Detection method, presentation device, detection device, detection system, detection program, and recording medium

Abstract

The detection method comprises: a measurement step for measuring the expression level of a plurality of nucleic acid molecules contained in each of a plurality of specimens; an evaluation step for evaluating the properties of each of the plurality of specimens on the basis of the expression level of the nucleic acid molecule measured in the measurement step using an evaluation algorithm for evaluating the properties of the specimens on the basis of the expression level of the nucleic acid molecule, and obtaining an evaluation value; and a detection step for assigning orders to the levels of the evaluation values of the plurality of specimens, and detecting, on the basis of the correlation between the orders of the plurality of specimens and the evaluation values, abnormalities occurring until the evaluation values are obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to a detection method, a presentation device, a detection device, a detection system, a detection program, and a recording medium. BACKGROUND

[0002] Japanese Patent No. 7021097 discloses a disease presence determination device provided with a sample data acquisition unit and a disease presence determination unit. The sample data acquisition unit acquires sample data containing the expression amounts of a plurality of miRNAs in a test sample derived from a living organism. The disease presence determination unit outputs a disease presence determination result for the plurality of diseases of the plurality of body parts with respect to the acquired sample data using a learned model. The learned model is a learned model that can determine the presence of each of the plurality of diseases including a plurality of malignant diseases or a plurality of benign diseases, including the presence of a plurality of diseases, obtained in advance by machine learning using training data containing a plurality of sample data having items for identifying the presence or absence of a plurality of diseases of a plurality of body parts.

[0003] In International Publication No. 2021 / 132547, a test method for testing a disease using a disease marker is disclosed, which includes a test body data acquisition step and a discrimination step. The test body data acquisition step acquires marker data representing a result of measuring a disease marker in a body fluid test body collected from a test subject and preparation data representing a preparation condition of the body fluid test body. In the discrimination step, it is discriminated whether or not the test subject has a disease by inputting the marker data and the preparation data acquired in the test body data acquisition step to a learned model obtained by machine learning on the correlation between a group of marker data representing a result obtained by measuring a disease marker in a body fluid test body and preparation data representing a preparation condition of the body fluid test body and the presence or absence of a disease of a test subject obtained by collecting the body fluid test body. SUMMARY

[0004] PROBLEMS TO BE SOLVED BY THE INVENTION

[0005] Here, in a case where an evaluation algorithm based on the expression amounts of a plurality of nucleic acid molecules is used to evaluate the properties of a plurality of test bodies, an abnormality sometimes occurs in the evaluation algorithm itself or in the process up to the use of the evaluation algorithm. Therefore, it is desirable to be able to easily detect an inadmissible abnormality occurring in the process up to the completion of the evaluation of the properties of a plurality of test bodies. As the abnormality, for example, an abnormality occurring in the following (1) to (3) can be cited.

[0006] (1) A case where the evaluation algorithm as an evaluation reference deteriorates due to some reason

[0007] (2) In a case where a plurality of specimens as evaluation targets are different from evaluation targets assumed in evaluation based on an evaluation algorithm as an evaluation reference

[0008] (3) In a case where a measurement result of the expression amount of the nucleic acid molecule is abnormal due to inappropriate processing such as pretreatment before measurement

[0009] An object of the present application is to provide a detection method, a detection device, a detection system, a detection program, and a recording medium, use an evaluation algorithm that evaluates a property of a specimen based on an expression amount of a nucleic acid molecule, evaluate a property of each of a plurality of specimens based on an expression amount of a nucleic acid molecule measured in advance, calculate an evaluation value, and in this case, an intolerable abnormality occurring in a process until the evaluation value is calculated can be easily detected.

[0010] Means for solving the problem

[0011] A detection method of one embodiment of the present application includes a measurement step of measuring an expression amount of a plurality of nucleic acid molecules contained in each of a plurality of specimens, an evaluation step of using an evaluation algorithm that evaluates a property of the specimen based on the expression amount of the nucleic acid molecule, evaluating a property of each of the plurality of specimens based on the expression amount of the nucleic acid molecule measured in the measurement step, and calculating an evaluation value, and a detection step of giving a rank to the evaluation value of the plurality of specimens, and detecting an abnormality occurring in a process until the evaluation value is calculated, based on a correlation between the rank of the plurality of specimens and the evaluation value.

[0012] Effects of the Invention

[0013] According to the present application, an evaluation algorithm that evaluates a property of a specimen based on an expression amount of a nucleic acid molecule is used, a property of each of a plurality of specimens is evaluated based on an expression amount of a nucleic acid molecule measured in advance, an evaluation value is calculated, and in this case, an intolerable abnormality occurring in a process until the evaluation value is calculated can be easily detected. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flowchart showing an example of each step of the detection method of the present embodiment.

[0015] Figure 2 is a graph showing an example of a correlation between a rank of a plurality of specimens and an evaluation value.

[0016] Figure 3 is a graph showing an example of a correlation between a rank of a plurality of specimens and an evaluation value, and an example of a determination result and incidental information attached to the determination result.

[0017] Figure 4 is a block diagram showing an example of a computer that functions as the detection device of the present embodiment.

