Multi-omics data management system

Abstract

The invention discloses a multi-omics data management system, and relates to the technical field of data management, an exception comparison module constructs a difference factor Cyyz by using a detection result and a second multi-omics data set, and judges an exception value based on the difference factor Cyyz, the process can identify and mark exception data as early as possible, the identification of the exception data is more accurate, and the efficiency is improved. And further analysis and research are facilitated. And the biological pathway identification module performs statistical analysis on the abnormal data, generates related state data information and analyzes a deviation coefficient Pcxs of the abnormal data in a corresponding biological pathway, and the analysis can reveal specific influence of the abnormal data in the biological pathway and help understand a biological mechanism of an abnormal state. The management module sets an evaluation threshold value and compares the deviation coefficient Pcxs with the evaluation threshold value so as to judge whether the samples in the experimental group are in a lesion state or not, the lesion state of the samples can be accurately evaluated in the process, and a scientific basis is provided for diagnosis and treatment of diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a multi-omics data management system. Background Art

[0002] In the field of bioinformatics, there is a growing demand for multi-omics data management systems. Multi-omics data involves rich information obtained from multiple omics levels (such as genomics, transcriptomics, proteomics, and metabolomics), which can provide a multi-faceted understanding of organisms. With the advancement of technology, researchers can obtain a large amount of data from these different omics levels, thus promoting the development of biomedical research. However, the complexity and huge amount of these data also bring challenges, especially in how to effectively collect, process, analyze, and detect anomalies. Therefore, multi-omics data integration and management has become a key area in order to provide a more comprehensive understanding of biology by integrating data from different levels.

[0003] In the field of multi-omics data management, especially in anomaly analysis, existing systems often face some challenges. Existing systems may lack multi-faceted comparative analysis of experimental and control groups, accurate labeling of outliers, and detailed analysis of biological pathways, which makes them inaccurate in disease research and clinical applications. Secondly, existing methods usually fail to take into account the complexity and diversity of data during processing, which may lead to insufficient sensitivity and accuracy of anomaly detection. This deficiency not only affects the control of data quality, but also affects the accurate understanding of disease states and biological mechanisms. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a multi-omics data management system that solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-omics data management system, including a dual sample module, a data acquisition module, a processing and analysis module, an abnormal comparison module, a biological pathway identification module and a management module;

[0006] The dual sample module is used to pre-allocate several groups of test samples, and the several groups of test samples are divided into an experimental group and a control group;

[0007] The data acquisition module is used to detect relevant proteomics data information and relevant metabolomics data information in the experimental group and the control group respectively;

[0008] The processing and analysis module is used to perform preprocessing operations on the relevant proteomics data information and the relevant metabolomics data information in the experimental group and the control group to construct a first multi-omics data set and a second multi-omics data set, and perform preliminary abnormality detection on the relevant information in the first multi-omics data set;

[0009] The abnormal comparison module is used to construct a difference factor Cyyz based on the detection results in the processing and analysis module and in combination with the second multi-omics data set, and to determine the abnormal value again based on the value of the difference factor Cyyz and mark it as abnormal data;

[0010] The biological pathway identification module is used to perform statistics on the abnormality data obtained in the abnormality comparison module to generate relevant state data information, and analyze the deviation coefficient Pcxs of the abnormality data in the corresponding biological pathway according to the relevant state data information;

[0011] The management module is used to pre-set an evaluation threshold value P, and compare and analyze the deviation coefficient Pcxs with the evaluation threshold value P to estimate whether the samples in the experimental group are in a pathological state.

[0012] Preferably, the dual sample module includes a grouping standard unit and an environment unification unit;

[0013] The grouping standard unit is used to extract biological materials from a disease-free individual in advance, and use the biological materials as samples in a control group, and randomly extract corresponding biological materials from several groups of individuals to be tested, and use the corresponding biological materials as samples in the experimental group, so as to obtain several experimental groups, and mark the several experimental groups with serial numbers;

[0014] The environmental unified unit is used to construct basic conditions, which include temperature, humidity, oxygen content and water content, and several experimental groups and one control group are treated under the basic conditions.

[0015] Preferably, the first multi-omics data set includes relevant proteomics data information and relevant metabolomics data information in several experimental groups, and the second multi-omics data set includes relevant proteomics data information and relevant metabolomics data information in a control group;

[0016] The data acquisition module includes a first sample detection unit and a second sample detection unit;

[0017] The first sample detection unit is used to detect and record relevant proteomics data information and relevant metabolomics data information in several experimental groups, wherein the relevant proteomics data information includes the absolute content of each protein in the corresponding biological material; the relevant metabolomics data information includes the absolute content and metabolite concentration of each metabolite in the corresponding biological material;

[0018] The second sample detection unit is used to detect and record relevant proteomics data information and relevant metabolomics data information in the control group, wherein the relevant proteomics data information includes the absolute content of each protein in the corresponding biological material; the relevant metabolomics data information includes the absolute content and metabolite concentration of each metabolite in the corresponding biological material.

