A ganoderma lucidum spore powder extracellular vesicle experimental result analysis method and system

Abstract

The application discloses a ganoderma lucidum spore powder extracellular vesicle experimental result analysis method and system, and relates to the technical field of data analysis. By introducing trend consistency analysis and sample dependence feature recognition mechanism, a trend stability evaluation process for high-dimensional omics data and animal experiment results is constructed. The process can not only identify false trends caused by extreme samples, batch effects or instrument bias, but also accurately locate trend reversal, instability or local non-reproducible areas, thereby avoiding over-attribution to experimental mechanisms and false interpretation. Compared with the traditional analysis method which only determines the reliability of the results according to statistical significance, the present method can grade the credibility of the trend results and output analysis suggestions in a structured manner, providing more instructive intelligent auxiliary decision basis for subsequent experimental design, index selection and data rechecking, and significantly improving the robustness of data interpretation and the reliability of result reproduction in ganoderma lucidum spore powder extracellular vesicle research.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method and system for analyzing the experimental results of extracellular vesicles from Ganoderma lucidum spore powder. Background Technology

[0002] In recent years, research on the mechanisms of action of natural medicinal substances has focused on Ganoderma lucidum spore powder, an important source of active ingredients. Extracellular vesicles extracted from Ganoderma lucidum spore powder are increasingly becoming important subjects in pharmacological mechanism research, cell communication research, and immunomodulation research due to their good cellular uptake capacity, stable biomembrane structure, and rich content of functional miRNAs and signal regulatory proteins. Current experimental analysis methods for Ganoderma lucidum spore powder extracellular vesicles mainly revolve around three technical lines: First, physicochemical characterization of the particle size distribution, morphological characteristics, and extraction purity of extracellular vesicles is achieved through transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and biomarker detection. Second, the functional effects of extracellular vesicles, such as immunomodulation, anti-inflammatory, or anti-tumor effects, are evaluated through cell activity experiments, cytokine expression detection, and animal model intervention. Third, molecular pathways that may be mediated by extracellular vesicles are explored through omics methods such as RNA sequencing, proteomics, and GO / KEGG enrichment analysis. The above analytical methods constitute the main experimental pathway for evaluating the extracellular vesicle function of Ganoderma lucidum spore powder, and provide important data support for the modernization research of traditional Chinese medicine and the development of health products.

[0003] However, with the expansion of experimental scale and the increase in data structure complexity, existing analytical methods have gradually revealed a crucial but easily overlooked problem: when faced with high-dimensional, multi-source omics data or animal experimental results, existing analytical systems generally lack the ability to intelligently identify and track anomalous data behavior. This can easily lead to misinterpreting spurious trends caused by extreme samples, instrument bias, or batch effects as genuine biological effects caused by extracellular vesicles. Particularly in the presentation and interpretation of PCA results, differential analysis heatmaps, and functional enrichment results, traditional analytical systems often fail to identify whether a trend is driven by a few anomalous samples, nor can they determine the consistency of related conclusions across different subsamples, or whether a trend reversal has occurred at the boundary samples. This can easily lead to problems such as over-attribution of mechanisms, inference bias, or failure to reproduce experiments during the interpretation of results. Therefore, there is an urgent need to introduce an intelligent analytical module with the capabilities of trend stability identification, anomalous response localization, and credibility-level output, as an important supplement to the existing data analysis system for Ganoderma lucidum spore powder extracellular vesicle experiments. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned in the background art above, and to propose a method and system for analyzing the experimental results of extracellular vesicles of Ganoderma lucidum spore powder.

[0005] A first aspect of this invention provides a method for analyzing the experimental results of extracellular vesicles in Ganoderma lucidum spore powder, the method comprising:

[0006] S1: Obtain the experimental results data related to extracellular vesicles of Ganoderma lucidum spore powder, and organize the response results of each experimental sample under the corresponding detection indicators into a structured experimental result set;

[0007] S2: Based on the experimental results set, the samples are divided into multiple subsets according to experimental batch, processing conditions or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance results of each detection index under different subsets.

[0008] S3: Based on the trend performance results, determine the consistency of the trends of each detection indicator across different subsets, and screen out trend candidates that are only valid in some subsets or show inconsistent performance across different subsets.

[0009] S4: For each trend candidate, analyze the impact of individual sample changes on the overall trend result, identify sensitive samples that have a significant impact on the trend result, and obtain the sample dependence characteristics of each trend candidate accordingly.

[0010] S5: Combining trend consistency and sample dependence characteristics, evaluate the stability of the trend results of each detection indicator and generate the corresponding trend stability judgment result.

[0011] S6: Based on the trend stability determination results, the reliability of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is marked, and the analysis results are output to assist in the judgment of experimental conclusions and subsequent experimental design adjustments.

[0012] Optionally, based on the trend performance results, the step of judging the trend consistency of each detection indicator across different subsets and screening out trend candidates that are valid only in some subsets or show inconsistent performance across different subsets is as follows:

[0013] Based on the trend performance results, a trend consistency index is calculated. Based on the trend consistency index and a preset threshold, trend candidates that are valid only in some subsets or whose performance is inconsistent in different subsets are selected.

[0014] Optionally, the calculation steps for the trend consistency index are as follows:

[0015] For a specific detection indicator, its trend performance results under multiple subsets are obtained, and the trend changes of the indicator over time in each subset are marked with symbols in sequence. An upward trend corresponds to a positive symbol, a downward trend corresponds to a negative symbol, and an unstable trend corresponds to a zero symbol. Thus, a trend symbol sequence is formed in each subset. The sequence is arranged in chronological order, and each symbol corresponds to the trend direction of a time period.

[0016] For each subset's trend symbol sequence, the frequency ratios of the three trend symbols (positive, negative, and zero) are calculated, and the natural logarithm of the frequency ratios of the three trend symbols is calculated. The three frequency ratios are multiplied by their respective natural logarithms, and the sum of all the multiplication results is used as the trend structure information value of the corresponding subset. The trend structure information value is used to measure the trend complexity within the subset. The higher the proportion of a certain trend, the lower the information value, indicating that the trend of the subset is more singular. The higher the information value, the more chaotic the trend of the subset.

[0017] Based on the trend structure information values ​​corresponding to all subsets, calculate the absolute difference between each pair of subsets, and select the largest difference as the maximum trend structure deviation of the indicator among all subsets, which is used to represent the most significant difference in trend complexity between different subsets.

[0018] For each subset of trend symbol sequences, the symbol change process is traversed in chronological order. The number of positions where changes occur between two adjacent trend symbols is counted and divided by the total number of symbols minus one to obtain the trend break density of that subset.