[0018] Figure 5 is a block diagram showing an example of a functional configuration of the detection device of the present embodiment. DETAILED DESCRIPTION

[0019] Hereinafter, an example of an embodiment of the technology of the present application will be described based on the drawings. In addition, in all the drawings, the same reference numerals are assigned to the constituent elements and processing that bear the same action, role, and function, and sometimes the repeated description is appropriately omitted. Each drawing is merely schematically shown to the extent that the technology of the present application can be sufficiently understood. Therefore, the technology of the present application is not limited to the illustrated examples. In addition, in the present embodiment, the description is sometimes omitted for the structure not directly related to the present application and the well-known structure.

[0020] <Detection method 10>

[0021] First, the detection method 10 of the present embodiment will be described. Figure 1 is a schematic diagram showing an example of each process of the detection method 10 of the present embodiment.

[0022] The detection method 10 is a method of measuring the expression amount of a plurality of nucleic acid molecules contained in each of a plurality of test subjects, evaluating the property of each of the plurality of test subjects based on the expression amount and calculating an evaluation value, and detecting an abnormality generated in a process until the evaluation value is calculated. As a measurement target of the expression amount of the plurality of nucleic acid molecules, i.e., a sample, a body fluid (for example, blood, serum, urine, tears, saliva, sweat, semen, lymph, tissue fluid, a body cavity fluid (for example, pleural fluid, ascites, etc.), cerebral spinal fluid, amniotic fluid, vaginal fluid, nasal discharge, etc.), a tissue, and a cell, etc. can be given. As a collection target of the test subject, a human or a non-human animal can be given. As the non-human animal, a non-human mammal (monkey, dog, cat, mouse, rat, rabbit, cow, horse, pig, and sheep, etc.), a bird (chicken, quail, etc.), etc. can be given.

[0023] As the nucleic acid molecule, for example, microRNA can be given. In addition, as the nucleic acid molecule, a small RNA other than microRNA, other RNA, DNA, etc. can be given. In addition, as the nucleic acid molecule, a nucleic acid molecule not composed of only ATGCU bases can be given. Specifically, for example, a nucleic acid subjected to modification such as DNA / RNA methylation and editing such as A-to-I RNA editing, etc. can be given. In this way, various nucleic acid molecules can become the application target of the present detection method.

[0024] Specifically, as shown in Figure 1 the detection method 10 has a measurement process 12, an evaluation process 13, a detection process 14, a determination process 16, and a presentation process 17. In the present embodiment, as an example, the measurement process 12, the evaluation process 13, the detection process 14, the determination process 16, and the presentation process 17 are sequentially executed.

[0025] Further, the detection method 10 is a method having the measurement process 12, the evaluation process 13, the determination process 16, and the presentation process 17, and thus can also be said to be a measurement method, an evaluation method, a determination method, or a presentation method. In addition, in the determination process 16, in the case where inspection or analysis, or the like, is performed by determination, the detection method can also be said to be an inspection method or an analysis method. Hereinafter, each process of the detection method 10 will be described.

[0026] <Measurement process 12>

[0027] The measurement process 12 is a process of measuring the expression amounts of the plurality of nucleic acid molecules contained in each of the plurality of samples. Specifically, in the measurement process 12, using a next-generation sequencer (hereinafter, sometimes referred to as NGS) as a measurement device, the plurality of nucleic acid molecules contained in each of the plurality of specimens is measured, and the base sequence of each nucleic acid molecule is determined. Next, by counting the number of each nucleic acid molecule determined for each base sequence, the read of the nucleic acid molecule in the NGS is calculated. The read of the nucleic acid molecule is the number of the nucleic acid molecules contained in the specimen, and corresponds to the expression amount of the nucleic acid molecule (specifically, the absolute expression amount). In addition, the read of the nucleic acid molecule output by the NGS can be normalized as a relative expression amount (i.e., a relative expression amount) to measure the expression amount. As a means of normalization, for example, RPM (Read Per Million: per million) normalization, normalization using a small RNA of an internal standard, or the like can be used. In this way, as the expression amount of the nucleic acid molecule, it can be a relative expression amount.

[0028] In addition, as the measurement device, in addition to the next-generation sequencer, quantitative PCR, a flow cytometer, or the like can be used, as long as the expression amount of the plurality of nucleic acid molecules can be measured.

[0029] In the NGS, the expression amount of the plurality of nucleic acid molecules can be measured for the plurality of specimens together. That is, in the measurement process 12, for the plurality of specimens, the expression amount of the plurality of nucleic acid molecules is measured in the same process.

[0030] In addition, sometimes, a pretreatment (for example, centrifugal separation) is performed on the specimen before the execution of the measurement process 12. In the present embodiment, in the case where the pretreatment is performed, the same process is performed on the plurality of specimens. That is, in the present embodiment, for the plurality of specimens, the pretreatment is performed in the same process.

[0031] <Evaluation process 13>

[0032] The evaluation process 13 is a process of evaluating the property of each of the plurality of specimens based on the expression amount of the nucleic acid molecule measured in the measurement process 12 using an algorithm for evaluating the property of the specimen based on the expression amount of the nucleic acid molecule (hereinafter, referred to as evaluation algorithm), and calculating an evaluation value.

