Method, apparatus, device, medium for analyzing core molecules of aging process

Abstract

The application discloses an analysis method, device and equipment of core molecules in the aging process, and a medium, and relates to the technical field of aging analysis. The method comprises the following steps: obtaining original data, dividing the original data into a control group and a plurality of experimental groups, and preprocessing the original data; performing aging mode clustering on the preprocessed original data to obtain a clustering result; performing duplicate removal GO enrichment analysis on each clustering mode, and visualizing the analysis result; taking the union of the molecular data of each experimental group after missing value screening and the molecular data of the control group after missing value screening, and analyzing the molecular data in the union through a differential analysis method to obtain a differential analysis result; performing correlation analysis on each clustering mode to obtain a correlation analysis result; and determining core molecules related to aging according to the correlation analysis result and the differential analysis result. The method can improve the accuracy of aging analysis.

Description

Technical Field

[0001] This invention relates to the field of aging analysis technology, and in particular to a method, apparatus, equipment, and medium for analyzing core molecules in the aging process. Background Technology

[0002] Vascular aging involves various molecular changes (proteins, metabolites, lipids) over a wide time span, from 1 to 30 months (mice) or 0 to 80 years (humans). This results in numerous time points, complex molecular changes, and a large-scale omics dataset. Furthermore, proteomics, metabolomics, and lipidomics data differ significantly in molecular properties, magnitude, and dynamic range. For example, proteomics involves complex post-translational modifications, metabolomics data contains a large number of small molecule metabolites of different classifications, and lipidomics data is even more complex, with numerous carbon chains and species. Effectively integrating and analyzing this heterogeneous data presents a significant challenge. Summary of the Invention

[0003] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a method, apparatus, device, and medium for analyzing core molecules in the aging process, capable of accurately analyzing aging-related molecules.

[0004] On one hand, the analytical method for the core molecules of the aging process according to embodiments of the present invention includes the following steps:

[0005] The raw data is obtained, divided into a control group and multiple experimental groups, and preprocessed. The raw data includes at least one of proteomic data, metabolomic data, and lipidomic data. The control group and each experimental group correspond to different aging time points, and each control group and each experimental group has multiple samples.

[0006] The preprocessed raw data is subjected to aging pattern clustering to obtain clustering results; the clustering results include multiple clustering patterns.

[0007] Perform deduplication GO enrichment analysis on each of the clustering patterns and visualize the results;

[0008] According to each clustering pattern, after missing value screening is performed on the control group and each experimental group, the molecular data of each experimental group after missing value screening and the molecular data of the control group after missing value screening are taken as a union, and the molecular data in the union are analyzed by the difference analysis method to obtain the difference analysis results.

[0009] Perform correlation analysis on each of the clustering patterns to obtain the correlation analysis results;

[0010] Based on the correlation analysis results and the difference analysis results, the core molecules related to aging were identified.

[0011] According to some embodiments of the present invention, the preprocessing of the raw data includes:

[0012] The original data are normalized at both the sample and molecular levels.

[0013] The normalized original data is standardized through centralization, automatic scaling, or Pareto standardization.

[0014] According to some embodiments of the present invention, performing aging pattern clustering on the preprocessed original data to obtain clustering results includes:

[0015] Starting with a cluster size of 2, gradually increase the cluster size until the preset maximum value is reached;

[0016] Based on each number of clusters, the preprocessed original data is clustered, and the mean silhouette coefficient and total sum of squares corresponding to each number of clusters are determined;

[0017] Based on the inflection point of the total sum of squares and the peak value of the average profile coefficient, determine the number of candidate clusters.

[0018] Based on the number of clusters for each candidate, a correlation analysis is performed on each cluster center to calculate the Pearson correlation coefficient between each pair of cluster centers;

[0019] The optimal number of clusters is determined based on the Pearson correlation coefficient.

[0020] The clustering result is determined based on the optimal number of clusters.

[0021] According to some embodiments of the present invention, determining the clustering result based on the optimal number of clusters includes:

[0022] Calculate the membership degree of each molecule to each of the clustering patterns;

[0023] Each molecule is assigned to the corresponding clustering pattern based on the membership degree.

[0024] The membership degree of each molecule in each clustering pattern is sorted to obtain the top N high membership degree molecules; N is a positive integer;

[0025] Based on the high membership degree molecules, a clustering heatmap is drawn and visualized.

[0026] According to some embodiments of the present invention, after performing missing value screening on the control group and each experimental group according to each clustering pattern, the molecular data of each experimental group after missing value screening and the molecular data of the control group after missing value screening are taken as a union, and the molecular data in the union are analyzed by a difference analysis method to obtain the difference analysis results, including:

[0027] A missing threshold is set, and the control group and each of the experimental groups are screened separately to retain the effective molecules of each of the control group and each of the experimental groups; the effective molecules are characterized by the proportion of samples with effective expression values ​​in all samples of the corresponding group exceeding the missing threshold;

[0028] Take the union of the effective molecules of each experimental group with the effective molecules of the control group;

[0029] Perform a normality test on the preprocessed original data;

[0030] When the distribution conforms to a normal distribution, the effective molecules within the union are analyzed for differences using the t-test or limma method, and the analysis results are corrected to obtain a first corrected P-value; or, when the distribution does not conform to a normal distribution, the effective molecules within the union are analyzed for differences using the Wilcoxon method, and the analysis results are corrected to obtain a first corrected P-value.

[0031] After normalizing the data of effective molecules within the union, the difference fold of the effective molecules between the experimental group and the control group is calculated.

[0032] According to some embodiments of the present invention, the raw data includes proteomic data, metabolomic data, and lipidomic data; the step of performing correlation analysis on each of the clustering patterns to obtain correlation analysis results includes:

[0033] Using Spearman or Pearson methods, each cluster pattern of the proteome data is correlated with all cluster patterns of the metabolome data or lipidome data to obtain the correlation coefficient and correlation P value between molecules.

