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

Abstract

This detection method comprises: a measurement step for measuring the expression levels of a plurality of nucleic acid molecules included in each of a plurality of samples; an evaluation step for evaluating the properties of each of the plurality of samples 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 sample on the basis of the expression level of the nucleic acid molecule, and obtaining an evaluation value; and a detection step for ranking the levels of the evaluation values of the plurality of samples, and, on the basis of the correlation between the ranking of the plurality of samples and the evaluation value, detecting an abnormality that has occurred during the process of obtaining the evaluation value.

Description

DETECTION METHOD, PRESENTATION DEVICE, DETECTION DEVICE, DETECTION SYSTEM, DETECTION PROGRAM, AND RECORDING MEDIUM

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

[0002] Japanese Patent No. 7021097 discloses a disease prevalence assessment device including a sample data acquisition unit and a prevalence assessment unit. The sample data acquisition unit acquires sample data including expression levels of multiple types of miRNA in a biological sample. The prevalence assessment unit uses a trained model to output prevalence assessment results for the multiple diseases in the multiple body parts for the acquired sample data. The trained model is a trained model that is capable of determining the prevalence of each of the multiple diseases, including multiple malignant diseases or multiple benign diseases, including cases where the subject is affected by multiple diseases, obtained in advance by machine learning using training data including multiple sample data having items for identifying the presence or absence of the multiple diseases in the multiple body parts.

[0003] WO 2021 / 132547 discloses a testing method for testing for a disease using a disease marker, the testing method including a specimen data acquisition step and a discrimination step. The specimen data acquisition step acquires marker data indicating the results of measuring a disease marker in a body fluid sample collected from a subject, and preparation data indicating the preparation conditions of the body fluid sample. The discrimination step determines the presence or absence of a disease in the subject by inputting the marker data and preparation data acquired in the specimen data acquisition step into a trained model that has been machine-learned to determine the correlation between a set of marker data indicating the results of measuring a disease marker in a body fluid sample and preparation data indicating the preparation conditions of the body fluid sample, and the presence or absence of a disease in the subject from whom the body fluid sample was collected.

[0004] When using an evaluation algorithm that evaluates the properties of each of a plurality of samples based on the expression levels of a plurality of nucleic acid molecules, abnormalities may occur in the evaluation algorithm itself or in the process leading up to the use of the evaluation algorithm. Therefore, it is desirable to be able to easily detect unacceptable abnormalities that occur in the process leading up to the completion of evaluation of the properties of each of a plurality of samples. Examples of such abnormalities include abnormalities that occur in the following cases (1) to (3).

[0005] (1) When the evaluation algorithm, which is the evaluation standard, has deteriorated for some reason. (2) When the multiple specimens to be evaluated are different from the specimens to be evaluated that are assumed to be evaluated by the evaluation algorithm, which is the evaluation standard. (3) When an abnormality occurs in the measurement results of the expression level of nucleic acid molecules due to inappropriate processing such as pre-processing before measurement.

[0006] The present disclosure aims to provide a detection method, detection device, detection system, detection program, and recording medium that can easily detect unacceptable abnormalities that occur in the process of calculating an evaluation value when the properties of each of a plurality of samples are evaluated based on the expression levels of nucleic acid molecules measured in advance using an evaluation algorithm that evaluates the properties of the sample based on the expression levels of nucleic acid molecules to calculate an evaluation value.

[0007] A detection method according to one aspect of the present disclosure includes a measurement step of measuring the expression levels of multiple nucleic acid molecules contained in each of multiple samples; an evaluation step of using an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules to evaluate the properties of each of the multiple samples based on the expression levels of the nucleic acid molecules measured in the measurement step, thereby obtaining an evaluation value; and a detection step of ranking the multiple samples according to their high and low evaluation values, and detecting any abnormalities that occur in the process of obtaining the evaluation value based on the correlation between the rankings of the multiple samples and the evaluation values.

[0008] According to the present disclosure, when an evaluation algorithm is used to evaluate the properties of a sample based on the expression level of a nucleic acid molecule, the properties of each of multiple samples are evaluated based on the expression level of a nucleic acid molecule measured in advance, and an evaluation value is calculated, unacceptable abnormalities that occur in the process of calculating the evaluation value can be easily detected.

[0009] FIG. 1 is a flow diagram showing an example of each step of a detection method according to the present embodiment; FIG. 2 is a graph showing an example of the correlation between the ranking of a plurality of specimens and the evaluation value; FIG. 3 is a graph showing an example of the correlation between the ranking of a plurality of specimens and the evaluation value, as well as an example of a determination result and associated information accompanying the determination result; FIG. 4 is a block diagram showing an example of a computer that functions as the detection device of the present embodiment; and FIG. 5 is a block diagram showing an example of the functional configuration of the detection device of the present embodiment.

