Data processing device, data processing method, recording medium storing data processing program, data processing system, and computer program product including data processing program

Abstract

The invention provides a data processing device, a data processing method, a recording medium storing a data processing program, a data processing system, and a computer program product including the data processing program. A data processing device (100) is provided with: a data acquisition unit (105) that acquires detection data that is detected by a detection device (10) and indicates the signal strength corresponding to a component in a sample; and a calculation unit (101) that processes the detection data acquired by the data acquisition unit. The calculation unit performs: a process for generating a plurality of pieces of analysis data including peaks of signal strength on the basis of the detection data; and generating a plurality of clusters by grouping peaks included in each of the plurality of analysis data by hierarchical clustering on the basis of peak information corresponding to the peaks, the calculation unit prohibiting grouping of the plurality of peaks included in the same analysis data into the same cluster in the hierarchical clustering.

Description

Technical Field

[0001] The present disclosure relates to a data processing device, a data processing method, a data processing program, and a data processing system for processing data. Background Art

[0002] When analyzing components such as compounds in a sample, multiple analytical data points detected from multiple samples are compared. For example, chromatography is a well-known technique in which components contained in a sample to be analyzed are separated using a separation device such as a chromatograph, thereby obtaining a separation result known as a chromatogram. The chromatogram obtained by chromatography has peaks of signal intensity. By comparing the peaks contained in each of the multiple chromatograms, the analyst can determine the components contained in the sample to be analyzed.

[0003] For example, Japanese Patent Application Laid-Open No. 2023-004872 discloses that, for each of a plurality of chromatograms obtained by chromatography, a table summarizing peaks included in each chromatogram is generated based on peak information such as peak area or peak height. Summary of the Invention

[0004] When generating a table such as that disclosed in Japanese Patent Application Laid-Open No. 2023-004872, for example, an analyst observes multiple chromatograms, estimates similar peaks between the multiple chromatograms, and registers the estimated peaks of each chromatogram as peaks of the same component in the table. The analyst can determine the components of each peak contained in each chromatogram by performing an identification process on the peaks of each chromatogram registered in the table as peaks of the same component. However, in order to generate such a table, the analyst needs to observe the peaks contained in each of the multiple chromatograms separately and compare them with the peaks contained in other chromatograms, which requires a lot of work time. In addition, the accuracy of the generated table varies depending on the analyst's knowledge and experience.

[0005] The present disclosure has been made to solve this problem, and an object of the present disclosure is to provide a technology that can perform data analysis quickly and accurately while reducing the workload of analysts.

[0006] According to one aspect of the present disclosure, a data processing device includes: a data acquisition unit that acquires detection data indicating signal intensities corresponding to components in a sample detected by a detection device; and a calculation unit that processes the detection data acquired by the data acquisition unit. The calculation unit generates a plurality of analysis data including peaks in signal intensities based on the detection data; and generates a plurality of clusters by grouping peaks included in each of the plurality of analysis data using hierarchical clustering based on peak information corresponding to the peaks. In the hierarchical clustering, the calculation unit prohibits grouping peaks that meet specific conditions based on the peak information into the same cluster.

[0007] According to another aspect of the present disclosure, a data processing method includes the following steps: acquiring detection data representing signal intensities corresponding to components in a sample detected by a detection device; and processing the detection data acquired by the acquisition step. The step of processing the detection data includes the following steps: generating a plurality of analysis data including peaks of signal intensities based on the detection data; and generating a plurality of clusters by grouping peaks contained in each of the plurality of analysis data based on peak information corresponding to the peaks through hierarchical clustering. The step of generating a plurality of clusters includes the following steps: in hierarchical clustering, it is prohibited to group a plurality of peaks that meet specific conditions based on peak information into the same cluster.

[0008] According to another aspect of the present disclosure, a data processing program causes a computer to perform the following steps: acquiring detection data representing signal intensities corresponding to components in a sample detected by a detection device; and processing the detection data acquired by the acquisition step. The step of processing the detection data includes the following steps: generating a plurality of analysis data including peaks of signal intensities based on the detection data; and generating a plurality of clusters by grouping peaks contained in each of the plurality of analysis data based on peak information corresponding to the peaks through hierarchical clustering. The step of generating a plurality of clusters includes the following steps: in hierarchical clustering, it is prohibited to group a plurality of peaks that meet specific conditions based on peak information into the same cluster.

[0009] According to another aspect of the present disclosure, a data processing system includes a detection device and a data processing device for processing data. The data processing device includes: a data acquisition unit that acquires detection data indicating signal intensities corresponding to components in a sample detected by the detection device; and a calculation unit that processes the detection data acquired by the data acquisition unit. The calculation unit performs the following processing: generating a plurality of analysis data including peaks of signal intensities based on the detection data; and grouping peaks included in each of the plurality of analysis data by hierarchical clustering based on peak information corresponding to the peaks to generate a plurality of clusters, wherein in the hierarchical clustering, the calculation unit prohibits grouping a plurality of peaks that meet specific conditions based on the peak information into the same cluster.

[0010] The above objects, features, aspects and advantages and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a diagram showing the configuration of a data processing system and a data processing device according to the embodiment.

[0012] Figure 23D vector data consisting of spectrum vectors and chromatograms generated by the data processing device according to the embodiment.

[0013] Figure 3 This is a diagram for explaining peak tracking using a plurality of chromatograms.

[0014] Figure 4 This is a diagram for explaining an example of comparison of peaks among a plurality of chromatograms.

[0015] Figure 5 This is a diagram for explaining an example of generating a compound table.

[0016] Figure 6 This is a diagram for explaining an example of a graph that displays peak vectors corresponding to peaks included in a plurality of chromatograms in a two-dimensional coordinate system.

[0017] Figure 7 This is a diagram for explaining an application example of hierarchical clustering using the centroid method.

[0018] Figure 8 This is a diagram for explaining an application example of hierarchical clustering using the centroid method executed by the data processing device according to the embodiment.

[0019] Figure 9 This is a flowchart of data processing executed by the data processing device involved in the embodiment.

[0020] Figure 10 This is a diagram for explaining an example of weighting of peak information in hierarchical clustering.

[0021] Figure 11 This is a diagram for explaining an example of comparison between the matching rate and the reproduction rate in the creation of a compound table.

[0022] Figure 12 This is a diagram for explaining an example of excluding peaks from clusters in hierarchical clustering.

[0023] Figure 13 This is a diagram for explaining an example of calculation of a representative value in each cluster obtained by grouping by hierarchical clustering. DETAILED DESCRIPTION

[0024] The present embodiment will be described in detail with reference to the accompanying drawings. The same or corresponding parts in the drawings are denoted by the same reference numerals, and their description will not be repeated in principle.

[0025] [Structure of Data Processing System and Data Processing Device]

[0026] Reference Figure 1 The configurations of the data processing system 1 and the data processing device 100 according to the embodiment will be described. Figure 1 1 is a diagram showing the configuration of a data processing system 1 and a data processing device 100 according to an embodiment. Figure 1 As shown, the data processing system 1 includes a chromatograph 10 and a data processing device 100 .

[0027] The chromatograph 10 is an example of a "detection device." The chromatograph 10 includes a container 11, a liquid delivery pump 12, an injector 13, a column 14, and a detector 15. The container 11 is used to store a mobile phase. The liquid delivery pump 12 draws the mobile phase from the container 11 and delivers it at a constant flow rate. The injector 13 injects the sample to be analyzed into the mobile phase delivered by the liquid delivery pump 12. The column 14 is used to store a stationary phase and separates the various components contained in the sample injected by the injector 13. The detector 15 detects the components eluted from the column 14. As the detector 15, for example, an absorbance detector (PDA (Photo Diode Array) detector), a fluorescence detector, a differential refractive index detector, a conductivity detector, or a mass spectrometer is used. Detection data indicating the signal intensity corresponding to the components in the sample detected by the detector 15 is output to the data processing device 100.