[0019] The difference between the maximum and minimum values ​​of the trend break density for all subsets is calculated to obtain the degree of consistency of the trend break change of this index among different subsets.

[0020] The maximum trend structure deviation is added to the difference in trend break density, and the result of this addition is used as input to calculate the output value of the exponential compression function. The output value is the trend consistency index of the detection index in all subsets.

[0021] Optionally, the step of selecting trend candidates that are valid only in some subsets or inconsistent in different subsets based on the trend consistency index and a preset threshold is as follows:

[0022] The trend consistency index is compared with a preset consistency threshold. When the trend consistency index is less than the threshold, it is determined that the trend performance of the detection index is inconsistent in different subsets, and it is selected as a trend candidate for subsequent sample dependency analysis and stability determination.

[0023] Optionally, for each trend candidate, the steps of analyzing the impact of individual sample changes on the overall trend result, identifying sensitive samples that have a significant impact on the trend result, and obtaining the sample dependency characteristics of each trend candidate are as follows:

[0024] For each trend candidate, the impact of individual sample changes on the overall trend result is analyzed, and the sample dependence index is calculated. The calculation steps are as follows:

[0025] For a detection indicator that is identified as a trend candidate, in the corresponding subset, its current reference trend mark is first obtained as the baseline trend performance. Then, each sample in the subset is removed once in turn, and the trend mark of the detection indicator is recalculated after each removal operation. The trend mark after the removal of the sample is compared with the reference trend mark. If the trend direction changes or the trend state changes from stable to unstable or the opposite, the sample is marked as a trend disturbance sample. Otherwise, it is marked as a non-disturbance sample. Finally, a binary state sequence is formed in the order of the samples.

[0026] In all samples labeled as trend perturbation samples, their original position index in the sample sequence is extracted. The absolute difference between the position spacing of all adjacent perturbation samples is calculated according to the index order. The absolute values ​​of all adjacent spacings are summed to obtain the actual total distance between perturbation samples.

[0027] Based on the total number of perturbed samples, calculate their theoretical maximum possible distribution distance in the sample sequence. The maximum distribution distance is the sum of the distances between adjacent perturbed samples in the most uniform distribution state. Divide the actual total distance value by the maximum possible distribution distance to obtain a ratio value. The result of subtracting the ratio value is defined as the perturbed clustering factor.

[0028] The trend disturbance proportion factor is obtained by dividing the number of samples marked as trend disturbances in the statistical sample sequence by the total number of samples.

[0029] By combining the perturbation clustering factor and the trend perturbation proportion factor, and multiplying the perturbation clustering factor by one and subtracting the trend perturbation proportion factor, the sample dependence index of the detection index in the current subset is calculated.

[0030] Optionally, the steps for evaluating the stability of the trend results of each detection indicator and generating the corresponding trend stability judgment result, by combining trend consistency and sample dependency characteristics, are as follows:

[0031] For each trend candidate, first determine whether its trend consistency index is higher than the set consistency threshold. If it is not higher, the trend is directly marked as an unstable trend, and there is no need to perform sample dependency judgment. If it is higher than the threshold, proceed to the next step of sample dependency judgment.

[0032] In the determination of sample dependence, the sample dependence index of the trend is compared with the set sample dependence threshold. If the sample dependence index is higher than the threshold, it indicates that the trend is highly dependent on a few sensitive samples and the stability is still insufficient. Therefore, the trend is marked as an unstable trend. If the sample dependence index is lower than the threshold, it indicates that the trend is not affected by the disturbance of a few samples while maintaining consistency. Therefore, it is judged as a stable trend.

[0033] The trend consistency determination result is combined with the sample dependency determination result, and a trend stability determination label is generated according to a fixed rule. The label includes at least three types: unstable trend dependent on unstable trend and stable trend.

[0034] A second aspect of this invention provides a system for analyzing the experimental results of extracellular vesicles from Ganoderma lucidum spore powder, the system comprising:

[0035] Data Acquisition Module: Acquires experimental results related to extracellular vesicles of Ganoderma lucidum spore powder, and organizes the response results of each experimental sample under the corresponding detection indicators into a structured set of experimental results.

[0036] Partitioning Module: Based on the experimental results set, the samples are divided into multiple subsets according to experimental batch, processing conditions or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance results of each detection index under different subsets.

[0037] The first screening module: Based on the trend performance results, it judges the consistency of the trend of each detection indicator across different subsets and screens out trend candidates that are only valid in some subsets or whose performance is inconsistent across different subsets.

[0038] The second screening module analyzes the impact of individual sample changes on the overall trend results for each trend candidate, identifies sensitive samples that have a significant impact on the trend results, and obtains the sample dependence characteristics of each trend candidate accordingly.

[0039] Judgment module: Combining trend consistency and sample dependency characteristics, it evaluates the stability of the trend results of each detection indicator and generates the corresponding trend stability judgment result;

[0040] Analysis module: Based on the trend stability determination results, the reliability of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is marked, and the analysis results are output to assist in the judgment of experimental conclusions and subsequent experimental design adjustments.

[0041] The beneficial effects of this invention are:

[0042] This invention proposes a method and system for analyzing the results of Ganoderma lucidum spore powder extracellular vesicle experiments. By introducing trend consistency analysis and sample-dependent feature recognition mechanisms, a trend stability assessment process for high-dimensional omics data and animal experimental results is constructed. This process can not only identify spurious trends caused by extreme samples, batch effects, or instrument bias, but also accurately locate trend reversals, instabilities, or locally unreproducible regions, thereby avoiding over-attribution and misinterpretation of experimental mechanisms. Compared to traditional analytical methods that rely solely on statistical significance to determine the reliability of results, this invention can grade and label the credibility of trend results and output analytical suggestions in a structured manner. This provides more guiding intelligent auxiliary decision-making basis for subsequent experimental design, indicator selection, and data verification, significantly improving the robustness of data interpretation and the reliability of result reproducibility in Ganoderma lucidum spore powder extracellular vesicle research. Attached Figure Description

[0043] Figure 1 The flowchart illustrates a method for analyzing the experimental results of extracellular vesicles from Ganoderma lucidum spore powder, as provided in this embodiment of the invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0045] This invention provides a method for analyzing the results of an extracellular vesicle experiment using Ganoderma lucidum spore powder. See also... Figure 1 , Figure 1 This is a flowchart illustrating a method for analyzing the experimental results of extracellular vesicles from Ganoderma lucidum spore powder, provided in an embodiment of the present invention. The method includes the following steps:

[0046] S1: Obtain the experimental results data related to extracellular vesicles of Ganoderma lucidum spore powder, and organize the response results of each experimental sample under the corresponding detection indicators into a structured experimental result set;

[0047] S2: Based on the experimental results set, the samples are divided into multiple subsets according to experimental batch, processing conditions or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance results of each detection index under different subsets.