[0033] In the evaluation step 13, first, the expression amount of the nucleic acid molecule measured in the measurement step 12 is analyzed. Specifically, the number of molecules of each nucleic acid molecule is counted. Further, in the case where human blood (specifically, serum) is used as the test object, for example, there are 500 kinds of microRNAs as the nucleic acid molecules measured.

[0034] In the evaluation step 13, next, the property of the test object is evaluated on the basis of the counted number of molecules (specifically, for example, the distribution of the counted number of molecules by the kind of nucleic acid molecule). As the evaluation of the property of the test object, for example, disease prediction of the provider of the test object can be given. Disease prediction can be performed using a disease prediction algorithm as an evaluation algorithm.

[0035] Specifically, the number of molecules of each of the microRNAs to be the object and the information on the presence or absence of a disease are subjected to machine learning as training data in advance, and a learning model as a disease prediction algorithm is constructed. Then, the counted number of molecules is input as a factor, and a score value (evaluation score) of disease prediction can be calculated as an output factor by the learning model.

[0036] In addition, as the evaluation of the property of the test object, it is not limited to disease prediction, but basic biological analysis, regression prediction, various anomaly detection, and the like can be performed.

[0037] <Detection step 14>

[0038] In the detection step 14, the order of magnitude of the score value of a plurality of test objects (hereinafter, sometimes referred to as a test object group) is given, and on the basis of the correlation between the order of magnitude of the plurality of test objects and the score value, an anomaly occurring in the process until the score value is calculated is detected. Hereinafter, the anomaly to be the detection object in the detection step 14 is sometimes referred to as an "object anomaly". Further, the test object group is not limited to a plurality of test objects of which the expression amount of a plurality of nucleic acid molecules is measured in a measurement device such as NGS. As the test object group, for example, a plurality of test objects for which the measurement is continuously performed by the same measurement device with the same process can be given. Therefore, the test object group also includes a plurality of test objects for which the measurement is performed a plurality of times.

[0039] Hereinafter, an example in which the order of magnitude of a plurality of test objects is given in the order from high to low of the score value will be described, but the order of magnitude of a plurality of test objects can be given in the order from low to high of the score value. That is, as the order of magnitude, it can be the order of magnitude arranged in the order from high to low of the score value, or it can be the order of magnitude arranged in the order from low to high of the score value.

[0040] In addition, the rank, for example, can be given as an absolute value when a plurality of samples are arranged in order. In the case where the rank is given in this way, for example, in the case where 50 samples are arranged in order from high to low in evaluation value, the rank of the twentieth from the side of high evaluation value is "20" places. In addition, the rank, for example, can also be given as a ratio indicating a relative value of the relative position in a plurality of samples. In the case where the rank is given in this way, for example, in the case where 50 samples are arranged in order from high to low in evaluation value, the rank of the twentieth from the side of high evaluation value is "40" %.

[0041] The subject abnormality detected in the detection process 14 is an abnormality that occurs in the process until the evaluation value is obtained. Therefore, the subject abnormality includes an abnormality from the sample as the evaluation target, an abnormality that occurs in the pretreatment before execution of the measurement process 12, the measurement process 12, and the evaluation process 13. Specifically, as the subject abnormality, for example, an abnormality that occurs in the following (1) to (3) can be cited.

[0042] (1) A case where the evaluation algorithm as the evaluation reference deteriorates due to some reason

[0043] (2) A case where the sample group as the evaluation target is different from the evaluation target assumed in the evaluation based on the evaluation algorithm as the evaluation reference

[0044] (3) A case where the measurement result of the expression amount of the nucleic acid molecule becomes abnormal due to the presence of inappropriate processing in the pretreatment before execution of the measurement process 12 and the measurement process 12 and the like

[0045] As an example of the above (1), there is a data drift (also referred to as a feature quantity drift, a covariate shift) caused by a shift in the statistical distribution of the input data as the training data at the time of learning model creation and the statistical distribution of the input data at the time of evaluation due to some change. As an example of the above (2), in the evaluation based on a disease prediction algorithm that assumes a low frequency of patients with a disease, there is a case where a sample group in which most of the samples are patients with a disease is used as the evaluation target. As an example of the above (3), there is a case where the worker has inappropriate processing of the sample required for the measurement process 12, or the reagent used for the processing of the sample is abnormal.

[0046] In the detection process 14, for example, a case where an arbitrary evaluation value of a sample whose rank is lower than the first threshold value is a value equal to or higher than the second threshold value is detected as the subject abnormality. Here, in the graph shown in FIG. 10, for example, the vertical axis is the evaluation value, and the horizontal axis is the rank given to the high and low of the evaluation value. In addition, in the example shown in FIG. 10, for example, the first threshold value is set to a value of 10% of the rank. Figure 2 Figure 2

[0047] ​​Here, the second threshold value is, for example, a threshold value used in the determination process 16 as a determination index. That is, as described later, in the determination process 16 in which the presence or absence of a disease is predicted, for example, when the evaluation value is determined to be a disease in a case where the evaluation value is equal to or higher than a predetermined threshold value, the threshold value is set as the second threshold value.

