Information processing device, information processing method, computer program, and computer-readable recording medium

Abstract

The information processing device includes a P-value calculation unit that calculates a module P-value for testing whether or not a formed module is correct for a module constituted by elements contained in multivariate data that is the subject of correlation network analysis.

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, a computer program, and a computer-readable recording medium that analyzes the relationships between multiple influencing components, identifies modules that have a high influence on a specific effect, and extracts candidates. [Background technology]

[0002] Patent Document 1 describes a technique for performing correlation network analysis on multivariate data, which is comprehensive molecular information obtained by omics analysis. Non-Patent Document 1 describes a technique for performing correlation network analysis on multivariate data such as big data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6318334 [Non-patent literature]

[0004] [Non-Patent Document 1] Kazuto Mannen, Yoshiyuki Ogata, Hideyuki Suzuki, "A new direction in multivariate analysis - Development of ConfeitoGUIplus", Journal of Bioengineering, Vol. 95, No. 7, July 2017 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the techniques described in Patent Document 1 and Non-Patent Document 1 have insufficient analytical precision.

[0006] An object of the present invention is to improve the accuracy of correlation network analysis for multivariate data. [Means for solving the problem]

[0007] One aspect of the present invention is an information processing device including a P-value calculation unit that calculates a module P-value for a module constituted by elements contained in multivariate data that is the subject of correlation network analysis.

[0008] One aspect of the present invention is an information processing method executed by an information processing device, which calculates a module P value for a module constituted by elements contained in multivariate data that is the subject of correlation network analysis.

[0009] One aspect of the present invention is a computer program for causing a computer to execute a step of calculating a module P value for a module constituted by elements contained in multivariate data that is the subject of correlation network analysis.

[0010] One aspect of the present invention is a computer-readable recording medium having recorded thereon a computer program for causing a computer to execute a step of calculating a module P value for a module composed of elements contained in multivariate data that is the subject of correlation network analysis. Effect of the Invention

[0011] According to the present invention, it is possible to obtain an effect of improving the accuracy of correlation network analysis for multivariate data. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an example of a schematic configuration of an information processing device according to an embodiment. [Diagram 2] 1 is a flowchart illustrating an example of a procedure of an information processing method according to an embodiment. [Diagram 3] FIG. 2 is a diagram illustrating an example of a configuration of multivariate data according to an embodiment. [Figure 4A] FIG. 11 is a diagram for explaining an example of a correlation coefficient between elements according to an embodiment. [Figure 4B] FIG. 11 is a diagram for explaining an example of a correlation coefficient between elements according to an embodiment. [Figure 5A] 1 is a diagram for explaining an FPO module selection method according to an embodiment. [Figure 5B] 1 is a diagram for explaining an FPO module selection method according to an embodiment. [Figure 5C] FIG. 1 is a diagram for explaining an example of FPO analysis according to an embodiment. [Figure 6A] FIG. 13 is a diagram for explaining an example of a final element F-score according to an embodiment. [Figure 6B] FIG. 13 is a diagram for explaining an example of a final element F-score according to an embodiment. [Figure 6C] FIG. 13 is a diagram for explaining an example of a method for calculating a final element F-score according to an embodiment. [Figure 7A] FIG. 11 is a diagram illustrating an example of an integration process according to an embodiment. [Figure 7B] FIG. 11 is a diagram illustrating an example of an integration process according to an embodiment. [Figure 8] FIG. 13 illustrates an example of a final FPO module integration result according to one embodiment. [Figure 9A] 9 is a diagram showing an example of an integration result of an FPO module corresponding to the final integration result of the FPO module in FIG. 8. [Figure 9B] FIG. 9 is a diagram for explaining an example of calculation of a distance metric of the final FPO module of FIG. 8; [Figure 10] FIG. 13 is a diagram for explaining an FNI analysis process according to an embodiment. [Figure 11] FIG. 13 is a diagram showing an example of a result of comparing analysis times for element P values ​​with and without a recursive formula. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Next, the information processing device, the information processing method, and the computer program of the present embodiment will be described with reference to the drawings. The embodiment described below is merely an example, and the embodiment to which the present invention is applied is not limited to the following embodiment. In all the drawings for explaining the embodiment, the same reference numerals are used for the parts having the same functions, and the repeated description will be omitted. In addition, "based on XX" in this application means "based on at least XX," and includes cases where it is based on other elements in addition to XX. Furthermore, "based on XX" is not limited to cases where XX is used directly, but also includes cases where it is based on XX that has been calculated or processed. "XX" is any element (for example, any information).

[0014] The terms used in this embodiment are explained in Tables 1, 2, and 3 below.

[0015] [Table 1]

[0016] [Table 2]

[0017] [Table 3]

[0018] 1 is a diagram showing an example of a schematic configuration of an information processing device 1 according to this embodiment. In FIG. 1, the information processing device 1 includes an input / output unit 11, a storage unit 12, and a control unit 13.

[0019] The input / output unit 11 receives input of data and outputs data between the information processing device 1 and an external device. The method of inputting and outputting data is not limited. The input / output unit 11 may transmit and receive data by communication via a communication line, for example. The input / output unit 11 may input and output data via a recording medium, for example.

[0020] The storage unit 12 stores various data such as programs executed by the control unit 13 and data used by the control unit 13. The storage unit 12 is configured from a hard disk device, a magneto-optical disk device, a non-volatile memory such as a flash memory, a read-only recording medium such as a CD-ROM, a volatile memory such as a dynamic random access memory (DRAM), or a combination of these.

[0021] The control unit 13 includes a CPU (Central Processing Unit) and a memory. Various functions are realized by executing the program stored in the storage unit 12. When the control unit 13 according to this embodiment executes the correlation network analysis program 121 stored in the storage unit 12, the functions of a correlation coefficient calculation unit 131, an FPO analysis unit 132, an FPO module selection unit 133, an FPO module integration unit 134, an FNI analysis unit 135, an FNI assignment processing unit 136, a P value calculation unit 137, and an output data generation unit 138 are realized.

[0022] The information processing device 1 may be configured using a general-purpose computer device, or may be configured as a dedicated hardware device. For example, the information processing device 1 may be configured using a server computer connected to a communication network such as the Internet. Furthermore, each function of the information processing device 1 may be realized by cloud computing. Furthermore, the information processing device 1 may be realized by a single computer, or may be realized by distributing the functions of the information processing device 1 among multiple computers. Furthermore, the information processing device 1 may be configured to open a website using, for example, a WWW system or the like.

[0023] The storage unit 12 has storage areas for storing a correlation coefficient list 122, module information for each rank by element 123, module integration information for each module F score (MF) and element F score (VF) threshold 124, final module integration information 125, evaluation value information for each target step 126, final module information 127, and an FPO analyzed module list 128. Note that a hash value may be calculated and added to each piece of information to speed up searching for information stored in the storage unit 12.

[0024] Next, an information processing method according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a flowchart showing an example of the procedure of the information processing method according to this embodiment.

[0025] (Step S1) The input / output unit 11 accepts input data. The input data is multivariate data on which correlation network analysis is performed. The input data may be, for example, comprehensive molecular information acquired by omics analysis. The input data may be, for example, information on a product acquired in the manufacturing and inspection process of the product for the purpose of analyzing the cause of defective products. The input data may be, for example, big data. The input data may be, for example, time-series data.

