting data includes, for example, data relating to the type of gate setting in a plot displayed on a worksheet and / or data relating to the gate parameters. The data relating to the type of gate setting includes, for example, data relating to the shape of the gate, which may be, for example, a rectangle, a linear shape, a polygon, an ellipse, or a quadrant. The data relating to the gate parameters may include, for example, data relating to the position and / or size of the gate.

[0076] The axis parameter setting data may include, for example, data relating to the type of field in the plot displayed on the worksheet and / or data relating to the type of signal used in the plot. The type of field may be, for example, forward scatter (FSC), side scatter (SSC), fluorescence, wavelength, channel (type or number of the photodetector channel), number of events, or light intensity. Any of these types of fields may be used as axis parameters. The data relating to the type of signal may be, for example, data relating to area, height, or width.

[0077] The plot setting data may include data relating to the type of plot displayed on the worksheet. The plot type may be, for example, a spectral plot, a histogram plot, a dot plot, or a density plot.

[0078] The axis scale setting data may include data relating to the axis scales of the plot displayed on the worksheet. This data may include, for example, one or more of the following: data relating to the scale of the scale, data relating to the maximum and / or minimum values ​​of the scale, and data relating to whether the setting is associated with a biexponential function.

[0079] The aforementioned statistical setting data may include, for example, data specifying the statistics to be output on the worksheet. These statistics may be one or more of the following: the number of events, the percentage of events within a given gate relative to the total number of events within a parent gate (parent%), the percentage of events within a given gate relative 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] Figure 9 shows an example of the hardware configuration of the server devices that make up the server system 10. The server system 10 may have multiple such server devices. Furthermore, these multiple server devices may reside in a single data center, or they may be distributed and located in multiple data centers in different locations or different countries.

[0081] The server device 1000 shown in Figure 9 comprises a CPU (Central Processing Unit) 1001, RAM 1002, and ROM 1003. The CPU 1001, RAM 1002, and 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 wireless connection. The communication device 1006 allows the server device 1000 to acquire various types of data (e.g., image data) via the network 1011. The acquired data can be stored, for example, in the storage device 1007. The type of communication device 1006 may be appropriately selected by those skilled in the art.

[0084] The storage device 1007 may store an operating system, a program for implementing the information processing method according to this disclosure on a server system, and various other programs, as well as image data, various data used in the information processing method according to this disclosure, and various other data. The operating system may be, for example, a UNIX®-based OS, particularly a LINUX®-based OS, or a WINDOWS®-based OS.

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

[0086] The output unit 1009 may be connected to an output device, such as a display device. The input unit 1010 may accept 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 Figure 10. Figure 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 processes 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 the screen of the output device (particularly the 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 sends a request to the server system 10 to start the interactive analysis process described above. Furthermore, the analysis instruction unit 22 receives input of analysis commands used in the interactive analysis process described above, and transmits said analysis commands to the server system 10. The said analysis commands may include the worksheet setting data described above. The interactive analysis processing unit 12 executes the analysis process by referring to the said worksheet setting data, thereby generating output data according to the said worksheet setting data. Thus, the data analysis client terminal according to this disclosure may be configured to transmit the analysis commands for the output data output to the output device to the server system. In addition, the data analysis client terminal according to this disclosure may be configured to cause the output device to output a window in which the output data is displayed, and to accept input of the analysis commands in that 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 analysis commands entered in the data analysis client terminal 20 to be sent 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 created by the server system 10 based on the fluorescence label intensity data from the server system.

[0094] Figure 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 (especially a computer).

[0095] The information processing device 1100 shown in Figure 11 comprises a CPU (Central Processing Unit) 1101, RAM 1102, and ROM 1103. The CPU 1101, RAM 1102, and ROM 1103 are interconnected via a bus 1004. 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 the network 1111 by wire or wireless connection. The communication device 1106 allows the information processing device 1100 to transmit or receive various types of data via the network 1111. For example, the communication device 1106 can transmit various types of data to or receive various types of data from the server system 10. The type of communication device 1106 may be appropriately selected by those skilled in the art.

[0098] The storage device 1107 may store an operating system, a program for outputting output data by an output data output unit, a program for realizing interactive analysis processing, and various other programs, as well as various data used in information processing according to this disclosure, and various other data. The operating system may be, for example, a UNIX®-based OS, particularly a LINUX®-based OS, or a WINDOWS®-based OS.

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

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

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

[0102] The data acquisition client terminal 30 will be described with reference to Figure 12. Figure 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] The data acquisition unit 31 acquires measurement data transmitted from the biological sample analyzer 40. The measurement data includes light intensity data that is subject to processing by the automatic analysis processing unit 11. This light intensity data may be light intensity data obtained by irradiating a biological sample with light.

[0104] The transmitting unit 32 transmits the measurement data (including light intensity data) acquired by the data acquisition unit 31, or the measurement 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 addition to the measurement data, the transmitting unit 32 may also transmit analysis setting data to the server system 10. Preferably, the transmitting unit 32 transmits the light intensity data, or the light intensity data and analysis setting data, to the server system in response to the acquisition of the light intensity data. For example, the transmitting unit 32 may automatically start transmitting the measurement data or the processed measurement data (and analysis setting data) in response to the acquisition of the measurement data. In this way, a data client terminal according to this disclosure may transmit the light intensity data to the server system 10. Furthermore, a data acquisition client terminal according to this disclosure may be configured to transmit the light intensity data (and analysis setting data) to the server system 10 in response to the acquisition of the light intensity data. In one embodiment of this disclosure, the data acquisition client terminal 30 may perform a calculation process to calculate the fluorescence label intensity data from the light intensity data. This calculation process may be performed 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 fluorescence label intensity data (or the fluorescence 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). Thus, a data client terminal according to this disclosure may transmit the fluorescence label intensity data to the server system 10. Furthermore, a data acquisition client terminal according to this disclosure may be configured to transmit the fluorescence label intensity data to the server system 10 in response to acquiring the fluorescence label intensity data. Furthermore, in this disclosure, the transmission unit 32 may transmit both the measurement data and the fluorescence labeling intensity data to the server system 10.

[0105] The data acquisition client terminal 30 has a separate functional unit called a transmission unit 32, in addition to the data acquisition unit 31, which allows the data acquisition process and the upload process to be executed separately. This makes it possible to upload measurement data (including light intensity data), fluorescence label intensity data calculated from the measurement data, or both of these data to the server system 10 without affecting the data acquisition process from the biological sample analyzer 40.

[0106] The data processing unit 33 may perform predetermined processing on the measurement data and / or fluorescence label intensity data acquired by the data acquisition unit 31. This processing may be compression processing or data processing (e.g., format conversion processing). This data processing allows the measurement data and / or fluorescence label intensity data to be converted into a format suitable for information processing in the server system 10, thereby improving the efficiency of processing in the server system 10. Thus, a data acquisition client terminal according to this disclosure may be configured to perform predetermined processing on the light intensity data or fluorescence label intensity data upon acquisition of the light intensity data or fluorescence label intensity data, and then transmit the processed light intensity data or fluorescence label intensity data to the server system.