[0028] Furthermore, the chromatograph 10 according to the embodiment is a liquid chromatograph (LC) that uses a liquid as a mobile phase, but the chromatograph 10 may be another chromatograph such as a gas chromatograph that uses a gas as a mobile phase.

[0029] In the chromatograph 10, the sample to be analyzed is injected into the mobile phase via the injector 13. The injected sample reaches the column 14 along with the flow of the mobile phase delivered by the liquid delivery pump 12 and passes through the column 14. The time it takes for the various components contained in the sample to pass through the column 14 varies depending on their affinity with the stationary phase or the mobile phase. For example, the time it takes for the components contained in the sample that are easily adsorbed on the stationary phase to pass through the column 14 (also known as the "retention time") is longer than that of the components that are less easily adsorbed on the stationary phase. As a result, the various components contained in the sample are separated in the time direction by the column 14. The elution liquid containing the components separated in the column 14 is introduced from the column 14 to the detector 15. The detector 15 outputs detection data, which represents the signal intensity corresponding to the concentration (amount) of the components introduced by the column 14. The data processing device 100 processes the detection data to generate a chromatogram. In addition, the solution that has passed through the detector 15 is discharged as waste liquid.

[0030] The data processing device 100 may be a general-purpose computer or a computer dedicated to the data processing system 1 for processing detection data from the chromatograph 10 . The data processing device 100 includes a calculation device 101 , a memory 102 , a storage device 103 , and an interface 105 .

[0031] The computing device 101 is an example of a "computing unit." The computing device 101 is a computing entity (computer) that performs various processes by executing various programs. The computing device 101 is composed of a processor such as a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit). In addition, the processor as an example of the computing device 101 has the function of performing various processes by executing programs, but a dedicated hardware circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array) can also be used to implement part or all of these functions. A "processor" is not limited to a processor in the narrow sense that performs processes in a stored program manner such as a CPU or MPU, and can include hard-wired circuits such as ASICs or FPGAs. Therefore, the "processor" as an example of the computing device 101 can also be replaced by a processing circuit (processing circuitry) in which processing is predefined by computer-readable code and / or hard-wired circuits. In addition, the computing device 101 can be composed of either a chip or multiple chips. Furthermore, the processor and associated processing circuitry may be comprised of multiple computers interconnected by wired or wireless means via a local area network or wireless network. The processor and associated processing circuitry may also be comprised of a cloud computer that remotely performs calculations based on input data and outputs its calculation results to other devices in a remote location.

[0032] The memory 102 includes a volatile storage area (e.g., a work area) that temporarily stores program code, work memory, and the like when the computing device 101 executes various programs. Examples of the storage unit include volatile memories such as DRAM (Dynamic Random Access Memory) and SRAM (Static Random Access Memory), and nonvolatile memories such as ROM (Read Only Memory) and flash memory.

[0033] The storage device 103 stores various programs or various data executed by the computing device 101. The storage device 103 can be one or more non-transitory computer readable mediums or one or more computer readable storage mediums. As an example of the storage device 103, an HDD (Hard Disk Drive) and an SSD (Solid State Drive) can be cited. The storage device 103 involved in the embodiment stores a data processing program 130 for the computing device 101 to perform data processing, in which the detection data obtained from the chromatograph 10 is processed.

[0034] The interface 105 is an example of a "data acquisition unit". The interface 105 sends and receives data with an external device or an external equipment via wired communication or wireless communication. For example, the interface 105 acquires the detection data output from the chromatograph 10 by communicating with the chromatograph 10. In addition, the interface 105 may also be a communication device that sends the detection data acquired from the chromatograph 10 to the cloud server, or sends the execution result of the data processing performed by the computing device 101 to the cloud server, by communicating with a cloud server not shown in the figure. Furthermore, the interface 105 may also send and receive data with the display unit 110 or the input unit 120 as a user interface via wired communication or wireless communication. The data processing device 100 is not limited to one interface 105, and may also have multiple interfaces 105 according to the number of communication objects.

[0035] The display unit 110 is a display such as a liquid crystal panel, and displays the results of data processing performed by the data processing device 100. The input unit 120 is a pointing device such as a keyboard or a mouse, and receives commands from the user. When a touch panel is used as the user interface, the display unit 110 and the input unit 120 may be integrally formed. Alternatively, the display unit 110 and the input unit 120 may be components included in the data processing device 100.

[0036] In the data processing system 1 configured as described above, the data processing device 100 acquires detection data in time series via the interface 105. This detection data represents the signal intensities corresponding to the components in the sample detected by the chromatograph 10. Based on the acquired detection data, the data processing device generates a chromatogram representing the temporal changes in signal intensities. This allows the data processing device 100 to acquire a chromatogram related to the components contained in the sample being analyzed.

[0037] [Example of a chromatogram]

[0038] Reference Figure 2 A chromatogram to be processed by the data processing device 100 according to the embodiment will be described. Figure 2 3D vector data consisting of spectrum vectors and chromatograms generated by the data processing device 100 according to the embodiment is shown. In the embodiment described below, a "compound" is used as an example of a "component."

[0039] The data processing device 100 can generate a spectrum vector based on the detection data acquired from the chromatograph 10. For example, if the chromatograph 10 is configured to detect the ion intensity of various compounds contained in the sample as signal intensity for each mass-to-charge ratio, the data processing device 100 can generate a mass spectrum vector representing the signal intensity of the mass-to-charge ratio relative to a specific time based on the detection data acquired from the chromatograph 10. Alternatively, if the chromatograph 10 is configured to detect the absorbance of various compounds contained in the sample as signal intensity for each wavelength, the data processing device 100 can generate a wavelength spectrum vector representing the signal intensity of the wavelength relative to a specific time based on the detection data acquired from the chromatograph 10.

[0040] like Figure 2 As shown, data processing device 100 can generate three-dimensional vector data by arranging the mass spectrum vectors or wavelength spectrum vectors in a time series according to retention time. Data processing device 100 can generate a chromatogram by plotting retention time on the horizontal axis and signal intensity (ion intensity or absorbance) on the vertical axis. Specifically, a chromatogram is data representing the temporal changes in signal intensity.

[0041] [An example of peak tracking]

[0042] Reference Figure 3 and Figure 4 Peak tracking will be described. Figure 3 This figure is used to illustrate peak tracking using multiple chromatograms. For example, assume the following case: the hydrogen ion concentration index in the solution serving as the mobile phase is set to multiple indices between pH 2.5 and pH 6.0, and a chromatogram is generated at each index.

[0043] By comparing multiple chromatograms obtained under different separation conditions, such as the hydrogen ion concentration index, analysts can determine the optimal separation conditions for separating compounds contained in a sample. This can be done by tracking how the retention time of each peak in each chromatogram changes depending on the separation conditions. This peak tracking allows analysts to identify peaks of the same compound in each chromatogram.

[0044] Here, when performing peak tracking, it is required to extract two chromatograms with similar peak information of each peak from multiple chromatograms, and perform peak tracking between the two extracted chromatograms. Peak information includes, for example, at least one of retention time, peak number, peak width, peak area, peak area ratio, peak height and peak height ratio. The peak number is the order of the peaks detected by the chromatograph 10. The peak width is the width between the time point when the peak starts to rise and the time point when the peak ends to fall. The peak area is the area of ​​the part surrounded by the peak. The peak area ratio is the ratio of the peak area of ​​the target peak to the total peak area of ​​all peaks contained in the same chromatogram. The peak height is the maximum value of the signal intensity of the peak. The peak height ratio is the ratio of the peak height of the target peak to the total peak height of all peaks contained in the same chromatogram.