[0019] Preferably, the processing and analysis module includes a pre-processing unit and a preliminary abnormality identification unit;

[0020] The preprocessing unit is used to perform preprocessing operations on the relevant proteomics data information and relevant metabolomics data information in the experimental group and the control group. The preprocessing includes noise removal, missing value filling and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling.

[0021] Preferably, the preliminary abnormality identification unit is used to sort the relevant proteomics data information and the relevant metabolomics data information in several groups of experimental groups by numerical values, so as to obtain the sorted sets of the corresponding omics data in each experimental group, and extract the 25th percentile and the 75th percentile in the sorted sets of several omics data, so as to obtain the first ordinal position c and the second ordinal position d of the corresponding omics data, that is, the value of the 25th position in the sorted set and the value of the 75th position in the sorted set, respectively. The specific extraction method is as follows:

[0022]

[0023] In the formula, n represents the number of corresponding learning data;

[0024] According to the values ​​of the first digit c and the second digit d, the safety range [a, b] is obtained. The specific method of obtaining the safety range [a, b] is as follows: a = c-1.5*(dc); b = d-1.5*(dc);

[0025] The corresponding learning data is compared with the safety range [a, b] to perform preliminary anomaly detection. The specific preliminary detection contents are as follows:

[0026] If the corresponding learning data falls within the safe range [a, b], it will be preliminarily determined that the current learning data is not in an abnormal state;

[0027] If the corresponding learning data does not fall within the safety range [a, b], it will be preliminarily determined that the current learning data is in a preliminary abnormal state.

[0028] Preferably, the abnormal comparison module includes a secondary judgment unit and a screening unit;

[0029] The secondary judgment unit is used to obtain several groups of abnormal data according to the result of the preliminary judgment in the preliminary abnormality identification unit, and record the several groups of abnormal data as abnormal values ​​Ysx1, and extract the same type of data as the data in the second multi-omics data set in combination with the second multi-omics data set, and record it as the standard value Ysx2; based on the abnormal value Ysx1 and the standard value Ysx2, a difference factor Cyyz is constructed, and the difference factor Cyyz is obtained by the following formula:

[0030]

[0031] Where, Ysx1 avg It is represented as the average abnormal value, Ysx2 is represented as the standard value, δYsx1 is represented as the variance of the abnormal value, and m is represented as the number of abnormal data.

[0032] Preferably, the screening unit is used to preset a threshold value, and compare and analyze the difference factor Cyyz with the preset threshold value to determine the abnormal value again. The specific analysis content is as follows:

[0033] If the difference factor Cyyz exceeds a preset threshold, the currently detected learning data will be judged as abnormal again and marked as abnormal learning data;

[0034] If the difference factor Cyyz does not exceed the preset threshold, it is determined that the currently detected learning data is not in an abnormal state and no marking is performed.

[0035] Preferably, the biological pathway identification module includes a data collection unit and a pathway state analysis unit;

[0036] The data acquisition unit is used to count the abnormal scientific data obtained in the abnormal comparison module to generate relevant status data information, wherein the relevant status data information includes the total number Cz of abnormal scientific data in the corresponding experimental group, the number Ys of abnormal scientific data in the corresponding experimental group appearing in the pathway, the total number Zjs of scientific data in the corresponding experimental group, and the number Ts of all scientific data in the corresponding experimental group included in the pathway.

[0037] Preferably, the pathway state analysis unit is used to analyze the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway according to the relevant state data information, which is specifically obtained by the following formula:

[0038]

[0039] In the formula, Ts represents the number of all the study data in the corresponding experimental group included in the pathway, Ys represents the number of abnormal study data in the corresponding experimental group appearing in the pathway, Zjs represents the total number of study data in the corresponding experimental group, and Cz represents the total number of abnormal study data in the corresponding experimental group. Expressed as the number of ways to select Ys elements from Ts elements, It is expressed as the number of ways to select Cs elements from Zjs elements, Expressed as the number of ways to select Cs-Ys elements from Zjs-Ts elements.

[0040] Preferably, the management module includes a comparison unit and a feedback unit;

[0041] The comparison unit is used to compare and analyze the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway with the evaluation threshold P to estimate whether the sample in the experimental group is in a pathological state. The specific contents are as follows:

[0042] If the coefficient of deviation Pcxs is greater than the evaluation threshold P, it is estimated that the samples in the corresponding experimental group are not in a pathological state;

[0043] If the coefficient of deviation Pcxs is equal to the evaluation threshold P, it is estimated that the samples in the corresponding experimental group are developing towards a pathological state;

[0044] If the coefficient of deviation Pcxs is less than the evaluation threshold P, the samples in the corresponding experimental group are estimated to be in a pathological state;

[0045] The feedback unit is used to summarize the experimental groups with lesions according to the lesion results obtained in the comparison unit, and generate an evaluation report for reference by medical staff, and to remind medical staff to make further analysis and intervention.