[0107] The data storage unit 34 may store the measurement data or the measurement data after processing. The data storage unit 34 may also store the fluorescence label intensity data or the fluorescence label intensity data after processing.

[0108] The hardware configuration example for the data acquisition client terminal 30 is the same as that described for the data analysis client terminal 20 in (3-2) above. Note that the data acquisition client terminal 30 may also be, for example, a general-purpose information processing device (especially 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 thereto. An example of the configuration of the biological sample analyzer is shown in Figure 13. As shown in Figure 13, the biological sample analyzer 40 includes a light irradiation unit 101 that irradiates light onto the biological sample S flowing through the flow path C, a detection unit 102 that detects the 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 the biological sample analyzer 40 include, for example, a flow cytometer and an imaging cytometer. The biological sample analyzer 40 may also include a sorting unit 104 that sorts specific biological particles P within the biological sample. An example of the biological sample analyzer 40 including a sorting unit is, for example, a cell sorter.

[0111] (Biological sample) The biological sample S may be a liquid sample containing biological particles. These biological particles may be, for example, cells or non-cellular biological particles. The cells may be living cells, and more specifically, blood cells such as red blood cells and white blood cells, and germ cells such as sperm and fertilized eggs. The cells may be directly collected from a sample such as whole blood, or they may be cultured cells obtained after culturing. Examples of non-cellular biological particles include extracellular vesicles, particularly exosomes and microvesicles. The biological particles may be labeled with one or more labeling substances (for example, dyes (particularly fluorescent dyes) and fluorescent dye-labeled antibodies). The biological sample analyzer of this disclosure may also analyze particles other than biological particles, and beads may be analyzed for calibration purposes.

[0112] (Flow channel) The channel C may be configured to allow a biological sample to flow, particularly in such a way that the biological particles contained in the biological sample are arranged in a substantially straight line. The channel structure including channel C may be designed to form a laminar flow, and in particular, to form a laminar flow in which the flow of the biological sample (sample flow) is surrounded by the flow of the sheath fluid. The design of the channel structure may be appropriately selected by those skilled in the art, and known designs may be adopted. Channel C may be formed in a channel structure (flow channel structure, particularly a channel structure on which focusing is performed) such as a microchip (a chip having a channel on the order of micrometers) or a flow cell. The width of channel C may be 1 mm or less, and in particular, 10 μm or more and 1 mm or less. Channel C and the channel structure including it may be formed from materials such as plastic or glass.

[0113] The apparatus of this disclosure may be configured such that light from the light irradiation unit irradiates a biological sample, particularly biological particles, flowing through the channel C. The apparatus of this disclosure may be configured such that the light irradiation point (interrogation point) for the biological sample is located within the channel structure in which the channel C is formed, or it may be configured such that the light irradiation point is located outside the channel structure. An example of the former is a configuration in which the light irradiates the channel C in a microchip or flow cell. In the latter case, the light irradiates biological particles after they have exited the channel structure (particularly its nozzle), for example, a Jet-in-Air type flow cytometer.

[0114] (Light-irradiated area) The light irradiation unit 101 includes a light source unit that emits light and a light guide optical system that guides the light to the channel 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 the wavelengths of ultraviolet light, visible light, or infrared light. The light guide optical system includes optical components such as a beam splitter group, a mirror group, or an optical fiber. The light guide optical system may also include a lens group for focusing the light, for example, an objective lens. There may be one or more light irradiation points on the biological sample. The light irradiation unit 101 may be configured to focus light irradiated from one or more different light sources onto a single irradiation point.

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

[0116] The detection unit includes a detection optical system that directs light of a predetermined detection wavelength to a corresponding photodetector. The detection optical system includes a spectroscopic unit such as a prism or diffraction grating, or a wavelength separation unit such as a dichroic mirror or optical filter. The detection optical system may be configured, for example, to spectrally analyze light from biological particles and detect light in different wavelength ranges using multiple photodetectors, more than the number of fluorescent dyes. A flow cytometer including such a detection optical system is called a spectral flow cytometer. Alternatively, the detection optical system may be configured, for example, to separate light corresponding to the fluorescence wavelength range of a fluorescent dye from light from biological particles and to detect the separated light using a corresponding photodetector.

[0117] Furthermore, the detection unit may 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 the 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 treated by the information processing unit as data related to light (hereinafter also referred to as "optical data"). The optical data may be optical data including fluorescence data, for example. More specifically, the optical data may be light intensity data, and the light intensity may be light intensity data of light including fluorescence (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 performs processing of various types of data (e.g., optical data) and a storage unit that stores various types of data.

[0119] If the biological sample analysis device includes a sorting unit as described below, the information processing unit may determine whether to sort biological particles based on optical data and / or morphological information. Based on the result of this determination, the information processing unit may control the sorting unit to sort the biological particles.

[0120] The information processing unit may be configured as a general-purpose computer, for example, as an information processing unit equipped with a CPU, RAM, and ROM. The information processing unit may be contained within a housing that houses the light irradiation unit and the detection unit, or it may be located outside of the housing. The information processing unit may be implemented, for example, by a data acquisition client terminal 30.

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

[0122] The biological sample analysis device 40 may be, for example, a microscope device for performing multicolor fluorescence imaging, and more particularly, a fluorescence microscope device. In recent years, the number of phosphors used in fluorescence imaging has been increasing, and the information processing system of this disclosure may be performed on light intensity data acquired by the microscope device.

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

[0124] The information processing method performed by the information processing system 1 may include an automated analysis processing step. In addition to the automated analysis processing step, the information processing method may also include an interactive analysis processing step that uses the output data generated by the automated analysis processing step. Furthermore, the information processing method performed by the information processing system 1 may include an output data acquisition step of acquiring output data based on analysis result data existing in the server system, and an interactive analysis processing step of using the output data obtained through the acquisition process. The following describes the automatic analysis process, the output data acquisition process, and the interactive analysis process.

[0125] (4-1) Automated analysis process

[0126] The flow of the automated analysis process performed by Information Processing System 1 will be explained with reference to Figure 14.

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

[0128] In step S101, the data acquisition client terminal 30 may acquire, in addition to the light intensity data, supplementary data that is referenced or used in the analysis process of the light intensity data. Such supplementary data may include, for example, data relating to the biological sample itself and / or data relating to the analysis settings of the biological sample. Examples of data relating to the biological sample itself include, for example, the attributes of the biological sample (e.g., the type of organism from which the biological sample originates, the type of bodily fluid from which the biological sample originates, the type of organ from which the biological sample originates, 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 relating to the analysis settings of the biological sample include, but are not limited to, the settings of the analyzer, the unmixing settings, and the measurement conditions. In step S101, the data acquisition client terminal 30 may perform a process to calculate fluorescence label intensity data from 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 supplementary data in addition to the light intensity data. Furthermore, if the process for calculating the fluorescence label intensity data is executed in step S101, the data acquisition client terminal 30 may transmit the fluorescence 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 perform the transmission in response to acquiring light intensity data or fluorescence 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 as a trigger when it starts receiving the light intensity data. This transmission may be performed via the network 50. The data acquisition client terminal 30 may also start transmitting the light intensity data to the server system 10 as a trigger when it has finished receiving the light intensity data, but as described above, triggering the start of transmission of light intensity data can speed up the completion of the upload.

