A method and system for controlled cell death ordering

Abstract

The application relates to the technical field of bioinformatics, in particular to a method and system for ordering controlled cell death. The method obtains transcriptome expression data of a target sample, and filters a target gene list related to controlled cell death; based on the target gene list, a plurality of enrichment analysis methods are used to analyze a preset pathway gene set, and significance p values and ordering results of each pathway are obtained; further, the p values obtained by different methods are combined and processed, and the ordering results are aggregated and analyzed to obtain consistency scores; finally, a comprehensive evaluation index is constructed based on the combined p values and the consistency scores, priority of each pathway is evaluated, and a final ordering result of the controlled cell death pathway is output. The application realizes stable and accurate priority evaluation of the controlled cell death pathway, thereby significantly improving the reliability and biological interpretation ability of key pathway identification.

Description

Technical Field

[0001] This application relates to the field of bioinformatics technology, and in particular to a controlled cell death sequencing method and system. Background Technology

[0002] Among related technologies, Regulated Cell Death (RCD), as a programmed cell death process under the influence of specific molecular regulatory mechanisms, plays an important role in biological processes such as tumorigenesis and development, immune regulation, and drug response. With the development of high-throughput sequencing technology, researchers typically use transcriptome data combined with signal pathway enrichment analysis methods to assess the activity of different RCD pathways, thereby analyzing the involvement of cell death mechanisms under specific biological conditions.

[0003] However, existing RCD pathway analysis methods still face significant challenges in integrating results from multiple methods and ensuring the stability of the ranking. Differences in statistical models, input data structures, and significance evaluation indicators among different enrichment analysis methods lead to poor consistency and even contradictions in the RCD pathway ranking results obtained by each method. Furthermore, individual methods are highly sensitive to data noise and sample differences, easily causing large fluctuations in analytical results and making it difficult to stably reflect true biological patterns. In addition, existing methods typically rank based on a single indicator (such as p-value or enrichment score), lacking a mechanism for consistent evaluation and comprehensive integration of results from multiple methods, making it difficult to effectively distinguish between dominant and secondary RCD pathways, thus affecting the accuracy and reliability of the analytical results. Moreover, related analytical processes often rely on scattered tools or manual operation, lacking a unified automated analysis platform, resulting in high barriers to entry and low efficiency.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this application is to propose a controlled cell death sequencing method and system to achieve stable and accurate priority assessment of controlled cell death pathways, thereby significantly improving the reliability of key pathway identification and biological interpretation capabilities.

[0006] To achieve the above objectives, one aspect of this application proposes a controlled cell death sequencing method, the method comprising the following steps: Obtain transcriptomic expression data of the target sample; A list of target genes was determined based on the transcriptome expression data; Based on the target gene list, various enrichment analysis methods were used to perform enrichment analysis on the preset controlled cell death pathway gene set to obtain the p-value and ranking results for each pathway. For each controlled cell death pathway, the p-values ​​obtained from multiple enrichment analysis methods were merged to obtain the merged p-value; Based on the ranking results corresponding to various enrichment analysis methods, a ranking aggregation process is performed to obtain a consistency score. Based on the merged p-value and the consistency score, the controlled cell death pathways are prioritized to determine the set of controlled cell death pathways with higher priority, and the final ranking result is generated.

[0007] In some embodiments, the transcriptome expression data includes gene expression matrix data and differentially expressed gene list data; The gene expression matrix data is used to perform expression profile-based enrichment analysis; The differentially expressed gene list data is used to perform enrichment analysis based on the gene list, and the gene list supports a single-column gene identifier format or a two-column data format containing gene identifiers and corresponding numerical indicators.

[0008] In some embodiments, determining the target gene list based on the transcriptome expression data includes: The transcriptome expression data were subjected to gene identification unification and preprocessing to obtain standardized gene data; Based on the standardized gene data, a list of target genes is obtained according to preset screening rules.

[0009] In some embodiments, the step of performing enrichment analysis on a pre-defined set of genes for controlled cell death pathways using multiple enrichment analysis methods based on the target gene list to obtain the p-value and ranking results for each pathway includes: Based on the target gene list and the pre-defined controlled cell death pathway gene set, enrichment analysis was performed on each pathway using both overrepresentation analysis and functional category scoring methods. The overrepresentation analysis method was used to calculate the significance p-value of each pathway based on the gene overlap relationship between the target gene list and the background gene set. The functional category scoring method was used to calculate the enrichment score and corresponding significance index of each pathway based on gene expression ranking information or sample expression profiles. The significance p-values ​​of each controlled cell death pathway were calculated based on each enrichment analysis method, and the pathways were ranked to obtain the corresponding pathway ranking results. Summarize the p-values ​​and pathway ranking results obtained from various enrichment analysis methods.

[0010] In some embodiments, the calculation process of the overrepresentation analysis method includes: Based on the target gene list, the preset controlled cell death pathway gene set, and the background gene set, determine the number of genes in each controlled cell death pathway and the number of genes overlapping with the target gene list. Based on the number of genes and their overlap, the significance p-values ​​of each pathway were calculated using the hypergeometric test or Fisher's exact test. The p-values ​​are corrected by multiple tests, and the pathways are ranked based on the corrected p-values ​​to obtain the corresponding pathway ranking results.