[0034] The correlation P-value was corrected using the Benjiamini-Hochberg correction method to obtain a second corrected P-value.

[0035] According to some embodiments of the present invention, determining the core molecules related to aging based on the correlation analysis results and the differential analysis results includes:

[0036] Based on the difference analysis results, the first molecule to be analyzed is determined, and based on the correlation analysis results, the second molecule to be analyzed is determined.

[0037] Based on the ranking of the first corrected P-values ​​of each first molecule to be analyzed, a first-order contribution is assigned to each first molecule to be analyzed, and based on the ranking of the absolute values ​​of the difference multiples of each first molecule to be analyzed, a second-order contribution is assigned to each first molecule to be analyzed.

[0038] Assign a corresponding first weight to each aging time point;

[0039] Multiply the first weight of each of the first molecules to be analyzed by the sum of the first and second rank contributions to obtain the first score.

[0040] Based on the second corrected P-value and correlation coefficient of the second molecule to be analyzed, determine the significance contribution and the contribution of different degrees of correlation of each of the second molecules to be analyzed;

[0041] The score is obtained by summing the significance contribution and the contribution of different degrees of correlation of the second molecule to be analyzed;

[0042] Based on the ranking of the scores of all the second molecules to be analyzed, a corresponding third-rank contribution is assigned to each score.

[0043] Multiply each of the above scores by the corresponding third-rank contribution to obtain the second score value;

[0044] The average of the first and second scores is taken to obtain the evaluation value of the first or second molecule to be analyzed.

[0045] Based on the assessment values, core molecules associated with aging were identified.

[0046] On the other hand, the analysis apparatus for core molecules of the aging process according to an embodiment of the present invention includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the above-described method for analyzing core molecules of the aging process.

[0047] On the other hand, an electronic device according to an embodiment of the present invention includes an analysis device for the core molecules of the aging process described above.

[0048] On the other hand, according to an embodiment of the present invention, a computer-readable storage medium stores computer-executable instructions for causing a computer to perform the above-described method for analyzing the core molecules of the aging process.

[0049] The method, apparatus, device, and medium for analyzing core molecules in the aging process according to embodiments of the present invention have at least the following beneficial effects: by preprocessing, clustering, and deduplication-removing GO enrichment analysis of the data, the expression patterns of different molecules at different aging time points are determined; by performing differential and correlation analysis on each clustering pattern, the results of differential and correlation analysis are combined to obtain core molecules related to aging, thereby improving the accuracy of aging analysis.

[0050] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0051] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0052] Figure 1 This is a flowchart illustrating the steps of the analytical method for the core molecules in the aging process according to an embodiment of the present invention.

[0053] Figure 2 This is a WSS-K curve diagram of an embodiment of the present invention;

[0054] Figure 3 This is a silhouette_avg-k curve diagram of an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the clustering results in an embodiment of the present invention;

[0056] Figure 5 This is a correlation diagram when the number of clusters is 5 in an embodiment of the present invention;

[0057] Figure 6 This is a heatmap of the top 50 molecules by membership degree in an embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of the BP and KEGG results in the GO enrichment analysis of this invention.

[0059] Figure 8 These are multiple sets of volcano diagrams from embodiments of the present invention;

[0060] Figure 9 These are multiple sets of heat maps from embodiments of the present invention;

[0061] Figure 10 These are multiple box plots from embodiments of the present invention;

[0062] Figure 11 This is a correlation network diagram of protein molecules and lipid molecules in an embodiment of the present invention;

[0063] Figure 12 This is a correlation network diagram of protein molecules and metabolic molecules in an embodiment of the present invention. Detailed Implementation

[0064] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0065] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0066] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0067] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0068] Conventional multi-omics data analysis of aging often focuses on cross-sectional studies between aging and younger groups, losing information about changes over time. It only focuses on molecular changes within these two groups, failing to capture the overall trajectory and patterns of molecular change throughout the aging process. This one-sided omics analysis only identifies the core molecules at two specific time points, not the core molecules involved in the entire aging process. Given the numerous time points in aging, effectively and comprehensively identifying and screening molecules associated with core aging-related changes is a challenge. Aging is not a linear process; each time point may be a significant turning point, and changes at each point cannot be ignored.

[0069] Based on this, we developed a data analysis software for studying the multi-omics change profile of vascular aging and an algorithm for screening core change molecules related to aging.

[0070] The following describes in detail, with reference to the accompanying drawings, the analytical methods, apparatus, equipment, and media for the core molecules of the aging process according to embodiments of the present invention.

[0071] On the one hand, embodiments of the present invention propose an analytical method for the core molecules of the aging process, such as... Figure 1 As shown, the method includes the following steps:

[0072] Step S100: Obtain raw data, divide the raw data into a control group and multiple experimental groups, and preprocess the raw data; the raw data includes at least one of proteomic data, metabolomic data, and lipidomic data, the control group and each experimental group correspond to different aging time points, and each control group and each experimental group has multiple samples;

[0073] Specifically, in this example, the raw data refers to multi-omics data, including proteomic, metabolomic, and lipidomic data. In subsequent analysis, the proteomic, metabolomic, and lipidomic data are analyzed separately to identify aging-related protein molecules, metabolic molecules, and lipid molecules. After obtaining the raw data, it needs to be divided into a control group and multiple experimental groups based on the corresponding aging time points. For example, assuming the analysis is of the human aging process, data from ages 20-29 can be used as the control group, and data from ages 30-39, 40-49, 50-59, and 60-69 can be used as experimental groups. Comparative analysis can then identify core molecules related to aging. It should be noted that the control and experimental groups can be set according to different experimental subjects and needs. Each control group and each experimental group contains multiple samples, each containing various protein / metabolic / lipid molecules. To facilitate the analysis of the raw data, preprocessing is also required. In this example, the preprocessing process includes the following two steps:

[0074] Step S110: Normalize the original data at both the sample level and the molecular level;

[0075] Step S120: Standardize the normalized original data by means of centralization, automatic scaling, or Pareto standardization.