[0010] An example of an embodiment of the technology of the present disclosure will be described below with reference to the drawings. Note that components and processes that perform the same operations, actions, and functions are given the same reference numerals throughout the drawings, and redundant explanations may be omitted as appropriate. Each drawing is merely a schematic illustration to allow a sufficient understanding of the technology of the present disclosure. Therefore, the technology of the present disclosure is not limited to the illustrated examples. Furthermore, in this embodiment, explanations of configurations that are not directly related to the present disclosure or well-known configurations may be omitted.

[0011] <Detection Method 10> First, a description will be given of a detection method 10 according to this embodiment. Fig. 1 is a schematic diagram showing an example of each step of the detection method 10 according to this embodiment.

[0012] Detection method 10 is a method for measuring the expression levels of multiple nucleic acid molecules contained in multiple samples, evaluating the properties of each of the samples based on the expression levels to determine an evaluation value, and detecting abnormalities that occur in the process of determining the evaluation value. Examples of samples from which the expression levels of multiple nucleic acid molecules are measured include body fluids (e.g., blood, serum, urine, tears, saliva, sweat, semen, lymph, tissue fluid, body cavity fluid (e.g., pleural effusion, ascites, etc.), cerebrospinal fluid, amniotic fluid, vaginal fluid, nasal mucus, etc.), tissues, and cells. Samples may be collected from humans or non-human animals. Examples of non-human animals include non-human mammals (monkeys, dogs, cats, mice, rats, rabbits, cows, horses, pigs, sheep, etc.), birds (chickens, quail, etc.), etc.

[0013] Examples of nucleic acid molecules include microRNA. The nucleic acid molecule may be small RNA other than microRNA, other RNA, DNA, or the like. Furthermore, the nucleic acid molecule does not have to be a nucleic acid molecule composed only of ATGCU bases. Specific examples include nucleic acids that have undergone modifications such as DNA / RNA methylation and editing such as A-to-I RNA editing. Thus, various nucleic acid molecules can be targets for application of this detection method.

[0014] 1 , the detection method 10 specifically includes a measurement step 12, an evaluation step 13, a detection step 14, a determination step 16, and a presentation step 17. In the present embodiment, the measurement step 12, the evaluation step 13, the detection step 14, the determination step 16, and the presentation step 17 are performed in this order, for example.

[0015] Since the detection method 10 is a method including a measurement step 12, an evaluation step 13, a determination step 16, and a presentation step 17, it can also be called a measurement method, an evaluation method, a determination method, or a presentation method. Furthermore, when an inspection or analysis is performed based on the determination in the determination step 16, the detection method can also be called an inspection method or an analysis method. Each step of the detection method 10 will be described below.

[0016] <Measurement Step 12> Measurement Step 12 is a step of measuring the expression levels of multiple nucleic acid molecules contained in each of multiple samples. Specifically, in Measurement Step 12, a next-generation sequencer (hereinafter sometimes referred to as NGS) is used as a measurement device to measure multiple nucleic acid molecules contained in each of multiple samples and identify the base sequence of each nucleic acid molecule. Next, the number of identified nucleic acid molecules is counted for each base sequence to determine the number of reads of the nucleic acid molecule in NGS. This number of reads of the nucleic acid molecule is the number of nucleic acid molecules contained in the sample and corresponds to the expression level (specifically, absolute expression level) of the nucleic acid molecule. Note that the number of reads of the nucleic acid molecule output by NGS may be normalized to measure the expression level as a relative expression level (i.e., relative expression level). Examples of normalization methods that can be used include RPM (Reads Per Million) normalization and normalization using small RNA as an internal standard. In this way, the expression level of the nucleic acid molecule may be a relative expression level.

[0017] In addition to next-generation sequencers, quantitative PCR and flow cytometers can also be used as measuring devices as long as they can measure the expression levels of multiple nucleic acid molecules.

[0018] NGS allows for simultaneous measurement of the expression levels of multiple nucleic acid molecules in multiple samples. That is, in measurement step 12, the expression levels of multiple nucleic acid molecules are measured for multiple samples in the same process.

[0019] Note that a pretreatment (e.g., centrifugation) may be performed on the sample before the measurement step 12 is performed. In this embodiment, when the pretreatment is performed, the same treatment is performed on multiple samples. That is, in this embodiment, the pretreatment is performed on multiple samples using the same process.

[0020] <Evaluation step 13> Evaluation step 13 is a step of evaluating the properties of each of a plurality of samples based on the expression levels of nucleic acid molecules measured in measurement step 12 using an algorithm for evaluating the properties of the sample based on the expression levels of nucleic acid molecules (hereinafter referred to as evaluation algorithm), and calculating an evaluation value.