[0131] In step S103, the server system 10 receives light intensity data or fluorescence 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 fluorescence label intensity data in the optical data storage unit 15.

[0132] In step S104, the server system 10 performs an automatic analysis process on the light intensity data or the fluorescence label intensity data. This automatic analysis process is performed particularly by the automatic analysis processing unit 11. In this automatic analysis process, the server system 10 calculates the fluorescence label intensity data from the light intensity data and can perform an analysis process on the fluorescence label intensity data to generate output data. Furthermore, if the server system 10 receives fluorescence label intensity data, in the automatic analysis process, the server system 10 can perform an analysis process on the received fluorescence label intensity data without performing the fluorescence label intensity data calculation process. The details of the process in step S104 will be explained below with reference to Figure 15.

[0133] In step S151 shown in Figure 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 fluorescence label intensity data) in the optical 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 performed by the automated analysis processing unit 11 may be executed, for example, in a serverless manner, and may be configured as a so-called serverless architecture. Within this specification, "serverless processing" does not mean processing that does not use a server, but rather processing that executes only predetermined information processing in an event-driven manner on a pre-built server architecture, that is, processing that information processing on a function-by-function basis. The pre-built server architecture may be one that has been built in advance by a service provider that provides analysis services to users of data analysis client terminals 20 via a server system 10, or it may be one that has been built by a so-called cloud service provider. If the server system 10 is, for example, Amazon Web Services (trademark), the processing by the automated analysis processing unit 11 may be executed 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 automated analysis processing unit 11 secures computation resources on the server system 10 for performing the analysis of the light intensity data (or fluorescence label intensity data). These computation resources function as a virtual server for performing the analysis. In step S152, the virtual server is started.

[0136] In step S153, the automated analysis processing unit 11 (particularly the virtual server) downloads the light intensity data (or fluorescence labeling intensity data) stored in the optical data storage unit 15.

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

[0138] In step S155, the automatic analysis processing unit 11 (particularly the virtual server) may perform a process to calculate fluorescence label intensity data from the light intensity data. Then, the automatic analysis processing unit 11 performs a process to generate analysis result data using the calculated fluorescence label intensity data. Note that if the server system 10 receives fluorescence label intensity data in step S103, this calculation process may be omitted. The process for calculating the fluorescence label intensity data may be performed using the data used for calculating the fluorescence label intensity data as described in step S154. This calculation process may include, for example, a process of obtaining fluorescence label intensity data by performing an unmixing process on the light intensity data using the said data. Furthermore, the automated analysis processing unit 11 may perform advanced analysis processes such as clustering and dimensionality reduction. The process for generating the aforementioned analysis result data may be performed using the analysis command mentioned in step S154. This generation process may include, for example, using the analysis command to generate analysis result data that includes one or more of the following: a plot image, a clustering result display figure, and statistics, based on the fluorescence label intensity data.

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

[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. This output data may be a part of the analysis result data. This output data may include, for example, one or more of the following: a two-dimensional plot image, a spectral plot image, and a clustering result display diagram. This output data may include data for generating these images, but may not include the fluorescence label intensity data used in the analysis process. This output data may be configured so that the data analysis client terminal 20 can edit these images. This allows the data analysis client terminal 20 to perform editing on the output data, which is easy to use in the interactive analysis process described later. In addition, this output data may include numerical data, such as statistical analysis result data.

[0141] Within this specification, "analysis result data" means data generated by the analysis process in the server system 10. "Output data" is a part of the said analysis result data, and in particular, data used for outputting analysis results on the data analysis client terminal 20.

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

[0143] In step S105, the automated analysis processing unit 11 (particularly the virtual server) sends an automated analysis completion notification to the data analysis client terminal 20. This automated analysis completion notification may be sent by email or by a notification method using server-side push technology. Upon completion of sending the automated analysis completion notification, the automated analysis processing unit 11 stops the virtual server. This prevents the virtual server from running for an unnecessarily long time and reduces 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 the output device. In response to receiving the automatic analysis completion notification, the data analysis client terminal 20 may display a screen asking the user whether to send an output data request to the server system 10. This screen may include, for example, a button to prompt the data analysis client terminal 20 to send an output data request to the server system 10.

[0145] In step S107, the data analysis client terminal 20 sends an output data request to the server system 10. For example, the data analysis client terminal 20 may perform this transmission when a button on the screen is clicked or selected by the user of the data analysis client terminal 20.

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

[0147] In step S109, the server system 10 sends 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) causes the output data to be output to the output device. An example of the output data output to the output device is shown in Figure 23. As shown in Figure 23, the output device displays a window on which the output data is displayed. In the lower left of this window, seven plot images are displayed. To the right of this window, one clustering result display diagram is displayed. Furthermore, in the upper left of this window, statistical data corresponding to each gate (number of events, Parent%, and Total%) is displayed. Thus, the window may display image data and / or statistical data based on the output data. The image data may include one or more plot images and / or one or more clustering result display diagrams as described above. The statistical data may include one or more statistics.

[0150] The automated analysis process described above allows virtual servers to be run only when necessary. Therefore, running costs can be significantly reduced compared to always-on servers. Scalability can also be ensured.

[0151] (4-2) Example of automated analysis process

[0152] Below, an example of the automated analysis processing flow when the server system 10 is Amazon Web Services (trademark) will be explained with reference to Figures 14 and 15.

[0153] In step S101, the data acquisition client terminal 30 acquires light intensity data of a biological sample from the 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 having acquired 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 an Amazon S3 bucket that functions as an optical data storage unit 15.

[0156] In step S104, the server system 10 performs an automated analysis process on the light intensity data. The details of the process in step S104 are described below with reference to Figure 15.

[0157] In step S151, in response to an object containing, for example, light intensity data being uploaded to an Amazon S3 bucket that functions as an optical data storage unit 15, the AWS Lambda functioning as an automatic analysis processing unit 11 starts the automatic analysis process in step S151.

[0158] In step S152, AWS Lambda reserves computation resources on the server system 10 to perform the analysis process described later. Specifically, it launches an Amazon EC2 instance as a virtual server to perform the analysis process described later in step S155. AWS Lambda may be terminated once the Amazon EC2 instance has been launched.

[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 retrieves the data necessary for the analysis, which is pre-stored in Amazon Aurora. The data necessary 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 fluorescence label intensity data from the light intensity data, for example, using the spectral reference data. This fluorescence label intensity data may be stored in the bucket. Furthermore, the instance generates analysis result data using the calculated fluorescence label intensity data.

[0162] The instance stores the analysis result data generated in step S155 in the bucket. The instance may also store the fluorescence labeling 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. For example, the instance identifies or extracts output data from the analysis result data.