[0048] S3: Based on the trend performance results, determine the consistency of the trends of each detection indicator across different subsets, and screen out trend candidates that are only valid in some subsets or show inconsistent performance across different subsets.

[0049] S4: For each trend candidate, analyze the impact of individual sample changes on the overall trend result, identify sensitive samples that have a significant impact on the trend result, and obtain the sample dependence characteristics of each trend candidate accordingly.

[0050] S5: Combining trend consistency and sample dependence characteristics, evaluate the stability of the trend results of each detection indicator and generate the corresponding trend stability judgment result.

[0051] S6: Based on the trend stability determination results, the reliability of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is marked, and the analysis results are output to assist in the judgment of experimental conclusions and subsequent experimental design adjustments.

[0052] This invention provides a method for analyzing the results of Ganoderma lucidum spore powder extracellular vesicle experiments. By introducing trend consistency analysis and sample dependence feature identification mechanisms, it constructs a trend stability assessment process for high-dimensional omics data and animal experimental results. This method can not only identify spurious trends caused by extreme samples, batch effects, or instrument bias, but also accurately locate trend reversals, instabilities, or locally unreproducible regions, thereby avoiding over-attribution and misinterpretation of experimental mechanisms. Compared to traditional analysis methods that rely solely on statistical significance to determine the reliability of results, this invention can grade and label the credibility of trend results and output analytical suggestions in a structured manner. This provides more guiding intelligent auxiliary decision-making basis for subsequent experimental design, indicator selection, and data verification, significantly improving the robustness of data interpretation and the reliability of result reproducibility in Ganoderma lucidum spore powder extracellular vesicle research.

[0053] In one embodiment, S1: The step of acquiring the experimental results data related to extracellular vesicles of Ganoderma lucidum spore powder and uniformly organizing the response results of each experimental sample under the corresponding detection index into a structured experimental result set is as follows:

[0054] The system automatically categorizes all data files for detection indicators in extracellular vesicle-related experiments, splits the raw data into multiple parsing groups based on indicator type, experimental batch, and sample source label, and generates group index identifiers.

[0055] For the raw experimental data in each group, a five-element data template consisting of time sequence identifier, sampling method, detection platform, response value, and unit standardization rules is constructed, and semantic completion rules are used to automatically fill in missing fields to ensure data structure consistency under different detection platforms.

[0056] Anomaly structure conflict stripping is performed on the response values ​​in each five-dimensional data template. This process includes detecting whether the rate of change of different indicators at the same time point exceeds a set threshold, marking data segments that exceed the threshold as interference points and isolating them from the original dataset.

[0057] After completing the structural cleaning and anomaly removal, all five data templates are integrated into a multidimensional response matrix according to a unified field order. Each dimension corresponds to the experimental sample number, processing conditions, detection index, response value and data credibility label, forming a structured set of experimental results as input for subsequent analysis processes.

[0058] It should be noted that, firstly, the raw results data from experiments related to extracellular vesicles of Ganoderma lucidum spore powder should be obtained. These raw results data typically include, but are not limited to, the following categories: One category is physicochemical characterization data of extracellular vesicles, such as morphological images obtained under transmission electron microscopy, particle size distribution and concentration results obtained from nanoparticle tracking analysis, Zeta potential detection results, and corresponding band images or expression levels of biomarkers; another category is cellular function response data, such as cell viability detection results, cell uptake experiment results, cytokine expression levels, inflammatory factor release levels, apoptosis rates, or proliferation levels; another category is animal experiment evaluation data, such as changes in animal signs after drug administration, histopathological scores, serum index detection results, immune factor expression results, and intervention response data at different time points; and the third category is omics analysis data, such as miRNA sequencing results, proteomics detection results, differential expression analysis results, principal component analysis results, clustering heatmap results, and GO / KEGG enrichment analysis results. The aforementioned data types can be presented in various formats, such as numerical tables, image files, time-series records, text annotations, or analysis files exported from a platform. Examples include a particle size distribution data table generated after NTA detection of a batch of samples, a cytokine expression result table generated by ELISA detection of cells in a specific treatment group, or a continuous response data table generated from multiple observation time points in an animal experiment. Because data structures, field naming, and recording methods often differ between different experimental platforms, detection indicators, and batches of samples, it is necessary to first extract, classify, and structure the aforementioned raw data to form a unified set of experimental results that can be used for subsequent trend analysis, anomaly localization, and stability assessment.

[0059] In this embodiment, step S1 involves intelligently structuring the various detection result data files generated in the Ganoderma lucidum spore powder extracellular vesicle experiment. Specifically, this involves: firstly, automatically identifying and classifying the raw data files from different experimental types and detection platforms; archiving data such as particle size distribution and concentration data tables obtained from nanoparticle tracking analysis (NTA), morphological image files corresponding to transmission electron microscopy (TEM), expression level data tables output from cytokine detection (such as ELISA), and differential expression result files generated from miRNA sequencing or proteomics analysis according to the detection index category; subsequently, combining experimental batch information, processing condition labels (such as different drug concentrations or processing times), and sample source identification, grouping and organizing various types of data, and... Key fields such as time point, sample number, detection index, response value, and unit are uniformly extracted. Based on this, format alignment and unit standardization are performed on data from different sources. For example, the concentration units output from different platforms are uniformly converted to a consistent dimension, and missing fields are semantically completed according to preset rules. At the same time, abnormal records are preliminarily screened. For example, data segments with abnormal increases or decreases in a certain index at the same time point are identified and marked or isolated. Finally, the processed data are integrated into a structured multidimensional dataset according to a unified field order. This transforms the originally scattered and inconsistent experimental results into a standardized and comparable set of structured experimental results, providing a unified and reliable data input foundation for subsequent trend identification, abnormal sample location, and stability assessment.

[0060] In one embodiment, S2: Based on the experimental result set, the samples are divided into multiple subsets according to experimental batch, processing conditions, or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance of each detection index under different subsets.

[0061] Based on the experimental batch identifier, processing condition label and sampling time information in the structured experimental results set, the samples are initially grouped according to the time dimension. All samples are divided into multiple overlapping time windows according to the processing time. Each time window may contain cross samples to preserve the continuity information in the experimental evolution process.

[0062] For the sample set within each time window, conditional clustering is further performed according to the treatment conditions. Samples with similar response patterns are automatically grouped into the same treatment subgroup. Treatment conditions include, but are not limited to, extracellular vesicle concentration gradient, drug administration frequency, inoculation method, or differences in animal models. This clustering process uses a custom feature similarity matrix instead of the traditional K-means algorithm to ensure that the sample division is more consistent with the actual biological intervention structure.