[0046] The present invention provides a multi-omics data management system, which has the following beneficial effects:

[0047] (1) Through the setting of the dual-sample module, the system can effectively divide the test samples into experimental and control groups, so as to conduct systematic comparative analysis. This grouping method enables the system to detect the differences between the experimental and control groups from multiple dimensions to further ensure the comprehensiveness and accuracy of data analysis. The data acquisition module can detect the proteomics and metabolomics data in the experimental and control groups respectively, ensuring the comprehensiveness and accuracy of the data. This precise data acquisition provides a reliable basis for subsequent processing and analysis, and helps to improve the credibility of the final analysis results. The abnormal comparison module uses the test results and the second multi-omics data set to construct the difference factor Cyyz, and judges the abnormal value based on this. This process can identify and mark abnormal data as early as possible, ensuring that the identification of abnormal data is more accurate, which is conducive to further analysis and research. The biological pathway identification module performs statistical analysis on abnormal data, generates relevant state data information, and analyzes the deviation coefficient Pcxs of abnormal data in the corresponding biological pathway. This analysis can reveal the specific impact of abnormal data in biological pathways and help understand the biological mechanism of abnormal states. The management module sets the evaluation threshold and compares the deviation coefficient Pcxs with it to determine whether the samples in the experimental group are in a pathological state. This process can accurately evaluate the pathological state of the samples and provide a scientific basis for the diagnosis and treatment of the disease. Overall, the system can not only systematically process and analyze multi-omics data, but also provide accurate abnormality detection and biological pathway analysis, thus playing an important role in disease research and precision medicine.

[0048] (2) The grouping standard unit ensures that biological materials extracted from disease-free individuals are used as control group samples, and biological materials extracted from individuals to be tested are used as experimental group samples. The accuracy of this grouping and labeling helps to more accurately identify the differences between disease states and healthy states. The environmental unification unit ensures that the experimental and control groups are treated under the same environmental conditions by constructing basic conditions. This unification of the environment reduces the impact of environmental variables on experimental results and further improves the comparability of data and the reliability of the experiment.

[0049] (3) The preliminary anomaly identification unit sorts the learning data, extracts the 25th and 75th percentiles, and calculates the safety range to identify potential abnormal data. This method uses the first order c and the second order d as the basis to ensure the scientificity and accuracy of anomaly detection. By comparing the data with the safety range, it can be preliminarily determined whether the data is in an abnormal state. This process helps to discover and mark abnormal data in a timely manner, further avoiding incorrect data analysis and decision-making.

[0050] (4) Based on the preliminary anomaly identification, the secondary judgment unit further analyzes the abnormal data by comparing the abnormal data with the standard data to construct the difference factor Cyyz. This process utilizes the difference between the abnormal data and the standard data to more accurately judge the abnormal state of the data. By providing a secondary screening mechanism, anomaly detection is not limited to the preliminary identification results, but through more detailed analysis, it is confirmed whether the data is indeed in an abnormal state. This not only improves the accuracy of abnormal data identification, but also enhances the comprehensiveness of data analysis and the reliability of the system, thereby providing strong support for the in-depth analysis and scientific research of multi-omics data.

[0051] (5) By calculating the coefficient of deviation Pcxs, we can reveal the importance and role of abnormal data in specific biological pathways. This analysis helps to identify key pathways that may be related to diseases and further reveal the potential mechanisms in biological processes. Such in-depth analysis not only helps to understand the biological significance of the data, but also provides important clues for the study of disease mechanisms and the discovery of markers. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a block diagram of a multi-omics data management system of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] See also Figure 1 , the present invention provides a multi-omics data management system, including a dual sample module, a data acquisition module, a processing and analysis module, an abnormal comparison module, a biological pathway identification module and a management module;

[0056] The dual sample module is used to pre-allocate several groups of test samples, and the several groups of test samples are divided into an experimental group and a control group;

[0057] The data acquisition module is used to detect relevant proteomics data information and relevant metabolomics data information in the experimental group and the control group respectively;

[0058] The processing and analysis module is used to perform preprocessing operations on the relevant proteomics data information and the relevant metabolomics data information in the experimental group and the control group to construct a first multi-omics data set and a second multi-omics data set, and perform preliminary abnormality detection on the relevant information in the first multi-omics data set;

[0059] The abnormal comparison module is used to construct a difference factor Cyyz based on the detection results in the processing and analysis module and in combination with the second multi-omics data set, and to determine the abnormal value again based on the value of the difference factor Cyyz and mark it as abnormal data;

[0060] The biological pathway identification module is used to perform statistics on the abnormality data obtained in the abnormality comparison module to generate relevant state data information, and analyze the deviation coefficient Pcxs of the abnormality data in the corresponding biological pathway according to the relevant state data information;

[0061] The management module is used to pre-set an evaluation threshold value P, and compare and analyze the deviation coefficient Pcxs with the evaluation threshold value P to estimate whether the samples in the experimental group are in a pathological state.