[0021] In the evaluation step 13, first, the expression levels of the nucleic acid molecules measured in the measurement step 12 are analyzed. Specifically, the number of molecules for each type of nucleic acid molecule is tallied. Note that when human blood (specifically, serum) is used as the sample, there are, for example, 500 types of microRNAs as nucleic acid molecules to be measured.

[0022] In the evaluation step 13, the properties of the sample are evaluated based on the counted number of molecules (specifically, for example, the distribution of the counted number of molecules for each type of nucleic acid molecule). Examples of evaluation of the properties of the sample include disease prediction for the donor who provided the sample. Disease prediction can be performed using a disease prediction algorithm as an evaluation algorithm.

[0023] Specifically, the number of molecules of each target microRNA and information on the presence or absence of disease are used as training data for machine learning in advance to construct a learning model as a disease prediction algorithm.Then, using the aggregated number of molecules as an input factor, the learning model can determine an evaluation value (evaluation score) for disease prediction as an output factor.

[0024] The evaluation of the properties of the specimen is not limited to disease prediction, but may also include basic biological analysis, regression prediction, and various abnormality detection.

[0025] <Detection Step 14> In the detection step 14, multiple samples (hereinafter sometimes referred to as a sample group) are ranked according to their evaluation values, and abnormalities that occur during the process of determining the evaluation values ​​are detected based on the correlation between the rankings of the multiple samples and the evaluation values. Hereinafter, abnormalities that are the target of detection in the detection step 14 may be referred to as "target abnormalities." Note that the sample group is not limited to multiple samples in which the expression levels of multiple nucleic acid molecules have been measured collectively using a measurement device such as an NGS. The sample group may also be, for example, multiple samples that have been measured consecutively using the same measurement device in the same process. Therefore, the sample group also includes multiple samples that have been measured multiple times.

[0026] Although an example in which multiple samples are ranked in descending order of evaluation value will be described below, multiple samples may also be ranked in descending order of evaluation value. That is, the ranking may be in descending order of evaluation value, or in descending order of evaluation value.

[0027] Furthermore, the ranking can be assigned, for example, as an absolute numerical value when multiple samples are arranged in order. In this case, for example, when 50 samples are arranged in order from highest to lowest evaluation value, the 20th sample from the highest evaluation value will be ranked "20th." In addition, the ranking can be assigned, for example, as a ratio, which is a relative numerical value representing the relative position among multiple samples. In this case, for example, when 50 samples are arranged in order from highest to lowest evaluation value, the 20th sample from the highest evaluation value will be ranked "40"%.

[0028] The target abnormality detected in the detection step 14 is an abnormality that occurs in the process leading up to the calculation of the evaluation value. Therefore, the target abnormality includes an abnormality originating in the specimen to be evaluated, and an abnormality that occurs in the pre-processing before the execution of the measurement step 12, the measurement step 12, and the evaluation step 13. Specifically, examples of the target abnormality include an abnormality that occurs in the following cases (1) to (3).

[0029] (1) When the evaluation algorithm, which is the evaluation standard, has deteriorated for some reason. (2) When the sample group to be evaluated is different from the evaluation target assumed in the evaluation by the evaluation algorithm, which is the evaluation standard. (3) When an abnormality occurs in the measurement result of the expression level of a nucleic acid molecule due to improper processing in the pre-processing before the execution of the measurement step 12, the measurement step 12, etc.

[0030] An example of the above (1) is data drift (also called feature drift or covariate shift) caused by some kind of change misaligning the statistical distribution of input data, which is training data used to create a learning model, and the statistical distribution of input data used at the time of evaluation. An example of the above (2) is a case in which a group of samples, most of which are disease patients, is used as the evaluation target in an evaluation using a disease prediction algorithm that assumes a low frequency of disease patients. An example of the above (3) is a case in which an operator improperly processes samples required for the measurement step 12, or a reagent used to process the samples is abnormal.

[0031] In the detection step 14, for example, when any evaluation value of a sample ranked lower than the first threshold is equal to or greater than the second threshold, the sample is detected as a target abnormality. In the graph shown in Fig. 2, for example, the vertical axis represents the evaluation value, and the horizontal axis represents the ranking of the evaluation value. In the example shown in Fig. 2, for example, the first threshold is set to the value of the 10% ranking.

[0032] Here, the second threshold value is, for example, a threshold value that serves as a determination index in the determination step 16. That is, as will be described later, for example, in the determination step 16 for predicting the presence or absence of a disease, if the evaluation value is equal to or greater than a predetermined threshold value, the threshold value is set as the second threshold value.

[0033] On the other hand, the first threshold value is, for example, a rank lower than the lowest rank at which a diagnosis of the presence of a disease is expected in the disease-presence prediction determination step 16. For example, if a diagnosis of the presence of a disease in the determination step 16 is expected at a rank higher than 10%, for example, a value of 10% is set as the first threshold value.