[0076] Specifically, after obtaining the raw data, it is input into the data analysis software of this application. The software allows for normalization and standardization of the raw data. Normalization and standardization can be performed on a sample or molecule level. Normalization is applied to the total intensity, and standardization can be achieved through methods such as centering scaling, automatic scaling, and Pareto scaling. Normalization can be performed at the sample level or the molecular level. For proteomic or metabolomic data, sample-level normalization (e.g., normalization based on total peak area; normalization based on expression intensity) can eliminate systematic biases caused by factors such as experimental batches and instrument sensitivity, ensuring the comparability of data from different samples. Molecular-level normalization may be necessary in some cases, requiring normalization of specific molecules (e.g., proteins, metabolites, lipids). This strategy is suitable for cross-platform analysis or multi-omics data integration scenarios, such as finding potential correlations between proteomic and lipidomic data. Depending on the specific research needs, appropriate standardization strategies can be selected. For example, centering scaling standardizes data to a mean of zero and a variance of one, suitable for eliminating the influence of dimensions and enhancing data comparability. Auto scaling performs mean-removal and unit variance standardization on each variable, particularly suitable for identifying features with significant differences between groups. Pareto standardization divides each variable by the square root of its standard deviation, assigning higher weights to variables with low variance compared to auto scaling, suitable for scenarios prioritizing readability. This analysis software provides these normalization and standardization methods for processing data.

[0077] Step S200: Perform aging pattern clustering on the preprocessed raw data to obtain clustering results; the clustering results include multiple clustering patterns;

[0078] Specifically, after preprocessing the original data, an improved k-means clustering method is used to cluster the data according to different expression patterns. Unlike traditional k-means, this data analysis software introduces a method based on dynamically adjusting the initial centroid position to reduce the algorithm's sensitivity to initial conditions and improve the stability of the clustering results. In this example, step S200 specifically includes the following six steps:

[0079] Step S210: Starting with a cluster size of 2, gradually increase the cluster size until the preset maximum value is reached;

[0080] Step S220: Cluster the preprocessed raw data according to the number of clusters, and determine the mean silhouette coefficient and total sum of squares corresponding to the number of clusters;

[0081] Step S230: Determine the number of candidate clusters based on the inflection point of the total sum of squares and the peak value of the average profile coefficient;

[0082] Step S240: Based on the number of clusters for each candidate cluster, perform correlation analysis on each cluster center and calculate the Pearson correlation coefficient between each pair of cluster centers;

[0083] Step S250: Determine the optimal number of clusters based on the Pearson correlation coefficient;

[0084] Step S260: Determine the clustering results based on the optimal number of clusters.

[0085] Specifically, an improved k-means clustering method is used to cluster the data into different cluster patterns, starting with the number of clusters k=2 and gradually increasing the number of clusters until a preset maximum value (e.g., k_max). The silhouette coefficient and the sum of squares (WSS) are used to evaluate the clustering quality at different k values. For each k value (e.g., k=2 to k=10), the corresponding WSS value and average silhouette coefficient (silhouette_avg-k) are calculated, and then... Figure 2 and Figure 3 As shown, draw the wss-k curve and silhouette_avg-k curve, observe where their respective optimal regions are, and identify any overlapping high points or inflection points. Figure 2 The x-axis represents the k value, and the y-axis represents the wss value. Figure 3 The x-axis represents the k value, and the y-axis represents the average silhouette coefficient (silhouette_avg-k). Based on the inflection point of the WSS-K curve and the peak value of the silhouette_avg-k curve, candidate k values ​​are listed, for example, through... Figure 2 It was found that the WSS-K curve has a clear inflection point when k is 3 or 4. Figure 3We found that k values ​​were high for all ranges from 1 to 4, so we tentatively set the candidate number of clusters to 3 or 4. To further evaluate these candidate k values, we performed correlation analysis on the center values ​​of different clustering patterns under each k value, calculating the Pearson correlation coefficient between each pair of potential cluster centers. When the correlation coefficient between two cluster centers exceeds 0.85, we consider them to belong to the same category of expression patterns. Otherwise, they are classified into different categories. The maximum k value where all clusters are less than 0.85 is selected as the optimal number of clusters. For example, when the optimal number of clusters is 4, the original data is clustered into four clustering patterns, such as... Figure 4 As shown, these are clusters 1-4 respectively. Figure 5 As shown, when the number of clusters is increased to 5, the correlation between cluster1 and cluster4 is found to be 0.962, which is greater than 0.85. Therefore, k=4 is determined to be the optimal number of clusters.

[0086] Furthermore, in some embodiments of this application, the following four steps are also included when performing aging pattern clustering:

[0087] Step S270: Calculate the membership degree of each molecule to each cluster pattern;

[0088] Step S280: Assign each molecule to the corresponding clustering pattern based on its membership degree;

[0089] Step S290: Sort the membership degree of each molecule in each clustering pattern and obtain the top N high membership degree molecules;

[0090] Step S291: Draw a cluster heatmap based on molecules with high membership degree, and visualize the cluster heatmap.

[0091] Specifically, after determining the optimal number of clusters, the membership value of each molecule is tested. Only if the membership value of a molecule to a particular cluster pattern exceeds 0.2 is it assigned to that cluster pattern; otherwise, it is considered not to belong to any cluster pattern. This determines the final number of clusters and the molecules belonging to each cluster pattern. The data analysis software in this embodiment also visualizes the results: for each determined cluster pattern, the top 50 (or other numbers) high-membership molecules are extracted, and a hierarchical clustering heatmap is drawn, such as... Figure 6As shown, this method clearly displays the core molecular characteristics of each clustering pattern. The data analysis software provides a K-means clustering analysis method based on dynamically adjusting the initial centroid position, along with a visualization module. By using dynamic centroid initialization, it overcomes the dependence of traditional k-means on initial random values, improving the reliability and stability of the clustering results. A dual screening criterion—combining a correlation threshold and membership score—ensures the identification of independent and significant expression patterns. The data analysis software implements an automated workflow: a complete analysis process from data processing to result visualization has been developed, reducing manual intervention and supporting high-throughput data analysis.