[0062] During the operation of this system, the dual sample module effectively divides the test samples into experimental and control groups, providing a reliable basis for subsequent comparative analysis. Through clear sample grouping, the comparison between the experiment and the control can be ensured to be efficient and scientific. The data acquisition module ensures the comprehensive detection of relevant proteomics and metabolomics data in the experimental and control groups. This multi-faceted data acquisition can ensure the integrity and accuracy of multi-omics data and provide a solid data foundation for subsequent analysis. The processing and analysis module plays a key role in data preprocessing. By constructing the first and second multi-omics data sets for preliminary anomaly detection, it can effectively eliminate data noise and interference, and further improve the quality and reliability of the data. The abnormal comparison module can further identify and mark abnormal data in a timely manner by constructing the difference factor Cyyz and analyzing its value. This process ensures the accurate detection of outliers and helps to find potential problems in the data in a timely manner. The biological pathway identification module performs detailed statistics on abnormal data and analyzes its deviation coefficient Pcxs in the biological pathway, thereby revealing the biological significance of the data. This in-depth analysis helps researchers understand the potential biological mechanism of abnormal data and provides valuable insights for subsequent research. The management module can scientifically determine whether the experimental group samples are in a pathological state by setting the evaluation threshold and comparing it with the deviation coefficient Pcxs. This evaluation method provides a reliable tool for disease diagnosis and helps identify potential disease states or disease progression. In general, the system further improves the accuracy of abnormal data detection and identifies abnormal experimental groups through the effective integration of the above modules, thereby promoting a deeper understanding of biological pathways and providing strong support for disease diagnosis and research.

[0063] Example 2

[0064] Please refer to Figure 1 ,Specifically: the dual sample module includes a grouping standard unit and an environmental unified unit;

[0065] The grouping standard unit is used to extract biological materials from a disease-free individual in advance, and use the biological materials as samples in a control group, wherein the biological materials include but are not limited to blood, urine and saliva; and randomly extract corresponding biological materials from several groups of individuals to be tested, and use the corresponding biological materials as samples in the experimental group, so as to obtain several experimental groups, and mark the several experimental groups with serial numbers;

[0066] The environmental unified unit is used to construct basic conditions, which include temperature, humidity, oxygen content and water content, and several experimental groups and one control group are treated under the basic conditions.

[0067] The first multi-omics data set includes relevant proteomics data information and relevant metabolomics data information in several experimental groups, and the second multi-omics data set includes relevant proteomics data information and relevant metabolomics data information in a control group;

[0068] The data acquisition module includes a first sample detection unit and a second sample detection unit;

[0069] The first sample detection unit is used to detect and record relevant proteomics data information and relevant metabolomics data information in several experimental groups, wherein the relevant proteomics data information includes the absolute content of each protein in the corresponding biological material; the relevant metabolomics data information includes the absolute content and metabolite concentration of each metabolite in the corresponding biological material;

[0070] The second sample detection unit is used to detect and record relevant proteomics data information and relevant metabolomics data information in the control group, wherein the relevant proteomics data information includes the absolute content of each protein in the corresponding biological material; the relevant metabolomics data information includes the absolute content and metabolite concentration of each metabolite in the corresponding biological material.

[0071] Among them, mass spectrometry is used to detect and quantify proteins. In the experiment, the proteins in the sample are enzymatically hydrolyzed into peptides, then separated by liquid chromatography, and finally identified and quantified by mass spectrometry. The mass spectrometer will output information about the absolute content, abundance and identification of the protein, and the expression level and identification information of the protein can be obtained.

[0072] Using the principle of liquid chromatography, the metabolites in the sample are separated by a chromatographic column, and then quantified by a detector to obtain the absolute content and concentration of each metabolite. Of course, the metabolite concentration in the sample can also be analyzed by mass spectrometry;

[0073] Each of the proteins mentioned above includes but is not limited to enzyme proteins, structural proteins, immune proteins, and signal transduction proteins, and each metabolite includes but is not limited to amino acids, sugars, lipids, and uric acid.

[0074] In this embodiment, the grouping standard unit in the double sample module ensures that the biological material extracted from the healthy individual is used as the control group sample, and the biological material extracted from the individual to be tested is used as the experimental group sample, which ensures the scientificity and representativeness of the experimental group and control group samples. This grouping method ensures the reliability of the experimental results and provides a solid foundation for subsequent data analysis. The environmental unification unit further ensures that all experimental group and control group samples are processed and analyzed under a consistent environment by setting standardized basic conditions, thereby reducing the interference of environmental factors on the experimental results and improving the comparability and accuracy of the data. The first sample detection unit and the second sample detection unit of the data acquisition module respectively perform comprehensive detection and recording of the proteomics and metabolomics data information in the experimental group and the control group. This method ensures the integrity of the absolute content of proteins and the absolute content and concentration of metabolites obtained from each biological material, making the analysis of multi-omics data more comprehensive and in-depth. By recording the absolute content and concentration of each protein and metabolite, detailed data support is provided for subsequent abnormal detection and biological pathway analysis. This detailed quantitative data is the key to revealing disease mechanisms, discovering biomarkers and conducting enrichment analysis. By constructing the No. 1 multi-omics data set and the No. 2 multi-omics data set, the system can effectively perform differential analysis between the experimental group and the control group, thereby identifying and marking abnormal data. This differential analysis capability helps to more accurately understand the biological differences between disease states and healthy states, and provides a scientific basis for clinical applications and personalized medicine. In summary, the system has significant beneficial effects in sample processing, data collection, abnormality detection, and biological pathway analysis, which helps to improve the accuracy and reliability of experimental results and promote the development of related biomedical research.