[0034] As described above, since the first threshold and the second threshold are set, if the evaluation value of any of the samples ranked lower than the first threshold is equal to or higher than the second threshold, there will be a sample (i.e., a sample with a value equal to or higher than the second threshold) that will be determined to have a disease in the determination step 16 at a rank (i.e., a rank lower than the first threshold) at which the determination step 16 is not expected to determine that the sample has a disease, and therefore, in the detection step 14, it will be possible to detect that an abnormality has occurred in the process leading up to the determination of the evaluation value.

[0035] As shown in FIG. 2 , in the sample group indicated by solid black dots, the evaluation value (see symbol 51) of a sample ranked lower than the first threshold is equal to or greater than the second threshold. In contrast, in the sample group indicated by open dots, the evaluation value (see symbol 52) of a sample ranked lower than the first threshold is equal to or less than the second threshold. Therefore, in the example shown in FIG. 2 , the sample group indicated by solid black dots corresponds to a case where any evaluation value of a sample ranked lower than the first threshold is equal to or greater than the second threshold. That is, this corresponds to a case where a sample that is determined to have a disease in the determination step 16 (i.e., a sample with a value equal to or greater than the second threshold) exists in a rank where a determination of the presence of a disease in the determination step 16 is not expected (i.e., a rank lower than the first threshold). Therefore, in the detection step 14, the sample group indicated by solid black dots is detected as a target abnormality.

[0036] On the other hand, in the sample group indicated by open dots, all evaluation values ​​of samples ranked lower than the first threshold are less than the second threshold. That is, samples that are determined to be disease-free in the determination step 16 (i.e., samples with evaluation values ​​below the second threshold) are present in a rank where a determination of disease presence is not expected in the determination step 16 (i.e., a rank lower than the first threshold). Therefore, in the detection step 14, the sample group indicated by open dots is detected as having normal evaluation values. That is, in the detection step 14, target abnormalities are not detected for the sample group indicated by open dots.

[0037] In the case of the graph shown in Figure 2, if any of the dots falls within the area indicated by the diagonal lines in Figure 2 (hereinafter sometimes referred to as the second quadrant), it can be said that the target abnormality is detected.

[0038] The detection method described with reference to FIG. 2 is effective for a group of specimens in which there are almost no specimens showing high evaluation values.

[0039] Furthermore, in the above description, when any dot is included in the second quadrant, it is detected as a target abnormality, but when multiple dots are included in the second quadrant, it may also be detected as a target abnormality.

[0040] Furthermore, if the dots ranked nth or n% (evaluation values) have a value equal to or greater than a second threshold, it may be detected as a target abnormality.

[0041] The second threshold value used in the detection process 14 is desirably determined based on, for example, the content of the pre-processing performed before the measurement process 12 is performed, the measurement device used in the measurement process 12, the judgment content in the judgment process 16, the evaluation content in the evaluation process 13, etc.

[0042] On the other hand, the first threshold value used in the detection step 14 is a ranking and a relative index that does not depend on the content executed in each step, so a fixed value is desirable. Note that, if it is expected that the sample group to be evaluated will differ from the evaluation target assumed in the evaluation by the evaluation algorithm that is the evaluation standard, it is desirable to change the first threshold value according to the attributes of the sample group to be evaluated.

[0043] Furthermore, multiple patterns of combinations of the first threshold and the second threshold used in the detection step 14 may be prepared in advance. In this case, the user selects one pattern from the multiple patterns and uses that pattern to detect the target abnormality in the detection step 14. Furthermore, in the detection step 14, it is possible to determine at least the first threshold based on information about the origin from which the sample group was collected and detect the target abnormality. Note that, since at least the first threshold is based on information about the origin from which the sample group was collected, the user may be allowed to set it to any value.

[0044] For example, if the specimen is human blood (specifically, serum), the information on the source from which the specimen group was collected is information indicating the source (e.g., a medical checkup, a cancer center, etc.).

[0045] If the origin from which the sample group was collected is one that tends to have a high evaluation value, a combination in which the first threshold is set relatively low is selected.

[0046] <Determination step 16> The determination step 16 is a step of making a determination regarding the sample for a group of samples based on the evaluation value obtained in the evaluation step 13. This determination step 16 is executed when a target abnormality is not detected in the detection step 14. Therefore, if a target abnormality is detected in the detection step 14, the determination step 16 is not executed. In this case, for example, the fact that a target abnormality has been detected may be presented to the user (i.e., the person who executes the detection method).