[0092] Step S300: Perform deduplication GO enrichment analysis on each cluster pattern and visualize the analysis results;

[0093] Specifically, after clustering, for different cluster patterns, we perform deduplication GO enrichment analysis and multiple visualizations within each cluster. Setting qvalueCutoff = 0.05, we use the "Wang" algorithm from the R package "simplifyEnrichment" to calculate the similarity between GO terms. If different GO terms have a similarity score greater than 0.7, they are considered the same GO term, and only the GO term with the smallest p.adjust is retained. If the similarity score is less than 0.7, they are considered different GO terms and are all retained. This processing effectively reduces redundant information in the results, making the GO enrichment analysis results more concise and easier to interpret, while maintaining the accuracy of its biological meaning. For GO enrichment analysis visualization, we have set up a multi-group enrichment analysis top term visualization module, automatically generating multiple bubble charts of the GO enrichment analysis results. Figure 7 As shown in the figure, the GO enrichment of proteins in different tissues and clusters can be displayed, allowing for simultaneous comparison of GO enrichment across multiple groups and dimensions. Among them, Figure 7 This section presents the results of BP and KEGG enrichment analyses in GO enrichment analysis. Based on semantic similarity, only terms with the smallest p.adj value for terms greater than 0.7 are retained. A clear and comprehensive comparative presentation of the enrichment results is provided.

[0094] Step S400: Based on each clustering pattern, after screening for missing values ​​in the control group and each experimental group, take the union of the molecular data of each experimental group after missing value screening and the molecular data of the control group after missing value screening, and analyze the molecules in the union using the difference analysis method to obtain the difference analysis results.

[0095] Specifically, for differential analysis during the aging process, we built an automated differential analysis module in our data analysis software. It's important to note that omics data often contains missing values. Missing values ​​indicate that a molecule is not expressed in the corresponding sample or its expression level is below the detection limit (0). To facilitate data analysis, missing value screening is necessary to remove missing values. However, we cannot arbitrarily screen or replace missing values, especially in aging omics data, because there are numerous time point groups during aging, and each time point group represents the expression status at that time point in the aging process. In existing methods, missing value screening typically involves setting a percentage, such as 75%, 50%, or other values. Taking 75% as an example, for all samples, if a molecule has a valid expression value in more than 75% of the samples, the molecule is retained; if the percentage of samples with a valid expression value for a molecule does not exceed 75%, the molecule is rejected. However, this method of screening for missing values ​​may miss some aging-related molecules, leading to inaccurate analysis results. This is because some molecules may only appear in the older experimental group, but not in the younger experimental group or the control group. If traditional screening methods are used, these molecules may be excluded because the proportion of samples with effective expression values ​​for such molecules is small. However, these molecules are themselves related to aging, and excluding them would affect the accuracy of the analysis. To avoid this problem, the analysis software in this application maximizes the retention of aging-related molecular information when screening for missing values, ensuring the accuracy and scientific validity of the analysis results.

[0096] Unlike traditional missing value screening methods, the missing value screening method in this application does not calculate the proportion of samples in which each molecule has an effective expression value across all samples. Instead, it calculates the proportion of samples in which each molecule has an effective expression value within a single group (control group / experimental group). For example, assuming a threshold of 75%, and a control group or experimental group has 100 samples, if a molecule has an effective expression value in more than 75 samples in that control group or experimental group, its proportion exceeds 75%, and the molecule in that group is retained. If a molecule has an expression value of 0 in more than 25 samples in that control group or experimental group, its proportion is below the 75% missing value threshold, and the molecule in that group is removed. By performing the above missing value screening operation on each control group and experimental group respectively, and then taking the union of the molecular data retained in each experimental group with the molecular data retained in the control group, molecular information related to aging is preserved as much as possible. Traditional missing value screening methods do not screen for missing values ​​separately for the control and experimental groups. Instead, they remove molecules with a proportion of effective expression values ​​of less than 75% or 50% from all the samples obtained, which may result in the loss of some aging-related molecules.

[0097] Specifically, in this example, step S400 above includes the following five steps:

[0098] Step S410: Set a missing threshold, screen the control group and each experimental group separately, and retain the effective molecules of the control group and each experimental group respectively; the effective molecules are those molecules whose proportion of effective expression values ​​in all samples of the corresponding group exceeds the missing threshold.

[0099] Step S420: Take the union of the effective molecules of each experimental group with the effective molecules of the control group;

[0100] Step S430: Perform a normality test on the original data;

[0101] Step S440: When the distribution conforms to normality, use t-test or limma method to perform differential analysis on the effective molecules within the union, and correct the analysis results to obtain the first corrected P value; or, when the distribution does not conform to normality, use Wilcoxon method to perform differential analysis on the effective molecules within the union, and correct the analysis results to obtain the first corrected P value.

[0102] Step S450: After normalizing the data of effective molecules in the union, calculate the fold difference of effective molecules between the experimental group and the control group.