[0045] For example, the analyst selects two chromatograms and uses peak tracking to associate peaks with similar peak information between the two selected chromatograms. Figure 3 In the example, the chromatograms at pH 6.0 and pH 5.5 are most similar among the multiple chromatograms. In this case, the analyst performs peak tracking between the pH 6.0 and pH 5.5 chromatograms. By correlating similar peaks across multiple chromatograms with different separation conditions, the analyst can efficiently identify optimal separation conditions.

[0046] In addition, when the analyst compares peaks between multiple chromatograms, he or she can also compare the peaks based on the spectrum vectors (mass spectrum vectors, wavelength spectrum vectors) of each peak. Figure 4 This is a diagram for explaining an example of comparison of peaks among a plurality of chromatograms.

[0047] like Figure 4 As shown, the analyst is Figure 1 Peak 1 and chromatogram Figure 2 In the case of comparing peak 1, the chromatogram Figure 1 Peak 1 and chromatogram Figure 2 The analyst can predict that: Figure 1 Peak 1 and chromatogram Figure 2 The shape of the spectral vector of peak 1 is similar to that of the chromatogram Figure 1 Peak 1 and chromatogram Figure 2 Peak 1 is a peak corresponding to the same compound.

[0048] [An example of a compound table]

[0049] Reference Figure 5 The compound table is described. Figure 5 This is a diagram for explaining an example of generating a compound table.

[0050] The analyst observes a plurality of chromatograms, estimates similar peaks between the plurality of chromatograms, and registers the estimated peaks of the respective chromatograms as peaks of the same compound in the compound table.

[0051] For example, Figure 5 As shown, the analyst in the chromatographic Figure 1 In the compound table 1, the peak information of peak 1 is registered as the peak corresponding to compound 1, the peak information of peak 2 is registered as the peak corresponding to compound 2, and the peak information of peak 3 is registered as the peak corresponding to compound 3. Figure 2 In the compound table 2, the peak information of peak 1 is registered as the peak corresponding to compound 1, the peak information of peak 3 is registered as the peak corresponding to compound 2, and the peak information of peak 2 is registered as the peak corresponding to compound 3. Figure 3 In compound table 1, peak information of peak 1 is registered as the peak corresponding to compound 1, peak information of peak 2 is registered as the peak corresponding to compound 2, and peak information of peak 3 is registered as the peak corresponding to compound 3.

[0052] That is, the analyst will chromatogram Figures 1 to 3 The result of comparing the peaks of Figure 1 Peak 1, chromatogram Figure 2 Peak 1 and chromatogram Figure 3 These peaks are similar to those of compound 1. Figures 1 to 3 The result of comparing the peaks of Figure 1 Peak 2, chromatogram Figure 2 Peak 3 and chromatogram Figure 3 These peaks are similar to the peak 2 of compound 2. Figures 1 to 3 The result of comparing the peaks of Figure 1 Peak 3, chromatogram Figure 2 Peak 2 and chromatogram Figure 3 These peaks are similar to those of compound 3.

[0053] In this way, the analyst observes multiple chromatograms, estimates similar peaks between the multiple chromatograms, and registers the estimated peaks of each chromatogram as peaks of the same compound in the compound table. However, in order to generate a compound table, the analyst needs to observe at least one peak contained in each of the multiple chromatograms and compare it with at least one peak contained in other chromatograms, which requires a lot of work time. In addition, if Figure 5 As shown in the example, even for the same compound, peak numbers may change between multiple chromatograms, or there may be many peaks with similar spectral vectors. The accuracy of the generated compound table may vary depending on the analyst's knowledge and experience.

[0054] Therefore, the data processing device according to the first embodiment is configured to generate a plurality of clusters by grouping peaks included in each of a plurality of chromatograms based on peak information corresponding to the peaks, thereby rapidly and accurately analyzing chromatogram data while reducing the workload of the analyst.

[0055] [Generation of compound tables based on clustering]

[0056] Reference Figures 6 to 12 Generation of a compound table based on clustering performed by the data processing device 100 according to the embodiment will be described. Figure 6 This figure illustrates an example of a graph displaying peak vectors corresponding to peaks in multiple chromatograms in a two-dimensional coordinate system. A "peak vector" is data obtained by converting peak information for each peak into a multidimensional vector in a multidimensional coordinate system, where multiple peak information such as retention time and peak area are plotted along different dimensional axes. Hereinafter, points where peak vectors are converted into multidimensional vectors in a multidimensional coordinate system are also referred to as "peak points."

[0057] exist Figure 6 shows a graph obtained by converting peak information for each peak included in multiple chromatograms into a two-dimensional vector in a two-dimensional coordinate system obtained by plotting peak area on the horizontal axis and retention time on the vertical axis. In this graph, the peak points corresponding to Compound 1 are represented by circles, the peak points corresponding to Compound 2 are represented by triangles, and the peak points corresponding to Compound 3 are represented by quadrilaterals.

[0058] like Figure 6 As shown, the peak points corresponding to the respective compounds tend to be concentrated in similar locations on the graph. This is because multiple peaks corresponding to the same compound have similar peak information.

[0059] The data processing apparatus 100 clusters the data having Figure 6 By grouping the peak points of each peak included in a plurality of chromatograms showing the tendency shown, the peaks can be aggregated for each type of compound.

[0060] Here, types of clustering algorithms include non-hierarchical clustering and hierarchical clustering.

[0061] Non-hierarchical clustering is a method in which the most similar data are grouped and classified into a specified number of clusters. Non-hierarchical clustering includes the k-means method (k-means method). For example, the k-means method is a method in which, after the number of clusters is predetermined, each data is randomly assigned to a cluster, the distance between each data and the center of gravity of each cluster is calculated, and each data is redistributed to the cluster with the smallest distance based on the calculated distance. If the k-means method is used, multiple data with close distances can be aggregated into the same cluster, but the number of clusters needs to be predetermined. Therefore, the analyst must know in advance the number of compounds contained in the sample to be detected by the chromatogram (i.e., the number of clusters). If the number of compounds contained in the sample (the number of clusters) is not known in advance, the k-means method cannot be used.

[0062] Hierarchical clustering is a method that clusters two data points with the smallest inter-cluster distance to form a single cluster. Furthermore, among multiple clusters, clusters with the smallest inter-cluster distance to form a single cluster. Depending on how the distance between clusters is defined, hierarchical clustering is categorized into the shortest distance method, the longest distance method, and the centroid method.

[0063] Reference Figure 7 An example of applying hierarchical clustering using the centroid method to group peak points in a chromatogram will be described. Figure 7 This is a diagram for explaining an application example of hierarchical clustering using the centroid method. Figure 7 In the example shown, peak points A to E corresponding to a plurality of peaks A to E, respectively, are shown in a two-dimensional coordinate system in which the horizontal axis represents the peak area and the vertical axis represents the retention time.

[0064] If we follow the center of gravity method, then Figure 7 As shown in (A), peak points A and B, which have the smallest distance between them, are clustered together to create a cluster (A, B). Similarly, peak points C and D, which have the smallest distance between them, are clustered together to create a cluster (C, D). Thus, peak points A to E are divided into cluster (A, B) consisting of peak points A and B, cluster (C, D) consisting of peak points C and D, and peak point E.

[0065] Then, if Figure 7 As shown in (B), the centroid of cluster (A, B) (e.g., the center position between peak points A and B), the centroid of cluster (C, D) (e.g., the center position between peak points C and D), and cluster (A, B) and cluster (C, D) with the smallest distance between two points in peak point E are aggregated to generate a single cluster (A, B, C, D). Thus, peak points A to E are divided into cluster (A, B, C, D) consisting of peak point A, peak point B, peak point C, and peak point D, and peak point E.

[0066] Then, if Figure 7As shown in (C), clusters (A, B, C, D) and peak point E are aggregated to generate a cluster (A, B, C, D, E).