[0048] On the other hand, the first threshold value is, for example, a value lower than the lowest rank in which the presence of a disease is determined in the determination process 16 in which the presence or absence of a disease is predicted. For example, in a case where the determination of the presence of a disease in the determination process 16 is assumed to be a rank higher than 10%, for example, 10% is set as the first threshold value.

[0049] As described above, since the first threshold value and the second threshold value are set, in a case where the evaluation value of the specimen of a rank lower than the first threshold value is a value equal to or higher than the second threshold value, there is a specimen determined to be a disease in the determination process 16 (that is, a specimen equal to or higher than the second threshold value) in the rank in which the determination of the presence of a disease is not assumed in the determination process 16 (that is, a rank lower than the first threshold value), and thus in the detection process 14, it is possible to detect that an abnormality has occurred in a process up to the evaluation value being calculated.

[0050] As Figure 2 indicated in the specimen group indicated by a black dot, the evaluation value of the specimen of a rank lower than the first threshold value (refer to reference numeral 51) is a value equal to or higher than the second threshold value. On the contrary, in the specimen group indicated by a white dot, the evaluation value of the specimen of a rank lower than the first threshold value (refer to reference numeral 52) is a value lower than the second threshold value. Thus, in the example indicated in Figure 2 indicated by a black dot corresponds to a case where the evaluation value of the specimen of a rank lower than the first threshold value is a value equal to or higher than the second threshold value. That is, it is a case where the specimen determined to be a disease in the determination process 16 (that is, a specimen equal to or higher than the second threshold value) exists in the rank in which the determination of the presence of a disease is not assumed in the determination process 16 (that is, a rank lower than the first threshold value). Thus, in the detection process 14, the specimen group indicated by a black dot is detected as an object abnormality.

[0051] On the other hand, the evaluation value of the specimen of a rank lower than the first threshold value in the specimen group indicated by a white dot is a value lower than the second threshold value. That is, in the rank in which the determination of the presence of a disease is not assumed in the determination process 16 (that is, a rank lower than the first threshold value), there is a specimen determined to be no disease in the determination process 16 (that is, a specimen lower than the second threshold value). Thus, in the detection process 14, for the specimen group indicated by a white dot, it is detected that the evaluation value is normal. That is, in the detection process 14, for the specimen group indicated by a white dot, an object abnormality is not detected.

[0052] Further, in the case of the graph indicated in Figure 2 indicated in the graph, in a case where an arbitrary point is included in Figure 2In the case of the area indicated by the diagonal line portion (hereinafter, sometimes referred to as the second quadrant), it can be said that the abnormality is detected as a target abnormality.

[0053] In addition, in the case where the evaluation value is high, the abnormality is detected as a target abnormality. Figure 2 The detection method described above is effective in a specimen group in which a specimen in which the evaluation value is high hardly exists.

[0054] In addition, in the above, in the case where the arbitrary point is included in the second quadrant, the abnormality is detected as a target abnormality, but it can be that, in the case where a plurality of points are included in the second quadrant, the abnormality is detected as a target abnormality.

[0055] Further, it can be that, in the case where the point (evaluation value) of the bit order of n bits or n% or more is a value equal to or higher than the second threshold value, the abnormality is detected as a target abnormality.

[0056] The second threshold value used in the detection process 14 is preferably determined, for example, in accordance with the content of the pretreatment performed before the execution of the measurement process 12, the measurement device used in the measurement process 12, the determination content in the determination process 16, the evaluation content in the evaluation process 13, and the like.

[0057] On the other hand, the first threshold value used in the detection process 14 is the bit order, which is a relative index that does not depend on the content executed in each process, and thus is preferably a value determined. Further, in the case where the specimen group assumed as an evaluation target is different from the evaluation target assumed in the evaluation based on the evaluation algorithm assumed as an evaluation reference, it is desirable to change the first threshold value in accordance with the attribute of the specimen group as the evaluation target.

[0058] In addition, it is also possible to prepare combinations of the first threshold value and the second threshold value used in the detection process 14 in advance in a plurality of modes. In this case, in the detection process 14, the user selects one mode from the plurality of modes, and detects a target abnormality using the mode. In addition, in the detection process 14, it is possible to determine at least the first threshold value based on the source information of the specimen group collected, and detect a target abnormality. In addition, at least the first threshold value is based on the source information of the specimen group collected, and thus the user can set it to an arbitrary value.

[0059] For example, in the case where the specimen is blood (specifically, serum) of a person, as the source information of the specimen group collected, it is information indicating that the source is a certain place (for example, a comprehensive medical examination and a cancer center, or the like).

[0060] In the case where the source of the specimen group collected is a source having a tendency that the evaluation value becomes high, a combination in which the first threshold value is set relatively low is selected.

[0061] <Determination Process 16>

[0062] The determination step 16 is a step that makes a determination related to the specimens based on the evaluation value obtained from the evaluation step 13. This determination step 16 is performed if no abnormality is detected in the detection step 14. Therefore, if an abnormality is detected in the detection step 14, the determination step 16 is not performed. Alternatively, in this case, for example, the user (i.e., the person performing the detection method) may be notified that an abnormality has been detected.