[0103] Specifically, the original data is first normalized by sample strength. Then, for each aging time point (i.e., the experimental group) and the control group, a 75% missing value (or other value) screening is performed, retaining only molecules that have effective expression values ​​in at least 75% of the samples within the corresponding group (i.e., effective molecules), and removing other molecules. The molecular data from each time point in the aging process (i.e., each experimental group) after the 75% missing value screening is combined with the molecular data from the control group after the same screening, and the molecules in the union of the different time points and the control group are retained. For all molecules in each union, differential analysis is performed to obtain the difference analysis results between each experimental group and the control group. This result accurately reflects the molecular changes related to aging, ensuring that key molecular information potentially related to aging is not lost during the comparison process. This approach avoids the problem of important molecules being excluded due to traditional methods. When performing differential analysis, the molecular data undergoes a normality test (Shapiro-Wilk or Kolmogorov-Smirnov test). If the data conforms to a normal distribution, parametric tests such as t-test and limma can be used; otherwise, a Wilcoxon nonparametric test is performed. After selecting the differential analysis method, the raw data of all molecules to be analyzed are normalized and standardized, and then analyzed using the selected differential analysis method. A suitable multiple correction method, including Benjamini-Hochberg (BH), False Discovery Rate (FDR), or Bonferroni method, is selected based on research needs to control the false positive rate, thus obtaining the first corrected P-value. Simultaneously, after normalizing the data of effective molecules within the union, the fold change in expression intensity of effective molecules between the experimental and control groups is calculated. It should be noted that when the expression intensity of a molecule in the control group is 0, since the denominator cannot be 0, a smaller value is used to replace 0; for example, 1 / 5 of the minimum non-zero expression intensity in each sample is used to replace 0. It should be noted that the difference factor is represented by log2FC, where FC is the ratio of the same molecule in the experimental group to that in the control group.

[0104] This data analysis software supports hierarchical comparisons across multiple time points and parallel comparisons with control groups using the aforementioned differential analysis methods. By merging the differential analysis results from different time points and control groups, a global molecular change spectrum can be generated, ensuring that key changes at each time point are accurately captured. For visualization and interpretation of differential analysis results, this module provides multiple sets of differential volcano plots, heatmaps, and box plots: displaying the significance level (corrected P-value) and effect size (fold change). For example... Figure 8The diagram shown is a volcano plot, illustrating the differential molecular weights between the experimental and control groups at different time points (p.adj (corrected p-value) < 0.05, log2FC (fold change) > 1). Figure 9 The following are multiple heatmaps showing the fold differences and significance between the experimental and control groups at different time points. * represents p.adj < 0.05, ** represents p.adj < 0.01, and *** represents p.adj < 0.001. Figure 10 Multiple box plots were used to illustrate the significance of each molecule in the experimental and control groups at different time points, as well as the overall distribution of data at each time point. Different graphs can be used to demonstrate the expression patterns and clustering characteristics of key molecules at different time points and groups. Users can further add dynamic heatmaps to visually display molecular expression trends with aging, and can also use expression trend analysis tools to perform linear regression or nonlinear fitting combined with age gradients to assess the correlation between molecular expression and aging stages. This automated and systematic aging data differential analysis module has the following advantages: 1. Time stratification and union screening strategy (low information loss rate): Through flexible time point grouping and union integration strategies, the loss of key molecules due to missing value filtering in traditional data analysis is significantly reduced. This method is closer to the actual scenario of aging research, ensuring that potential biological changes can be captured. 2. Multi-level differential analysis integration (automation and enhanced scalability): This module is designed to support flexible configuration, including adaptive grouping strategies, diverse statistical test combinations, and multiple visualization methods. Furthermore, this method can be extended to the analysis of other complex time-series data (such as developmental biology or disease progression studies), demonstrating broad applicability. For visualization of aging differences, multiple visualization modules such as differential volcano plots, heatmaps, and box plots have been set up.

[0105] Step S500: Perform correlation analysis on each clustering pattern to obtain the correlation analysis results;

[0106] It should be noted that difference analysis targets analysis within the same omics dataset, while correlation analysis targets analysis between different omics datasets. For example, correlation analysis can be performed between proteomics and metabolomics data, or between proteomics and lipidomics data. For multi-omics correlation analysis during aging, the data analysis software includes a correlation analysis module that supports Spearman and Pearson correlation coefficient calculation methods. Pearson correlation is suitable for linear relationships and relatively normal data distributions, while Spearman correlation, based on rank, has better robustness to nonlinear and non-normally distributed data. The Halla algorithm is introduced to optimize the efficiency of traditional correlation calculations, especially when processing large-scale multi-omics datasets, significantly improving computational speed and stability. Specifically, step S500 includes the following two steps:

[0107] Step S510: Using Spearman or Pearson methods, perform correlation analysis between each cluster pattern of the proteomics data and all cluster patterns of the metabolomics data or lipidomics data to obtain the correlation coefficient and correlation P value.

[0108] Step S520: Use the Benjiamini-Hochberg correction method to correct the correlation P-value and obtain the second corrected P-value.

[0109] Specifically, assuming the proteomic data ultimately clusters into four patterns, and the metabolomic or lipidomic data clusters into five patterns, correlation analysis needs to be performed between the four proteomic cluster patterns and the five metabolomic or lipidomic cluster patterns to identify metabolic or lipid molecules significantly correlated with protein molecules. Correlation coefficients and correlation p-values ​​are calculated, and then a second-corrected p-value is obtained using correction methods such as the Benjamini-Hochberg (BH) to control the false positive error rate. The results are then screened using the adjusted p-value (i.e., the second-corrected p-value). In addition to the dual thresholds of correlation coefficient (e.g., |r|≥0.5) and p.adj (e.g., p.adj≤0.05), network analysis methods (e.g., molecular network degree centrality) are used to further screen core related molecules. These molecules may play a pivotal role in cross-omics data or have significant biological meaning.

[0110] Therefore, this data analysis software allows users to flexibly group data based on different aging stages, sample types, or other experimental conditions (e.g., young vs. aging groups, different time points, etc.), enabling more detailed analysis of the dynamic changes in correlation patterns. Users can select the molecular categories of focus (e.g., lipids, proteins, or metabolites) according to their research needs, or set specific targets of interest for targeted analysis. We screen the correlation results between different omics to identify core related molecules based on the number of correlations. For visualization of correlation analysis, network-based visualization functions are provided, such as modular clustering and forced-directed layout, to intuitively display highly correlated molecular communities. Figure 11 The correlation network diagram between protein and lipid molecules is shown (|r|>0.6, p.adj<0.05). Figure 12 The correlation network diagram of protein molecules and metabolic molecules (|r|>0.6, p.adj<0.05).