[0026] The multivariate data to be subjected to the correlation network analysis according to the present embodiment is not particularly limited in terms of its acquisition route, acquisition method, type, attribute, etc. For example, multivariate data obtained by omics analysis can be mentioned. "Omics analysis" generally means an integrated analysis of individual comprehensive molecular information, and representative examples of comprehensive molecular information include transcriptome data, which is comprehensive information on gene transcripts, and metabolome data, which is comprehensive information on metabolites. These comprehensive data can be obtained by any method or means, for example, various gene analyses, gene expression analyses, and various mass analyses such as liquid chromatography mass spectrometry (LC-MS), gas chromatography mass spectrometry (GC-MS), and capillary electrophoresis-mass spectrometry (CE-MS). Furthermore, there is no particular limit to the source of acquisition of these comprehensive data, and examples include parts, organs, tissues, and cells derived from various types of animals, plants, microorganisms, and bacteria. Furthermore, the multivariate data may be information obtained by any method from samples obtained from a certain environment and artificial products (e.g., processed foods, etc.).

[0027] The input data may have a format including a plurality of data sets each consisting of a quantitative value corresponding to each of a plurality of elements. FIG. 3 is a diagram showing an example of the configuration of multivariate data (input data) according to this embodiment. The input data may be a quantitative value table consisting of a row corresponding to each element and a column corresponding to each data set, as exemplified in FIG. 3. In the quantitative value table shown in FIG. 3, the first column is the element name (e.g., element identifier (ID001, ID002, etc. in FIG. 3)), the first row is the data set name (DATA_a, DATA_b, etc. in FIG. 3), and the quantitative value of the data set y of the element x is stored in the xth row and yth column (x and y are integers of 2 or more). For example, the data "data_b002" in the 3rd row and 3rd column is the quantitative value of the data set "DATA_b" of the element "ID002".

[0028] (Step S2) The correlation coefficient calculation unit 131 calculates the correlation coefficient between elements based on the quantitative values ​​of the input data. The correlation coefficient calculation unit 131 generates a correlation coefficient list 122 in which the correlation coefficients are sorted in descending order for each element of all elements in the input data. In this way, correlation coefficient lists 122 are generated for all elements in the input data. The correlation coefficient list 122 for element x is a list in which pairs of element names and correlation coefficients are sorted in descending order of correlation coefficient with element x. The correlation coefficient lists 122 for each element generated by the correlation coefficient list 122 are stored in the storage unit 12.

[0029] The correlation coefficient between elements according to this embodiment is, for example, any one of the following indices: Pearson's product moment correlation coefficient, Spearman's rank correlation coefficient, and cosine similarity. The correlation coefficient between elements is a real number between -1 and 1.

[0030] In addition, as for the correlation coefficient between elements according to this embodiment, the negative correlation coefficient may be replaced with "0", or the negative correlation coefficient may not be replaced with "0". When the negative correlation coefficient is not replaced with "0", "negative correlation" and "no correlation" can be distinguished. Therefore, even if two elements that have a strong positive correlation with another element and a negative correlation belong to the same module, they can be made to correspond to each other. Therefore, the module can be easily interpreted.

[0031] 4A and 4B are hypothetical image diagrams for explaining an example of a correlation coefficient between elements according to this embodiment. FIG. 4A shows an example of a module, with elements shown as circles. Elements with a correlation coefficient value equal to or greater than a specific correlation coefficient are connected by lines. The elements include seed elements and elements within the module. Symbols shown in the circles are identification information of the elements. In the example shown in FIG. 4A, the correlation coefficient ρ between element X and element Y is XY is greater than zero, and the correlation coefficient ρ between element X and element Z X Z Consider the case where is greater than zero. In this case, the correlation coefficient ρ between element Y and element Z YZ It is unclear whether is greater than zero. Figure 4B shows the correlation coefficient ρ YZThe following is an image of the possible values ​​of the correlation coefficient ρ YZ It can be seen that can be greater than zero, can be less than zero, or can be unpredictable. Note that ρ indicates the correlation coefficient. Figure 4B shows the range of the Pearson correlation value (-1 to 1) on the X and Y axes.

[0032] (Step S3) The FPO analysis unit 132 sets all elements in the input data as seed elements, and excludes false-positive non-seed elements from among non-seed elements that are ranked higher (with larger correlation coefficients) in the correlation coefficient list 122 of the seed elements (Out ) process (FPO analysis process). The FPO analysis process will be described below.

[0033] The FPO analysis unit 132 constructs a provisional module by connecting a predetermined top (up to Mth) non-seed element in the correlation coefficient list 122 of the seed element with an edge. M is any value in a predetermined range of module size (from the lower limit to the upper limit of the module size). The number of elements (excluding the seed element) constituting the module of the seed element is the rank (rank=M). The predetermined range of the module size can be arbitrarily set by an operator, for example, by setting a MIN value and a MAX value in advance in the information processing device 1. Here, the MIN value and the MAX value are the module size excluding the seed element, and are the minimum and maximum values ​​that the rank can take. Therefore, the lower limit of the total number of elements (seed elements and non-seed elements) constituting the module (module size) is "MIN value + 1", and the upper limit is "MAX value + 1".

[0034] Next, the FPO analysis unit 132 generates a list of correlation coefficients between all elements included in the correlation coefficient list 122 of the seed element and each non-seed element, sorted in descending order, for the provisional module. The FPO analysis unit 132 further connects elements in the generated list having correlation coefficients greater than a predetermined correlation threshold with edges for the provisional module. Here, multiple correlation thresholds are set, thereby configuring multiple provisional modules with different edge patterns. For example, all possible values ​​of the correlation threshold are set as the correlation threshold, thereby configuring provisional modules with edge patterns for each of the correlation thresholds. The lower the correlation threshold, the more edges there are. For example, instead of setting multiple correlation thresholds at the same time, the correlation threshold may be initialized to the maximum value "1", and then the correlation threshold may be reduced while configuring a provisional module with each correlation threshold. Next, the FPO analysis unit 132 calculates the F score and MF of each of the provisional modules having different edge patterns by the following formula.

[0035]

number

[0036] n is the number of elements in the module. edge(i) is the number of edges of element i, which is the number of elements connected to element i in the same module for the edge pattern corresponding to the current correlation coefficient. i is an integer value between 1 and n, and is the index of the element. degree(i) is the degree of element i, which is the total number of elements connected to element i both inside and outside the module for the edge pattern corresponding to the current correlation coefficient.

[0037] MF is the harmonic mean of module density (MD) and module specificity (MS). MD represents the degree to which a module is internally connected as a whole. MS represents the degree to which a module is isolated from the outside (exclusively connected). Therefore, a module with a large MF can be said to be isolated from the outside and to have strong connections as a whole.

[0038] The FPO analysis unit 132 stores in the storage unit 12 the maximum value of the MFs calculated for each of the plurality of provisional modules with different edge patterns, and the correlation threshold value and edge pattern corresponding to the maximum value of the MF. The FPO analysis unit 132 calculates the correlation threshold corresponding to the maximum value of MF and the F score, VF(i), of each non-seed element i for the edge pattern according to the following equation.