[0063] As a determination related to the specimen, one could cite the presence or absence of a disease predicted by the provider of the specimen. In determination step 16, for example, the evaluation value calculated in evaluation step 13 is a second threshold (refer to...). Figure 2 In the above situations, it can be determined that there is a disease. Figure 2 In the case of the diagram shown, the region adjacent to the right side of the second quadrant is provided ( Figure 2 The provider of the specimen corresponding to the point in the first quadrant of the image is determined to have a disease.

[0064] As described above, if an object anomaly is detected in inspection step 14, the determination step 16 is not executed, but this is not a limitation. For example, even if an object anomaly is detected in inspection step 14, the determination step 16 may be executed, for example, if a determination result is obtained as reference data. In this case, for example, a prompt may be made to the user that a determination has been made based on the detection of the object anomaly.

[0065] <Step 17>

[0066] The prompting step 17 is a step that indicates the correlation between the position and evaluation value of multiple specimens. For example, prompting step 17 indicates the correlation between the position and evaluation value assigned to each specimen in the specimen group during testing step 14.

[0067] Specifically, in the prompting step 17, the distribution of the positions and evaluation values ​​of multiple samples relative to a first threshold set for the positions and a second threshold set for the evaluation values ​​is prompted (e.g., Figure 2 The chart shown.

[0068] In addition, in the prompting step 17, besides indicating the correlation between the position and evaluation value assigned to each specimen in the specimen group in the detection step 14, the judgment result 56 in the judgment step 16 can also be prompted (e.g., Figure 3 (The chart shown). Figure 3 In the example shown, the judgment result for the presence or absence of disease indicates whether cancer is predicted (specifically, whether the prediction is positive or negative), as well as the accompanying information (e.g., the patient's position within the specimen group as a patient (equivalent to the specimen provider), and opinions on the judgment result, etc.).

[0069] As the presentation method, there are a method of presenting by display on a display section, a method of printing on a recording medium such as paper, and the like. As the display section, for example, a liquid crystal display, an organic EL (Electro Luminescence) display, and the like can be given.

[0070] As the presentation target of the correlation, for example, a provider of the specimen, a doctor who treats a disease in the case of performing disease prediction, and the like can be given.

[0071] <Detection system 20>

[0072] Next, a detection system 20 that is a system that executes the above-described detection method 10 will be described. As shown in FIG. 1, the detection system 20 has a measurement device 21 and a detection device 30. Figure 4

[0073] <Measurement device 21>

[0074] The measurement device 21 is an example of a measurement section, and is a device that executes the above-described measurement process 12. That is, the measurement device 21 measures the expression amounts of the plurality of nucleic acid molecules contained in each of the plurality of samples. As the measurement device 21, for example, NGS is used.

[0075] <Detection device 30>

[0076] The detection device 30 is an example of an evaluation section, and is also an example of a detection section. The detection device 30 is a device that executes the above-described evaluation process 13 and the above-described detection process 14. That is, the detection device 30 evaluates the properties of the plurality of specimens using an evaluation algorithm that evaluates the properties of the specimens based on the expression amounts of the nucleic acid molecules, evaluates the properties of each of the plurality of specimens based on the expression amounts of the nucleic acid molecules that are measured in advance, and obtains evaluation values. Further, the detection device 30 assigns ranks to the evaluation values of the plurality of specimens, and detects an abnormality that occurs in a process up to the evaluation values are obtained, based on a correlation between the ranks of the plurality of specimens and the evaluation values.

[0077] In addition, the detection device 30 executes the above-described determination process 16 and the presentation process 17. That is, the detection device 30 performs a determination related to the specimens with respect to a specimen group based on the evaluation values that are obtained by itself. Further, the detection device 30 functions as a presentation device that evaluates based on the expression amounts of the nucleic acid molecules that are measured in advance, obtains evaluation values, assigns ranks to the evaluation values of the plurality of specimens, and presents a correlation between the ranks of the plurality of specimens and the evaluation values. Specifically, the detection device 30 presents the ranks of the plurality of specimens and the evaluation values with respect to a distribution of a first threshold value that is set with respect to the ranks and a second threshold value that is set with respect to the evaluation values (for example, a graph shown in FIG. 2). Figure 2

[0078] The detection device 30 has a function as a computer, and as shown in FIG. 3, for example, has a CPU 31, a ROM 32, a RAM 33, an input device 34, an output device 35, and a bus 36 that connects these elements to each other.​​Figure 4 As shown in FIG. 1, the detection device 30 has a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, a storage 34, an input section 35, a display section 36, and a communication interface (I / F) 37. The respective constituent sections are connected via a bus 39 in a manner that enables communication with each other.

[0079] The CPU 31, which is an example of a processor, is a central arithmetic processing unit that executes various programs or controls the respective sections. That is, the CPU 31 reads out a program from the ROM 32 or the storage 34 and executes the program using the RAM 33 as a work area. The CPU 31 performs the control of the respective structures and various arithmetic processing in accordance with the program stored in the ROM 32 or the storage 34. The CPU 31 is an example of a processor.

[0080] The ROM 32 records various programs and various data. The RAM 33 temporarily stores programs or data as a work area. The storage 34 is constituted by a HDD (Hard Disk Drive) or an SSD (Solid State Drive) and records various programs including an operating system and various data.