[0111] This approach significantly improves the accuracy and biological significance of core molecule screening by combining correlation coefficients (e.g., |r|), corrected p-values, and network centrality indices (e.g., degree, betweenness number). High computational efficiency and flexible configuration—the application of the Halla algorithm greatly enhances computational efficiency, making it suitable for processing large-scale multi-omics datasets; simultaneously, its modular grouping design allows users to perform highly customizable analyses according to their research needs.

[0112] Step S600: Based on the results of correlation analysis and differential analysis, identify the core molecules related to aging.

[0113] Identifying core molecules related to aging during the aging process remains a challenge with current methods. Conventional methods, such as relying on significance analysis and fold changes, are limited and incomplete, failing to fully identify all aging-related core molecules. This is because aging is a non-linear process, not concentrated at a single point in time; dramatic changes can occur at any given point, with different weights assigned to different time points. Therefore, we developed a core aging molecule screening algorithm—a multi-factor weighted screening algorithm. This algorithm comprehensively considers the weight of each time point in the aging process, as well as the weights of the corrected p-value, log2FC (fold change), correlation coefficient, and number of related connections, ultimately calculating the molecules that undergo core changes during aging. Specifically, in this example, step S600 includes the following 10 steps:

[0114] Step S610: Based on the difference analysis results, determine the first molecule to be analyzed, and based on the correlation analysis results, determine the second molecule to be analyzed;

[0115] Step S620: Based on the ranking results of the first corrected P-values ​​of each first molecule to be analyzed, assign the corresponding first-rank contribution to each first molecule to be analyzed; based on the ranking results of the absolute values ​​of the difference folds of each first molecule to be analyzed, assign the corresponding second-rank contribution to each first molecule to be analyzed.

[0116] Step S630: Assign a corresponding first weight to each aging time point;

[0117] Step S640: Multiply the first weight of each first molecule to be analyzed by the sum of the corresponding first and second contribution values ​​to obtain the first score.

[0118] Step S650: Based on the second corrected P-value and correlation coefficient of the second molecule to be analyzed, determine the significance contribution and the contribution of different degrees of correlation of each second molecule to be analyzed;

[0119] Step S660: Sum the significance contribution and the contribution of different degrees of correlation of the second molecule to be analyzed to obtain the scoring result;

[0120] Step S670: Based on the ranking of the scores of all the second molecules to be analyzed, assign a corresponding third-rank contribution to each score.

[0121] Step S680: Multiply each score result by the corresponding third-rank contribution to obtain the second score value;

[0122] Step S690: Take the average of the corresponding first score and second score to obtain the evaluation value of the corresponding first molecule to be analyzed or the second molecule to be analyzed;

[0123] Step S691: Identify the core molecules associated with aging based on the assessment values.

[0124] Specifically, firstly, based on the results of the difference analysis, molecules with high significance are selected as the first molecules to be analyzed. For example, molecules are screened based on the magnitude of the first corrected P-value; the smaller the first corrected P-value, the more significant the molecule. In this example, molecules with a first corrected P-value ≤ 0.05 are selected as the first molecules to be analyzed. Then, weights are assigned according to the time point corresponding to the experimental group to which the molecule belongs, as follows:

[0125]

[0126] Here, min and max represent the minimum and maximum time points for all samples, respectively, and time represents the time point corresponding to that molecule. Traditional aging analysis methods mainly rely on the significance and fold change of difference analysis, which is not comprehensive enough, especially since aging is a non-linear process that does not concentrate on a single time point, and the importance of different time points varies. Therefore, this method considers the weight of each time point in the overall process, which has a significant impact on the results. The weight of each time point in the aging process is not exactly the same, so a time period is used to calculate the weight of each time point. The last time point minus the first time point gives the total time period, and the weight ω of that time point is obtained by subtracting the first time point from the last time point and then dividing by the total time period. n ω at different time points n Calculated separately. This allows for adjusting the importance of each time point according to the time span, ensuring that early or late time points are not overestimated or underestimated, fully considering the non-linear characteristics of aging, and ensuring that key time nodes receive appropriate attention.

[0127] Then, the first correction p-values ​​of each first molecule to be analyzed are sorted from smallest to largest. The first-order contribution of the smallest first correction p-value is set to 1, the first-order contribution of the second-to-last first correction p-value is (n-1) / n, and so on, until the first-order contribution of the largest first correction p-value is 1 / n (n represents the total number of molecules in the first molecule to be analyzed). The first-order contribution of the first correction p-value is multiplied by the weight ω. n This yields the contribution of the molecule to the significance of the molecule at that time point. Similarly, the difference factor is ranked using the absolute value of log2FC. The second-ranked contribution of the largest difference factor is 1, decreasing sequentially until the second-ranked contribution of the smallest difference factor is 1 / n. This is then multiplied by the corresponding weight ω. n This gives the contribution of the molecule to the difference fold at that time point. The formula for calculating the first fraction value is as follows:

[0128] D n =ω n *First-time contribution +ω n *Second-ranked contribution;

[0129] This formula means that it considers not only statistical significance but also the actual magnitude of change in expression levels. This combination more accurately reflects changes in biological significance, rather than just statistical differences. For protein molecules that appear multiple times at different time points, the values ​​for each time point are calculated separately and then summed. The contribution of significance and the contribution of fold change are added together and then sorted from largest to smallest to obtain the ranking of the differentially expressed molecules; the higher the score, the more important the molecule.