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

[0165] The light intensity data acquired in step S101 may be one or more. If there are multiple light intensity data, these may be, for example, multiple light intensity data acquired by measuring each of multiple biological samples. In this case, in step S102, the data acquisition client terminal 30 transmits the multiple light intensity data to the server system 10. The data acquisition client terminal 30 may also transmit data necessary for analysis to the server system 10. Next, in step S103, the server system 10 may record the multiple light intensity data and / or the data necessary for the analysis in a database such as Amazon DynamoDB. Next, in step S104, the instance may perform the automatic analysis process for each of the multiple light intensity data. The instance may update the database upon completion of the automatic analysis process. If the analysis process has been completed for all of the multiple light intensity data, the instance may proceed to step S105.

[0166] In step S105, the instance sends an automatic analysis completion notification to the data analysis client terminal 20. The instance may be stopped once the transmission of the automatic analysis completion notification is complete.

[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 the output device. In response to receiving the automatic analysis completion notification, the data analysis client terminal 20 may display a screen asking the user whether to send an output data request to the server system 10. This screen may include, for example, a button to prompt the data analysis client terminal 20 to send an output data request to the server system 10.

[0168] In step S107, the data analysis client terminal 20 sends an output data request to the server system 10. For example, the data analysis client terminal 20 may perform this transmission when a button on the screen is clicked or selected by the user of the data analysis client terminal 20.

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

[0170] In step S109, the server system 10 sends 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 data to be output to the output device.

[0173] (4-3) Data acquisition process for output

[0174] The data acquisition process for output by Information Processing System 1 will be explained below with reference to Figure 16.

[0175] In step S201, the data analysis client terminal 20 sends 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 may include information used to identify analysis result data and / or information used to generate output data from analysis result data. In addition to the request, the information sent in step S201 may also include user authentication information of the data analysis client terminal 20. The transmission in step S201 may be performed via the network 50.

[0176] In step S202, the server system 10 receives an output data request sent 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 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 the following: 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 fluorescence label intensity data used in the analysis process. The output data may be configured to allow editing of these images. 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 performed by a virtual server that is always running within the server system 10. The continuous operation of this virtual server allows for high-speed execution of the output data acquisition process. Furthermore, the output data generation unit 13 may be configured as a serverless architecture.

[0179] The data acquisition or generation process for output data by the output data generation unit 13 may be performed in a container created or managed by, for example, Amazon ECS if the server system 10 is, for example, Amazon Web Services (trademark). In such a container, for example, AWS Fargate may perform 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 output data to the data analysis client terminal 20 via the network 50.

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

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

[0183] (4-4) Interactive analysis processing

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

[0185] In step S301, the data analysis client terminal 20 sends 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 the output device to output output data. For example, step S301 may be executed after step S107 as described in (4-1) above, or after step S206 as 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 like the one shown in Figure 20. Then, in step S206, the acquired output data (plot data) is displayed in the window as shown in Figure 21.

[0187] The aforementioned request includes, for example, identification data used to identify data to be analyzed for interactive analysis processing. This identification data includes, for example, information identifying a biological sample and information identifying an 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, the server system 10 (particularly the interactive analysis processing unit 12) reserves computation resources on the server system 10 for executing the interactive analysis process in response to receiving the request. These computation resources function as a virtual server for executing the interactive analysis process.

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

[0191] The data to be analyzed may include analysis result data derived from the output data. Furthermore, the data to be analyzed may also include light intensity data and / or fluorescence labeling intensity data derived from the analysis result data. After acquiring the data to be analyzed in step S304, the interactive analysis processing unit 12 may monitor whether an analysis command described later has been received, that is, wait until the said analysis command is received.

[0192] After step S304 is executed 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 such a session, the server system 10 may send a notification to the data analysis client terminal 20 informing it that computation resources have been allocated. The establishment may then be performed upon receipt of such notification. This notification may be, for example, a notification using server-side push technology. Alternatively, the establishment may be performed by obtaining the status from the data analysis client terminal 20 to the server system 10 by polling. By establishing the RPC, the server system 10 can execute analysis processing in accordance with the analysis commands sent from the data analysis client terminal 20 from step S305 onward. Both the server system 10 and the data analysis client terminal 20 may include connection units, which are functional elements 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. MagicOnion can be given as an example of these connection units, 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 the analysis. The data necessary for the analysis may include, for example, data used for calculating fluorescence labeling intensity data and data used in the analysis processing of fluorescence labeling data. The data used to calculate the fluorescence labeling intensity data may include, for example, spectral reference data. This data is used for unmixing. The data used in the analysis process for fluorescence intensity labeling data may further include the aforementioned analysis commands.

[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 enters the analysis command through this 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 modified. For example, the input analysis command may include at least one of the following: a modified gate setting command, a modified plot setting command, a modified axis setting command, a modified axis display setting command, a modified statistics setting command, and a modified region setting command. The analysis command may also include a modified clustering command.

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

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

[0197] The processing performed by the interactive analysis processing unit 12 may be executed serverlessly, for example, and may be configured as a so-called serverless architecture. If the server system 10 is, for example, Amazon Web Services (trademark), the processing performed by the interactive analysis processing unit 12 may be executed by, for example, AWS Lambda, and in particular 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 to calculate fluorescence label intensity data from the light intensity data. Then, the interactive analysis processing unit 12 executes a process to generate analysis result data using the calculated fluorescence label intensity data. If there are no changes to the fluorescence labeling intensity data, the interactive analysis processing unit 12 may omit the process of calculating the fluorescence labeling intensity data, that is, it may only perform the process of generating analysis result data using the existing fluorescence labeling intensity data. The interactive analysis processing unit 12 stores the analysis result data generated in step S308 in, for example, the analysis result data storage unit 16. The interactive analysis processing unit 12 also stores the fluorescence label intensity data generated in step S308 in, for example, the optical data storage unit. These storage processes may be performed, for example, after the processing in step S308, or after the processing in step S310.

[0199] In step S309, the interactive analysis processing unit 12 generates output data to be output to 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. This output data may be a part of the analysis result data. This output data may include, for example, one or more of the following: a two-dimensional plot image, a spectral plot image, and a clustering result display diagram. This output data may include data for generating these images, but may not include the fluorescence label intensity data used in the analysis process. This output data may be configured to allow editing of these images. This allows, for example, the data analysis client terminal to perform editing 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 sends the generated output data to the data analysis client terminal 20. The output data may be sent for each data unit that makes up the output data, or the entire output data may be sent as a whole. Regarding the former, for example, it is conceivable that the output data includes two components: 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, once the output statistical data is generated, the server system 10 may transmit the statistical data, and then, once the output image data is generated, the server system 10 may transmit the image data. Alternatively, the opposite may occur: the image data may be transmitted first, followed by the statistical data. 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 Figure 22, a plot image in which a portion of the plot image in Figure 21 has been modified is displayed in the window.