[0039]

number

[0040] VF(i) is the harmonic mean of element density (VD(i)) and element singularity (VS(i)). VD(i) represents the proportion of element i that is connected to other elements within the module. VS(i) represents the proportion of the degree that element i has relative to the entire network that is connected to elements of the module. i is an integer value between 1 and n, and is the index of the element. Therefore, an element i with a large VF(i) is closely and exclusively connected to the module, and can be said to be an element suitable as a component of the module.

[0041] The FPO analysis unit 132 judges the non-seed element i with the smallest VF(i) of the provisional module to be a false positive, and removes the non-seed element i judged to be a false positive from the provisional module. Next, the FPO analysis unit 132 calculates the maximum MF and the VF(i) of each non-seed element i again for the provisional module from which the non-seed element i judged to be a false positive has been removed. This FPO analysis process for removing the false positive non-seed element i is repeated until the number of elements of the provisional module becomes the "module size lower limit", that is, "MIN value + 1". The FPO analysis unit 132 judges the time when the MF was maximum as a state in which the false positives have been removed without excess or deficiency, and stores the configuration information of the provisional assembly module at the time when the MF was maximum in the module information 123 as the maximum MF module in the "rank" of the seed element.

[0042] The FPO analysis unit 132 executes FPO analysis processing for each rank in a predetermined range of ranks (from the lower limit (MIN value + 1) to the upper limit (MAX value + 1) of the module size at the time when the provisional module is constructed). As a result, for one seed element, for each rank in the predetermined range of ranks, the configuration information of the provisionally assembled module at the time when the MF was maximum is stored in the module information 123 as a maximum MF module.

[0043] The FPO analysis unit 132 performs the FPO analysis in step S3 while increasing the rank from the lower limit (MIN value) to the upper limit (MAX value), and stores the configuration information of the module for which the FPO analysis in step S3 has been completed (FPO analyzed module) in the FPO analyzed module list 128. If a module identical to an FPO analyzed module that already exists in the FPO analyzed module list 128 is constructed, the FPO analysis process for removing false positive non-seed elements may be stopped, and the search for the maximum MF module of that rank may be terminated early.

[0044] The FPO analysis unit 132 uses a correlation threshold and an edge pattern to determine whether the maximum value of the MF is not updated, and if it determines that the maximum value of the MF is not updated in a provisional module for which the MF has not been calculated among multiple provisional modules with different edge patterns, it may terminate the calculation of the MF and terminate the search for the maximum MF module of that rank early. By configuring in this manner, when the FPO analysis unit 132 determines that the maximum MF value will not be updated for a provisional module for which the MF has not been calculated among multiple provisional modules with different edge patterns, it can terminate the MF calculation and terminate the search for the maximum MF module of that rank early.Therefore, even if the maximum MF value is not updated, the amount of calculation can be reduced and the FPO analysis processing time can be shortened compared to when the MF calculation is continued and the module optimization processing is continued.

[0045] (Step S4) The FPO module selection unit 133 selects an optimal module (referred to as an FPO module) of the FPO analysis result based on the module information 123 for one seed element.

[0046] FIG. 5A is a diagram for explaining the FPO module selection method according to the present embodiment. The example of FIG. 5A is time-series metabolome data of grated radish (2305 elements x 10-point time-series data set). The result is an FPO analysis process performed using an element with Pearson's product-moment correlation coefficient, MIN value "2", MAX value "2305", and identifier "c1487" as a seed element. FIG. 5A shows the maximum value of MF (vertical axis) corresponding to the rank (horizontal axis). The FPO module selection unit 133 determines the module M_PTmax of the rank that shows the maximum value PTmax (longest plateau length) of the period in which the maximum value of MF is not updated even if the rank value is increased. The FPO module selection unit 133 selects the determined module M_PTmax as the FPO module.

[0047] In the example shown in FIG. 5A, the MF of the module constructed at "Rank=623" was calculated to be 0.683, and no modules with an MF exceeding 0.683 were constructed up to "Rank=761". Therefore, the plateau length, which is the period during which the maximum MF is not updated even if the rank value is increased, is "138", which is obtained by subtracting "Rank=623" from "Rank=761". In this example, since "138" was the longest plateau length (longest plateau length) PTmax, the module constructed at "Rank=623" was selected as the module M_PTmax of rank "623" which shows the longest plateau length PTmax.

[0048] Previously, the module with the rank showing the maximum MF across all ranks was selected as the FPO module. However, the rank showing the maximum MF across all ranks is often at or near the upper limit of the rank (MAX value). For this reason, previously, huge, biased modules were often constructed. Also, previously, it was possible that optimal FPO analysis results could not be obtained because it depended on the setting of the upper limit of the rank (MAX value).

[0049] In contrast, according to the present embodiment, a module with a large MF and an appropriate number of elements can be obtained, which improves the quality of the FPO analysis results. In addition, the FPO analysis results are less affected by the setting of the upper limit value (MAX value) of the rank. This allows for a more robust FPO analysis.

[0050] Furthermore, the module information 123 for each rank can be used to verify the selection criteria for the FPO module. The output data generating unit 138 may generate data for a graph of the maximum value of MF, as exemplified in Fig. 5A, using the module information 123 for each rank. This graph data is data that can be used to display the graph on a display screen or print the graph. This allows the operator to easily verify the selection criteria for the FPO module.

[0051] As another FPO module selection method, a criterion that maintains robustness regardless of the number of elements in the module or the distribution of correlation coefficients between elements may be adopted. For example, a criterion using the plateau length of the graph of the moving average of the maximum value of MF as exemplified in FIG. 5B, or a criterion that the increase rate of the graph is below a certain value may be adopted. Also, a criterion optimized for each data set based on the distribution of correlation coefficients between elements may be adopted. In this case, the upper limit value of the rank, i.e., the MAX value, does not need to be adjusted as a setting parameter, thereby reducing the burden on the operator.

[0052] FIG. 5B is a diagram for explaining the FPO module selection method according to the present embodiment. The example of FIG. 5B is time-series metabolome data of grated radish (number of elements 2305×time-series 10-point data set). The result is an FPO analysis process performed using an element with identifier “c1487” as a seed element with Pearson's product-moment correlation coefficient, MIN value “2”, MAX value “2305”. FIG. 5B shows a 10-point moving average (vertical axis) of the maximum value of MF corresponding to the rank (horizontal axis). In the example shown in FIG. 5B, the MF of the module constructed at “rank=152” is calculated as 0.590, and no module with an MF exceeding 0.590 was constructed up to “rank=385”. Therefore, the plateau length of the moving average, which is the period during which the maximum value of MF is not updated even if the rank value is increased, is “233”, which is obtained by subtracting “rank=152” from “rank=385”. In this example, since “233” was the longest plateau length (longest plateau length) MAPTmax of the moving average, the module constructed with “rank=152” was selected as the module M_MAPTmax with rank “152” indicating the longest plateau length MAPTmax.