[0047] An example of a sample-related determination is predicting whether or not the donor who provided the sample has a disease. In the determination step 16, for example, if the evaluation value obtained in the evaluation step 13 is equal to or greater than a second threshold value (see FIG. 2), it is possible to determine that the donor has a disease. In the case of the graph shown in FIG. 2, the donor who provided the sample corresponding to the dot present in the area immediately to the right of the second quadrant (first quadrant in FIG. 2) is determined to have a disease.

[0048] As described above, when a target anomaly is detected in the detection step 14, the determination step 16 is not executed, but this is not limited to this. For example, even when a target anomaly is detected in the detection step 14, the determination step 16 may be executed, for example, if a determination result is to be obtained as reference data. In this case, for example, after the target anomaly is detected, the fact that a determination has been made may be presented to the user.

[0049] <Presentation step 17> The presentation step 17 is a step of presenting the correlation between the ranks of a plurality of samples and the evaluation values. In the presentation step 17, for example, the correlation between the ranks assigned to each sample in the sample group in the detection step 14 and the evaluation values ​​is presented.

[0050] Specifically, in the presentation step 17, the distribution of the rankings and evaluation values ​​of multiple samples is presented (for example, the graph shown in Figure 2) relative to a first threshold value set for the rankings and a second threshold value set for the evaluation values.

[0051] Furthermore, in the presentation step 17, in addition to the correlation between the rank and evaluation value assigned to each sample in the sample group in the detection step 14, the determination result 56 in the determination step 16 (for example, the graph shown in Fig. 3) may be presented. In the example shown in Fig. 3, the determination result predicting the presence or absence of disease includes a cancer presence / absence prediction (specifically, a prediction of whether it is positive or negative), and accompanying information accompanying the determination result (for example, the rank of the examinee (corresponding to the sample provider) within the sample group, and comments on the determination result, etc.).

[0052] The display method may be a method of displaying on a display unit, a method of printing on a recording medium such as paper, etc. Examples of the display unit include a liquid crystal display and an organic EL (Electro Luminescence) display.

[0053] Examples of recipients to whom the correlation is presented include the provider of the sample, and in the case of disease prediction, the doctor who treats the disease.

[0054] <Detection System 20> Next, a description will be given of the detection system 20 as a system that executes the above-described detection method 10. The detection system 20 includes a measurement device 21 and a detection device 30, as shown in FIG.

[0055] <Measuring Device 21> The measuring device 21 is an example of a measuring unit, and is a device that performs the above-mentioned measuring step 12. That is, the measuring device 21 measures the expression levels of multiple nucleic acid molecules contained in each of multiple samples. As the measuring device 21, for example, an NGS is used.

[0056] <Detection Device 30> The detection device 30 is an example of an evaluation unit and an example of a detection unit. The detection device 30 is a device that executes the evaluation step 13 and the detection step 14 described above. That is, the detection device 30 uses an evaluation algorithm that evaluates the properties of each of a plurality of samples based on the expression levels of nucleic acid molecules measured in advance to determine an evaluation value. Furthermore, the detection device 30 ranks the evaluation values ​​of the plurality of samples, and detects abnormalities that occur in the process of determining the evaluation value based on the correlation between the rankings of the plurality of samples and the evaluation values.

[0057] The detection device 30 also executes the aforementioned determination step 16 and presentation step 17. That is, the detection device 30 performs a sample-related determination for a group of samples based on the evaluation values ​​it has determined. Furthermore, the detection device 30 performs evaluations based on the expression levels of nucleic acid molecules measured in advance, determines evaluation values, ranks the evaluation values ​​of multiple samples, and functions as a presentation device that presents the correlation between the rankings of the multiple samples and the evaluation values. Specifically, the detection device 30 presents the distribution of the rankings and evaluation values ​​of the multiple samples (e.g., the graph shown in FIG. 2 ) relative to a first threshold value set for the rankings and a second threshold value set for the evaluation values.

[0058] 4, the detection device 30 has a function as a computer, and includes a CPU (Central Processing Unit) 31, a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, a storage 34, an input unit 35, a display unit 36, and a communication interface (I / F) 37. The components are connected to each other via a bus 39 so as to be able to communicate with each other.

[0059] The CPU 31 (an example of a processor) is a central processing unit that executes various programs and controls each part. That is, the CPU 31 reads 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 controls each of the above components and performs 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.

[0060] The ROM 32 stores various programs and various data. The RAM 33 temporarily stores programs or data as a working area. The storage 34 is configured by an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including the operating system and various data.

[0061] In this embodiment, for example, a detection program for executing a detection process that performs the above-described detection method 10 is recorded in the storage 34. The detection program may be a single program, or a group of programs configured from multiple programs or modules. The detection program may be recorded in the ROM 32. The ROM 32 or the storage 34 functions as an example of a non-transitory recording medium.