[0075] Example 3

[0076] Please refer to Figure 1 ,Specifically: the processing and analysis module includes a pre-processing unit and a preliminary anomaly recognition unit;

[0077] The preprocessing unit is used to perform preprocessing operations on the relevant proteomics data information and relevant metabolomics data information in the experimental group and the control group. The preprocessing includes noise removal, missing value filling and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling.

[0078] The preliminary abnormality identification unit is used to sort the relevant proteomics data information and the relevant metabolomics data information in several experimental groups by numerical value, so as to obtain the sorted sets of the corresponding omics data in each experimental group, and extract the 25th percentile and the 75th percentile in the sorted sets of several omics data, so as to obtain the first ordinal position c and the second ordinal position d of the corresponding omics data, that is, the value of the 25th position in the sorted set and the value of the 75th position in the sorted set, respectively. The specific extraction method is as follows:

[0079]

[0080] In the formula, n represents the number of corresponding learning data;

[0081] According to the values ​​of the first digit c and the second digit d, the safety range [a, b] is obtained. The specific method of obtaining the safety range [a, b] is as follows: a = c-1.5*(dc); b = d-1.5*(dc);

[0082] The corresponding learning data is compared with the safety range [a, b] to perform preliminary anomaly detection. The specific preliminary detection contents are as follows:

[0083] If the corresponding learning data falls within the safe range [a, b], it will be preliminarily determined that the current learning data is not in an abnormal state;

[0084] If the corresponding learning data does not fall within the safety range [a, b], it will be preliminarily determined that the current learning data is in a preliminary abnormal state.

[0085] In this embodiment, the preprocessing unit preprocesses the relevant proteomics data information and metabolomics data information of the experimental group and the control group, including removing noise, filling missing values ​​and data smoothing operations. Through a variety of methods for filling missing values, the system can handle various data missing situations, further ensure the integrity and accuracy of the data, and this preprocessing method greatly reduces the impact of data noise and missing on the analysis results, thereby improving the reliability of subsequent data analysis. The preliminary abnormality identification unit sorts the relevant data of the experimental group, calculates the 25th percentile and the 75th percentile, and determines the safety range [a, b]. This abnormality detection method can effectively identify abnormal data beyond the normal range, which helps to accurately identify and mark abnormal data. By comparing the data with the safety range, the preliminary abnormality identification unit can effectively perform preliminary abnormality detection. If the data falls within the safety range, the data is judged to be in a normal state; if the data is not within the safety range, it is preliminarily judged to be in an abnormal state. This preliminary abnormality detection can identify potential problems in the early stages of data analysis, and provides important clues for subsequent in-depth analysis and processing. Through systematic data preprocessing and preliminary abnormality detection, the system reduces the complexity and manual intervention of data processing and improves the efficiency and reliability of data analysis. Preprocessing operations ensure high data quality, while anomaly detection provides clear standards and methods to identify abnormal data, thus providing a solid foundation for in-depth data analysis and decision-making. In general, the processing and analysis module of the system further improves the accuracy and efficiency of data analysis through data preprocessing and scientific preliminary anomaly detection, provides strong support for the management and analysis of multi-omics data, and helps to improve the reliability of experimental results and the interpretability of data.

[0086] Example 4

[0087] Please refer to Figure 1 ,Specifically: the abnormal comparison module includes a secondary judgment unit and a screening unit;

[0088] The secondary judgment unit is used to obtain several groups of abnormal data according to the result of the preliminary judgment in the preliminary abnormality identification unit, and record the several groups of abnormal data as abnormal values ​​Ysx1, and extract the same type of data as the data in the second multi-omics data set in combination with the second multi-omics data set, and record it as the standard value Ysx2; based on the abnormal value Ysx1 and the standard value Ysx2, a difference factor Cyyz is constructed, and the difference factor Cyyz is obtained by the following formula:

[0089]

[0090] In the formula, Ysx1 avgIt is represented as the average abnormal value, Ysx2 is represented as the standard value, δYsx1 is represented as the variance of the abnormal value, and m is represented as the number of abnormal data.

[0091] The screening unit is used to preset a threshold value, and compare and analyze the difference factor Cyyz with the preset threshold value to determine the abnormal value again. The specific analysis content is as follows:

[0092] If the difference factor Cyyz exceeds a preset threshold, the currently detected learning data will be judged as abnormal again and marked as abnormal learning data;

[0093] If the difference factor Cyyz does not exceed the preset threshold, it is determined that the currently detected learning data is not in an abnormal state and no marking is performed.