[0130] The fine stratification of correlation analysis—the ranking of correlation coefficient scores—includes the number of significant molecules and the contribution of different correlation coefficients. The formula for calculating the second score is as follows:

[0131]

[0132] In this study, the absolute value of the correlation coefficient r is defined as follows: |0.5≤r<0.7| indicates low correlation, |0.7≤r<0.8| indicates moderate correlation, and |r≥0.8| indicates high correlation. The significance contribution is calculated by dividing the number of significant molecules (i.e., the number of other omics molecules significantly correlated with a given molecule, p.adj<0.05) by the total number of significant molecules (i.e., the total number of molecules significantly correlated with all second-order molecules). Then, the contribution of different levels of correlation is calculated: the contribution of a molecule with low correlation is divided by the total number of significantly correlated molecules, multiplied by the average low correlation value (0.6). Similarly, the contributions of moderate and high correlation are calculated. Finally, the significance contribution and the contributions of different levels of correlation are added together to obtain the correlation coefficient score. The scores are then ranked from highest to lowest, with the highest score in the third position having a contribution of 1, decreasing sequentially, and the lowest score in the third position having a contribution of 1 / n. By introducing different intervals of r (0.5-0.7, 0.7-0.8, ≥0.8) and their corresponding weights, this method can distinguish different correlation strengths, assigning higher weights to higher correlations. This ensures that not only significance is considered, but also the tightness of the association, enabling the algorithm to more accurately identify key molecules with strong biological associations.

[0133] After obtaining the first and second scores, the average of the first and second scores is taken to obtain the evaluation value of the corresponding first or second analyte, as follows:

[0134]

[0135] By combining the contribution of average difference molecular scores and the contribution of correlation coefficient molecular scores, the contribution of molecules exhibiting core changes in aging (comprehensive assessment of molecules exhibiting core changes in aging) is obtained. Molecules with the largest contribution of their position in core changes in aging are those that are important in the aging process. This method ensures both statistical significance and a comprehensive assessment of expression changes, while avoiding the dominant effect of a single indicator. By ranking and converting the data into a unified scoring range, Dn and Cn are successfully standardized, allowing for a reasonable weighted average calculation. This balanced approach avoids the excessive influence of any single indicator and the unfair weighting or distortion of results caused by directly using raw data, making the selected core molecules more reliable.

[0136] The analytical method for core molecules in the aging process according to embodiments of this application breaks through the limitations of traditional differential analysis. Compared with traditional methods based on significance and fold difference, this algorithm captures deeper molecular change patterns by introducing innovative techniques such as time weighting and fine correlation stratification. Simultaneously, the algorithm fully considers the dynamic characteristics of each stage of the aging process, rather than simply focusing on a single time point. This modeling approach better reflects real biological processes. Furthermore, by integrating information from multiple dimensions such as differential analysis, correlation coefficients, and time weighting, the algorithm provides a more comprehensive data perspective, resulting in higher authenticity and reliability of the selected molecules. This method provides a more systematic and scientific research tool for the field of aging research, compensating for the shortcomings of traditional methods in modeling complex biological processes.

[0137] On the other hand, embodiments of this application also provide an analytical apparatus for core molecules in the aging process, comprising:

[0138] The processor can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solutions provided in the embodiments of this application.

[0139] The memory can be implemented in the form of read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory and called by the processor to execute the analysis method of the core molecules of the aging process in the embodiments of this application.

[0140] Input / output interfaces are used to implement information input and output;

[0141] The communication interface is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0142] A bus is used to transfer information between various components of a device, such as processors, memory, input / output interfaces, and communication interfaces.

[0143] The processor, memory, input / output interfaces, and communication interfaces communicate with each other within the device via a bus.

[0144] This application also provides an electronic device, including an analysis apparatus for the core molecules of the aging process as described above.

[0145] This application also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the analysis method for the core molecules of the aforementioned aging process.

[0146] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Although specific embodiments are described herein, those skilled in the art will recognize that many other modifications or alternative embodiments are also within the scope of this disclosure. For example, any of the functions and / or processing capabilities described in connection with a particular device or component can be performed by any other device or component. Furthermore, while various exemplary embodiments and architectures have been described according to embodiments of this disclosure, those skilled in the art will recognize that many other modifications to the exemplary embodiments and architectures described herein are also within the scope of this disclosure.

[0148] The foregoing description, with reference to block diagrams and flowcharts of systems, methods, systems, and / or computer program products according to exemplary embodiments, has described certain aspects of this disclosure. It should be understood that one or more blocks in the block diagrams and flowcharts, as well as combinations of blocks in the block diagrams and flowcharts, can be implemented by executing computer-executable program instructions, respectively. Similarly, according to some embodiments, some blocks in the block diagrams and flowcharts may not need to be executed in the order shown, or may not all need to be executed. Furthermore, additional components and / or operations beyond those shown in the blocks in the block diagrams and flowcharts may exist in some embodiments.

[0149] Therefore, blocks in block diagrams and flowcharts support combinations of means for performing a specified function, combinations of elements or steps for performing a specified function, and program instruction means for performing a specified function. It should also be understood that each block in a block diagram and flowchart, and combinations of blocks in block diagrams and flowcharts, can be implemented by a dedicated hardware computer system or a combination of dedicated hardware and computer instructions that performs a specific function, element, or step.

[0150] The program modules, applications, etc., described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the exemplary methods described herein) to be performed.

[0151] Software components can be coded using any of a variety of programming languages. An exemplary programming language could be a low-level programming language, such as assembly language associated with a specific hardware architecture and / or operating system platform. Software components including assembly language instructions may need to be converted into executable machine code by an assembler before being executed by the hardware architecture and / or platform. Another exemplary programming language could be a higher-level programming language that is portable across multiple architectures. Software components including higher-level programming languages ​​may need to be converted into an intermediate representation by an interpreter or compiler before execution. Other examples of programming languages ​​include, but are not limited to, macro languages, shell or command languages, job control languages, scripting languages, database query or search languages, or report writing languages. In one or more exemplary embodiments, a software component containing instructions from one of the above-described programming language examples can be executed directly by the operating system or other software components without first being converted into another form.

[0152] Software components can be stored as files or other data storage structures. Software components of similar type or related function can be stored together in a specific directory, folder, or library. Software components can be static (e.g., pre-defined or fixed) or dynamic (e.g., created or modified at runtime).