[0062] An example of a processor is not limited to the above-mentioned CPU, which is a general-purpose processor, but may be, for example, a dedicated processor configured with a circuit designed specifically for executing a specific process. Also, an example of a processor is not limited to a single processor, but may be a processor configured by multiple processors located in physically separate locations working together.

[0063] The input unit 35 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs. The input unit 35 also receives as input information on the expression levels of multiple nucleic acid molecules measured by the measurement device 21. The input unit 35 also receives as input information used in the evaluation step 13 and the detection step 14, such as the aforementioned origin information, evaluation content, and evaluation target.

[0064] The display unit 36 ​​is, for example, a liquid crystal display, and displays various information. The detection device 30 can notify the user through the display unit 36 ​​that a target abnormality has been detected.

[0065] Furthermore, the detection device 30 can present the correlation between the rankings of a plurality of specimens and the evaluation values ​​to the user via the display unit 36. Therefore, in this embodiment, the display unit 36 ​​can be understood as an example of a presentation device. The display unit 36 ​​may also function as the input unit 35 by employing a touch panel system.

[0066] The communication interface 37 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI (Fiber Distributed Data Interface), and Wi-Fi (registered trademark).

[0067] As shown in FIG. 5 , in the detection device 30 , the CPU 31 executes the detection program to function as an evaluation function unit 150 , a detection function unit 160 , a determination unit 170 , and a presentation unit 180 .

[0068] The evaluation function unit 150 executes the aforementioned evaluation step 13. That is, the evaluation function unit 150 uses an evaluation algorithm that evaluates the properties of a specimen based on the expression level (expression amount) of a nucleic acid molecule to evaluate the properties of each of a plurality of specimens based on the expression level of the nucleic acid molecule measured by the measurement device 21, and calculates an evaluation value (see the aforementioned evaluation step 13).

[0069] The detection function unit 160 executes the above-mentioned detection step 14. That is, the detection function unit 160 ranks the samples in the sample group according to the high or low evaluation value of each sample, and detects target abnormalities based on the correlation between the rankings of the multiple samples and the evaluation values ​​(see the above-mentioned detection step 14).

[0070] The determining section 170 executes the determining step 16. That is, the determining section 170 makes a determination regarding the sample for the sample group based on the evaluation value determined by the evaluation function section 150 (see the determining step 16 described above).

[0071] The presenting unit 180 executes the above-mentioned presenting step 17. That is, the presenting unit 180 presents the correlation between the ranks of a plurality of samples and the evaluation values ​​(see the above-mentioned presenting step 17).

[0072] In this embodiment, the detection system 20 includes the measurement device 21 and the detection device 30, but the detection system 20 may be configured with a single device. In this case, the single device functions as an example of a measurement unit, an evaluation unit, and a detection unit.

[0073] The detection device 30 may be configured with a plurality of devices. For example, the detection device 30 may be configured with a plurality of devices (for example, four devices) that share the tasks of the evaluation step 13, the detection step 14, the determination step 16, and the presentation step 17.

[0074] <Effects of this embodiment> In this embodiment, as described above, in the detection step 14, the evaluation values ​​of the multiple samples are ranked, and target abnormalities (i.e., abnormalities that occur in the process leading up to the determination of the evaluation values) are detected based on the correlation between the rankings of the multiple samples and the evaluation values ​​(see FIG. 2).

[0075] This makes the distribution of evaluation values ​​for a plurality of specimens (i.e., a specimen group) clear, and makes it possible to easily detect unacceptable target abnormalities.

[0076] Furthermore, in this embodiment, in the detection step 14, for example, if any evaluation value of a sample ranked lower than the first threshold is equal to or greater than the second threshold, it is detected as a target abnormality.

[0077] This makes it possible to detect a target anomaly when any dot falls in the second quadrant in the graph shown in FIG. 2, for example, and makes it possible to easily detect an unacceptable target anomaly.

[0078] In the measurement step 12, the expression levels of a plurality of nucleic acid molecules are measured for a plurality of samples in the same process.

[0079] Since the properties of the sample are evaluated based on the expression levels of multiple nucleic acid molecules measured under the same measurement conditions, the reliability of the evaluation value is increased, and target abnormalities can be detected with high accuracy in the detection step 14.

[0080] In this embodiment, in the determination step 16, if no target abnormality is detected in the detection step 14, a determination regarding the sample is made for the sample group based on the evaluation value obtained in the evaluation step 13.

[0081] In the determination step 16, it is possible to prevent the execution of determinations that result in low reliability compared to when determinations regarding samples are always made for a sample group regardless of the detection results in the detection step 14.

[0082] In this embodiment, in the detection step 14, a target abnormality is detected using one pattern from a combination of a plurality of patterns based on the first threshold value and the second threshold value.