[0081] In the present embodiment, for example, a detection program for executing a detection process that performs the detection method 10 described above is recorded in the storage 34. The detection program can be one program or a program group constituted by a plurality of programs or modules. In addition, the detection program can be recorded in the ROM 32. The ROM 32 or the storage 34 functions as an example of a non-transitory recording medium.

[0082] As an example of a processor, for example, the CPU described above, which is a general-purpose processor, is not limiting, and for example, a dedicated processor constituted by a circuit specially designed to execute a specific process can be used. In addition, as an example of a processor, not only a case where one processor is constituted, but also a case where a plurality of processors disposed at physically separate locations cooperate to constitute a processor can be used.

[0083] The input section 35 includes a pointing device such as a mouse and a keyboard and is used for various inputs. In addition, the input section 35 accepts, as an input, information on the expression amounts of the plurality of nucleic acid molecules measured by the measurement device 21. In addition, the input section 35 accepts, as an input, information used in the evaluation process 13 and the detection process 14, such as the source information, the evaluation content, and the evaluation object.

[0084] The display section 36, which is, for example, a liquid crystal display, displays various information. In the detection device 30, it is possible to prompt the user that an object abnormality is detected by the display section 36.

[0085] In addition, in the detection device 30, the user can be prompted of the correlation between the ranks and the evaluation values of the plurality of specimens by the display section 36. Therefore, in the present embodiment, the display section 36 can be held as an example of a prompting device. Further, the display section 36 can be of a touch panel type, and function as the input section 35.

[0086] The communication interface 37 is an interface for communication with other devices, using, for example, a standard such as Ethernet (registered trademark), FDDI (Fiber Distributed Data Interface), Wi-Fi (registered trademark), or the like.

[0087] As shown in Fig. 1, in the detection device 30, the CPU 31 functions as the evaluation function section 150, the detection function section 160, the determination section 170, and the prompting section 180 by executing a detection program. Figure 5

[0088] The evaluation function section 150 executes the above-described evaluation process 13. That is, the evaluation function section 150 evaluates the properties of the plurality of specimens based on the expression amounts of the nucleic acid molecules measured by the measurement device 21 using an evaluation algorithm that evaluates the properties of the specimens based on the expression amounts of the nucleic acid molecules (expression amounts), and obtains evaluation values (see the above-described evaluation process 13).

[0089] The detection function section 160 executes the above-described detection process 14. That is, the detection function section 160 assigns ranks to the respective evaluation values of the specimens in the specimen group, and detects an abnormality of the object based on the correlation between the ranks and the evaluation values of the plurality of specimens (see the above-described detection process 14).

[0090] The determination section 170 executes the above-described determination process 16. That is, the determination section 170 performs a determination related to the specimens based on the evaluation values obtained by the evaluation function section 150 (see the above-described determination process 16).

[0091] The prompting section 180 executes the above-described prompting process 17. That is, the prompting section 180 prompts the correlation between the ranks and the evaluation values of the plurality of specimens (see the above-described prompting process 17).

[0092] Further, in the present embodiment, the detection system 20 has the measurement device 21 and the detection device 30, but the detection system 20 can also be constituted by one device. In this case, the one device functions as an example of a measurement section, an evaluation section, and a detection section.

[0093] ​Further, the detection device 30 can also be constituted by a plurality of devices. For example, the detection device 30 can also be constituted by a plurality of (for example, four) devices that share the execution of the evaluation process 13, the detection process 14, the determination process 16, and the prompting process 17 described above.

[0094] <Effects of the Present Embodiment>

[0095] In the present embodiment, as described above, in the detection process 14, the ranks are assigned to the evaluation values of the plurality of test subjects based on the correlation between the ranks and the evaluation values of the plurality of test subjects (see FIG. 6), and the object abnormality is detected (i.e., an abnormality that occurs in the process up to the evaluation value being obtained). Figure 2

[0096] Thus, the distribution of the evaluation values in the plurality of test subjects (i.e., the test subject group) becomes clear, and the unallowable object abnormality can be easily detected.

[0097] Further, in the present embodiment, in the detection process 14, for example, a case in which an arbitrary evaluation value of a test subject whose rank is lower than the first threshold value is a value equal to or higher than the second threshold value is detected as the object abnormality.

[0098] Thus, for example, in the graph shown in FIG. 6, in a case in which an arbitrary point is included in the second quadrant, the object abnormality can be detected, and the unallowable object abnormality can be easily detected. Figure 2

[0099] In the measurement process 12, the expression amounts of the plurality of nucleic acid molecules are measured in the same process for the plurality of test subjects.

[0100] Based on the expression amounts of the plurality of nucleic acid molecules that are measured under the same measurement conditions, the evaluation of the properties of the test subjects is performed, and thus the reliability of the evaluation values is improved, and the object abnormality can be detected with high accuracy in the detection process 14.

[0101] Further, in the present embodiment, in the determination process 16, in a case in which the object abnormality is not detected in the detection process 14, the test subject group is subjected to the determination related to the test subject based on the evaluation values obtained by the evaluation process 13.

[0102] In the determination process 16, regardless of the detection result of the detection process 14, compared to a case in which the determination related to the test subject is always performed for the test subject group, the execution of the determination in which the reliability of the result is low can be suppressed.