[0053] In addition, the FPO module selection unit 133 may end the FPO analysis and the selection of the FPO module when a predetermined condition is satisfied. FIG. 5C is a diagram for explaining an example of the FPO analysis according to this embodiment. FIG. 5C is time-series metabolome data of grated radish (2305 elements x 10-point time-series data set). The result is that the FPO analysis process was performed using an element having an identifier "c1487" as a seed element with a Pearson product-moment correlation coefficient, a MIN value of "2", and a MAX value of "2305". FIG. 5C shows the maximum value (vertical axis) of the MF corresponding to the rank (horizontal axis). In this example, since there are 1569 elements having a positive correlation coefficient with the identifier "c1487", the maximum rank of the identifier "c1487" is 1569. FIG. 5C shows the process in the case where the FPO module selection unit 133 is executed each time the FPO analysis unit 132 finishes the calculation of the maximum MF for each rank in step S3, and it is determined whether or not the longest plateau length is updated. When it is determined that the longest plateau length is not updated, the FPO module selection unit 133 may determine that the maximum value of the plateau length is not updated even if the rank changes (even if the rank increases), end the increase in rank, and end the FPO analysis and the selection of the FPO module. When the rank exceeds 1432 in FIG. 5C, the remaining number of ranks is "137" obtained by subtracting the rank "1432" from the maximum rank "1569". Since the number of ranks "137" is below PTmax "138", it is determined that the part of the rank equal to or greater than 1432 does not exceed PTmax, and the calculation of the part described as not requiring calculation (the part of the rank equal to or greater than 1432) is not performed. By configuring in this manner, the FPO module selection unit 133 can terminate FPO analysis and selection of FPO modules when the longest plateau length is not updated even when an element is added (even when the rank is increased). Therefore, even when the longest plateau length is not updated, the amount of calculations can be reduced and the time required for FPO analysis and selection of FPO modules can be shortened compared to when FPO analysis and selection of FPO modules are continued for each rank.

[0054] (Step S5) When the selection of FPO modules is completed with all elements in the input data as seed elements (Step S5, YES), proceed to Step S6-1. On the other hand, if there are elements remaining for which an FPO module has not yet been selected (Step S5, NO), return to Step S3 and perform FPO analysis and FPO module selection with the elements for which an FPO module has not yet been selected as seed elements.

[0055] (Step S6-1) The FPO analysis unit 132 calculates, for each seed element's FPO module, the maximum value of VF(i) for each element (i) in the FPO module (referred to as the final VF) and an MF based on the final VF (referred to as the final MF). The final MF and final VF calculated for each seed element's FPO module are stored in the evaluation value information 126.

[0056] 6A and 6B are virtual image diagrams for explaining an example of the final VF according to this embodiment. FIG. 6A shows an example of a module and an element. In FIG. 6A, elements are indicated by circles. The elements include an element of interest (an element for which the final VF is calculated), an element within a module, and an element outside the module. The numerical value indicated in the circle is a correlation rank for the element of interest. As an example here, the higher the correlation rank, the higher (the smaller the value). In FIG. 6A, lines are used to connect elements with correlation coefficients equal to or greater than a specific value between the element of interest and the element within the module, and between the element of interest and the element outside the module. A solid line indicates the relationship between elements included in an edge, i.e., between elements within the module. A dotted line indicates the relationship between elements not included in an edge, i.e., between elements outside the module.

[0057] The FPO analysis unit 132 calculates a final VF for each element (i) in an FPO module for one FPO module. Specifically, the FPO analysis unit 132 sets each element (i) in the FPO module as an element of interest, and calculates a VF(i) for the element of interest. FIG. 6B shows a calculation result of VF(i) for one element (i) (element of interest). In FIG. 6B, the horizontal axis indicates the correlation rank, and the vertical axis indicates VF(i). The FPO analysis unit 132 obtains the maximum value of VF(i) (final VF) for one element (i) (element of interest). In the example of Figure 6B, when the correlation ranking is "5", there are three connections to elements within the module and two connections to elements outside the module, the degree is "5 = 2 + 3", the edge is 3, the number of elements in the module (n) is 5, and VF(i) is calculated from equation (2) above as "VF(i) = (2 × edge(i)) / (degree(i) + n-1) = (2 × 3) / (5 + 5-1) = 0.67", which shows the maximum value of VF(i) (final VF).

[0058] In addition, the FPO analysis unit 132 may determine whether or not VF(i) does not exceed the maximum value even if the correlation rank is increased in the calculation of the final VF. When it is determined that VF(i) does not exceed the maximum value even if the correlation rank is increased, the FPO analysis unit 132 may end the process of calculating VF(i).

[0059] FIG. 6C is a virtual image diagram for explaining an example of a method for calculating the final VF according to this embodiment. For one FPO module, the FPO analysis unit 132 calculates a final VF for each element (i) in the FPO module. Specifically, the FPO analysis unit 132 sets each element (i) in the FPO module as an element of interest, and calculates a VF(i) for the element of interest. FIG. 6C shows the calculation result of VF(i) for one element (i) (element of interest). In FIG. 6C, the horizontal axis indicates the correlation rank, and the vertical axis indicates VF(i). When the correlation rank is 8, the FPO analysis unit 132 determines that VF(i) does not exceed the maximum value, and ends the process of calculating VF(i). The reason why the calculation of VF(i) can be ended will be described below.

[0060]

number

[0061] In the above formula (3), VF is the element F score. n is the number of elements in the target module. edge is the number of edges in the target module. degree is the degree number of elements. From the above formula (3), even if all elements in a module are connected to elements in other modules, edge = n-1, so we can say edge ≦ n-1. When calculating the final VF, the number of elements n in the module is a fixed value, so it is possible to find the upper limit of the achievable final VF using only the degree number.

[0062]

number

[0063] In the above formula (4), VF is the element F score. a is the maximum VF value at the time of correlation rank a. n is the number of elements in the target module. degree is the number of degrees of the elements. In the example of FIG. 6C, n=5, and at the time of correlation rank a=5, VF a = 0.67, so VF aIn order to exceed this, d≦8 must be satisfied according to the above formula (4). Here, the value of degree depends on the correlation rank, and in the case of a correlation rank of "9", the value of degree is 9. Therefore, after calculating the case of a correlation rank of "8", it is not necessary to calculate the cases of correlation ranks of "9" or higher. In the example of FIG. 6C, in the case of a correlation rank of "6", there are three connections to elements within the module and three connections to elements outside the module, the number of degrees (degree) is "6=3+3", the number of edges (edge) is 3, and the number of elements in the module (n) is 5. Therefore, according to the above formula (2), VF(i) is "VF(i)=(2×edge(i)) / (degree(i)+n-1)=(2×3) / (6+5-1)=0.60". In the case of a correlation ranking of "7", there are three connections to elements within the module and four connections to elements outside the module, so the number of degrees is "7 = 4 + 3", the number of edges is 3, and the number of elements in the module (n) is 5. Therefore, from the above formula (2), VF(i) is "VF(i) = (2 x edge(i)) / (degree(i) + n-1) = (2 x 3) / (7 + 5-1) = 0.54". In the case of a correlation ranking of "8", there are three connections to elements within the module and five connections to elements outside the module, so the number of degrees is "8 = 5 + 3", the number of edges is 3, and the number of elements in the module (n) is 5. Therefore, from the above formula (2), VF(i) is "VF(i) = (2 x edge(i)) / (degree(i) + n-1) = (2 x 3) / (8 + 5-1) = 0.50". In this way, the requirements are sorted in descending order of correlation rank, as determined by equation (4). It can be confirmed that even if an element is added, VF(i) does not exceed 0.67 (when the correlation rank is "5") if the element is an element outside the module. By configuring in this way, it is possible to reduce the amount of calculation and the processing time compared to the case where VF(i) is calculated for all correlation ranks.