[0083] Therefore, the pattern used to detect the target abnormality can be selected depending on the specimen to be detected, and the target abnormality can be detected with high accuracy in the detection step 14.

[0084] Furthermore, in this embodiment, in the detection step 14, a first threshold is determined based on information about the origin from which the sample group was collected, and a target abnormality is detected.

[0085] Since the first threshold value used to detect the target anomaly is determined according to the origin information, the target anomaly can be detected with high accuracy in the detection step 14 .

[0086] In this embodiment, the detection device 30 evaluates the expression levels of nucleic acid molecules measured in advance, calculates an evaluation value, ranks the evaluation values ​​of multiple samples, and presents a correlation between the rankings of the multiple samples and the evaluation values. This allows a recipient of the presented correlation to detect a target abnormality based on the correlation.

[0087] Specifically, the detection device 30 presents a distribution of the rankings and evaluation values ​​of multiple samples (e.g., the graph shown in FIG. 2 ) relative to a first threshold value set for the rankings and a second threshold value set for the evaluation values, thereby enabling the recipient to easily detect unacceptable target abnormalities.

[0088] The present disclosure is not limited to the above-described embodiment, and various modifications, changes, and improvements are possible without departing from the spirit of the present disclosure. The above-described modifications may be appropriately combined.

[0089] <Additional Notes> (Aspect 1) A detection method comprising: a measurement step of measuring the expression levels of multiple nucleic acid molecules contained in each of multiple samples; an evaluation step of evaluating the properties of each of the multiple samples based on the expression levels of the nucleic acid molecules measured in the measurement step using an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules, thereby obtaining an evaluation value; and a detection step of ranking the multiple samples according to their evaluation values ​​and detecting an abnormality that occurred in the process of obtaining the evaluation values ​​based on the correlation between the rankings of the multiple samples and the evaluation values. (Aspect 2) The detection method according to Aspect 1, wherein the detection step ranks the multiple samples in descending order of evaluation value, and detects the abnormality when any evaluation value of a sample ranked lower than a first threshold is equal to or greater than a second threshold. (Aspect 3) The detection method according to Aspect 1 or Aspect 2, wherein the measurement step measures the expression levels for the multiple samples using the same process. (Aspect 4) The detection method according to any one of Aspects 1 to 3, further comprising a determination step of performing a determination on the samples based on the evaluation value determined in the evaluation step when the detection step does not detect the abnormality. (Aspect 5) The detection method according to any one of Aspects 2 to 4, wherein there are a plurality of patterns for the combination of the first threshold and the second threshold, and the detection step detects the abnormality using one of the plurality of patterns. (Aspect 6) The detection method according to Aspect 5, wherein the detection step determines the first threshold based on information on the origin from which the plurality of samples were collected, and detects the abnormality. (Aspect 7) A presentation device that ranks a plurality of samples based on the expression levels of nucleic acid molecules contained in each of the samples, in order of their evaluation values ​​for evaluating properties of the samples, and presents a correlation between the rankings of the plurality of samples and the evaluation values. (Aspect 8) The presentation device according to Aspect 7, which presents the rankings of the plurality of samples and a distribution of the evaluation values ​​with respect to a first threshold set for the rankings and a second threshold set for the evaluation values. (Aspect 9) The presentation device according to aspect 7 or 8, which presents accompanying information accompanying a determination result of a determination regarding the sample made based on the evaluation value.a detection unit that ranks the properties of the plurality of samples based on the expression levels of the nucleic acid molecules measured by the measurement unit, and detects an abnormality that occurred in the process of determining the evaluation value, using an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules, and detects an abnormality that occurred in the process of determining the evaluation value, based on the correlation between the rank of the plurality of samples and the evaluation value. (Aspect 10) A detection device comprising a processor that uses an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules to evaluate the properties of each of a plurality of samples based on the expression levels of the nucleic acid molecules measured in advance, and determines an evaluation value, using an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules, and detects an abnormality that occurred in the process of determining the evaluation value, based on the correlation between the rank of the plurality of samples and the evaluation value. (Aspect 11) A detection system comprising: a measurement unit that measures the expression levels of a plurality of nucleic acid molecules contained in each of a plurality of samples; an evaluation unit that uses an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules, and detects an abnormality that occurred in the process of determining the evaluation value, using an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules, and detects an abnormality that occurred in the process of determining the evaluation value, based on the correlation between the rank of the plurality of samples and the evaluation value. (Aspect 12) A detection program for causing a computer to execute a detection process in which the computer uses an evaluation algorithm that evaluates the properties of specimens based on the expression levels of nucleic acid molecules to evaluate the properties of each of a plurality of specimens based on the expression levels of the nucleic acid molecules that have been measured in advance, to determine an evaluation value, rank the specimens in order of their evaluation values, and detect any abnormalities that have occurred in the process of determining the evaluation values ​​based on the correlation between the ranks of the specimens and the evaluation values. (Aspect 13) A non-transitory recording medium having recorded thereon a detection program for causing a computer to execute a detection process in which the computer uses an evaluation algorithm that evaluates the properties of specimens based on the expression levels of nucleic acid molecules to evaluate the properties of each of a plurality of specimens based on the expression levels of the nucleic acid molecules that have been measured in advance, to determine an evaluation value, rank the specimens in order of their evaluation values, and detect any abnormalities that have occurred in the process of determining the evaluation values ​​based on the correlation between the ranks of the specimens and the evaluation values.