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

[0205] After the interactive analysis processing is completed, the interactive analysis processing unit 12 stops the virtual server. This prevents the virtual server from running for an unnecessarily long time, thereby reducing the cost of using the server. The interactive analysis process may be terminated, for example, when the data analysis client terminal 20 receives a termination instruction for the interactive analysis process. Upon receiving the termination instruction, the data analysis client terminal 20 sends the termination instruction to the server system 10. Upon receiving the termination instruction, the interactive analysis processing unit 12 stops the virtual server. This prevents the virtual server from running for an unnecessarily long time and reduces the cost of using the server. Alternatively, the interactive analysis processing unit 12 may stop the virtual server if a predetermined amount of time has elapsed without the server system 10 receiving an analysis command from the data analysis client terminal 20.

[0206] In the interactive analysis process described above, virtual servers can be run only when necessary. Therefore, running costs can be significantly reduced compared to always-on servers. Scalability can also be ensured.

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

[0208] Below, we will explain an example of the interactive analysis processing flow in Figure 17 when the server system 10 is Amazon Web Services (trademark), with further reference to Figures 18 and 19.

[0209] As shown in Figure 18, in step S301, the data analysis client terminal 20 sends 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, the AWS Lambda function, which functions as the interactive analysis processing unit 12, reserves computation resources on the server system 10 for executing the interactive analysis processing in response to receiving the request. Specifically, AWS Lambda causes Amazon ECS to run a container (specifically a Docker container) within Amazon EC2. The processing of the virtual server that executes the analysis processing described later is executed within that container. Alternatively, the processing of the virtual server may be executed without building the container.

[0212] In step S304, the instance retrieves the data to be analyzed, for example, stored in Amazon S3. Furthermore, in step S304, the instance may acquire data necessary for the analysis. For example, the instance may acquire data necessary for the analysis that is pre-stored in Amazon Aurora (such as data used to calculate fluorescence label intensity data). After step S304 is executed 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 it that computation resources have been allocated within Amazon EC2. The establishment of the session may occur upon receipt of such notification. Both the server system 10 and the data analysis client terminal 20 may be equipped with MagicOnion as a connection component, which is a functional element for executing RPCs. After acquiring the data to be analyzed in step S304, the instance monitors whether the analysis command described below has been received, that is, waits until the analysis command is received.

[0213] As shown in Figure 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 sends the analysis command entered in step S305 to the server system 10.

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

[0216] In step S308, the instance performs a process to calculate fluorescence label intensity data from the light intensity data. Then, the instance performs a process to generate analysis result data using the calculated fluorescence label intensity data. If there are no changes to the fluorescence labeling intensity data, the instance may omit the fluorescence labeling intensity data, meaning it may only perform the process of generating analysis result data using the existing fluorescence labeling intensity data. The instance saves the analysis result data generated in step S308 to a bucket, for example, in S3. The interactive analysis processing unit 12 also saves the fluorescence label intensity data generated in step S308 to the same 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 an analysis command at 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 constituted by a server group existing in each of a plurality of geographically dispersed data centers. The plurality of data centers may be dispersed and exist in a plurality of countries, for example. Each data center may be referred to as a region.

[0223] In the present disclosure, according to the location where the data analysis client terminal 20 and / or the data acquisition client terminal 30 exist, the server system 10 identifies, from among the plurality of data centers, a data center that executes automatic analysis processing and / or interactive analysis processing according to the present disclosure, and a server in the identified data center can execute these processings. Preferably, the server system 10 can identify, from among the plurality of data centers, a data center closer to said location as the data center that executes said processing.

[0224] In the present disclosure, according to preset information such as information relating to the location where the data analysis client terminal 20 and / or the data acquisition client terminal 30 exist or contract information, the server system 10 identifies, from among the plurality of data centers, a data center to which data used in the processing of the present disclosure is uploaded or stored, and the data can be uploaded or stored in a server within the identified data center. Preferably, the server system 10 can identify, from among the plurality of data centers, a data center closer to said location as the data center where said uploading or storing is performed.

[0225] Particularly preferably, the server that performs the processing and the server on which the upload or storage takes place may be located within the same data center.

[0226] As described above, selecting a data center can accelerate the processing required by this disclosure. For example, by configuring the data center where data is stored to be configurable on a per-user or per-contract basis, users can utilize a data center geographically closer to them, enabling faster data uploads and faster response times during interactive analysis.

[0227] (6) Data migration

[0228] In this disclosure, the optical data storage unit may include two or more types of storage with different access speeds. Furthermore, the server system may perform a migration process to move the optical intensity data and / or the fluorescence label intensity data stored in the storage with a higher access speed (hereinafter also referred to as "hot storage") to the storage with a lower access speed (hereinafter also referred to as "cold storage") when predetermined conditions are met.

[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 cost, and so-called cold storage, which has a slower response time to data access but a lower unit cost. In this disclosure, as described above, it is possible to migrate data from hot storage to cold storage by performing the migration process, thereby reducing costs. The predetermined conditions for performing the migration process may be set in advance by the user. For example, the predetermined conditions may be that the migration process is performed when a predetermined number of days have passed since the last access to the data and there has been no access to the data.

[0230] Preferably, the server system compresses the data subject to the migration process before executing the migration process. This further reduces storage costs.

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

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

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

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

[0235] External storage located outside the server system 10 may be, for example, online storage owned by a user of an information processing system compliant with this disclosure. The server system 10 (particularly the automatic analysis processing unit 11) may start the automatic analysis processing 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 light intensity data has been stored. Upon receiving such notification, the automatic analysis processing unit 11 may execute the event-driven analysis processing described in (4-1) above as a trigger.

[0236] In this case, steps S152 and S154 to S157 may be performed 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 optical data storage unit 15.

[0237] Furthermore, although (4-1) above states that the automatic analysis processing unit 11 secures computation resources on the server system 10 for executing the analysis process, these computation resources may be located outside the server system 10. For example, computation resources for executing the analysis process may be secured within an information processing device located outside the server system 10.

[0238] In recent years, research institutions such as universities and corporate laboratories have been acquiring 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 location or by performing analysis processing using external computational resources, as described above, information processing systems in accordance with this disclosure allow users to further reduce the operating costs of server systems.

[0239] (8) Splitting of data to be analyzed

[0240] In step S155 described in (4-2) above and step S309 described in (4-4) above, a process is performed to calculate fluorescence label intensity data from light intensity data. This process includes, for example, fluorescence correction or unmixing, and these processes are performed especially when the biological sample analyzer is a biological particle analyzer such as a flow cytometer. The 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) in accordance with this disclosure may, in the calculation process for calculating fluorescence 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, for example, concurrently. For example, the server system (particularly an automated analysis processing unit) may launch multiple AWS Lambdas and assign each of the divided light intensity data to each virtual server. Then, each virtual server may perform the calculation process for each of the divided light intensity data assigned to it.