[0063] In each processing subgroup, the local change trend is calculated based on the corresponding detection index data sequence and the time sequence. The change trend does not adopt the conventional slope calculation method, but introduces the order direction maintenance rate judgment, that is, to count whether each index value maintains a monotonically rising, falling or fluctuating unstable state over time, and generate a trend label for each detection index based on the trend structure.

[0064] For indicator items whose trends are marked as unstable or conflicting in direction, the system will automatically trace the data distribution boundary within the subgroup, locate the key sample subset that causes the trend uncertainty, and mark the trend result as a local abnormal trend.

[0065] The trend markers, directionality, stable state, and anomaly markers of each detection indicator in all subgroups are integrated into a unified trend performance result set, which serves as the input data source for subsequent trend consistency analysis and stability scoring.

[0066] It should be noted that, in this embodiment, the specific implementation of step S2 includes: First, based on the existing experimental batch numbers, processing condition labels, and sampling time information in the structured experimental results set, the sample data is initially grouped. The core principle of grouping is not to use hard-divided time points, but to adopt a cross-overlapping time window mechanism. The entire experimental time axis is slid along a preset time span to generate multiple overlapping windows. Each window covers samples from several adjacent time points. For example, when studying the 72-hour effect of extracellular vesicle intervention, each time window span can be set to 24 hours, with a sliding step of 12 hours. Then, the first window contains samples from 0–24h, the second window contains samples from 12–36h, and so on. This design can preserve the samples. To ensure the continuity of dynamic evolution between samples and avoid data fragmentation caused by abrupt trend changes, conditional clustering is further performed on the sample set within each time window based on processing conditions. This clustering process differs from traditional K-means or spectral clustering algorithms; instead, it employs a custom logic for constructing a response feature similarity matrix. This involves extracting the response trajectories of multiple key detection indicators for each sample within the current window as input vectors, calculating the weighted similarity between samples in terms of response curve shape, amplitude of change, and direction, forming a nonlinear similarity matrix. This matrix serves as the basis for conditional clustering, automatically grouping samples with similar response patterns into the same processing subgroup. For example, it distinguishes between 24h high-dose + intravenous injection and 24h medium-dose + intraperitoneal injection. Grouping based on low similarity, with 12h and 24h high-dose + intravenous injections grouped together; after completing the subgroup division, for all detection index data sequences in each subgroup, the local change trend is calculated in conjunction with time information. The trend calculation method does not use the conventional linear fitting slope, but introduces the order direction maintenance rate mechanism, that is, judging whether each index shows a continuous upward, downward or fluctuating trend within the current subgroup, statistically analyzing the step size ratio of continuous upward or downward and comparing it with the number of irregular jumps to generate trend labels, such as upward trend, downward trend or fluctuating instability; for indices labeled as fluctuating instability or directional conflict, the system will automatically trace the abnormal samples in the processing subgroup that caused the trend break. At boundary locations, if an animal's TNF-α value at 24 hours is found to be significantly higher than other samples in the same group and breaks the continuous downward trend, this sample is marked as a key interference point, and the corresponding trend is marked as a local abnormal trend. Finally, the system summarizes and integrates the trend direction, trend type, fluctuation state, and outlier labels of the detected indicators in all processing subgroups into a trend performance result set. The trend characteristics of each indicator under different subsets are encoded as structured trend information to support subsequent consistency analysis and trend stability scoring. This approach not only preserves the diversity of real trends but also improves the ability to judge the authenticity of trends in the context of complex data fluctuations through direction retention rate and outlier subset tracing mechanism.

[0067] In one embodiment, S3: Based on the trend performance results, the step of determining the trend consistency of each detection indicator across different subsets and filtering out trend candidates that are valid only in some subsets or show inconsistent performance across different subsets is as follows:

[0068] Based on the trend performance results, calculate the trend consistency index, and filter out trend candidates that are only valid in some subsets or whose performance is inconsistent in different subsets based on the trend consistency index and a preset threshold.

[0069] The calculation steps for the trend consistency index are as follows:

[0070] For a specific detection indicator, its trend performance results under multiple subsets are obtained, and the trend changes of the indicator over time in each subset are marked with symbols in sequence. An upward trend corresponds to a positive symbol, a downward trend corresponds to a negative symbol, and an unstable trend corresponds to a zero symbol. Thus, a trend symbol sequence is formed in each subset. The sequence is arranged in chronological order, and each symbol corresponds to the trend direction of a time period.

[0071] For each subset's trend symbol sequence, the frequency ratios of the three trend symbols (positive, negative, and zero) are calculated, and the natural logarithm of the frequency ratios of the three trend symbols is calculated. The three frequency ratios are multiplied by their respective natural logarithms, and the sum of all the multiplication results is used as the trend structure information value of the corresponding subset. The trend structure information value is used to measure the trend complexity within the subset. The higher the proportion of a certain trend, the lower the information value, indicating that the trend of the subset is more singular. The higher the information value, the more chaotic the trend of the subset.

[0072] Based on the trend structure information values ​​corresponding to all subsets, calculate the absolute difference between each pair of subsets, and select the largest difference as the maximum trend structure deviation of the indicator among all subsets, which is used to represent the most significant difference in trend complexity between different subsets.

[0073] For each subset's trend symbol sequence, the symbol change process is traversed in chronological order. The number of positions where changes occur between two adjacent trend symbols is counted and divided by the total number of symbols minus one to obtain the trend break density of that subset, which represents the degree of continuity of the trend change in that subset. Then, the difference between the maximum and minimum values ​​of the trend break density of all subsets is calculated to obtain the degree of consistency of the trend break change of this index among different subsets.

[0074] The maximum trend structure deviation is added to the difference in trend break density, and the result is used as input to calculate the output value of the exponential compression function. The output value is the trend consistency index of the detection index in all subsets. The closer the index value is to one, the higher the trend consistency of the index in all subsets. The closer it is to zero, the higher the degree of trend splitting in different subsets.

[0075] The trend consistency index is compared with a preset consistency threshold. When the trend consistency index is less than the threshold, it is determined that the trend performance of the detection index is inconsistent in different subsets, and it is selected as a trend candidate for subsequent sample dependency analysis and stability determination.