[0067] Thus, when hierarchical clustering using the centroid method is directly applied to group chromatogram peaks, peaks in multiple chromatograms are ultimately aggregated into a single cluster. This result means that the peaks in multiple chromatograms correspond to the same compound, a result that is unlikely to occur in chromatograms of samples containing multiple compounds. This is also true when using hierarchical clustering methods other than the centroid method.

[0068] Therefore, if Figure 8 As shown, the data processing device 100 according to Embodiment 1 is configured to prohibit grouping multiple peaks that meet specific conditions based on peak information into the same cluster when applying hierarchical clustering to group peaks in a chromatogram. The specific conditions include at least one of the following: the multiple peaks to be grouped are contained in the same chromatogram; the multiple peaks to be grouped have different spectra; and the difference in peak information between the multiple peaks to be grouped exceeds a specified range. More specifically, the data processing device 100 prohibits grouping multiple peaks contained in the same chromatogram into the same cluster during hierarchical clustering. Furthermore, the data processing device 100 prohibits grouping multiple peaks with different mass spectra (MS spectra) or wavelength spectra (PDA spectra) into the same cluster during hierarchical clustering. Furthermore, the data processing device 100 prohibits grouping multiple peaks whose differences in peak information (such as peak area or peak height) exceed a specified range into the same cluster during hierarchical clustering. Furthermore, the specified range can be set by the analyst. If the difference in peak information is within the specified range, the likelihood that the multiple peaks correspond to the same compound is higher than if the difference in peak information exceeds the specified range.

[0069] Figure 8 1 is a diagram for explaining an application example of hierarchical clustering according to the centroid method executed by the data processing device 100 according to the embodiment. Figure 8 The following example is shown: the data processing device 100 is for the chromatography Figures 1 to 3 Peak points 1 to 3 corresponding to the three peaks 1 to 3 respectively are grouped by hierarchical clustering, thereby generating a plurality of clusters.

[0070] like Figure 8 As shown, the data processing device 100 processes the chromatogram Figure 2 Peak 1 and chromatogram Figure 3 The peak points 2 of the chromatogram are grouped to generate a cluster 1A. Figure 1 Peak point 1 and cluster 1A are grouped to generate cluster 1.

[0071] The data processing device 100 performs chromatography Figure 1Peak 2 and chromatogram Figure 2 The peak points 3 of the chromatogram are grouped to generate a cluster 2A. Figure 3 The peak points of cluster 1 and cluster 2A are grouped to generate cluster 2.

[0072] The data processing device 100 performs chromatography Figure 2 Peak 2 and chromatogram Figure 3 The peak points 3 of the chromatogram are grouped to generate a cluster 3A. Figure 1 Peak point 3 and cluster 3A are grouped to generate cluster 3.

[0073] Regarding cluster 1, such as the chromatogram Figure 1 Peak point 1, chromatogram Figure 2 Peak 1 and chromatogram Figure 3 Peak 2, as shown in the chromatogram Figures 1 to 3 Only one peak point is selected from each chromatogram for grouping. Figure 1 Peak 1, chromatogram Figure 2 Peak 1 and chromatogram Figure 3 It is highly likely that the peaks in Peak 2 correspond to the same compound.

[0074] Regarding cluster 2, such as the chromatogram Figure 1 Peak point 2, chromatogram Figure 2 Peak 3 and chromatogram Figure 3 Peak 1, as shown in the chromatogram Figures 1 to 3 Only one peak point is selected from each chromatogram for grouping. Figure 1 Peak 2, chromatogram Figure 2 Peak 3 and chromatogram Figure 3 The peaks in Peak 1 are highly likely to correspond to the same compound.

[0075] Regarding cluster 3, such as the chromatogram Figure 1 Peak point 3, chromatogram Figure 2 Peak 2 and chromatogram Figure 3 Peak point 3, as shown in the chromatogram Figures 1 to 3 Only one peak point is selected from each chromatogram for grouping. Figure 1 Peak 3, chromatogram Figure 2 Peak 2 and chromatogram Figure 3 There is a high possibility that the peaks in Peak 3 correspond to the same compound.

[0076] The data processing device 100 can use the plurality of clusters generated in this way to generate Figure 5 For example, the data processing device 100 sets the compound corresponding to each peak included in cluster 1 as compound 1, and sets the chromatogram Figure 1Enter the peak information of Peak 1 into the column of Compound 1 in Compound Table 1, and Figure 2 Enter the peak information of Peak 1 into the column of Compound 1 in Compound Table 2, and Figure 3 The peak information of the peak 2 of cluster 1 is input into the column of compound 1 in compound table 3. The data processing device 100 sets the compound corresponding to each peak included in cluster 2 as compound 2, and Figure 1 Enter the peak information of Peak 2 into the column of Compound 2 in Compound Table 1, and Figure 2 Enter the peak information of Peak 3 into the column of Compound 2 in Compound Table 2, and convert the chromatogram Figure 3 The peak information of Peak 1 is input into the column of Compound 2 in Compound Table 3. The data processing device 100 sets the compound corresponding to each peak included in Cluster 3 as Compound 3 and Figure 1 Enter the peak information of Peak 3 into the column of Compound 3 in Compound Table 1, and convert the chromatogram Figure 2 Enter the peak information of Peak 2 into the column of Compound 3 in Compound Table 2, and convert the chromatogram Figure 3 The peak information of Peak 3 is input into the column of Compound 3 in Compound Table 3. Thus, the data processing device 100 can quickly and accurately generate the same peak information as the chromatogram while reducing the workload of the analyst. Figures 1 to 3 The corresponding compound tables.

[0077] In addition, the data processing apparatus 100 is not limited to generating Figure 5 The compound table for each chromatogram shown can also be generated by summarizing multiple chromatograms to generate a compound table. For example, the data processing device 100 can also calculate the representative value of each cluster 1 to 3 and generate a table obtained by summarizing the representative values ​​of each of the multiple clusters. Specifically, the data processing device 100 calculates the representative value of each cluster 1 to 3 based on the chromatogram included in cluster 1. Figure 1 Peak 1, chromatogram Figure 2 Peak 1 and chromatogram Figure 3 The data processing device 100 calculates the representative value of cluster 1 based on the peak information of each peak 2 of cluster 2. Figure 1 Peak 2, chromatogram Figure 2 Peak 3 and chromatogram Figure 3 The data processing device 100 calculates the representative value of cluster 2 based on the peak information of each peak 1 of cluster 3. Figure 1 Peak 3, chromatogram Figure 2 Peak 2 and chromatogram Figure 3 The data processing device 100 calculates the representative value of cluster 3 based on the peak information of each peak 3. The data processing device 100 inputs the representative value of cluster 1 into the column of compound 1 in the compound table, the representative value of cluster 2 into the column of compound 2 in the compound table, and the representative value of cluster 3 into the column of compound 3 in the compound table, thereby generating a compound table.

[0078] Furthermore, the representative value of each cluster may be the average value or the median value of the peak information of the peaks included in each cluster. For example, the representative value may be the average value or the median value of the retention time, the average value or the median value of the peak width, the average value or the median value of the peak area, the average value or the median value of the peak area ratio, the average value or the median value of the peak height, the average value or the median value of the peak height ratio, etc., of the peaks included in each cluster.

[0079] In this way, the data processing device 100 is used for the chromatography Figures 1 to 3 The peaks 1 to 3 contained in each of these clusters are grouped by hierarchical clustering based on the peak information corresponding to each of these peaks 1 to 3 to generate clusters 1 to 3. At this time, the data processing device 100 generates clusters 1 to 3 while prohibiting the grouping of multiple peaks contained in the same chromatogram into the same cluster. In other words, the data processing device 100 prohibits the grouping of each of clusters 1 to 3 with other clusters. The data processing device 100 generates a compound table based on the peak information of the peaks contained in each of the multiple clusters generated in this way. As a result, the data processing device 100 can quickly and accurately analyze the chromatogram data and generate a compound table while reducing the workload of the analyst.