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

[0243] (9) Data sharing

[0244] The metadata (particularly analysis setting data), light intensity data, and fluorescence label intensity data described in (4-1) above may be stored in the storage or memory unit included in the server system 10 in this disclosure. In this disclosure, these data may be stored in the server system 10 in a manner that is related to one another. The relationship between these data makes it easier to reproduce the measurement and / or the analysis of the measurement results. Further, the auxiliary data described in (4-1) above, the data used for calculating fluorescence-labeled intensity data, and the data used in analysis processing for fluorescence intensity labeled data may also be stored in the server system 10 in a state of being associated with each other. The association of these data with each other facilitates reproduction of measurement and / or reproduction of analysis of measurement results.

[0245] Further, these data (particularly these 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. That is, these data may be shared by two or more data analysis client terminals and / or data acquisition client terminals. For example, in the present disclosure, a plurality of data analysis client terminals can share one or more of light intensity data, fluorescence-labeled intensity data, and 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. Further, in the present disclosure, a plurality of data acquisition client terminals can share one or more of light intensity data, fluorescence-labeled intensity data, and analysis setting data in the server system. Particularly preferably, 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) as described above, information related to measurement and / or analysis performed by a certain user can be reused by other users, which facilitates reproduction of measurement and / or reproduction of analysis of measurement results.

[0246] (10) Unification of output data

[0247] The output data generated by the automated 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 types of output data may have the same metadata used to output them to an output device. This makes it easier to use, for example, the output data generated by the automated 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 automated analysis process on newly acquired light intensity data.

[0248] Since the output data is displayed on a worksheet with a similar user interface, users can view and execute automated analysis processing, output data generation processing, and interactive analysis processing on a consistent user interface. Furthermore, because the various analysis settings used for measurement are saved along with the analysis results, measurements and analyses can be easily reproduced using the same settings. These mechanisms, along with the standardization of data between devices, are crucial. By combining different technologies, it becomes easy to reproduce the same experiment across multiple different devices.

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

[0250] As described in (4-1) above, in this disclosure, the data analysis client terminal 20 causes the output device to output output data including a clustering result display diagram. The clustering result display diagram may be, for example, the clustering result display diagram shown in Figure 23, which is the 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 will also increase. In such cases, displaying data for all markers in the clustering results diagram can make the diagram complex, making it difficult to grasp the expression levels of each marker. For example, in FlowSOM's star chart and population pie chart, if the number of marker types displayed in each cluster increases, it can become difficult to grasp the expression levels of each cluster.

[0252] In a preferred embodiment of this disclosure, the data analysis client terminal 20 may be configured to change the number of markers displayed in the clustering results display diagram, and more specifically, to select markers to be included in each cluster in the clustering results display diagram. The ability to select markers to be included in each cluster allows users to customize the clustering result display according to 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, the data analysis client terminal 20 may form a clustering result display diagram for the selected one or more markers when one or more markers are selected from all the markers included in the output data. The formed clustering result display diagram may not include data for the one or more surface markers that were not selected. In other words, depending on the selection made, the data analysis client terminal 20 may form a clustering result display diagram based on the data of the selected one or more markers. The clustering result display diagram may be a star chart or a pie chart, but is not limited to these.

[0254] An example of the clustering result display diagram being a star chart will be further explained below with reference to the diagram.

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

[0256] In this case, in response to the user selecting (clicking or touching) the star chart, for example, or to the user selecting a predetermined button, the data analysis client terminal 20 causes the output device to display a marker selection window 501, as shown in Figure 25. The window includes a group of markers displayed in the star chart, a color-coded display element 502 indicating the color corresponding to each marker, and a marker list 503 displayed in the star chart. Here, the marker list is configured to allow selection of whether or not to display each marker in the star chart. Furthermore, although the color-coded display elements are shown as a pie in the figure, the color-coded display elements only need to be shown in a way that the markers and colors are associated, and other display formats are also acceptable.

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

[0258] When the five markers mentioned above are selected, the data analysis client terminal 20 displays only the color of the selected marker in the color-coded display element 513, as shown in the figure. This allows the user to understand the contents of the star chart after marker selection.

[0259] Depending on which of the five markers is selected, the data analysis client terminal 20 also changes the star chart shown in Figure 24. For example, depending on which of the five markers is selected, the data analysis client terminal 20 changes the star chart 500 to the star chart 510 shown in Figure 27.

[0260] As can be seen from the comparison of the enlarged view 504 on the left side of Figure 24 and the enlarged view 514 on the left side of Figure 27, the data analysis client terminal 20 changes the data elements (number of colors) displayed within 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. Furthermore, the change to the clustering result display diagram may be performed in such a way as to display only the data for the marker selected in the marker list (data based on expression levels and / or the color corresponding to each marker).

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

[0263] Although the above example shows a reduction in the number of markers, the number of markers may also be increased. Furthermore, in conjunction with the selection of markers, the data analysis client terminal 20 may change the color-coded display elements in the marker selection window. In addition, in conjunction with the selection of markers, the data analysis client terminal 20 may also change the clustering result display diagram. For example, Figure 28 shows that 10 markers have been selected in the marker list 522 within the marker selection window 521. The data analysis client terminal 20 displays the color-coded display element 523, which is color-coded to 10, in response to this selection. The data analysis client terminal 20 may then display the clustering result display diagram 520, as shown in Figure 29, in response to this selection. Furthermore, Figure 30 shows that three markers have been selected in the marker list 532 within the marker selection window 531. The data analysis client terminal 20 displays three color-coded display elements 533 in response to this selection. Then, in response to this selection, the data analysis client terminal 20 may display the clustering result display diagram 530 as shown in Figure 31. Furthermore, the display order of markers within the star chart may also be arbitrarily changed. That is, the data analysis client terminal 20 may be configured to change the display order of markers within the star chart. This change in display order may be performed, for example, in response to user operations on the marker list or color-coded display elements in the marker selection window, or in response to user operations on the star chart itself. For example, the data analysis client terminal 20 may change the display order in response to a user changing the relative positions of two or more selected markers in the marker list through a predetermined operation (e.g., drag operation). 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 elements through a predetermined operation (e.g., dragging). The data analysis client terminal 20 may also change the display order through a similar operation on nodes in a star chart.

[0264] Furthermore, in the above description, the data analysis client terminal 20 performs the modification of the clustering result display diagram, but the modification of the clustering result display diagram may also be performed by the server system 10. For example, when a user selects a marker in the marker selection window 501, the data analysis client terminal 20 sends data about the selected marker to the server system 10. The server system 10 then generates a modified clustering result display diagram based on the data about the selected marker and sends the modified clustering result display diagram to the data analysis client terminal 20. The data analysis client terminal 20 may then display the modified clustering result display diagram on an output device.

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

[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 meta-cluster function that groups clusters with similar marker expression tendencies, but star charts and population pie charts are displayed on a cluster basis. In such cases, if all clusters are displayed in the clustering result display diagram, it may become difficult to grasp the overall trend and overview of marker expression levels in the measured sample (e.g., the trend of expression levels at the meta-cluster level) from the clustering result display diagram.