[0094] In this embodiment, the secondary judgment unit compares the abnormal data with the standard value of the control group by combining the results of the preliminary abnormal identification unit to construct a difference factor Cyyz. By calculating the difference between the abnormal value Ysx1 and the standard value Ysx2, this process can more accurately identify abnormal data, reduce the risk of false positives and false negatives, and improve the accuracy of data anomaly detection. The calculation method of the difference factor Cyyz involves the mean of the abnormal value, the standard value, the variance of the abnormal value, and the number of abnormal data. This calculation method fully considers the distribution characteristics and data variability of the abnormal data, making the result of the difference factor more scientific and reliable. In this way, the difference between the abnormal data and the standard data can be accurately quantified, providing a solid foundation for further abnormal data marking. The screening unit compares the difference factor Cyyz with the preset threshold value to provide a dynamic abnormal state judgment mechanism. If the difference factor Cyyz exceeds the threshold, the data is marked as abnormal. This judgment mechanism can timely discover and mark the data with significant differences; if the difference factor Cyyz does not exceed the threshold, the current data will not be marked as abnormal, thus avoiding unnecessary marking and ensuring the accuracy of data processing. In short, through the above-mentioned secondary judgment and screening process, the abnormal comparison module optimizes the data marking process, reduces the influence of human errors and subjective judgments, and improves the automation and standardization of data abnormality identification. The module can process multiple sets of data and perform refined abnormal data analysis based on the difference factor, which enhances the overall data analysis capability of the multi-omics data management system. Accurate abnormal data identification and marking provide high-quality data support for further biological research and clinical applications, and help to gain a deeper understanding of disease mechanisms and discover potential biomarkers. In summary, the abnormal comparison module of the system provides an accurate data abnormality identification method, optimizes the data marking process, and enhances the analysis capability of the multi-omics data management system through the scientific construction of the difference factor Cyyz and dynamic abnormal state judgment.

[0095] Example 5

[0096] Please refer to Figure 1 ,Specifically: the biological pathway identification module includes a data collection unit and a pathway state analysis unit;

[0097] The data acquisition unit is used to count the abnormal scientific data obtained in the abnormal comparison module to generate relevant status data information, which includes the total number Cz of abnormal scientific data in the corresponding experimental group, the number Ys of abnormal scientific data in the corresponding experimental group appearing in the pathway, the total number Zjs of scientific data in the corresponding experimental group, and the number Ts of all scientific data in the corresponding experimental group included in the pathway. The number Ys of abnormal scientific data in the corresponding experimental group appearing in the pathway can be monitored and obtained by a pathway analysis tool, wherein the pathway analysis tool includes Pathway Studio or Cytoscape, etc.;

[0098] The pathway state analysis unit is used to analyze the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway according to the relevant state data information, which is specifically obtained by the following formula:

[0099]

[0100] In the formula, Ts represents the number of all the study data in the corresponding experimental group included in the pathway, Ys represents the number of abnormal study data in the corresponding experimental group appearing in the pathway, Zjs represents the total number of study data in the corresponding experimental group, and Cz represents the total number of abnormal study data in the corresponding experimental group. Expressed as the number of ways to select Ys elements from Ts elements, It is expressed as the number of ways to select Cs elements from Zjs elements, It is expressed as the number of ways to select Cs-Ys elements from Zjs-Ts elements, where As an example, the calculation method is as follows: where ! represents factorial.

[0101] Among them, pathway refers to a series of interacting enzymes and metabolites in an organism. Each pathway represents a biological process or function. Pathways include metabolic pathways, signal transduction pathways, transcriptional regulation pathways, and cell cycle pathways.

[0102] Each protein or metabolite in the KEGG database is usually associated with a specific biological pathway. Among them, the KEGG database (Kyoto Encyclopedia of Genes and Genomes) is a widely used bioinformatics database, mainly used to organize and analyze biological data, especially in the fields of genomics, proteomics, metabolomics and systems biology. It provides comprehensive information about biological pathways, genome functions and drug effects.

[0103] In this embodiment, the data collection unit is responsible for systematically counting the abnormal biological data obtained in the abnormal comparison module and generating detailed relevant state data information, including the total number of abnormal biological data in the experimental group Cz, the number of abnormal biological data in the pathway Ys, the total number of biological data in the experimental group Zjs, and the number of all biological data in the experimental group in the pathway Ts. The multi-dimensional collection of these data lays a solid foundation for subsequent analysis and helps to accurately evaluate the performance of abnormal data in biological pathways. Based on the relevant state data information, the pathway state analysis unit calculates the deviation coefficient Pcxs of the abnormal biological data in each experimental group in the corresponding biological pathway. Through the formula calculation, the deviation coefficient Pcxs can quantify the distribution difference of abnormal data relative to all data in the pathway, thereby revealing the actual impact of abnormal data in the biological pathway; the deviation coefficient Pcxs can reveal whether the abnormal data is excessively enriched or sparse in a specific pathway, thereby helping researchers identify possible biological mechanisms and potential disease markers. This in-depth analysis improves the understanding of the function of biological pathways and helps to identify key pathways related to diseases. The module uses a selection method in combinatorial mathematics, such as factorial calculation, to accurately measure the distribution of biological data, making the analysis results more reliable. In summary, the biological pathway identification module of this system improves the effect of abnormal data analysis through multi-dimensional data statistics, calculation of deviation coefficient Pcxs and pathway analysis, enhances the understanding of biological pathways, and supports deeper biological research and exploration of disease mechanisms.