[0103] Further, in the present embodiment, in the detection process 14, one of a plurality of patterns from among the first threshold value and the second threshold value is used to detect the object abnormality.

[0104] ​​Therefore, based on the specimen that becomes the object of detection, the mode used in the detection of object anomalies can be selected, and object anomalies can be detected with high precision in the detection step 14.

[0105] Furthermore, in this embodiment, in the detection step 14, a first threshold is determined based on the source information of the collected sample group, indicating that the object is abnormal.

[0106] The first threshold for detecting object anomalies is determined based on the source information, thus enabling high-precision detection of object anomalies in detection step 14.

[0107] Furthermore, in this embodiment, the detection device 30 evaluates based on pre-determined expression levels of nucleic acid molecules, calculates evaluation values, assigns rankings to the evaluation values ​​of multiple samples, and indicates the correlation between the rankings of the multiple samples and the evaluation values. Thus, the recipient, having been shown this correlation, can detect abnormalities in the target based on this correlation.

[0108] Specifically, the detection device 30 indicates the position of multiple samples and the distribution of evaluation values ​​relative to a first threshold set for the position and a second threshold set for the evaluation values ​​(e.g., Figure 2 (See the diagram shown). This allows the recipient to easily detect unacceptable object anomalies.

[0109] This invention is not limited to the embodiments described above, and various modifications, alterations, and improvements can be made without departing from its spirit. Multiple variations described above can also be appropriately combined to form a complete system.

[0110] <Postscript>

[0111] (Method 1)

[0112] A detection method, which has the following characteristics: The assay procedure measures the expression levels of multiple nucleic acid molecules contained in each of the multiple samples. The evaluation process uses an evaluation algorithm based on the expression levels of nucleic acid molecules to evaluate the properties of the specimens. Based on the expression levels of the nucleic acid molecules measured in the assay process, the algorithm evaluates the properties of each of the multiple specimens and calculates an evaluation value. The detection process assigns a ranking to the evaluation values ​​of the multiple samples based on their relative values, and detects any abnormalities that occur during the process until the evaluation value is determined.

[0113] (Method 2)

[0114] According to the detection method described in method 1, wherein, In the detection step, the plurality of samples are given a rank in order of evaluation value from high to low, and a case where any evaluation value of a sample whose rank is lower than a first threshold value is a value equal to or higher than a second threshold value is detected as the abnormality.

[0115] (Manner 3)

[0116] The detection method according to Manner 1 or 2, wherein In the measurement step, the expression amount is measured for the plurality of samples in the same procedure.

[0117] (Manner 4)

[0118] The detection method according to any one of Manners 1 to 3, wherein The detection method further has a determination step in which, in a case where the abnormality is not detected in the detection step, a sample-related determination is made on the plurality of samples based on the evaluation value obtained through the evaluation step.

[0119] (Manner 5)

[0120] The detection method according to any one of Manners 2 to 4, wherein The combination of the first threshold value and the second threshold value has a plurality of patterns, In the detection step, the abnormality is detected using one of the plurality of patterns.

[0121] (Manner 6)

[0122] The detection method according to Manner 5, wherein In the detection step, the first threshold value is decided based on source information of the plurality of samples, and the abnormality is detected.

[0123] (Manner 7)

[0124] A presentation device, wherein A rank of an evaluation value evaluating a property of a sample is given based on an expression amount of a nucleic acid molecule contained in the sample, and a correlation between the rank of the plurality of samples and the evaluation value is presented.

[0125] (Manner 8)

[0126] The presentation device according to Manner 7, wherein The rank of the plurality of samples and the evaluation value are presented with respect to a first threshold value set for the rank and a second threshold value set for the evaluation value.

[0127] (Manner 9)

[0128] The prompting device according to any one of the aspects 7 or 8, The prompting device according to any one of the aspects 7 or 8,

[0129] (Aspect 10)

[0130] A detection device, wherein The detection device includes a processor, The processor uses an evaluation algorithm that evaluates a property of a specimen based on an expression amount of a nucleic acid molecule, evaluates the property of each of a plurality of specimens based on an expression amount of the nucleic acid molecule determined in advance, calculates an evaluation value, assigns a rank to the evaluation values of the plurality of specimens, and detects an abnormality generated in a process until the evaluation value is calculated based on a correlation between the rank and the evaluation value of the plurality of specimens.

[0131] (Aspect 11)

[0132] A detection system including: a measurement unit that measures an expression amount of a plurality of nucleic acid molecules contained in each of a plurality of specimens; an evaluation unit that uses an evaluation algorithm that evaluates a property of the specimen based on an expression amount of a nucleic acid molecule, evaluates the property of each of the plurality of specimens based on the expression amount of the nucleic acid molecule measured by the measurement unit, and calculates an evaluation value; and a detection unit that assigns a rank to the evaluation values of the plurality of specimens and detects an abnormality generated in a process until the evaluation value is calculated based on a correlation between the rank and the evaluation value of the plurality of specimens.