[0267] Figure 32 shows an example of a clustering result display diagram (star chart) containing numerous nodes. In the clustering result display diagram 600 of the same figure, many clusters are displayed, and for example, in the area enclosed by the dotted line 601, many clusters are displayed overlapping, making it difficult to confirm the expression level trends in these clusters. Even when this area is enlarged, as shown on the right of the figure, it is difficult to confirm the expression level trends. In addition, it is even more difficult to grasp the expression level trends and characteristics at the metacluster level, which is a grouping of multiple clusters, and as a result, it is difficult to grasp an overview of the expression trend for the entire sample.

[0268] In a preferred embodiment of this disclosure, the data analysis client terminal 20 may be configured to output star charts and population pie charts in metacluster units, which are groups of one or more clusters having similar expression trends. The metaclusters may be formed by automatically grouping clusters with similar marker expression tendencies using an algorithm, or they may be formed in response to a user selecting one or more clusters themselves. This type of clustering result output diagram at the metacluster level, i.e., a metacluster chart, makes it easier to understand trends in expression levels.

[0269] An example of a metacluster chart formed from a clustering result display diagram is shown in Figure 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, 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 multiple clusters in the clustering result display figure 600 can be classified into seven cluster groups (referred to as meta-clusters 611 to 617) as shown in the figure. Each meta-cluster has common characteristics (for example, common characteristics regarding the expression status of markers). Cluster groups belonging to the same meta-cluster are assigned the same color. Conversely, meta-clusters are assigned different colors from each other. The data analysis client terminal 20 generates metacluster nodes for each of these seven metaclusters based on data from one or more clusters belonging to each metacluster. A metacluster chart 602, including the seven generated metacluster nodes 621-627, is shown on the right of the figure. The color of metacluster node 621 is the same as the color of metacluster 611. Similarly, the colors of metacluster nodes 622-627 are the same as the colors of metaclusters 612-617, respectively. In this way, assigning the same color to metaclusters and metacluster nodes before and after the formation of the metacluster chart makes it easier to understand the relationship between metaclusters and metacluster nodes.

[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 cells belonging to the metacluster node. In the figure, metacluster nodes containing more events have circles with larger diameters.

[0272] Furthermore, the data analysis client terminal 20 may place each metacluster node in the metacluster chart at a location determined based on the location of one or more clusters belonging to each metacluster node. For example, the data analysis client terminal 20 may determine the location of each metacluster node in the metacluster chart based on the number of events contained in each of the one or more clusters belonging to each metacluster node and the location of each of those clusters. In one example, the location of each metacluster node may be the centroid of the one or more clusters integrated into that metacluster node.

[0273] In addition, it is not necessary to specify the location of the metacluster nodes 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, as shown in the metacluster chart 603 of Figure 34, or it may arrange the generated one or more metacluster nodes in a grid.

[0274] Furthermore, in the above description, the data analysis client terminal 20 forms a metacluster chart from the clustering result display diagram, but the formation of the metacluster chart may be performed by the server system 10. For example, a user performs a predetermined operation to form a metacluster chart from a clustering result display diagram. Upon receiving this predetermined operation, the data analysis client terminal 20 sends instruction data to the server system 10 to generate a metacluster chart from the clustering result display diagram. Upon receiving this instruction data, the server system 10 generates a metacluster chart from the clustering result display diagram and sends 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) Relationship between metaclusters and two-dimensional plots in clustering result display diagrams

[0276] As described in (12) above, multiple clusters in the clustering result display can be classified into metaclusters consisting of one or more clusters that share common characteristics. If the metaclusters in the clustering result display can be associated with events in the two-dimensional plot, the events belonging to the metaclusters can be intuitively understood.

[0277] In a preferred embodiment of this disclosure, the data analysis client terminal 20 may display a two-dimensional plot so that, depending on the selection of a metacluster in the clustering result display diagram, it can identify events belonging to that metacluster from among the events included in the two-dimensional plot. For example, the data analysis client terminal 20 may assign a color common to the metacluster to the events belonging to that metacluster in the two-dimensional plot. This type of two-dimensional plot display allows for an intuitive understanding of events belonging to metaclusters, and also enables backgating.

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

[0279] As shown in Figure 37, the user selects the area 634 enclosed by the dashed line by dragging the mouse cursor from the position of reference numeral 632 to the position of reference numeral 633. The selection of area 634 also selects the meta-clusters 611-617 that overlap with it. In response to the selection of meta-clusters 611-617, the data analysis client terminal 20 assigns the corresponding color to the events belonging to each meta-cluster in the two-dimensional plots 630 and 631, as shown in the same figure. This color-coding display method allows for an intuitive understanding of events belonging to meta-clusters and also enables backgating.

[0280] In the figure, an example is shown where multiple metaclusters are selected by dragging; however, one or more metaclusters may also be selected by clicking, for example. In this case as well, the events added to the selected metaclusters may be colored according to the color assigned to each metacluster.

[0281] (14) Control of the pie display axis in the star chart

[0282] As shown in the diagrams described in (11) to (13) above, the expression level data of marker groups may be displayed as a pie in each node (cluster) of a star chart. For example, the higher the expression level of a marker, the longer the radial length of the region (pi) corresponding to that marker. For example, in the node 700 shown in Figure 38, the expression levels of multiple markers are displayed as a pie. The expression level of each marker corresponds to the radial length; for example, the expression level of the marker corresponding to the region indicated by reference numeral 701 corresponds to the radial length 702. In the same figure, the length 702 is indicated by an arrow, but such an arrow does not need to be displayed in the star chart.

[0283] In this disclosure, the data analysis client terminal 20 may be configured 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 that node. Here, each node, which is a collection of events, does not have to be a cluster formed by performing clustering. Each node may be formed, for example, based on a group of events present within a gate when a gate is created on a two-dimensional plot. The ability to change the axis settings in this way makes it easier to grasp the degree of expression. In addition, it becomes possible to more accurately understand the relationship with the two-dimensional plot.

[0284] Examples of axis scales for a two-dimensional plot include the Linear scale, Log scale, and Biexponential scale. Similarly, examples of axis scales for the aforementioned display include the Linear scale, Log scale, and Biexponential scale. In this disclosure, the data analysis client terminal 20 may adopt any of these as the axis scale for the pie display in each node. For example, if a biexponential scale is adopted as the axis scale for a two-dimensional plot, the data analysis client terminal 20 may adopt a biexponential scale as the axis scale for the pie display of one or more nodes corresponding to that two-dimensional plot.

[0285] In this disclosure, the data analysis client terminal 20 may be configured to output a two-dimensional plot settings window in order to change the axis settings as described above. Let's assume that the data analysis client terminal 20 is displaying, for example, the two-dimensional plot data 710 shown in Figure 39 on the output device as output data.

[0286] In this case, for example, when a user performs a predetermined operation, the data analysis client terminal 20 displays a plot setting window 711 on the output device. The plot setting window has areas 712 and 713 for adjusting the settings of the X and Y axes of the two-dimensional plot data 710, as shown in the figure. Area 712 for setting the X axis has a list box 714 for selecting the axis scale of the X axis, as shown in the figure. In the figure, "Biexponential" is displayed, but the list box is configured so that "Linear" and "Log" can also be selected in addition to "Biexponential". Area 713 for setting the Y axis also has a list box 715 for selecting the axis scale of the Y axis. This list box is also configured so that any of the three axis scales can be selected.