[0080] [Flow of Data Processing by Data Processing Device 100]

[0081] Reference Figure 9 The flow of data processing executed by the data processing device 100 will be described. Figure 9 This is a flowchart of data processing performed by the data processing device 100 according to the embodiment. Figure 9 The processing steps shown (hereinafter referred to as "S").

[0082] like Figure 9 As shown, the data processing device 100 acquires detection data indicating signal intensities corresponding to components in a sample detected by the chromatograph 10 (S1). Based on the detection data acquired from the chromatograph 10, the data processing device 100 generates a chromatogram indicating temporal changes in signal intensities (S2).

[0083] The data processing device 100 performs hierarchical clustering on the plurality of chromatograms generated in the process of S2 to generate a plurality of clusters (S3). At this time, the data processing device 100 prohibits grouping a plurality of peaks included in the same chromatogram into the same cluster.

[0084] The data processing device 100 generates a compound table based on the peak information of at least one peak included in each of the plurality of clusters generated in the process of S3 ( S4 ) Thereafter, the data processing device 100 ends this process.

[0085] As described above, the data processing device 100 generates a plurality of chromatograms based on the detection data obtained from the chromatograph 10, and for the peaks contained in each of the plurality of chromatograms, groups the peaks corresponding to the peaks through hierarchical clustering to generate a plurality of clusters, and uses the generated plurality of clusters to generate a compound table. Furthermore, the data processing device 100 prohibits grouping a plurality of peaks that meet specific conditions based on the peak information into the same cluster in the hierarchical clustering. Specifically, the data processing device 100 prohibits grouping a plurality of peaks contained in the same analytical data, a plurality of peaks with different spectra (MS spectra, PDA spectra), or a plurality of peaks whose difference in peak information (peak area or peak height, etc.) exceeds a specified range into the same cluster in the hierarchical clustering. Thus, the data processing device 100 can quickly and accurately perform data analysis of the chromatograms and generate a compound table while reducing the workload of the analyst.

[0086] [Modification]

[0087] The present disclosure is not limited to the above-described embodiment, and various modifications can be made and applied. Next, modifications applicable to the present disclosure will be described.

[0088] (Variation 1)

[0089] exist Figure 6 In the illustrated example, the data processing device 100 converts the peak information of each peak included in the multiple chromatograms into a two-dimensional vector in a two-dimensional coordinate system using the peak area in the peak information as the horizontal axis and the retention time in the peak information as the vertical axis. However, the data processing device 100 is not limited to using a two-dimensional coordinate system; a three-dimensional or higher-dimensional coordinate system may also be used. Furthermore, the data processing device 100 is not limited to using peak area and retention time; other peak information such as peak number, peak width, peak area, peak area ratio, peak height, or peak height ratio may also be used to generate a multidimensional coordinate system.

[0090] (Variation 2)

[0091] When analysts generate a compound table by themselves, they are not limited to observing the peak that is the observation target. They also observe the peak that appears before the peak in the chromatogram (the peak with the previous peak number) or the peak that appears after the peak (the peak with the next peak number) to comprehensively estimate the compound corresponding to the peak.

[0092] Therefore, when generating a multidimensional coordinate system, the data processing device 100 may also generate the multidimensional coordinate system by extracting, on the coordinate axes, peak information of peaks preceding or following the peak corresponding to the peak point plotted in the multidimensional coordinate system (the peak to be subjected to hierarchical clustering). This allows the data processing device 100 to generate multiple clusters through hierarchical clustering, taking into account the chronological relationship between multiple peaks appearing in the chromatogram.

[0093] (Variation 3)

[0094] When generating a multi-dimensional coordinate system, the data processing device 100 may also weight at least one peak information. For example, Figure 10 This is a diagram for explaining an example of weighting of peak information in hierarchical clustering.

[0095] like Figure 10 As shown, the data processing device 100 may also place greater emphasis on the area in the peak information than on the retention time in the peak information, and weight the area more than the retention time. For example, the data processing device 100 may multiply the scale of the area taken on the horizontal axis by a constant, while not multiplying the scale of the retention time taken on the vertical axis by a constant, thereby weighting the area more than the retention time. In this way, in a two-dimensional coordinate system generated based on the weighted area and the unweighted retention time, the horizontal axis corresponding to the area plays a dominant role in calculating the distance between points, compared to the vertical axis corresponding to the retention time. The data processing device 100 transforms the peak information of each peak contained in multiple chromatograms into a two-dimensional vector in the weighted two-dimensional coordinate system to perform hierarchical clustering, thereby being able to perform hierarchical clustering in a manner that further influences the peak information to be emphasized.

[0096] (Variation 4)

[0097] When calculating the distances between multiple peak points plotted in a multidimensional coordinate system, the data processing device 100 may also calculate the difference in real numbers (scalar quantities) of peak information such as retention time, peak area, and peak width for each peak point, and group the two peak points with the smallest calculated difference. For example, the data processing device 100 may calculate the difference in real numbers (scalar quantities) of peak information for peak points a and b by calculating the following equation (1).

[0098] [Number 1]

[0099] ab(1)

[0100] The data processing device 100 may also group the peak point a and the peak point b when the calculation result calculated by the formula (1) is minimized.

[0101] (Variant 5)

[0102] Peak points plotted in the multidimensional coordinate system are points whose peak vectors are displayed in the multidimensional coordinate system. Therefore, when calculating inter-point distances for multiple peak points plotted in the multidimensional coordinate system, data processing device 100 may also calculate the difference between the peak vectors corresponding to each peak point and group the two peak points with the smallest calculated difference. For example, data processing device 100 may calculate the difference between the peak vector corresponding to peak point a and the peak vector corresponding to peak point b by calculating the following equation (2).

[0103] [Number 2]

[0104]

[0105] In equation (2), θ is the angle formed by the peak vector corresponding to peak point a and the peak vector corresponding to peak point b. The data processing device 100 may group peak points a and b when the result calculated by equation (2) is minimized.

[0106] (Variation 6)

[0107] Figure 11 This is a diagram for explaining an example of comparison of matching rate and reproduction rate in the generation of compound table. Figure 11 The corresponding relationship between the correct answer and the prediction is shown in FIG. 1 , when it is predicted which of the compounds A to C the peaks are corresponding to for 30 peaks included in at least one chromatogram. Figure 11 In the example shown, among the 30 peaks, ten peaks correspond to compounds A to C, respectively.

[0108] exist Figure 11 In the example shown in (A), among the 30 peaks, ten peaks corresponding to compound A are grouped into cluster 1, ten peaks corresponding to compound B are grouped into cluster 2, and ten peaks corresponding to compound C are grouped into cluster 3. Figure 11 In the example of (A), the ten peaks corresponding to compounds A to C are equally divided and grouped into clusters 1 to 3. Needless to say, such a compound table has a high matching rate and a high reproducibility.

[0109] exist Figure 11 In the example shown in (B), among the 30 peaks, 6 peaks corresponding to compound A are grouped into cluster 1, 4 peaks corresponding to compound A are grouped into cluster 2, 10 peaks corresponding to compound B are grouped into cluster 3, and 10 peaks corresponding to compound C are grouped into cluster 4. Figure 11 In the example (B), the ten peaks corresponding to compounds B and C are equally divided and grouped into clusters 3 and 4, respectively. Meanwhile, the ten peaks corresponding to compound A are divided and grouped into two clusters, 1 and 2, rather than into a single cluster. Needless to say, such a compound table has a high matching rate but a low reproducibility rate.