[0064] The FPO analysis unit 132 calculates an MF (called a final MF) for one FPO module based on the final VF of each element (i) in the FPO module. Specifically, the FPO analysis unit 132 obtains edge(i) and degree(i) when the final VF is obtained for each element (i) in the FPO module, and calculates an MF (final MF) from the above formula (1) using the obtained edge(i) and degree(i) of each element (i).

[0065] The method of calculating the final VF and the final MF may be any method that calculates VF(i) and MF without depending on the seed element in the module for which the final VF and the final MF are calculated. For example, the VF(i) in the case where an element (i) in the module has the smallest element P value (described later) with respect to other elements in the module may be used as the final VF.

[0066] (Step S6-2) The P value calculation unit 137 calculates a module P value for the FPO module of each seed element by the following formula.

[0067]

number

[0068] N is the total number of elements in the multivariate data that is the input data. n is the number of elements in the module. edge avg is the integer closest to the average value of edge(i) of each element(i) used in the calculation of the final MF. avg is an integer closest to the average value of the degree(i) of each element(i) used in calculating the final MF. q and r are positive integers, and r≦q. In addition, edge avg Instead of the degree, we use the number of edges in the module that connect elements with correlation coefficients equal to or greater than the correlation threshold when the maximum value of the MF of the FPO module is obtained. avg Alternatively, the degree at which the maximum value of the MF of the FPO module is obtained may be used.

[0069] The module P value calculated for the FPO module of each seed element is stored in the evaluation value information 126 as the module P value after FPO analysis. The module P value has the same meaning as the probability used in statistical processing, and represents the probability of an event occurring that is rarer than the observed event. The module P value can be used to verify whether the formed module is correct or not. The module P value calculated by the above formula (5) is found by multiplying the probability of giving the same MD and the probability of giving the same MS when the module is randomly constructed. The module P-value after FPO analysis allows the operator to evaluate the significance of the FPO module of each seed element, which can add credibility to the module.

[0070] (Step S7) The FPO module integration unit 134 executes a module integration process (modularization process) for the FPO modules of each seed element. The FPO modules are configured separately for each seed element by the above-mentioned steps S3-S5. Therefore, FPO modules are configured for the total number of elements, and overlapping elements occur between the FPO modules. This overlapping of elements is eliminated by the module integration process. The module integration process will be described below.

[0071] The FPO module integration unit 134 reconstructs modules for each VF threshold within a predetermined range of the VF threshold (from the minimum to the maximum of the VF threshold). The VF threshold is preset at a constant interval from the minimum to the maximum of the VF threshold. The module reconstruction method is to delete all existing edges for all FPO modules and reconnect elements that are final VFs equal to or greater than the VF threshold with new edges. At this time, if there is an overlap of elements between FPO modules, the FPO modules are also integrated via the overlapping elements. This provides an integration result of the FPO modules for each VF threshold. The FPO module integration unit 134 stores the integration result of the FPO modules for each VF threshold in the module integration information 124. The VF threshold is not limited to being set at a constant interval from the minimum to the maximum of the VF threshold. For example, all possible values ​​of MF and VF may be listed as the VF threshold, and integration may be performed for all MF and VF. In this case, the VF threshold does not have to be set at a constant interval.

[0072] The FPO module integration unit 134 may perform the integration process only on some of the FPO modules, instead of performing the integration process on all the FPO modules configured by steps S3-S5. For example, the FPO modules that do not satisfy a predetermined condition may be excluded from the integration process based on the final VF, final MF, and module P value of each FPO module stored in the evaluation value information 126. For example, when the VF threshold is set from the minimum value to the maximum value of the VF threshold, the FPO modules whose final MF is less than the VF threshold and the FPO modules whose seed element final VF is less than the VF threshold may be excluded from the integration process. In this way, by totally excluding FPO modules whose quality or significance is low based on the evaluation value from the integration process, it is possible to improve the quality or significance of the modules being integrated.

[0073] FIG. 7 is a virtual image diagram for explaining an example of the integration process according to this embodiment. FIG. 7A is an image diagram of integrating modules by excluding edges between elements using the final VF in the modularization process. FIG. 7A shows three FPO modules M7-1, M7-2, and M7-3 as an example, with elements shown as circles, element identifiers "c001"-"c011", and the final MF and final VF of each FPO module calculated in step S6-1. In this example, element "c002" overlaps between FPO modules M7-1 and M7-2, and element "c008" overlaps between FPO modules M7-2 and M7-3. When the VF threshold is set to 0.5, a module M7-4 including all elements is constructed by the integration process. When the VF threshold is set to 0.75, the final VF of element "c002" in module M7-2 and element "c011" in module M7-3 is less than the VF threshold, so they are not connected by an edge and are excluded by the integration process. As a result, modules M7-5 and M7-6 are formed by the integration process. When the VF threshold is set to 0.85, modules M7-7, M7-8, and M7-9 are formed when the integration process is performed in the same way. Here, modules M7-8 and M7-9 have a small number of elements, 2 and 1, respectively, and are evaluated low. In addition, they do not show significant connections between multiple elements, which hinders the operator from analyzing the integration results of the FPO modules.

[0074] On the other hand, FIG. 7B is an image diagram of integrating modules by excluding components for each module using the final MF and the final VF of the seed element in the modularization process, and excluding edges between elements using the final VF. FIG. 7B shows an integration process in which, when the VF threshold is set from the minimum value to the maximum value of the VF threshold, FPO modules whose final MF is less than the VF threshold and FPO modules whose seed element's final VF is less than the VF threshold are excluded from the integration process. When the VF threshold is set to 0.85, modules M7-2 and M7-3 whose final MF is less than 0.85 are excluded from the integration process, and only module M7-7 is configured when the integration process is performed. In this way, the integration process can prevent the configuration of modules with low evaluation values, and can improve the quality or significance of the modules that are configured.

[0075] The FPO module integration unit 134 stores the integration result (FPO module group) of the FPO modules with the VF threshold value that has the maximum number of FPO modules that fall within a predetermined range of module sizes in the final module integration information 125 as the final FPO module integration result.

[0076] Conventionally, the final integration result of the FPO module alone does not provide an overall picture of the positional relationship of the elements, making it difficult to analyze the internal structure of the FPO module. However, according to this embodiment, the integration result of the FPO module for each VF threshold is stored in the module integration information 124, so that the operator can check how the integration result of the FPO module changes with the change in the VF threshold, based on the integration result of the FPO module for each VF threshold. This allows the operator to grasp the overall picture of the positional relationship of the elements in the final integration result of the FPO module, which contributes to the analysis of the internal structure of the FPO module.

[0077] 8 and 9 are diagrams showing an example of the final FPO module integration result according to the present embodiment. FIG. 8 and FIG. 9 show the results of FPO analysis using a data set showing the friendships of 34 people belonging to a karate club at a university in the United States, with Pearson's product-moment correlation coefficient, MIN value "5", and MAX value "34". Components are shown as circles, and the patterns inside the circles distinguish each of the separated members. The numbers are the identification numbers of the 34 members of the karate club. The solid lines show the relationships of the connections obtained by the modularization process. FIG. 8 shows the results of network depiction for each of the obtained modules. FIG. 9A shows an example of the FPO module integration result for each VF threshold corresponding to the final FPO module integration result. In the example of FIG. 9A, a graph is shown showing how the FPO module integration result (modularization of each element (vertical axis)) changes according to the change in the VF threshold (horizontal axis). For example, when the VF threshold (VF th ) is 0.81, the relationship between the modules M8-1, M8-2, and M8-3 for the three modules M8-1, M8-2, and M8-3 shown in FIG. 8 can be understood from FIG. 9A.