[0133] (Aspect 12)

[0134] A detection program that causes a computer to execute: an evaluation algorithm that evaluates a property of a specimen based on an expression amount of a nucleic acid molecule, evaluates the property of each of a plurality of specimens based on an expression amount of the nucleic acid molecule determined in advance, and calculates an evaluation value; and a detection process that assigns a rank to the evaluation values of the plurality of specimens and detects an abnormality generated in a process until the evaluation value is calculated based on a correlation between the rank and the evaluation value of the plurality of specimens.

[0135] (Aspect 13)

[0136] A non-transitory recording medium that records a detection program that causes a computer to execute: An evaluation algorithm that evaluates a property of a test object using an expression amount of a nucleic acid molecule, evaluates a property of each of a plurality of test objects based on an expression amount of the nucleic acid molecule determined in advance, and processes to obtain an evaluation value; and A detection process that gives a rank to each of the evaluation values of the plurality of test objects, and detects an abnormality generated in a process until the evaluation value is obtained, based on a correlation between the ranks and the evaluation values of the plurality of test objects.

[0137] The disclosure of Japanese Patent Application No. 2023-125051 filed on July 31, 2023 is incorporated herein by reference in its entirety. All documents, patent applications, and technical standards cited in this specification are incorporated herein by reference to the same extent as if each document, patent application, or technical standard were specifically and individually indicated to be incorporated by reference.

Claims

1. A detection method, comprising: The assay procedure measures the expression levels of multiple nucleic acid molecules contained in each of the multiple samples. The evaluation process uses an evaluation algorithm based on the expression levels of nucleic acid molecules to evaluate the properties of the specimens. Based on the expression levels of the nucleic acid molecules measured in the assay process, the algorithm evaluates the properties of each of the multiple specimens and calculates an evaluation value. The detection process assigns a ranking to the evaluation values ​​of the multiple samples based on their relative values, and detects any abnormalities that occur during the process until the evaluation value is determined.

2. The detection method according to claim 1, wherein, In the detection process, the multiple samples are assigned a ranking according to the evaluation value from high to low, and any sample with a ranking lower than the first threshold whose evaluation value is above the second threshold is detected as the abnormality.

3. The detection method according to claim 1, wherein, In the measurement process, the expression level is measured for the multiple samples using the same procedure.

4. The detection method according to claim 1, wherein, The detection method further includes a determination step, in which, if no abnormality is detected in the detection step, a determination related to the specimens is made based on the evaluation value obtained through the evaluation step.

5. The detection method according to claim 2, wherein, The combination of the first threshold and the second threshold has multiple patterns. In the detection process, one of the multiple modes is used to detect the anomaly.

6. The detection method according to claim 2, wherein, In the detection process, the first threshold is determined based on the source information of the multiple samples collected, and the abnormality is detected.

7. A prompting device, wherein, Based on the expression levels of nucleic acid molecules contained in each of the multiple specimens, the evaluation values ​​for assessing the properties of the specimens are assigned a ranking, indicating the correlation between the ranking of the multiple specimens and the evaluation values.

8. The prompting device according to claim 7, wherein, The system indicates the position of the plurality of specimens and the distribution of the evaluation values ​​relative to a first threshold set for the position and a second threshold set for the evaluation values.

9. The prompting device according to claim 7, wherein, The information provided includes supplementary information related to the determination of the specimen based on the evaluation value.

10. A detection device, wherein, The detection device includes a processor. The processor uses an evaluation algorithm based on the expression levels of nucleic acid molecules to assess the properties of specimens. It evaluates the properties of multiple specimens based on pre-determined expression levels of the nucleic acid molecules and calculates evaluation values. The evaluation values ​​of the multiple samples are assigned a ranking based on their relative values. Based on the correlation between the ranking of the multiple samples and the evaluation values, abnormalities that occur during the process until the evaluation values ​​are obtained are detected.

11. A detection system comprising: The assay unit measures the expression levels of multiple nucleic acid molecules contained in each of the multiple samples; The evaluation unit uses an evaluation algorithm based on the expression levels of nucleic acid molecules to evaluate the properties of the specimens, and evaluates the properties of each of the plurality of specimens based on the expression levels of the nucleic acid molecules measured by the measurement unit, and calculates an evaluation value; and The detection unit assigns a ranking to the evaluation values ​​of the plurality of specimens, and detects any abnormalities that occur during the process of determining the evaluation values ​​based on the correlation between the rankings of the plurality of specimens and the evaluation values.

12. A detection program that causes a computer to perform the following processing: An evaluation algorithm that uses the expression levels of nucleic acid molecules to assess the properties of specimens, evaluates the properties of multiple specimens based on pre-determined expression levels of the nucleic acid molecules, and calculates evaluation values; and The detection process assigns a ranking to the evaluation values ​​of the multiple samples based on their relative values, and detects any abnormalities that occur during the process until the evaluation value is determined.

13. A non-transitory recording medium having a detection program recorded thereon, the detection program causing a computer to perform the following processing: An evaluation algorithm that uses the expression levels of nucleic acid molecules to assess the properties of specimens, evaluates the properties of multiple specimens based on pre-determined expression levels of the nucleic acid molecules, and calculates evaluation values; and The detection process assigns a ranking to the evaluation values ​​of the multiple samples based on their relative values, and detects any abnormalities that occur during the process until the evaluation value is determined.

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