[0110] exist Figure 11 In the example shown in (C), among the 30 peaks, ten peaks corresponding to compound A are grouped into cluster 1, and ten peaks corresponding to compound B and ten peaks corresponding to compound C are grouped into cluster 2. Figure 11In the example of (C), the ten peaks corresponding to compound A are grouped into cluster 1, while the ten peaks corresponding to compounds B and C are not divided into two clusters but into one cluster 2. Needless to say, such a compound table has a low matching rate and a high reproducibility rate.

[0111] exist Figure 11 In the example shown in (D), among the 30 peaks, three peaks corresponding to compound A, four peaks corresponding to compound B, and three peaks corresponding to compound C are grouped into cluster 1, three peaks corresponding to compound A, three peaks corresponding to compound B, and four peaks corresponding to compound C are grouped into cluster 2, and four peaks corresponding to compound A, three peaks corresponding to compound B, and three peaks corresponding to compound C are grouped into cluster 3. In this way, Figure 11 In the example of (D), ten peaks corresponding to compounds A to C are grouped so as to be included in any of clusters 1 to 3. Needless to say, such a compound table has a low matching rate and a low reproduction rate.

[0112] Here, in Figure 11 The compound table with high matching rate and low reproducibility shown in (B) is similar to the table shown in Figure 11 When the compound table with low matching rate and high reproducibility shown in (C) is compared, in the data analysis of the chromatogram, as a result, Figure 11 The compound table of (B) is compared with Figure 11 The reason is that in a cluster generated by hierarchical clustering, Figure 11 When peaks corresponding to different compounds are mixed as shown in (C), the analyst cannot identify one compound based on the cluster, which causes inconvenience in practical application. Figure 11 As shown in (B) of FIG. 1 , when the number of data is small but only peaks corresponding to one compound are mixed, the analyst can identify one compound based on this one cluster.

[0113] Therefore, when the compound table is generated by the data processing device 100, the matching rate is given more importance than the reproducibility rate. Therefore, the data processing device 100 is configured to execute several processes as described below to improve the matching rate.

[0114] As a method for improving the matching rate, the data processing device 100 may also prohibit the use of a cluster to generate a compound table based on the number of peaks contained in the cluster. Figure 11In the case of a clustering result such as that shown in (B) of FIG, the data processing device 100 prohibits the use of cluster 1 containing peaks corresponding to the same compound A and cluster 2 having a smaller number of data in generating a compound table. Alternatively, the data processing device 100 may prohibit the use of one cluster to generate a compound table when the number of peaks contained in one of the plurality of clusters is less than a predetermined number. For example, the data processing device 100 may also prohibit the use of one cluster to generate a compound table when the number of peaks contained in the one cluster is less than a predetermined number. Figure 11 In the case of a clustering result such as that shown in (B), it is prohibited to generate a compound table using cluster 2 having less than five data points. The predetermined number serving as a threshold value may also be set by the analyst.

[0115] As a method for improving the matching rate, the data processing device 100 may exclude peaks contained in one cluster that are close to peaks contained in other clusters from the cluster. Figure 12 This is a diagram for explaining an example of excluding peaks from clusters in hierarchical clustering. Figure 12 As shown in FIG, the peak point surrounded by the dotted line is more than a specified distance away from other peak points corresponding to the same type of compound. Such peak points may be caused by errors or misdetections in waveform processing when generating the chromatogram. If such peak points are used to perform hierarchical clustering, the matching rate may be reduced. Therefore, the data processing device 100 preferably excludes peak points such as the peak point surrounded by the dotted line that are more than a specified distance away from other peak points corresponding to the same type of compound to perform clustering. Figure 12 In the example, the data processing device 100 may also exclude the peak point represented by a circle surrounded by a dotted line from the cluster corresponding to compound 1, exclude the peak point represented by a triangle surrounded by a dotted line from the cluster corresponding to compound 2, and exclude the peak point represented by a quadrilateral surrounded by a dotted line from the cluster corresponding to compound 3.

[0116] As a method for improving the matching rate, the data processing device 100 may also extract peaks for generating a compound table by calculating the following formula (3).

[0117] [Number 3]

[0118]

[0119] In the above formula (3), the first term represents the sum of the reciprocals of the distances between the target peak point included in one cluster and the other peak points included in other clusters, and the second term represents the sum of the reciprocals of the distances between the target peak point included in one cluster and the other peak points included in the same cluster. When the value pi calculated by formula (3) is negative, it can be said that the target peak point is close to the other peak points in its own cluster after grouping and is far away from the other peak points in other clusters. On the other hand, when the value pi calculated by formula (3) is positive, it can be said that the target peak point is far away from the other peak points in its own cluster after grouping and is close to the other peak points in other clusters. Therefore, the data processing device 100 can also extract only the peak points for which the value pi calculated by formula (3) is negative and generate a compound table using only the extracted peak points.

[0120] Furthermore, the above formula (3) can also be rewritten as the following formula (4).

[0121] [Number 4]

[0122]

[0123] In the above formula (4), the first term represents the sum of the values ​​obtained by transforming the distance between the object peak point contained in a cluster and the other peak points contained in other clusters using a function f that decreases monotonically relative to the distance, and the second term represents the sum of the values ​​obtained by transforming the distance between the object peak point contained in a cluster and the other peaks contained in the cluster using a function f that decreases monotonically relative to the distance.

[0124] Furthermore, the above-mentioned equations (3) and (4) can also be rewritten as the following equation (5).

[0125] [Number 5]

[0126]

[0127] In the above formula (5), the first term represents the sum of the values ​​obtained by transforming the distance between the object peak point contained in a cluster and the other peak points contained in other clusters using a function f that decreases monotonically relative to the distance, and the second term represents the sum of the values ​​obtained by transforming the distance between the object peak point contained in a cluster and the other peaks contained in the cluster using a function f that decreases monotonically relative to the distance.

[0128] (Variant 7)

[0129] When calculating the representative value of a cluster, the data processing device 100 may also determine a representative peak from the peaks included in the cluster and calculate the representative value of the cluster based on the peak information of the representative peak. Figure 13 This is a diagram for explaining an example of calculation of a representative value in each cluster obtained by grouping by hierarchical clustering.

[0130] For example, Figure 13 As shown, the data processing device 100 can also extract the medoid point of each cluster based on the peak information of the peak points included in each cluster corresponding to compounds 1 to 3 as a representative value. The data processing device 100 can also calculate the peak information of the extracted medoid point as a representative value. In addition, the medoid point is the point with the smallest sum of distances between the peak points included in a cluster and the other peaks included in the cluster (for example, the calculation result of the second term when f(x) = x in formula (5)).

[0131] Here, at the medoid point, the distance difference with other peak points in the cluster containing the medoid point itself is taken into account, but the distance difference with other peak points in other clusters is not taken into account (for example, the calculation result of the first term in equations (4) and (5)). Therefore, there may be a situation like Figure 13 The medoid point of the cluster corresponding to compound 2 is close to the cluster corresponding to compound 1.

[0132] Therefore, the data processing device 100 preferably selects the peak point at which the value Pi calculated using the above equations (3) to (5) is minimized as the representative peak point of each cluster, and calculates the representative value of the cluster based on the peak information of the representative peak point, and uses this as the representative value. In this way, the data processing device 100 extracts the peak point at which the value Pi calculated using equations (3) to (5) is minimized in each cluster, and generates a compound table by summarizing the representative values ​​of the extracted representative peak points, thereby generating a compound table with high accuracy.

[0133] (Variation 8)

[0134] In the above embodiment, a chromatogram is used as analytical data, but the data processing device 100 can also be applied to analytical data having a plurality of peaks other than a chromatogram.

[0135] Furthermore, the above-described embodiments and modifications can be appropriately combined and applied to one data processing system 1 and one data processing device 100 .

[0136] [Way]

[0137] It will be understood by those skilled in the art that the above-mentioned multiple exemplary embodiments are specific examples of the following aspects.