[0076] In the calculation of the trend consistency index, the required basic data comes from the structured trend performance result set output in the previous analysis step. The specific acquisition methods include the following: First, in step S2, the changing trends of each detection indicator under different subsets are used to generate trend labels through the sequential direction retention rate mechanism, and a trend result list is formed according to the time series. This list is organized in the system according to the subset dimension, and has fields such as subset number, time period division, trend direction, and trend category. When the system executes step S3, it uses this trend result list as input, extracts the trend direction sequence corresponding to each detection indicator within each subset, and constructs a trend direction symbol sequence after sorting according to the time label, which is used for subsequent structural entropy and fracture density analysis. To ensure the consistency of the trend direction data... In addition to stability, the system also logically fills in missing trend markers during the extraction process. For example, if the trend direction is the same before and after a time point and there is no data in between, the system will fill in the trend symbol with the same trend symbol as before and after to avoid misjudging the number of breaks. In addition, the system will add the source subset number to each symbol sequence so that it can be matched one by one when performing cross-subset difference and break density range calculation. All the trend direction data, frequency statistics data and trend change location records mentioned above are derived from the structured trend result set built in the previous steps and are obtained by calling through a programmatic interface without manual intervention or reading the original experimental data again. This ensures the continuity, traceability and structural integrity of the calculation process, thus providing high-quality data support for the calculation logic of each level of the trend consistency index.

[0077] It should be noted that the trend consistency index is a quantitative indicator used to measure whether the trend evolution structure of the same detection indicator remains consistent across multiple subsets. Its value ranges from 0 to 1, and it reflects whether the trend of the indicator possesses structural stability and logical coherence under different experimental conditions. When the trend consistency index is closer to 1, it indicates that the trend direction, complexity, and break behavior of the indicator are more consistent across subsets. That is, their trend evolution paths are highly consistent in form and logic, suggesting that the trend is very likely a true reflection of the extracellular vesicle intervention, rather than being caused by sample fluctuations, local anomalies, or experimental errors. Conversely, when the trend consistency index is close to 0, it indicates that the trend performance of the indicator has severely diverged across different subsets. Not only may the direction differ (e.g., some subsets increase, some decrease), but the trend structure may also differ significantly (e.g., some subsets show linear changes, while others show highly volatile changes), and the break density distribution differs significantly. In this case, the reliability of the trend is low, and it is not suitable as a basis for mechanism judgment or conclusion. For example, if an immune factor such as IL-6 shows a continuous decreasing trend across different administration periods or concentration subsets, with stable changes and few breakpoints, then the trend consistency index will be very close to 1. However, if it shows a significant decreasing trend in some subsets, an initial increase followed by a decrease in others, or even no significant change, then the differences in structural information entropy and breakpoint distribution between trend symbol sequences will significantly widen, leading to a significant decrease in the final trend consistency index. This index integrates three aspects: the symbol sequence structure of the trend direction, the complexity of the trend, and the morphological differentiation of trend breaks. It is uniformly mapped to a standardized interval using a nonlinear compression function, avoiding the one-sidedness of evaluating trend consistency with a single indicator. Therefore, in the analysis of Ganoderma lucidum spore powder extracellular vesicle experimental results, it can serve as a key criterion for determining whether a trend has broad applicability and result stability, providing data support for subsequent credibility annotation and mechanism explanation.

[0078] In one embodiment, S4: For each trend candidate, the step of analyzing the impact of individual sample changes on the overall trend result, identifying sensitive samples that have a significant impact on the trend result, and obtaining the sample dependency characteristics of each trend candidate is as follows:

[0079] For each trend candidate, the impact of individual sample changes on the overall trend result is analyzed, and the sample dependence index is calculated. The calculation steps are as follows:

[0080] For a detection indicator that is identified as a trend candidate, in the corresponding subset, its current reference trend mark is first obtained as the baseline trend performance. Then, each sample in the subset is removed once in turn, and the trend mark of the detection indicator is recalculated after each removal operation. The trend mark after the removal of the sample is compared with the reference trend mark. If the trend direction changes or the trend state changes from stable to unstable or the opposite, the sample is marked as a trend disturbance sample. Otherwise, it is marked as a non-disturbance sample. Finally, a binary state sequence is formed in the order of the samples, where the state corresponding to each sample is disturbance or non-disturbance.

[0081] In all samples labeled as trend perturbation samples, their original position index in the sample sequence is extracted. The absolute difference between the position spacing of all adjacent perturbation samples is calculated according to the index order. The absolute values ​​of all adjacent spacings are summed to obtain the actual total distance between perturbation samples.

[0082] Based on the total number of perturbation samples, calculate their theoretical maximum possible distribution distance in the sample sequence. The maximum distribution distance is the sum of the distances between adjacent perturbation samples in the most uniform distribution state. Divide the actual total distance value by the maximum possible distribution distance to obtain a ratio value. The result of subtracting the ratio value is defined as the perturbation clustering factor. The closer the perturbation clustering factor is to 1, the more concentrated the positions of all trend perturbation samples are in the sample sequence. The closer it is to 0, the more dispersed the distribution is.

[0083] The number of samples marked as trend disturbances in the statistical sample sequence is divided by the total number of samples to obtain the trend disturbance proportion factor, which indicates what proportion of samples in the trend candidates have a trend disturbance effect. The larger the proportion factor, the more the trend is affected by the majority of samples, and the smaller the proportion factor, the more the trend may be dominated by a minority of samples.

[0084] By combining the perturbation clustering factor and the trend perturbation proportion factor, and multiplying the perturbation clustering factor by one and subtracting the trend perturbation proportion factor, the sample dependence index of the detection index in the current subset is calculated. The sample dependence index is a dimensionless quantity between 0 and 1. The larger the sample dependence index value, the higher the dependence of the trend result on individual samples, and the smaller the value, the weaker the dependence of the trend on a single sample, and the more stable the structure.

[0085] It should be noted that in the calculation of the sample dependence index, all data involved are derived from the analysis results generated and structured in the preceding steps, rather than directly calling the original experimental measurement data. Specifically, after completing step S2, the system has generated a clear trend performance result for each trend candidate in the corresponding subset. This trend performance result includes the subset identifier, sample order, time order, and trend marker information corresponding to the detection index in each time period. When calculating the sample dependence index, the system first directly reads the reference trend marker from this trend performance result as the baseline trend state before sample removal. Then, according to the original arrangement order of the samples in the subset, it calls the sample index information one by one, temporarily masking the trend data corresponding to a single sample in the structured trend result set. Based on the remaining samples, the same trend determination rule is re-executed to obtain the trend marker after removing the sample. The data used to determine whether the trend has changed during this process is not recalculated. Instead of detecting numerical values ​​of the indicators, the trend marking results are compared based on existing trend judgment logic. Therefore, whether a sample is marked as a trend disturbance sample is directly determined by the change in trend marking. At the same time, the sample's position index in the subset, the total number of samples, and the sample order information are all directly derived from the sample number field that has been fixed in the structured experimental results set, without the need for additional inference. After completing the marking of disturbance samples, the calculation of the distance between disturbance samples, the determination of the maximum possible distribution distance, and the statistics of the proportion of disturbance samples are all based on the above sample index and sample quantity fields for deterministic calculation. This ensures that the data source used for the calculation of the sample dependence index is clear, the path is traceable, and the process is completely reproducible, avoiding the impact of human intervention or the introduction of uncertain data on the index results.