[0078] The output data generating unit 138 may generate data (e.g., a graph) showing the correspondence between the VF threshold (horizontal axis) and the integration result of the FPO module (modularization of each element (vertical axis)) illustrated in Fig. 9 using the module integration information 124 for each VF threshold. This graph data is data that can be displayed on a display screen or printed out. This allows the operator to easily analyze the final integration result of the FPO module.

[0079] The FPO module integration unit 134 may further calculate a distance index d indicating the distance of the final FPO module after module integration based on the integration results of the FPO modules for each VF threshold stored in the module integration information 124. The distance index is a positive real number and is calculated for all pairs of FPO modules. The lower the value of the distance index d, the closer the distance between the two modules is determined to be. By calculating the distance index d, the overall positional relationship between the FPO modules can be easily and quantitatively evaluated.

[0080] FIG. 9B is a diagram for explaining an example of a method for calculating a distance index d of the final FPO module according to the present embodiment. FIG. 9B shows one method for calculating the distance index d based on the integration result of the final FPO module illustrated in FIG. 9A. In FIG. 9B, the VF shown in FIG. th For the three modules M8-1, M8-2, and M8-3 separated at =0.81, we consider virtual modules M9-1, M9-2, and M9-3 that further include information on the structure of the modules when the VF threshold is raised. Point G in the graph is defined as the center point of the separated virtual modules M9-1, M9-2, and M9-3. Regarding the distance between the obtained FPO modules, the difference in the VF threshold and the number of components are used to calculate the distance to the center point of the virtual module, thereby more accurately grasping the overall picture of the positional relationship of the elements in the integration result of the FPO modules.

[0081] For example, when calculating the distance index between modules M9-1 and M9-2, the process is carried out in the following order (1) to (3). (1) The minimum VF threshold at which the components of modules M8-1 and M8-2 are separated by the module merging process, VF th,min In this example, VF th,min =0. The number is 76. (2) The components of the virtual modules M9-1 and M9-2 are VF thresholds. th When set to above, the average value of the minimum VF thresholds that are excluded by the module integration process is VF th, min avgFor example, in the case of the virtual module M9-1, the elements “23”, “21”, “19”, “16”, and “15” are excluded when the VF threshold is 0.95 or more, the elements “33”, “31”, “30”, and “24” are excluded when the VF threshold is 0.92 or more, the elements “34”, “29”, “28”, “27”, and “10” are excluded when the VF threshold is 0.91 or more, the element “9” is excluded when the VF threshold is 0.90 or more, and the element “32” is excluded when the VF threshold is 0.86 or more. Therefore, the average value of the minimum VF threshold at which 16 components are excluded, VF th, minavg (M8-1). Specifically, VF th, minavg This can be calculated as (M8-1) = (0.95 x 5 + 0.92 x 4 + 0.91 x 5 + 0.90 x 1 + 0.86 x 1) / 16 = 0.9212. Similarly, for the virtual module M9-2, elements “8”, “4”, “22”, “2”, and “18” are excluded if the VF threshold is 0.90 or more, elements “1”, “13”, and “14” are excluded if the VF threshold is 0.88 or more, element “20” is excluded if the VF threshold is 0.87 or more, and element “3” is excluded if the VF threshold is 0.86 or more. Thus, the average VF of the minimum VF threshold is 10, and 11. th, minavg (M8-2). Specifically, VF th, minavg (M8-2) = (0.9 x 5 + 0.88 x 3 + 0.87 x 1 + 0.86 x 1) / 10 = 0.8870. (3) Calculate the distance index d(M8-1, M8-2) using the following formula (6).

[0082]

number

[0083] In the above formula (6), d indicates the distance index between modules. th indicates the VF threshold. VF th,min denotes the minimum VF threshold at which the components of modules M8-1 and M8-2 are separated by the module merging process. th, minavgdenotes the respective averages of the minimum VF thresholds at which components of both modules are excluded. In this example, d(M8-1, M8-2) = 2(0.81-0.76) + (0.8870-0.81) / 2 + (0.9212-0.81) / 2 = 0.1000 + 0.0385 + 0.0556 = 0.1941.

[0084] When the same calculation is performed for the virtual modules M9-2 and M9-3, the results are d(M8-2, M8-3) = 0.0585 and d(M8-1, M8-3) = 0.1756. In the case of virtual module M9-3, elements “7”, “6”, “5”, “12”, and “11” are excluded when the VF threshold is 0.85 or more. Therefore, the average value of the VF threshold at which the five components are excluded, VF th, minavg (M8-3). Specifically, VF th, minavg (M8-3) = 0.85 × 5 / 5 = 0.8500. The minimum VF threshold at which the components of modules M8-2 and M8-3 are divided by the module integration process, VF th,min In this example, VF th,min = 0.81. In this example, it can be calculated as d(M8-2, M8-3) = 2(0.81-0.81) + (0.8870-0.81) / 2 + (0.8500-0.81) / 2 = 0.0385 + 0.0200 = 0.0585. In addition, in the case of d(M8-1, M8-3), the minimum VF threshold at which the components of modules M8-1 and M8-3 are divided by the module integration process, VF th,min In this example, VF th,min = 0.76. In this example, it can be calculated as d(M8-1, M8-3) = 2(0.81-0.76) + (0.9212-0.81) / 2 + (0.8870-0.81) / 2 = 0.1000 + 0.0556 + 0.0200 = 0.1756. From the above, it can be determined that M8-2 is closer to M8-1 than M8-3. In this way, we can get a more accurate overall picture of the positional relationship of elements in the final FPO module integration result.

[0085] 9B shows the distance index calculated based on the final integration result of the FPO module illustrated in FIG. 9A as a numerical value. In the module M8-1, the distance 0.1056 indicates the average value of the distance of each element from the minimum VF threshold at which the module M8-2 is divided to the minimum VF threshold at which the elements constituting the module M8-1 are excluded. In the module M8-1, the distance 0.0556 indicates the average value of the distance from the minimum VF threshold at which the module M8-3 is divided to the minimum VF threshold at which the elements constituting the module M8-1 are excluded. In the module M8-2, the distance 0.0885 indicates the average value of the distance of each element from the minimum VF threshold at which the module M8-1 is divided to the minimum VF threshold at which the elements constituting the module M8-2 are excluded. In the module M8-2, the distance 0.0385 indicates the average value of the distance of each element from the minimum VF threshold at which the module M8-3 is divided to the minimum VF threshold at which the elements constituting the module M8-2 are excluded. In module M8-3, the distance 0.0700 indicates the average distance from the minimum VF threshold at which module M8-1 is divided to the minimum VF threshold at which the elements constituting module M8-3 are excluded, and the distance 0.0200 indicates the average distance of each element from the minimum VF threshold at which module M8-2 is divided to the minimum VF threshold at which the elements constituting module M8-3 are excluded.

[0086] (Step S8-1) The FPO module integration unit 134 calculates a final VF and a final MF for each final FPO module after the module integration, similar to the FPO analysis unit 132 in step S6-1 above. The final VF and the final MF calculated for each final FPO module after the module integration are stored in the evaluation value information 126.