[0138] (Item 1) A data processing device according to one embodiment includes: a data acquisition unit that acquires detection data indicating signal intensities corresponding to components in a sample detected by a detection device; and a calculation unit that processes the detection data acquired by the data acquisition unit. The calculation unit generates a plurality of analysis data including peaks in signal intensities based on the detection data; and generates a plurality of clusters by grouping peaks included in each of the plurality of analysis data using hierarchical clustering based on peak information corresponding to the peaks. In the hierarchical clustering, the calculation unit prohibits grouping peaks that satisfy a specific condition based on the peak information into the same cluster.

[0139] The data processing device described in the first item can generate multiple clusters by grouping peaks contained in multiple sets of analysis data using hierarchical clustering based on peak information corresponding to the peaks. This eliminates the need for analysts to estimate common peaks across multiple sets of analysis data to generate multiple clusters, enabling rapid data analysis. Furthermore, the data processing device prohibits grouping multiple peaks that satisfy specific conditions based on peak information into the same cluster during hierarchical clustering. Therefore, multiple peaks that satisfy specific conditions based on peak information are not grouped into the same cluster as the same component, enabling highly accurate data analysis using hierarchical clustering.

[0140] (Item 2) In the data processing apparatus according to Item 1, the detection device is a chromatograph, and each of the plurality of analysis data is a chromatogram showing a peak with respect to signal intensity relative to retention time.

[0141] According to the data processing device described in the second aspect, the analyst does not need to personally estimate peaks common to a plurality of chromatograms to generate a plurality of clusters, and data analysis can be performed quickly and accurately.

[0142] (Item 3) In the data processing device according to Item 1 or Item 2, the calculation unit generates a table based on peak information corresponding to peaks included in each of the plurality of clusters.

[0143] According to the data processing device described in the third aspect, the analyst does not need to estimate common peaks among a plurality of chromatograms to generate a compound list, and can generate a compound list quickly and with high accuracy.

[0144] (Item 4) In the data processing device described in Item 3, the calculation unit performs the following processing: calculating a representative value for each of the plurality of clusters; and generating a table based on the representative value for each of the plurality of clusters.

[0145] According to the data processing device described in the fourth aspect, the analyst can create a compound table based on the representative values ​​of each of the plurality of clusters.

[0146] (Item 5) In the data processing device according to Item 3 or Item 4, the calculation unit prohibits use of the cluster generation table for one cluster among the plurality of clusters, based on the number of peaks included in the cluster.

[0147] According to the data processing device described in the fifth aspect, for example, the analyst can prohibit the use of cluster 2 containing a small number of peaks in generating a compound table, thereby improving the matching rate in generating a compound table.

[0148] (Item 6) In the data processing device described in any one of Items 3 to 5, the calculation unit uses peaks extracted based on the difference between a peak included in a cluster and other peaks included in the cluster, and the difference between a peak included in the cluster and peaks included in other clusters to generate a table.

[0149] According to the data processing device described in the sixth item, the analyst can use peaks extracted based on the differences between the peaks contained in a cluster and other peaks contained in the cluster, and the differences between the peaks contained in the cluster and the peaks contained in other clusters to generate a compound table, thereby improving the matching rate in the generation of the compound table.

[0150] (Item 7) In the data processing device described in Item 4, the calculation unit calculates the representative value using the peak extracted based on the difference between the peak included in a cluster and other peaks included in the cluster, and the difference between the peak included in the cluster and peaks included in other clusters.

[0151] According to the data processing device described in item 7, the analyst can generate a compound table based on representative values ​​calculated using peaks extracted based on the differences between peaks included in a cluster and other peaks included in the cluster, and the differences between peaks included in the cluster and peaks included in other clusters.

[0152] (Item 8) In the data processing device according to any one of Items 1 to 7, the calculation unit weights at least one peak information item more heavily than other peak information items, and performs hierarchical clustering based on the weighted peak information item.

[0153] According to the data processing device described in the eighth aspect, the analyst can perform hierarchical clustering in such a manner as to further influence the peak information to be emphasized.

[0154] (Item 9) In the data processing device according to any one of Items 1 to 8, the calculation unit excludes, from the one cluster, peaks included in one cluster that are close to peaks included in another cluster.

[0155] According to the data processing device described in item 9, the analyst can generate a compound table by excluding peaks included in one cluster that are close to peaks included in other clusters from the one cluster, thereby improving the matching rate in generating the compound table.

[0156] (Item 10) In the data processing device according to any one of Items 1 to 9, the peak information includes at least one of a retention time, a number, a width, an area, an area ratio, a height, and a height ratio of a peak.

[0157] According to the data processing device described in item 10, the analyst can generate a compound table based on at least one of the retention time, number, width, area, area ratio, height, and height ratio of peaks included in the chromatogram.

[0158] (Item 11) In the data processing device described in any one of Items 1 to 10, the peak information includes at least one of the retention time, number, width, area, area ratio, height and height ratio of the peak before or after the peak that becomes the target of hierarchical clustering.

[0159] According to the data processing device described in the eleventh aspect, the analyst can generate a plurality of clusters by hierarchical clustering in consideration of the chronological relationship of a plurality of peaks appearing in the chromatogram, thereby generating a compound table.

[0160] (Item 12) In the data processing device described in any one of Items 1 to 11, the hierarchical clustering is clustering performed using at least one of a shortest distance method, a longest distance method, and a centroid method.

[0161] According to the data processing device described in the twelfth item, the analyst can create a compound table using a plurality of clusters generated by hierarchical clustering using at least one of the shortest distance method, the longest distance method, and the centroid method.

[0162] (Item 13) In the data processing device described in any one of Items 1 to 12, the specific conditions include at least one of the following conditions: multiple peaks are included in the same analysis data; the spectra of the multiple peaks are different; and the difference in peak information of the multiple peaks exceeds a specified range.

[0163] According to the data processing device described in the thirteenth item, in hierarchical clustering, multiple peaks contained in the same analysis data, multiple peaks with different spectra, or multiple peaks whose peak information differences exceed the specified range will not be grouped into the same cluster as the same component, and hierarchical clustering can be used to perform data analysis with high precision.

[0164] (Item 14) In a data processing method according to one embodiment, the processing performed by a computer (computing unit) includes the following steps: acquiring detection data indicating signal intensities corresponding to components in a sample detected by a detection device; and processing the detection data acquired by the acquiring step. The step of processing the detection data includes the following steps: generating a plurality of analysis data including peaks of signal intensities based on the detection data; and grouping peaks included in each of the plurality of analysis data by hierarchical clustering based on peak information corresponding to the peaks to generate a plurality of clusters. The step of generating the plurality of clusters includes the following steps: in the hierarchical clustering, prohibiting the grouping of a plurality of peaks that satisfy a specific condition based on the peak information into the same cluster.

[0165] According to the data processing method described in item 14, a computer can generate multiple clusters by grouping peaks contained in each of a plurality of analysis data sets using hierarchical clustering based on peak information corresponding to the peaks. This eliminates the need for analysts to estimate peaks shared by the plurality of analysis data sets to generate multiple clusters, enabling rapid data analysis. Furthermore, the computer prohibits grouping multiple peaks that satisfy specific conditions based on the peak information into the same cluster during hierarchical clustering. Therefore, multiple peaks that satisfy specific conditions based on the peak information are not grouped into the same cluster as the same component, enabling highly accurate data analysis using hierarchical clustering.

[0166] (Item 15) A data processing program according to one embodiment causes a computer (computing unit) to execute the following steps: acquiring detection data indicating signal intensities corresponding to components in a sample detected by a detection device; and processing the detection data acquired by the acquiring step. The processing step includes the following steps: generating a plurality of analysis data including peaks in signal intensities based on the detection data; and grouping peaks included in each of the plurality of analysis data using hierarchical clustering based on peak information corresponding to the peaks to generate a plurality of clusters. The generating step includes the following steps: prohibiting, in the hierarchical clustering, from grouping a plurality of peaks that satisfy a specific condition based on the peak information into the same cluster.