[0153] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for analyzing core molecules in the aging process, characterized in that, Includes the following steps: The raw data is obtained, divided into a control group and multiple experimental groups, and preprocessed. The raw data includes at least one of proteomic data, metabolomic data, and lipidomic data. The control group and each experimental group correspond to different aging time points, and each control group and each experimental group has multiple samples. The preprocessed raw data is subjected to aging pattern clustering to obtain clustering results; The clustering results include multiple clustering patterns; Perform deduplication GO enrichment analysis on each of the clustering patterns and visualize the results; The deduplication GO enrichment analysis involves calculating the similarity of GO terms for different clustering patterns, retaining the GO term with the smallest p.adjust among the GO terms with a similarity greater than 0.7, and retaining all GO terms with a similarity less than 0.

7. According to each clustering pattern, after missing value screening is performed on the control group and each experimental group, the molecular data of each experimental group after missing value screening and the molecular data of the control group after missing value screening are taken as a union, and the molecular data in the union are analyzed by the difference analysis method to obtain the difference analysis results. Perform correlation analysis on each of the clustering patterns to obtain the correlation analysis results; Based on the correlation analysis results and the difference analysis results, the core molecules related to aging were identified.

2. The analytical method for the core molecules of the aging process according to claim 1, characterized in that, The preprocessing of the raw data includes: The original data are normalized at both the sample and molecular levels. The normalized original data is standardized through centralization, automatic scaling, or Pareto standardization.

3. The analytical method for the core molecules of the aging process according to claim 1, characterized in that, The step of performing aging pattern clustering on the preprocessed raw data to obtain clustering results includes: Starting with a cluster size of 2, gradually increase the cluster size until the preset maximum value is reached; Based on each number of clusters, the preprocessed original data is clustered, and the mean silhouette coefficient and total sum of squares corresponding to each number of clusters are determined; Based on the inflection point of the total sum of squares and the peak value of the average profile coefficient, determine the number of candidate clusters. Based on the number of clusters for each candidate, a correlation analysis is performed on each cluster center to calculate the Pearson correlation coefficient between each pair of cluster centers; The optimal number of clusters is determined based on the Pearson correlation coefficient. The clustering result is determined based on the optimal number of clusters.

4. The analytical method for the core molecules of the aging process according to claim 3, characterized in that, The step of determining the clustering result based on the optimal number of clusters includes: Calculate the membership degree of each molecule to each of the clustering patterns; Each molecule is assigned to the corresponding clustering pattern based on the membership degree. The membership degree of each molecule in each clustering pattern is sorted to obtain the top N high membership degree molecules; N is a positive integer; Based on the high membership degree molecules, a clustering heatmap is drawn and visualized.

5. The analytical method for the core molecules of the aging process according to claim 1, characterized in that, According to each clustering pattern, after filtering for missing values ​​in the control group and each experimental group, the molecular data of each experimental group after missing value filtering and the molecular data of the control group after missing value filtering are taken as a union. The molecular data within the union are then analyzed using a difference analysis method to obtain the difference analysis results, including: A missing threshold is set, and the control group and each of the experimental groups are screened separately to retain the effective molecules of each of the control group and each of the experimental groups; the effective molecules are characterized by the proportion of samples with effective expression values ​​in all samples of the corresponding group exceeding the missing threshold; Take the union of the effective molecules of each experimental group with the effective molecules of the control group; Perform a normality test on the original data; When the distribution conforms to a normal distribution, the effective molecules within the union are analyzed for differences using the t-test or limma method, and the analysis results are corrected to obtain a first corrected P-value; or, when the distribution does not conform to a normal distribution, the effective molecules within the union are analyzed for differences using the Wilcoxon method, and the analysis results are corrected to obtain a first corrected P-value. After normalizing the data of effective molecules within the union, the difference fold of the effective molecules between the experimental group and the control group is calculated.

6. The analytical method for the core molecules of the aging process according to claim 5, characterized in that, The raw data includes proteomic data, metabolomic data, and lipidomic data; The step of performing correlation analysis on each of the clustering patterns to obtain the correlation analysis results includes: Using Spearman or Pearson methods, each cluster pattern of the proteome data is correlated with all cluster patterns of the metabolome data or lipidome data to obtain the correlation coefficient and correlation P value between molecules. The correlation P-value was corrected using the Benjiamini-Hochberg correction method to obtain a second corrected P-value.

7. The analytical method for the core molecules of the aging process according to claim 6, characterized in that, Based on the correlation analysis results and the difference analysis results, the core molecules related to aging are identified, including: Based on the difference analysis results, the first molecule to be analyzed is determined, and based on the correlation analysis results, the second molecule to be analyzed is determined. Based on the ranking of the first corrected P-values ​​of each first molecule to be analyzed, a first-order contribution is assigned to each first molecule to be analyzed, and based on the ranking of the absolute values ​​of the difference multiples of each first molecule to be analyzed, a second-order contribution is assigned to each first molecule to be analyzed. Assign a corresponding first weight to each aging time point; Multiply the first weight of each of the first molecules to be analyzed by the sum of the first and second rank contributions to obtain the first score. Based on the second corrected P-value and correlation coefficient of the second molecule to be analyzed, determine the significance contribution and the contribution of different degrees of correlation of each of the second molecules to be analyzed; The score is obtained by summing the significance contribution and the contribution of different degrees of correlation of the second molecule to be analyzed; Based on the ranking of the scores of all the second molecules to be analyzed, a corresponding third-rank contribution is assigned to each score. Multiply each of the above scores by the corresponding third-rank contribution to obtain the second score value; The average of the first and second scores is taken to obtain the evaluation value of the first or second molecule to be analyzed. Based on the assessment values, core molecules associated with aging were identified.

8. An analytical apparatus for the core molecules of the aging process, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform the analytical method for the core molecules of the aging process as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, An analytical apparatus for the core molecules of the aging process as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform an analytical method for the core molecules of the aging process as described in any one of claims 1 to 7.

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