[0090] 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 described herein are incorporated herein by reference to the same extent as if each individual document, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

Claims

1. A detection method comprising: a measurement step of measuring the expression levels of multiple nucleic acid molecules contained in each of multiple samples; an evaluation step of evaluating the properties of each of the multiple samples based on the expression levels of the nucleic acid molecules measured in the measurement step using an evaluation algorithm that evaluates the properties of the samples based on the expression levels of the nucleic acid molecules, thereby obtaining an evaluation value; and a detection step of ranking the multiple samples based on their high and low evaluation values, and detecting any abnormalities that have occurred in the process of obtaining the evaluation value based on the correlation between the rankings of the multiple samples and the evaluation values.

2. The detection method according to claim 1, wherein in the detection process, the plurality of samples are ranked in order from highest evaluation value to lowest evaluation value, and if any evaluation value of a sample ranked lower than a first threshold value is equal to or greater than a second threshold value, the sample is detected as an abnormality.

3. The detection method according to claim 1, wherein in the measurement step, the expression levels are measured for the multiple samples in the same process.

4. The detection method according to claim 1, further comprising a determination step of making a determination regarding the samples for the plurality of samples based on the evaluation value obtained in the evaluation step if the abnormality is not detected in the detection step.

5. The detection method according to claim 2, wherein there are a plurality of combinations of the first threshold value and the second threshold value, and in the detection step, the abnormality is detected using one of the plurality of combinations.

6. The detection method according to claim 2, wherein in the detection step, the first threshold is determined based on information on the origin from which the multiple samples were collected, and the abnormality is detected.

7. A presentation device that ranks a plurality of samples based on the expression level of nucleic acid molecules contained in each of the samples, and presents the correlation between the rankings of the samples and the evaluation values.

8. The presentation device according to claim 7, which presents the ranks of the multiple samples and a distribution of the evaluation values ​​with respect to a first threshold value set for the ranks and a second threshold value set for the evaluation values.

9. The presentation device according to claim 7, which presents accompanying information accompanying a judgment result regarding the sample made based on the evaluation value.

10. A detection device comprising a processor, which uses an evaluation algorithm for evaluating the properties of a sample based on the expression level of a nucleic acid molecule to evaluate the properties of each of a plurality of samples based on the expression level of the nucleic acid molecule measured in advance, calculates an evaluation value, ranks the evaluation values ​​of the plurality of samples according to their highs and lows, and detects abnormalities that occur in the process of calculating the evaluation value based on the correlation between the ranks of the plurality of samples and the evaluation value.

11. A detection system comprising: a measurement unit that measures the expression levels of multiple nucleic acid molecules contained in each of multiple samples; an evaluation unit that uses an evaluation algorithm to evaluate the properties of the samples based on the expression levels of the nucleic acid molecules, and evaluates the properties of each of the multiple samples based on the expression levels of the nucleic acid molecules measured by the measurement unit to obtain an evaluation value; and a detection unit that ranks the evaluation values ​​of the multiple samples based on their highs and lows, and detects abnormalities that occur in the process of obtaining the evaluation value based on the correlation between the rankings of the multiple samples and the evaluation values.

12. A detection program for causing a computer to execute a detection process that uses an evaluation algorithm for evaluating the properties of a sample based on the expression level of a nucleic acid molecule to evaluate the properties of each of a plurality of samples based on the expression level of the nucleic acid molecule measured in advance, calculates an evaluation value, ranks the plurality of samples according to their high and low evaluation values, and detects abnormalities that occur in the process of calculating the evaluation value based on the correlation between the ranks of the plurality of samples and the evaluation value.

13. A non-transient recording medium having recorded thereon a detection program for causing a computer to execute a detection process that uses an evaluation algorithm for evaluating the properties of a sample based on the expression level of a nucleic acid molecule to evaluate the properties of each of a plurality of samples based on the expression level of the nucleic acid molecule measured in advance, calculates an evaluation value, ranks the plurality of samples according to their high and low evaluation values, and detects abnormalities that occur in the process of calculating the evaluation value based on the correlation between the ranks of the plurality of samples and the evaluation value.

Images