[0167] According to the data processing program described in item 15, the computer can generate multiple clusters by grouping peaks contained in each of the plurality of analysis data using hierarchical clustering based on the peak information corresponding to the peaks. This eliminates the need for the analyst to estimate peaks shared by the plurality of analysis data to generate the clusters, enabling rapid data analysis. Furthermore, the computer prohibits grouping multiple peaks that satisfy specific conditions based on the peak information into the same cluster during hierarchical clustering. Therefore, multiple peaks that satisfy specific conditions based on the peak information are not grouped into the same cluster as the same component, enabling highly accurate data analysis using hierarchical clustering.

[0168] (Item 16) A data processing system according to one embodiment includes a detection device and a data processing device for processing data. The data processing device includes: a data acquisition unit that acquires detection data indicating signal intensities corresponding to components in a sample detected by the detection device; and a calculation unit that processes the detection data acquired by the data acquisition unit. The calculation unit performs the following processing: generating a plurality of analysis data including peaks in signal intensities based on the detection data; and grouping peaks included in each of the plurality of analysis data using hierarchical clustering based on peak information corresponding to the peaks to generate a plurality of clusters, wherein in the hierarchical clustering, the calculation unit prohibits grouping a plurality of peaks that satisfy a specific condition based on the peak information into the same cluster.

[0169] The data processing system described in item 16 can generate multiple clusters by grouping peaks contained in multiple sets of analysis data using hierarchical clustering based on peak information corresponding to the peaks. This eliminates the need for analysts to estimate common peaks across multiple sets of analysis data to generate multiple clusters, enabling rapid data analysis. Furthermore, the data processing device 100 prohibits grouping multiple peaks that satisfy specific conditions based on peak information into the same cluster during hierarchical clustering. Therefore, multiple peaks that satisfy specific conditions based on peak information are not grouped into the same cluster as the same component, enabling highly accurate data analysis using hierarchical clustering.

[0170] While the embodiments of the present invention have been described, the embodiments disclosed herein are to be considered in all respects as illustrative and non-restrictive. The scope of the present invention is indicated by the claims, and all modifications within the meaning and scope equivalent to the claims are intended to be encompassed.

Claims

1. A data processing device for processing data, the data processing device comprising: a data acquisition unit that acquires detection data indicating the intensity of a signal corresponding to a component in the sample detected by the detection device; and a computing unit for processing the detection data acquired by the data acquisition unit, in, The operation unit performs the following processing: generating a plurality of analysis data including a peak of the signal intensity based on the detection data; and For each peak included in the plurality of analysis data, a plurality of clusters are generated by grouping the peaks by hierarchical clustering based on peak information corresponding to the peaks. In the hierarchical clustering, the calculation unit prohibits grouping a plurality of peaks that satisfy a specific condition based on the peak information into the same cluster.

2. The data processing device according to claim 1, wherein: The detection device is a chromatograph, Each of the plurality of analysis data is a chromatogram showing a peak of the signal intensity relative to a retention time.

3. The data processing device according to claim 1, wherein: The calculation unit generates a table based on the peak information corresponding to the peaks included in each of the plurality of clusters.

4. The data processing device according to claim 3, wherein: The operation unit performs the following processing: calculating a representative value for each of the plurality of clusters; and The table is generated based on the representative values ​​of each of the plurality of clusters.

5. The data processing device according to claim 3 or 4, wherein: The calculation unit prohibits generation of the table using one of the plurality of clusters based on the number of peaks included in the one cluster.

6. The data processing device according to claim 3 or 4, wherein: The calculation unit generates the table using peaks extracted based on differences between a peak included in one cluster and other peaks included in the one cluster and differences between a peak included in the one cluster and peaks included in other clusters.

7. The data processing device according to claim 4, wherein: The calculation unit calculates the representative value using peaks extracted based on differences between a peak included in one cluster and other peaks included in the one cluster and differences between a peak included in the one cluster and peaks included in other clusters.

8. The data processing device according to claim 1 or 2, wherein: The calculation unit weights at least one of the peak information more heavily than other peak information, and executes the hierarchical clustering based on the weighted peak information.

9. The data processing device according to claim 1 or 2, wherein: The calculation unit excludes, from the one cluster, peaks included in one cluster that are close to peaks included in another cluster.

10. The data processing device according to claim 2, wherein: The peak information includes at least one of the retention time, number, width, area, area ratio, height, and height ratio of the peak.

11. The data processing device according to claim 2, wherein: The peak information includes at least one of the retention time, number, width, area, area ratio, height, and height ratio of a peak before or after the peak to be subjected to the hierarchical clustering.

12. The data processing device according to claim 1 or 2, wherein: The hierarchical clustering is clustering performed according to at least one method among the shortest distance method, the longest distance method and the centroid method.

13. The data processing device according to claim 1 or 2, wherein: The specific condition includes at least one of the following conditions: the plurality of peaks are included in the same analysis data; the spectra of the plurality of peaks are different; and the difference between the peak information of the plurality of peaks exceeds a prescribed range.

14. A data processing method is a data processing method for processing data by a computer. As a process executed by the computer, the data processing method includes the following steps: Acquiring detection data indicating the intensity of a signal corresponding to a component in the sample detected by the detection device; as well as Processing the detection data acquired by performing the acquisition step, The step of processing the detection data includes the following steps: generating a plurality of analysis data including a peak of the signal intensity based on the detection data; and For each peak included in the plurality of analysis data, a plurality of clusters are generated by grouping the peaks by hierarchical clustering based on peak information corresponding to the peaks. The step of generating the plurality of clusters includes the step of prohibiting, in the hierarchical clustering, a plurality of peaks satisfying a specific condition based on the peak information from being grouped into the same cluster.

15. A recording medium storing a data processing program for processing data, wherein: The data processing program enables the computer to execute the following steps: acquiring detection data indicating the intensity of a signal corresponding to a component in the sample detected by the detection device; and Processing the detection data acquired by performing the acquisition step, The step of processing the detection data includes the following steps: generating a plurality of analysis data including a peak of the signal intensity based on the detection data; and For each peak included in the plurality of analysis data, a plurality of clusters are generated by grouping the peaks by hierarchical clustering based on peak information corresponding to the peaks. The step of generating the plurality of clusters includes the step of prohibiting, in the hierarchical clustering, a plurality of peaks satisfying a specific condition based on the peak information from being grouped into the same cluster.

16. A data processing system for processing data, the data processing system comprising: detection device; and A data processing device, which processes the data, in, The data processing device comprises: a data acquisition unit that acquires detection data indicating the intensity of a signal corresponding to a component in the sample detected by the detection device; and a computing unit for processing the detection data acquired by the data acquisition unit, The operation unit performs the following processing: generating a plurality of analysis data including a peak of the signal intensity based on the detection data; and For each peak included in the plurality of analysis data, a plurality of clusters are generated by grouping the peaks by hierarchical clustering based on peak information corresponding to the peaks. In the hierarchical clustering, the calculation unit prohibits grouping a plurality of peaks that satisfy a specific condition based on the peak information into the same cluster.

17. A computer program product comprising a data processing program for processing data, wherein: The computer program product causes a computer to execute the following steps: acquiring detection data indicating the intensity of a signal corresponding to a component in the sample detected by the detection device; and Processing the detection data acquired by performing the acquisition step, The step of processing the detection data includes the following steps: generating a plurality of analysis data including a peak of the signal intensity based on the detection data; and For each peak included in the plurality of analysis data, a plurality of clusters are generated by grouping the peaks by hierarchical clustering based on peak information corresponding to the peaks. The step of generating the plurality of clusters includes the step of prohibiting, in the hierarchical clustering, a plurality of peaks satisfying a specific condition based on the peak information from being grouped into the same cluster.

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