[0086] It's important to note that the sample dependence index is a dimensionless numerical value used to measure whether the trend of a certain detection indicator is highly dependent on a few key samples in a multi-sample dataset. Its value ranges from 0 to 1; a higher value indicates a stronger dependence of the trend on individual samples, while a lower value indicates that the trend is supported by the majority of samples and is more stable. Essentially, this index reflects whether the currently observed trend is merely an illusion driven by one or two sensitive samples, or a general behavior of the entire sample group. Its calculation logic comprehensively considers two core dimensions: first, whether trend disturbance samples appear in clusters; that is, if removing only a few adjacent samples in the sample sequence leads to a significant change in the trend, it indicates that the trend is highly dependent on these samples; second, whether the trend change is a general phenomenon; if the trend is sensitive to a large number of samples, it indicates that the trend itself is unstable rather than dependent on a single point. Therefore, by combining the clustering degree of disturbance samples with the proportion of disturbance, the sample dependence index can effectively identify the structural risks behind the trend. For example, in an experiment involving extracellular vesicles of Ganoderma lucidum spores, if IL-10 shows a decreasing trend, but this trend only becomes increasing or insignificant when a specific abnormal animal is removed, while the removal of other samples has no effect, then that single abnormal animal constitutes a key factor in the trend result. The calculated sample dependence index is close to 1, indicating that the trend conclusion lacks overall support and is easily influenced by incidental samples, and should not be directly used as a basis for mechanistic judgment. Conversely, if the trend remains stable regardless of which sample is removed, the sample dependence index is close to 0, indicating that the trend has broad consistency and is more reliable. Therefore, the sample dependence index is not only an important component in assessing trend stability but also a key auxiliary tool for identifying spurious trends, incidental trends, or abnormally dominant trends.

[0087] In one embodiment, S5: The step of combining trend consistency and sample dependency characteristics to evaluate the stability of the trend results of each detection indicator and generate the corresponding trend stability judgment result is as follows:

[0088] For the selected trend candidates, obtain their corresponding trend consistency index and sample dependence index, which serve as quantitative criteria for evaluating the evolutionary stability of the trend across multiple subsets and its sensitivity to individual samples, respectively.

[0089] For each trend candidate, first determine whether its trend consistency index is higher than the set consistency threshold. If it is not higher, the trend is directly marked as an unstable trend, and there is no need to perform sample dependency judgment. If it is higher than the threshold, proceed to the next step of sample dependency judgment.

[0090] In the determination of sample dependence, the sample dependence index of the trend is compared with the set sample dependence threshold. If the sample dependence index is higher than the threshold, it indicates that the trend is highly dependent on a few sensitive samples and the stability is still insufficient. Therefore, the trend is marked as an unstable trend. If the sample dependence index is lower than the threshold, it indicates that the trend is not affected by the disturbance of a few samples while maintaining consistency. Therefore, it is judged as a stable trend.

[0091] The trend consistency judgment result is combined with the sample dependency judgment result, and a trend stability judgment label is generated according to a fixed rule. The label includes at least three types: unstable trend dependent on unstable trend and stable trend, which are used for subsequent credibility labeling and strategy recommendation.

[0092] The above trend stability determination results are bound with the identification information of the corresponding detection indicators and uniformly organized into a set of structured trend assessment results, which are used as part of the system output results to support the screening of experimental conclusions, screening of mechanisms and pathways, or subsequent data visualization module calls.

[0093] It should be noted that, in this implementation, the overall process of trend stability assessment is based on the calculated trend consistency index and sample dependence index. First, for each detection indicator selected as a trend candidate, the system automatically extracts its trend consistency index value and sample dependence index value within the corresponding subset range. These are used to measure whether the evolutionary structure of the trend is consistent across multiple subsets and whether it is overly dependent on a few dominant samples. Then, the system executes the consistency judgment logic, comparing the trend consistency index of the current indicator with a preset consistency threshold. If it is lower than the threshold, the trend is directly judged as an unstable trend, indicating significant structural breaks and severe differentiation in evolutionary direction, and no further sample dependence judgment is needed. If the consistency index is higher than the threshold, it indicates that the trend has a certain degree of structural consistency across different subsets. The system then proceeds to the sample dependence judgment step, comparing its sample dependence index with a set dependence threshold. If the dependence index is higher than the threshold, it indicates that although the current trend is consistent in direction, it is highly dependent on one or two key samples and lacks broad support, thus being judged as an unstable trend. If the dependence index is lower than the threshold, it indicates that the trend not only has good structural consistency but is also not disturbed by a few dominant samples, possessing good overall support. If the trend has good reproducibility potential, it is considered a stable trend. After the trend consistency determination result and the sample dependence determination result are obtained separately, the system merges them according to a fixed combination rule to generate a trend stability label. This label value is strictly limited to three types: unstable trend, dependent unstable trend, and stable trend. The unstable trend corresponds to a trend with poor consistency, the dependent unstable trend corresponds to a trend with consistent structure but strong dependence, and the stable trend corresponds to a trend with consistent structure and low sample dependence. This ensures that the determination result has a clear logical source and a unique output. For example, if the trend consistency index of a certain detection indicator is 0.85, a consistency index higher than 0.8 is considered stable. If a trend is identified as having an unstable dependency trend, and its sample dependency index is 0.75 (higher than the dependency threshold of 0.6), the system will mark it as having an unstable dependency trend. The system will then bind the stability label to the identification information of the detection indicator, including the indicator name, subset number, time window, and original trend data number, and write it into the trend stability judgment result set in a structured form. This will be used for subsequent data visualization, credibility labeling, and mechanism explanation and screening, ensuring that each trend judgment process is traceable and verifiable. At the same time, it will have the ability to directly schedule downstream strategy systems, forming the core output interface of the overall trend intelligent judgment module.

[0094] In one embodiment, S6: Based on the trend stability determination result, the confidence level of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is labeled, and the analysis results used to assist in the judgment of experimental conclusions and subsequent adjustment of experimental design are output as follows:

[0095] Obtain trend stability judgment labels corresponding to each detection indicator. The labels include three types: stable trend, dependent unstable trend, and unstable trend. Assign a corresponding confidence level to each trend label, where stable trend corresponds to high confidence level, dependent unstable trend corresponds to medium confidence level, and unstable trend corresponds to low confidence level.