[0287] When a user selects an axis scale for the X and / or Y axis in the plot settings 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 settings window, in accordance with the selected axis scale. This change is made so that the axis scale of the pie display matches the axis scale selected in the plot settings.

[0288] This disclosure provides the information processing system described above, as well as a server system, a data acquisition client terminal, and a data analysis client terminal included in said information processing system. Details thereof are as described above.

[0289] 2. Information Processing Method

[0290] This 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 that performs analysis on light intensity data obtained by irradiating a biological sample with light or on fluorescence label intensity data calculated from said light intensity data to generate output data; an analysis result data storage step that stores the output data generated based on the light intensity data or the fluorescence label intensity data; and an interactive analysis processing step that analyzes the fluorescence label intensity data based on an analysis command for the output data output to an output device and outputs analysis result data. The description in (4) above applies to each of these steps.

[0291] Furthermore, this disclosure may also take the following form. [1] An automated analysis processing unit that generates output data by performing analysis processing on light intensity data or fluorescence label intensity data obtained 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 fluorescence labeling intensity data, An interactive analysis processing unit analyzes the fluorescence label intensity data based on an analysis command for the output data output to the output device and outputs analysis result data. A server system including this. [2] The server system described in [1], wherein the automatic analysis processing unit calculates fluorescence labeling intensity data from the light intensity data. [3] The server system described in [1] or [2], wherein the processing by the automated analysis processing unit and the processing by the interactive analysis processing unit are executed on different computation resources. [4] The server system is one of the following: [1] to [3], wherein the server system reserves computation 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 according to 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 for storing 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 performs a process of transferring the light intensity data and / or the fluorescence label intensity data stored in storage with a higher access speed to storage with a lower access speed when predetermined conditions are 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 dimensionality-reduced 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 fluorescence label intensity data by performing calculation processing on said light intensity data; and A light data storage unit that stores the light intensity data or the fluorescence label intensity data transmitted from the data acquisition client terminal, An automatic analysis processing unit that generates output data by performing an analysis on the light intensity data or the fluorescence labeling intensity data obtained from the above, An analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescence labeling intensity data, A server system including: an interactive analysis processing unit that analyzes the fluorescence label intensity data based on analysis commands for the output data output to the output device and outputs analysis result data. An information processing system that includes this.

[10] The information processing system according to [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 performs predetermined processing on the light intensity data or the fluorescent label intensity data in response to acquiring the light intensity data or the fluorescent label intensity data, and transmits the processed light intensity data or fluorescent label intensity data to the server system.

[12] The server system has in advance the analysis setting data used in the processing by the automatic analysis processing unit, The automated analysis processing unit uses the analysis setting data to calculate the fluorescence labeling intensity data from the light intensity data. An information processing system described in 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 performs a process to calculate fluorescence 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 described above further includes a data analysis client terminal including the output device, as described in any one of [9] to

[13] .

[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 data analysis client terminal causes the output device to output a window displaying the output data, and accepts input of the analysis command in the window, as described in

[14] or

[15] , the information processing system described in

[14] or

[15] .

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

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

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

[17] , wherein multiple data acquisition client terminals can share analysis setting data within the server system.

[19] A data acquisition unit that acquires light intensity data obtained by irradiating a biological sample with light, The system includes a transmission unit that transmits the light intensity data to a server system in response to the acquisition of the light intensity data. In the server system, fluorescence labeling 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 fluorescence labeling intensity data from the server system, A processing unit that performs processing to output the output data to the output device, A data analysis client terminal that includes this feature. 〔twenty one〕 The data analysis client terminal described in

[20] causes the output device to output a window on which the output data is displayed, and accepts input of analysis commands for the output data in the window. 〔twenty two〕 An automated analysis process that generates output data by performing analysis on light intensity data obtained by irradiating a biological sample with light, or on fluorescence label intensity data calculated from said light intensity data, An analysis result data storage step that stores the output data generated based on the light intensity data or the fluorescence labeling intensity data, An interactive analysis processing step which analyzes the fluorescence label intensity data based on an analysis command for the output data output to the output device and outputs analysis result data; Information processing methods including [Explanation of Symbols]

[0292] 1. Information Processing System 10 Server Systems 20 Data Analysis Client Terminals 30 Data Acquisition Client Terminals 40. Biological sample analyzer

Claims

1. An automated analysis processing unit that generates output data by performing an analysis on fluorescence 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 analyzes the fluorescence label intensity data based on an analysis command for the output data output to the output device and outputs analysis result data, A server system including this.

2. The server system according to claim 1, wherein the processing by the automated 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 reserves computation 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 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.

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

6. The optical data storage unit includes two or more types of storage with different access speeds. The server system according to claim 5, wherein the server system performs a process of transferring the light intensity data and / or the fluorescence label intensity data stored in storage with a higher access speed to storage with a lower access speed when predetermined conditions are met.

7. The server system according to claim 1, 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 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 fluorescence label intensity data by performing calculation processing on said light intensity data; and A light data storage unit that stores the light intensity data or the fluorescence 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 fluorescence labeling intensity data calculated from the light intensity data, An analysis result data storage unit that stores the output data, A server system including: an interactive analysis processing unit that analyzes the fluorescence label intensity data based on analysis commands for the output data output to the output device and outputs analysis result data. An information processing system that includes this.

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

11. The server system has in advance the analysis setting data used in the processing by the automatic analysis processing unit, The automated analysis processing unit uses the analysis setting data to calculate the fluorescence labeling intensity data from the light intensity data. The information processing system according to claim 8.

12. The information processing system according to claim 8, wherein the automatic analysis processing unit performs a process to calculate fluorescence 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. 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. The information processing system according to claim 13, wherein the data analysis client terminal causes the output device to output a window on which the output data is displayed, and accepts input of the analysis command in the window.

16. The information processing system according to claim 8, wherein multiple data analysis client terminals can share one or more of the light intensity data, fluorescence label intensity data, and analysis setting data within the server system.

17. The information processing system according to claim 8, wherein multiple data acquisition client terminals can share analysis setting data within the server system.

18. A data acquisition unit that acquires light intensity data obtained by irradiating a biological sample with light, The system includes a transmission unit that transmits the light intensity data to a server system in response to the acquisition of the light intensity data. In the server system, fluorescence labeling intensity data is calculated from the light intensity data. Data acquisition client terminal.

19. A data acquisition client terminal as described in Claim 18, A data analysis client terminal includes a communication unit that receives output data created by the server system based on fluorescence label intensity data from the server system, and a processing unit that processes the output data to output to an output device. Information processing systems, including those mentioned above.

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

21. An automated analysis process that generates output data by performing analysis on fluorescence label intensity data calculated from light intensity data obtained by irradiating a biological sample with light, The analysis result data storage step stores the output data, An interactive analysis processing step which analyzes the fluorescence label intensity data based on an analysis command for the output data output to the output device and outputs analysis result data, Information processing methods including

Images