[0087] (Step S8-2) The P value calculation unit 137 calculates a module P value for each of the final FPO modules after the module integration by the above formula (5). The module P value calculated for each of the final FPO modules after the module integration is stored in the evaluation value information 126 as the module P value after the module integration. The module P value after module integration allows the operator to evaluate the significance of each final FPO module after module integration, which adds credibility to the module.

[0088] (Step S9) The FNI analysis unit 135 executes a process (FNI analysis process) of incorporating (In) false-negative elements for some or all of the final FPO modules (final FPO modules) after module integration.

[0089] The final FPO module to be the target of the FNI analysis process may be, for example, a plurality of modules that the user can specify. Alternatively, the conditions for the final FPO module to be the target of the FNI analysis process (FNI analysis process target conditions) may be set in advance in the information processing device 1, and the FNI analysis unit 135 may exclude the final FPO module that does not satisfy the FNI analysis process target conditions, and determine the remaining final FPO module as the target of the FNI analysis process. The FNI analysis process target condition is, for example, a module having a certain level of significance (module P value) or more. The FNI analysis process is described below.

[0090] FIG. 10 is a virtual image diagram for explaining the FNI analysis process according to this embodiment. In FIG. 10, elements are indicated by circles. The elements include an element of interest, a candidate element, an element in a target module, and an element outside the target module. An element in a target module is an element included in a final FPO module (final FPO module) after module integration, and an element outside the target module is an element not included in a final FPO module (final FPO module) after module integration. The numbers shown in the circles indicate correlation rankings with respect to the candidate elements. Each element is connected by a line. A solid line indicates a relationship between elements in a module including the element of interest. A dotted line indicates a relationship between elements outside the module including the candidate element. The FNI analysis unit 135 repeats the FNI analysis process for the element of interest while sequentially setting all elements included in all final FPO modules targeted for the FNI analysis process as the element of interest. Therefore, the FNI analysis process is executed for the number of all elements included in all final FPO modules targeted for the FNI analysis process. Here, with reference to FIG. 10, one element of interest shown in FIG. 10 will be described as an example.

[0091] The FNI analysis unit 135 sets a candidate element to be incorporated into the final FPO module (target module) to which the element of interest belongs. For example, the candidate element to be incorporated into the target module may be all elements not belonging to the target module (i.e., all elements outside the target module). For example, the candidate element to be incorporated into the target module may be limited to a predetermined upper rank of correlation ranking with respect to the element of interest among all elements outside the target module. For example, the candidate element to be incorporated into the target module may be limited to a predetermined upper rank of correlation ranking with respect to the element of interest among all elements outside the target module, and a correlation coefficient with respect to the element of interest may be equal to or greater than a predetermined correlation threshold. For example, the candidate element to be incorporated into the target module may be limited to an element not belonging to any final FPO module. The FNI analysis unit 135 performs a false negative judgment with respect to the target module for each candidate element.

[0092] Specifically, the FNI analysis unit 135 refers to the correlation coefficient list 122 of the candidate element i in order from the top (rank from the highest correlation (correlation rank)), and calculates, for each rank, VS(i) of the above formula (2) for the target module of the candidate element i, "VS(i) = (number of edges in the target module of the candidate element i edge(i)) ÷ (number of degrees of the candidate element i degree(i))". The FNI analysis unit 135 records information such as correlation rank for VS(i) that exceeds a predetermined VS threshold among all the calculated VS(i) in the storage unit 12. The VS threshold can be set arbitrarily by, for example, an operator. When a VS(i) that exceeds a predetermined VS threshold is calculated, the FNI analysis unit 135 determines that the candidate element is a false negative element for the element of interest, and incorporates the candidate element into the target module.

[0093] (Step S10) The P value calculation unit 137 calculates an element P value for a target module in a correlation rank corresponding to a VS(i) that exceeds the VS threshold value, using the following formula.

[0094]

number

[0095] In the above formula (7), N is the total number of elements of the multivariate data that is the input data. n is the number of elements in the target module. edge is the number of edges edge(i) of the candidate element i in the target module. degree is the number of degrees degree(i) of the candidate element i. q and r are positive integers, and r≦q.

[0096] The element P value may be calculated using the recursive formula of equation (8) while calculating VS(i) at each correlation rank in step S9.

[0097]

number

[0098] In the above formula (8), N is the total number of elements of the multivariate data that is the input data. n is the number of elements of the module. edge is the number of edges edge(i) of the candidate element i in the target module. degree is the number of degrees degree(i) of the candidate element i. By calculating the element P value using the recursive formula of equation (8) while calculating VS(i) in step S9, it is possible to omit the calculation of the number of combinations, thereby improving the processing efficiency.

[0099] 11 shows the time required to perform FNI analysis (including steps S9 to S13-2 (described below)) after completing FPO analysis using Arabidopsis microarray data (a data set of 27127 x 9442 samples with the number of elements) with Pearson's product-moment correlation coefficient, MIN value of 2, and MAX value of 250. This shows that the analysis time was significantly reduced to 1 / 92 by simultaneously executing steps S9 and S10 and adopting a recursive formula to calculate the element P value, as in the present invention.

[0100] The P value calculation unit 137 stores information about the correlation rank with the smallest element P value among the correlation ranks corresponding to VS(i) exceeding the VS threshold in the evaluation value information 126 in the FNI analysis. The information about the correlation rank with the smallest element P value is the information about the correlation rank that can be determined to be the most significant.

[0101] (Step S11) When the FNI analysis process has been completed for all elements of all final FPO modules that are the target of the FNI analysis process (step S11, YES), proceed to step S12. On the other hand, if there are still elements for which the FNI analysis process has not been executed (step S11, NO), return to step S9 and execute the FNI analysis process on the elements for which the FNI analysis process has not been executed as the element of interest.

[0102] (Step S12) The FNI assignment processing unit 136 executes an assignment process for eliminating overlapping candidate elements among multiple final FPO modules generated by the FNI analysis process. In the assignment process, the following assignment determination criteria are used.

[0103] Assignment Criteria: For multiple target modules with overlapping candidate elements i, the following priorities are used: VS(i)> "The module P value of the above formula (5) when the candidate element i is incorporated into the target module"> The number of elements in the module VS(i) has the highest priority, then the module P value, then the number of elements in the module.

[0104] First, the candidate element i is left only in the target module with the largest VS(i), and the candidate element i is removed from the other target modules. On the other hand, if VS(i) is equivalent, the candidate element i is retained only in the target module having the smallest “module P value in equation (5) above when the candidate element i is incorporated into the target module,” and the candidate element i is removed from the other target modules. If VS(i) is equivalent and the “module P value of equation (5) above when candidate element i is incorporated into the target module” is equivalent between the target modules, then candidate element i is left only in the target module with the largest number of elements, and candidate element i is removed from the other target modules.

[0105] The assignment process is not limited to the process based on the above-mentioned assignment criteria, as long as it can eliminate the overlapping of elements between multiple modules that occurs due to the FNI analysis process. For example, modules having overlapping elements may be integrated by a module integration process (modularization process).

[0106] The FNI assignment processing unit 136 stores information on the final FPO module after the assignment process in the final module information 127. The final module information 127 may be a file for each module, a file that compiles all the modules, or both a file for each module and a file that compiles all the modules. Note that information on the final FPO module before the assignment process may be recorded in the storage unit 12.