[0096] Based on the above credibility levels, credibility labels are generated for each trend result according to the preset mapping rules. The labels include a credibility level name, a risk warning field, and a suggested handling strategy field. The risk warning field is used to indicate the possible abnormal driving mechanism or sample structure risk of the trend result, and the suggested handling strategy field is used to give the suggested actions to be taken for the trend result in subsequent experimental design or mechanism explanation.

[0097] The content of the suggested processing strategy field is automatically generated based on the trend type. For indicators with stable trends, it is recommended to maintain the current processing strategy and prioritize their inclusion in the mechanism explanation model. For indicators that depend on unstable trends, it is recommended to expand the sample or perform cross-validation. For indicators with unstable trends, it is recommended to temporarily not use them or redesign the subset grouping structure.

[0098] The generated credibility labels are bound to the original trend results, detection indicator labels, subset numbers, and time window information to construct a trend result data structure with enhanced credibility.

[0099] The enhanced credibility trend results are then aggregated into a structured analysis result set, output as a data object that the system can call, and made available to the visualization interface, experimental report generation module, or subsequent strategy recommendation interface to support researchers in screening, interpreting and adjusting experimental conclusions and optimizing the next round of experimental design. It should be noted that after determining the trend stability of each detection indicator, the system first reads the corresponding trend stability label for each indicator. This label includes three states: stable trend, dependent on unstable trend, and unstable trend. Based on this label and preset rules, the system assigns three confidence levels: high, medium, and low confidence, for subsequent unified labeling. Next, the system generates a corresponding confidence labeling structure based on these confidence levels. This structure contains three fields: confidence level name, risk warning field, and suggested handling strategy field. The risk warning field indicates potential risks such as structural inconsistencies, sample dependence, or non-mechanistic drivers. For example, if a trend is determined to be unstable, the system will automatically indicate in the risk warning that the trend may only exist in some subsets and that batch effect interference is possible. The suggested handling strategy field automatically generates operational suggestions based on the trend state. For example, for stable trends, the system recommends prioritizing inclusion in the mechanism pathway model; for dependent unstable trends, the system recommends expanding the sample size or validating sensitive samples; and for unstable trends, the system suggests temporarily suspending use and re-implementing the model. The new evaluation grouping method is followed by the system binding the credibility label to the original trend result structure at the field level, specifically including the detection indicator name, subset number, time window label, trend symbol sequence, and trend status code, etc., which are then integrated into a single credibility-enhanced trend result record and stored in the trend credibility analysis result table. Finally, the system summarizes the credibility-enhanced result structures corresponding to all detection indicators into a standardized analysis result set and outputs it as a data object that can be called within the system. This allows access to downstream visualization modules, report generation modules, and mechanism pathway analysis interfaces, enabling researchers to quickly identify high-credibility trend results as mechanism support when screening experimental conclusions, interpreting results, or adjusting the next round of experimental design. Alternatively, different levels of processing schemes can be set for medium- and low-credibility trends, thereby achieving a closed-loop process from trend identification to result classification and then to experimental optimization suggestions. For example, if a detection indicator is marked as having an unstable dependence trend, the system will automatically prompt in the report that the trend is mainly dominated by a single sample and suggest adding a replication group, realizing automatic linkage and structured output of credibility evaluation, result reminders, and design adjustment suggestions.

[0100] Based on the same inventive concept, this invention also provides a system for analyzing the experimental results of extracellular vesicles in Ganoderma lucidum spore powder, including:

[0101] Data Acquisition Module: Acquires experimental results related to extracellular vesicles of Ganoderma lucidum spore powder, and organizes the response results of each experimental sample under the corresponding detection indicators into a structured set of experimental results.

[0102] Partitioning Module: Based on the experimental results set, the samples are divided into multiple subsets according to experimental batch, processing conditions or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance results of each detection index under different subsets.

[0103] The first screening module: Based on the trend performance results, it judges the consistency of the trend of each detection indicator across different subsets and screens out trend candidates that are only valid in some subsets or whose performance is inconsistent across different subsets.

[0104] The second screening module analyzes the impact of individual sample changes on the overall trend results for each trend candidate, identifies sensitive samples that have a significant impact on the trend results, and obtains the sample dependence characteristics of each trend candidate accordingly.

[0105] Judgment module: Combining trend consistency and sample dependency characteristics, it evaluates the stability of the trend results of each detection indicator and generates the corresponding trend stability judgment result;

[0106] Analysis module: Based on the trend stability determination results, the reliability of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is marked, and the analysis results are output to assist in the judgment of experimental conclusions and subsequent experimental design adjustments.

[0107] Based on the Ganoderma lucidum spore powder extracellular vesicle experimental result analysis system provided in this invention, a trend stability assessment process for high-dimensional omics data and animal experimental results is constructed by introducing trend consistency analysis and sample dependence feature recognition mechanisms. This system can not only identify spurious trends caused by extreme samples, batch effects, or instrument bias, but also accurately locate trend reversals, instabilities, or locally unreproducible regions, thereby avoiding over-attribution and misinterpretation of experimental mechanisms. Compared to traditional analysis methods that rely solely on statistical significance to determine the reliability of results, this invention can grade and label the credibility of trend results and output analysis suggestions in a structured manner. This provides more guiding intelligent auxiliary decision-making basis for subsequent experimental design, indicator selection, and data verification, significantly improving the robustness of data interpretation and the reliability of result reproducibility in Ganoderma lucidum spore powder extracellular vesicle research.

[0108] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should still fall within the scope of the claims of the present invention.