[0104] Example 6

[0105] Please refer to Figure 1 ,Specifically: the management module includes a comparison unit and a feedback unit;

[0106] The comparison unit is used to compare and analyze the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway with the evaluation threshold P to estimate whether the sample in the experimental group is in a pathological state. The specific contents are as follows:

[0107] If the coefficient of deviation Pcxs is greater than the evaluation threshold P, it is estimated that the samples in the corresponding experimental group are not in a pathological state;

[0108] If the coefficient of deviation Pcxs is equal to the evaluation threshold P, it is estimated that the samples in the corresponding experimental group are developing towards a pathological state;

[0109] If the coefficient of deviation Pcxs is less than the evaluation threshold P, the samples in the corresponding experimental group are estimated to be in a pathological state;

[0110] The feedback unit is used to summarize the experimental groups with lesions according to the lesion results obtained in the comparison unit, and generate an evaluation report for reference by medical staff, and to remind medical staff to make further analysis and intervention.

[0111] In this embodiment, the comparison unit can further accurately judge the pathological state of the experimental group samples by comparing and analyzing the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway with the preset evaluation threshold P. Specifically, if the deviation coefficient Pcxs is greater than the evaluation threshold P, it is judged that the sample is not in a pathological state; if it is equal to the evaluation threshold P, it means that the sample may be developing towards a pathological state; if it is less than the evaluation threshold P, it is judged that the sample is already in a pathological state. This precise judgment mechanism helps to carefully evaluate the health status of the experimental group samples, thereby further ensuring the accurate identification of the pathological state. According to the pathological results of the comparison unit, the feedback unit can systematically summarize the experimental groups with lesions and generate detailed evaluation reports. These reports provide comprehensive information about the pathological state of the samples, so that medical staff can quickly obtain key data, optimize clinical decisions, and implement necessary intervention measures. The summary and reporting functions of the feedback unit can promptly remind medical staff to further analyze and intervene in the pathological samples in the experimental group. This timely feedback mechanism can help medical staff identify potential health risks, conduct targeted diagnosis and treatment, thereby improving the response speed and processing efficiency of lesions. The management module provides a powerful clinical decision support tool for medical staff through accurate lesion status judgment and systematic report generation. This support can help doctors extract useful information from complex multi-omics data, optimize treatment plans, and improve the overall effect of patient management. By comparing and analyzing the deviation coefficient Pcxs and the evaluation threshold P, potential disease development trends can be discovered early, thereby enhancing the ability of disease prevention and early intervention. This ability is of great significance for improving the early detection rate of diseases and improving prognosis, and helps to achieve personalized medicine and precision treatment. In summary, the management module of the system provides timely and accurate lesion information and decision support for medical staff through the accurate lesion status judgment of the comparison unit and the systematic result summary of the feedback unit. This not only improves the analysis efficiency of the lesion status of the experimental group samples, but also optimizes the clinical decision-making process and promotes early intervention and prevention of diseases.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-omics data management system, characterized in that: It includes dual sample module, data acquisition module, processing and analysis module, abnormal comparison module, biological pathway identification module and management module; The dual sample module is used to pre-allocate several groups of test samples, and the several groups of test samples are divided into an experimental group and a control group; The data acquisition module is used to detect relevant proteomics data information and relevant metabolomics data information in the experimental group and the control group respectively; The processing and analysis module is used to perform preprocessing operations on the relevant proteomics data information and the relevant metabolomics data information in the experimental group and the control group to construct a first multi-omics data set and a second multi-omics data set, and perform preliminary abnormality detection on the relevant information in the first multi-omics data set; The abnormal comparison module is used to construct a difference factor Cyyz based on the detection results in the processing and analysis module and in combination with the second multi-omics data set, and to determine the abnormal value again based on the value of the difference factor Cyyz and mark it as abnormal data; The biological pathway identification module is used to perform statistics on the abnormality data obtained in the abnormality comparison module to generate relevant state data information, and analyze the deviation coefficient Pcxs of the abnormality data in the corresponding biological pathway according to the relevant state data information; The management module is used to pre-set an evaluation threshold value P, and compare and analyze the deviation coefficient Pcxs with the evaluation threshold value P to estimate whether the samples in the experimental group are in a pathological state.

2. A multi-omics data management system according to claim 1, characterized in that: The dual sample module includes a grouping standard unit and an environment uniform unit; The grouping standard unit is used to extract biological materials from a disease-free individual in advance, and use the biological materials as samples in a control group, and randomly extract corresponding biological materials from several groups of individuals to be tested, and use the corresponding biological materials as samples in the experimental group, so as to obtain several experimental groups, and mark the several experimental groups with serial numbers; The environmental unified unit is used to construct basic conditions, which include temperature, humidity, oxygen content and water content, and several experimental groups and one control group are treated under the basic conditions.

3. A multi-omics data management system according to claim 1, characterized in that: The first multi-omics data set includes relevant proteomics data information and relevant metabolomics data information in several experimental groups, and the second multi-omics data set includes relevant proteomics data information and relevant metabolomics data information in a control group; The data acquisition module includes a first sample detection unit and a second sample detection unit; The first sample detection unit is used to detect and record relevant proteomics data information and relevant metabolomics data information in several experimental groups, wherein the relevant proteomics data information includes the absolute content of each protein in the corresponding biological material; the relevant metabolomics data information includes the absolute content and metabolite concentration of each metabolite in the corresponding biological material; The second sample detection unit is used to detect and record relevant proteomics data information and relevant metabolomics data information in the control group, wherein the relevant proteomics data information includes the absolute content of each protein in the corresponding biological material; the relevant metabolomics data information includes the absolute content and metabolite concentration of each metabolite in the corresponding biological material.