[0107] According to this embodiment, when duplicate candidate elements occur in multiple final FPO modules as a result of performing FNI analysis processing on multiple final FPO modules, the duplication of the candidate elements can be eliminated. This saves the operator the trouble of individually specifying the final FPO module for executing the FNI analysis processing, and allows the operator to perform the FNI analysis processing on multiple final FPO modules at once, and also allows the operator to obtain an FNI analysis processing result without duplicate elements.

[0108] (Step S13-1) The FNI assignment processing unit 136 calculates a final VF and a final MF for each final FPO module after the assignment process, similar to the FPO analysis unit 132 in the above step S6-1. The final VF and the final MF calculated for each final FPO module after the assignment process are stored in the evaluation value information 126.

[0109] (Step S13-2) The P value calculation unit 137 calculates a module P value for each final FPO module after the assignment process by the above formula (5). The module P value calculated for each final FPO module after the assignment process is stored in the evaluation value information 126 as the module P value after the assignment process. The module P-value after the assignment process allows the operator to evaluate the significance of each final FPO module after the assignment process, which adds credibility to the module.

[0110] (Step S14) The output data generating unit 138 generates various output data. The output data is data that can be displayed on a display screen or printed. For example, the output data generating unit 138 generates drawing data that expresses the configuration of each final FPO module after the assignment process. The configuration of each final FPO module after the assignment process is drawn and visualized by this drawing data.

[0111] For example, the output data generating unit 138 may generate graph data (such as the graph data illustrated in FIG. 5A) showing MFs corresponding to each rank for each seed element obtained by the FPO analysis process. For example, the output data generating unit 138 may generate graph data (such as the graph data illustrated in FIG. 9) showing the integration results by VF threshold obtained by the FPO module integration process.

[0112] The output data generated by the output data generation unit 138 may be output by the input / output unit 11 to a device external to the information processing device 1, or may be stored in the storage unit 12. The output data stored in the storage unit 12 can be output by the input / output unit 11 to an external device.

[0113] According to this embodiment, it is possible to obtain an effect of improving the accuracy of correlation network analysis of multivariate data such as comprehensive molecular information obtained by omics analysis or big data.

[0114] Although an embodiment of the present invention has been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and design changes and the like that do not deviate from the gist of the present invention are also included.

[0115] In addition, a computer program for implementing the functions of each of the above-mentioned devices may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed. Note that the "computer system" referred to here may include hardware such as an OS and peripheral devices. In addition, if a WWW system is used, the "computer system" also includes the homepage providing environment (or display environment). In addition, the term "computer-readable recording medium" refers to writable non-volatile memory such as a flexible disk, an optical magnetic disk, a ROM, or a flash memory, a portable medium such as a DVD (Digital Versatile Disc), or a storage device such as a hard disk built into a computer system.

[0116] Furthermore, the term "computer-readable recording medium" includes devices that retain a program for a certain period of time, such as volatile memory (e.g., DRAM) within a computer system that serves as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. The program may be transmitted from a computer system in which the program is stored in a storage device or the like to another computer system via a transmission medium, or by a transmission wave in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for implementing some of the functions described above, or may be a so-called differential file (differential program) that can implement the functions described above in combination with a program already recorded in the computer system. [Explanation of symbols]

[0117] 1...information processing device, 11...input / output unit, 12...storage unit, 121...correlation network analysis program, 122...correlation coefficient list, 123...module information, 124...module integration information, 125...final module integration information, 126...evaluation value information, 127...final module information, 128...FPO analyzed module list, 13...control unit, 131...correlation coefficient calculation unit, 132...FPO analysis unit, 133...FPO module selection unit, 134...FPO module integration unit, 135...FNI analysis unit, 136...FNI assignment processing unit, 137...P value calculation unit, 138...output data generation unit

Claims

1. A P-value calculation unit is provided for calculating a module P-value for a module composed of elements of multivariate data to be subjected to correlation network analysis by the following formula: [0010] N is the total number of elements in the multivariate data, n is the number of elements in the module, Edge ａｖｇ is the integer closest to the average number of edges of each element used in calculating the module F-score, degree ａｖｇ is the integer closest to the average degree of each element used in calculating the module F score, q and r are positive integers, and r≦q; Information processing device.

2. The P-value calculation unit calculates an element P-value for elements outside a module composed of elements contained in multivariate data to be subjected to correlation network analysis, using the following formula: [0025] N is the total number of elements in the multivariate data, n is the number of elements in the target module, edge is the number of edges of the candidate element in the target module; degree is the degree of the candidate element, q and r are positive integers, and r≦q; The information processing device according to claim 1 .

3. The P-value calculation unit records information about a correlation rank having a minimum element P-value among correlation ranks corresponding to element uniqueness rates exceeding a predetermined threshold value. The information processing device according to claim 2 .

4. An information processing method executed by an information processing device, Calculate the module P value for a module composed of elements of the multivariate data to be subjected to correlation network analysis using the following formula: [0030] N is the total number of elements in the multivariate data, n is the number of elements in the module, Edge ａｖｇ is the integer closest to the average number of edges of each element used in calculating the module F-score, degree ａｖｇ is the integer closest to the average degree of each element used in calculating the module F score, q and r are positive integers, and r≦q; Information processing methods.

5. For a module composed of elements of the multivariate data to be subjected to correlation network analysis, calculate the element P value for elements outside the module using the following formula: [0045] N is the total number of elements in the multivariate data, n is the number of elements in the target module, edge is the number of edges of the candidate element in the target module; degree is the degree of the candidate element, q and r are positive integers, and r≦q; The information processing method according to claim 4.

6. On the computer, Calculating a module P value for a module composed of elements of the multivariate data to be subjected to correlation network analysis using the following formula: [0050] N is the total number of elements in the multivariate data, n is the number of elements in the module, Edge ａｖｇ is the integer closest to the average number of edges of each element used in calculating the module F-score, degree ａｖｇ is the integer closest to the average degree of each element used in calculating the module F score, q and r are positive integers, and r≦q; A computer program for executing the above.

7. On the computer, A step of calculating an element P value for elements outside a module composed of elements contained in multivariate data to be subjected to correlation network analysis by the following formula: [006] N is the total number of elements in the multivariate data, n is the number of elements in the target module, edge is the number of edges of the candidate element in the target module; degree is the degree of the candidate element, q and r are positive integers, and r≦q; 7. The computer program product of claim 6 further comprising:

8. On the computer, Calculating a module P value for a module composed of elements of the multivariate data to be subjected to correlation network analysis using the following formula: [0070] N is the total number of elements in the multivariate data, n is the number of elements in the module, Edge ａｖｇ is the integer closest to the average number of edges of each element used in calculating the module F-score, degree ａｖｇ is the integer closest to the average degree of each element used in calculating the module F score, q and r are positive integers, and r≦q; A computer-readable recording medium having a computer program recorded thereon for executing the above.

9. On the computer, A step of calculating an element P value for elements outside a module composed of elements contained in multivariate data to be subjected to correlation network analysis by the following formula: [0080] N is the total number of elements in the multivariate data, n is the number of elements in the target module, edge is the number of edges of the candidate element in the target module; degree is the degree of the candidate element, q and r are positive integers, and r≦q; 9. The computer-readable recording medium according to claim 8, further comprising a computer program recorded thereon for causing the computer to execute the steps of:

Images