Claims

1. A method for analyzing the experimental results of extracellular vesicles in Ganoderma lucidum spore powder, characterized in that, Includes the following steps: S1: Obtain the experimental results data related to extracellular vesicles of Ganoderma lucidum spore powder, and organize the response results of each experimental sample under the corresponding detection indicators into a structured experimental result set; S2: Based on the experimental results set, the samples are divided into multiple subsets according to experimental batch, processing conditions or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance results of each detection index under different subsets. S3: Based on the trend performance results, determine the consistency of the trends of each detection indicator across different subsets, and screen out trend candidates that are only valid in some subsets or show inconsistent performance across different subsets. The specific process is as follows: Based on the trend performance results, calculate the trend consistency index, and filter out trend candidates that are only valid in some subsets or whose performance is inconsistent in different subsets based on the trend consistency index and a preset threshold. The calculation steps for the trend consistency index are as follows: For a specific detection indicator, its trend performance results under multiple subsets are obtained, and the trend changes of the indicator over time in each subset are marked with symbols in sequence. An upward trend corresponds to a positive symbol, a downward trend corresponds to a negative symbol, and an unstable trend corresponds to a zero symbol. Thus, a trend symbol sequence is formed in each subset. The sequence is arranged in chronological order, and each symbol corresponds to the trend direction of a time period. For each subset of trend symbol sequences, the frequency ratios of the three trend symbols (positive, negative, and zero) are counted, and the natural logarithm of the frequency ratios of the three trend symbols is calculated. The three frequency ratios are multiplied by their respective natural logarithms, and the sum of all the multiplication results is used as the trend structure information value of the corresponding subset. Based on the trend structure information values ​​corresponding to all subsets, calculate the absolute difference between each pair of subsets, and select the largest difference as the maximum trend structure deviation. For each subset of trend symbol sequences, the symbol change process is traversed in chronological order. The number of positions where changes occur between two adjacent trend symbols is counted and divided by the total number of symbols minus one to obtain the trend break density of the subset. The difference between the maximum and minimum values ​​of the trend break density for all subsets is calculated to obtain the degree of consistency of the trend break change of the index among different subsets. The maximum trend structure deviation is added to the difference in trend break density, and the result is used as input to calculate the output value of the exponential compression function. The output value is the trend consistency index of the detection index in all subsets. S4: For each trend candidate, analyze the impact of individual sample changes on the overall trend result, identify sensitive samples that have a significant impact on the trend result, and obtain the sample dependence characteristics of each trend candidate accordingly. S5: Combining trend consistency and sample dependence characteristics, evaluate the stability of the trend results of each detection indicator and generate the corresponding trend stability judgment result. S6: Based on the trend stability determination results, the reliability of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is marked, and the analysis results are output to assist in the judgment of experimental conclusions and subsequent experimental design adjustments.

2. The method for analyzing the results of an extracellular vesicle experiment on Ganoderma lucidum spore powder according to claim 1, characterized in that, The steps for selecting trend candidates that are valid only in some subsets or inconsistent across different subsets based on the trend consistency index and a preset threshold are as follows: The trend consistency index is compared with a preset consistency threshold. When the trend consistency index is less than the threshold, it is determined that the trend performance of the detection index is inconsistent in different subsets, and it is selected as a trend candidate for subsequent sample dependency analysis and stability determination.

3. The method for analyzing the experimental results of extracellular vesicles in Ganoderma lucidum spore powder according to claim 1, characterized in that, The steps for analyzing the impact of individual sample changes on the overall trend outcome for each trend candidate are as follows: Identifying sensitive samples that have a significant impact on the trend outcome, and obtaining the sample dependency characteristics of each trend candidate accordingly. For each trend candidate, the impact of individual sample changes on the overall trend result is analyzed, and the sample dependence index is calculated. The calculation steps are as follows: For a detection indicator that is identified as a trend candidate, in the corresponding subset, its current reference trend mark is first obtained as the baseline trend performance. Then, each sample in the subset is removed once in turn, and the trend mark of the detection indicator is recalculated after each removal operation. The trend mark after removing the sample is compared with the reference trend mark. If the trend direction changes or the trend state changes from stable to unstable or the opposite, the sample is marked as a trend disturbance sample. Otherwise, it is marked as a non-disturbance sample, and finally a binary state sequence is formed in the order of the samples. In all samples labeled as trend perturbation samples, their original position index in the sample sequence is extracted. The absolute difference between the position spacing of all adjacent perturbation samples is calculated according to the index order. The absolute values ​​of all adjacent spacings are summed to obtain the actual total distance between perturbation samples. Based on the total number of perturbed samples, calculate their theoretical maximum possible distribution distance in the sample sequence. The maximum possible distribution distance is the sum of the distances between adjacent perturbed samples in the most uniform distribution state. Divide the actual total distance value by the maximum possible distribution distance to obtain a ratio value. The result of subtracting the ratio value is defined as the perturbed clustering factor. The trend disturbance proportion factor is obtained by dividing the number of samples marked as trend disturbances in the statistical sample sequence by the total number of samples. By combining the perturbation clustering factor and the trend perturbation proportion factor, and multiplying the perturbation clustering factor by one and subtracting the trend perturbation proportion factor, the sample dependence index of the detection index in the current subset is calculated.

4. The method for analyzing the results of an extracellular vesicle experiment on Ganoderma lucidum spore powder according to claim 1, characterized in that, The steps for evaluating the stability of the trend results of each detection indicator and generating the corresponding trend stability judgment result, by combining trend consistency and sample dependency characteristics, are as follows: For each trend candidate, first determine whether its trend consistency index is higher than the set consistency threshold. If it is not higher, the trend is directly marked as an unstable trend, and there is no need to perform sample dependency judgment. If it is higher than the threshold, proceed to the next step of sample dependency judgment. In the determination of sample dependence, the sample dependence index of the trend is compared with the set sample dependence threshold. If the sample dependence index is higher than the threshold, it means that the trend is highly dependent on a few sensitive samples and the stability is still insufficient. Therefore, the trend is marked as an unstable trend. If the sample dependence index is lower than the threshold, it means that the trend is not affected by the disturbance of a few samples while maintaining consistency. Therefore, it is judged as a stable trend. The trend consistency determination result is combined with the sample dependency determination result, and a trend stability determination label is generated according to a fixed rule. The label includes at least three types: unstable trend dependent on unstable trend and stable trend.

5. A system for analyzing the results of an extracellular vesicle experiment on Ganoderma lucidum spore powder, used to implement the method for analyzing the results of an extracellular vesicle experiment on Ganoderma lucidum spore powder as described in any one of claims 1-4, characterized in that, The systems included are: Data Acquisition Module: Acquires experimental results related to extracellular vesicles of Ganoderma lucidum spore powder, and organizes the response results of each experimental sample under the corresponding detection indicators into a structured set of experimental results. Partitioning Module: Based on the experimental results set, the samples are divided into multiple subsets according to experimental batch, processing conditions or time dimension, and the changing trend of the corresponding detection index is calculated in each subset to obtain the trend performance results of each detection index under different subsets. The first screening module: Based on the trend performance results, it judges the consistency of the trend of each detection indicator across different subsets and screens out trend candidates that are only valid in some subsets or whose performance is inconsistent across different subsets. The second screening module analyzes the impact of individual sample changes on the overall trend results for each trend candidate, identifies sensitive samples that have a significant impact on the trend results, and obtains the sample dependence characteristics of each trend candidate accordingly. Judgment module: Combining trend consistency and sample dependency characteristics, it evaluates the stability of the trend results of each detection indicator and generates the corresponding trend stability judgment result; Analysis module: Based on the trend stability determination results, the reliability of the trend results in the Ganoderma lucidum spore powder extracellular vesicle experiment is marked, and the analysis results are output to assist in the judgment of experimental conclusions and subsequent experimental design adjustments.

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