4. A multi-omics data management system according to claim 3, characterized in that: The processing and analysis module includes a pre-processing unit and a preliminary abnormality identification unit; The preprocessing unit is used to perform preprocessing operations on the relevant proteomics data information and relevant metabolomics data information in the experimental group and the control group. The preprocessing includes noise removal, missing value filling and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling.

5. A multi-omics data management system according to claim 4, characterized in that: The preliminary abnormality identification unit is used to sort the relevant proteomics data information and the relevant metabolomics data information in several experimental groups by numerical value, so as to obtain the sorted sets of the corresponding omics data in each experimental group, and extract the 25th percentile and the 75th percentile in the sorted sets of several omics data, so as to obtain the first ordinal position c and the second ordinal position d of the corresponding omics data, that is, the value of the 25th position in the sorted set and the value of the 75th position in the sorted set, respectively. The specific extraction method is as follows: In the formula, n represents the number of corresponding learning data; According to the values ​​of the first digit c and the second digit d, the safety range [a, b] is obtained. The specific method of obtaining the safety range [a, b] is as follows: a = c-1.5*(dc); b = d-1.5*(dc); The corresponding learning data is compared with the safety range [a, b] to perform preliminary anomaly detection. The specific preliminary detection contents are as follows: If the corresponding learning data falls within the safe range [a, b], it will be preliminarily determined that the current learning data is not in an abnormal state; If the corresponding learning data does not fall within the safety range [a, b], it will be preliminarily determined that the current learning data is in a preliminary abnormal state.

6. A multi-omics data management system according to claim 5, characterized in that: The abnormal comparison module includes a secondary judgment unit and a screening unit; The secondary judgment unit is used to obtain several groups of abnormal data according to the result of the preliminary judgment in the preliminary abnormality identification unit, and record the several groups of abnormal data as abnormal values ​​Ysx1, and extract the same type of data as the data in the second multi-omics data set in combination with the second multi-omics data set, and record it as the standard value Ysx2; based on the abnormal value Ysx1 and the standard value Ysx2, a difference factor Cyyz is constructed, and the difference factor Cyyz is obtained by the following formula: In the formula, Ysx1 avg It is represented as the average abnormal value, Ysx2 is represented as the standard value, δYsx1 is represented as the variance of the abnormal value, and m is represented as the number of abnormal data.

7. A multi-omics data management system according to claim 6, characterized in that: The screening unit is used to preset a threshold value, and compare and analyze the difference factor Cyyz with the preset threshold value to determine the abnormal value again. The specific analysis content is as follows: If the difference factor Cyyz exceeds a preset threshold, the currently detected learning data will be judged as abnormal again and marked as abnormal learning data; If the difference factor Cyyz does not exceed the preset threshold, it is determined that the currently detected learning data is not in an abnormal state and no marking is performed.

8. A multi-omics data management system according to claim 1, characterized in that: The biological pathway identification module includes a data collection unit and a pathway state analysis unit; The data acquisition unit is used to count the abnormal scientific data obtained in the abnormal comparison module to generate relevant status data information, wherein the relevant status data information includes the total number Cz of abnormal scientific data in the corresponding experimental group, the number Ys of abnormal scientific data in the corresponding experimental group appearing in the pathway, the total number Zjs of scientific data in the corresponding experimental group, and the number Ts of all scientific data in the corresponding experimental group included in the pathway.

9. A multi-omics data management system according to claim 8, characterized in that: The pathway state analysis unit is used to analyze the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway according to the relevant state data information, which is specifically obtained by the following formula: In the formula, Ts represents the number of all the study data in the corresponding experimental group included in the pathway, Ys represents the number of abnormal study data in the corresponding experimental group appearing in the pathway, Zjs represents the total number of study data in the corresponding experimental group, and Cz represents the total number of abnormal study data in the corresponding experimental group. Expressed as the number of ways to select Ys elements from Ts elements, It is expressed as the number of ways to select Cs elements from Zjs elements, Expressed as the number of ways to select Cs-Ys elements from Zjs-Ts elements.

10. A multi-omics data management system according to claim 1, characterized in that: The management module includes a comparison unit and a feedback unit; The comparison unit is used to compare and analyze the deviation coefficient Pcxs of the abnormal data in each experimental group in the corresponding biological pathway with the evaluation threshold P to estimate whether the sample in the experimental group is in a pathological state. The specific contents are as follows: If the coefficient of deviation Pcxs is greater than the evaluation threshold P, it is estimated that the samples in the corresponding experimental group are not in a pathological state; If the coefficient of deviation Pcxs is equal to the evaluation threshold P, it is estimated that the samples in the corresponding experimental group are developing towards a pathological state; If the coefficient of deviation Pcxs is less than the evaluation threshold P, the samples in the corresponding experimental group are estimated to be in a pathological state; The feedback unit is used to summarize the experimental groups with lesions according to the lesion results obtained in the comparison unit, and generate an evaluation report for reference by medical staff, and to remind medical staff to make further analysis and intervention.

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