[0011] In some embodiments, the calculation process of the functional category scoring method includes: Based on the gene expression sorting information or expression matrix data corresponding to the target gene list, construct a whole gene sorting list or sample expression profile; For a pre-defined set of genes for controlled cell death pathways, enrichment scores are calculated on the sorted list or expression profile using gene set enrichment analysis methods to obtain the enrichment scores and significance indices for each pathway. The gene set enrichment analysis methods include at least one of the following: phenotypic grouping-based enrichment analysis methods, pre-sorted gene lists-based enrichment analysis methods, and single-sample enrichment analysis methods. Based on the permutation test, the enrichment score was evaluated for significance to obtain the corresponding p-value, and the controlled cell death pathways were ranked to obtain the corresponding pathway ranking results.

[0012] In some embodiments, the process of merging p-values ​​obtained from multiple enrichment analysis methods for each controlled cell death pathway to obtain a merged p-value includes: Based on the significance p-values ​​obtained by each of the enrichment analysis methods, a multi-source p-value set corresponding to the same controlled cell death pathway is constructed. Based on the multi-source p-value set, a p-value merging algorithm is used to statistically integrate each p-value to obtain the merged p-value of each controlled cell death pathway; the p-value merging algorithm includes at least one of a combination statistical method based on the independence test hypothesis or a weighted fusion method based on rank statistics.

[0013] In some embodiments, the ranking aggregation process based on the ranking results corresponding to multiple enrichment analysis methods to obtain a consistency score includes: Based on the pathway ranking results obtained by each of the enrichment analysis methods, a multi-source ranking list corresponding to the same controlled cell death pathway is constructed. The multi-source sorting list is uniformly standardized to generate a standardized multi-source sorting list; Based on the standardized multi-source sorting list, a robust sorting aggregation algorithm is used to fuse the sorting information of each controlled cell death pathway to obtain a consistent sorting value for each pathway. Based on the consistency ranking value, the consistency score of each controlled cell death pathway is calculated and generated.

[0014] In some embodiments, the step of prioritizing each controlled cell death pathway based on the merged p-value and the consistency score, determining the set of controlled cell death pathways with higher priority, and generating a final ranking result includes: A comprehensive evaluation index for each controlled cell death pathway is constructed based on the merged p-value and the consistency score. The comprehensive evaluation indicators are standardized to generate standardized comprehensive evaluation indicators; The standardized comprehensive evaluation indexes are weighted and fused based on preset weights to obtain a comprehensive priority score for each controlled cell death pathway. Based on the comprehensive priority score, each controlled cell death pathway is ranked, and a set of controlled cell death pathways with higher priority is selected to generate the final ranking result.

[0015] To achieve the above objectives, another aspect of this application proposes a controlled cell death sequencing system, the system comprising: The acquisition module is used to acquire transcriptome expression data of the target sample; A gene list determination module is used to determine a list of target genes associated with controlled cell death based on the transcriptome expression data. The enrichment analysis module is used to perform enrichment analysis on the preset controlled cell death pathway gene set based on the target gene list using multiple enrichment analysis methods, and obtain the p-value and ranking results for each pathway. The p-value merging module is used to merge p-values ​​obtained from multiple enrichment analysis methods for each controlled cell death pathway to obtain a merged p-value. The sorting and aggregation module is used to perform sorting and aggregation processing based on the sorting results corresponding to multiple enrichment analysis methods to obtain a consistency score. The priority evaluation module is used to evaluate the priority of each controlled cell death pathway based on the merged p-value and the consistency score, determine the set of controlled cell death pathways with higher priority, and generate the final ranking result.

[0016] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0018] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0019] The embodiments of this application include at least the following beneficial effects: This application provides a controlled cell death ranking method and system. This scheme acquires and analyzes the transcriptome expression data of target samples, constructs a list of target genes related to controlled cell death, and combines multiple enrichment analysis methods to comprehensively evaluate preset pathways, thereby achieving a multi-dimensional characterization of different controlled cell death pathways. Compared with traditional technical solutions that rely solely on a single enrichment analysis strategy, by introducing a multi-method parallel analysis mechanism, the bias of a single algorithm on the results is effectively reduced, significantly improving the comprehensiveness and accuracy of pathway identification. Furthermore, by merging the p-values ​​obtained from multiple enrichment analysis methods, the stability of statistical significance evaluation can be enhanced. Simultaneously, by combining the consistency score obtained from ranking aggregation, the ranking information of each pathway under different analysis methods is effectively integrated, thereby constructing a more reliable comprehensive evaluation index. Based on this, prioritizing controlled cell death pathways can more accurately screen key pathways, improving the discriminative power and interpretability of the results. This method can efficiently identify key controlled cell death pathways in the context of complex biological data, providing strong data support for disease mechanism research, target screening and precision medicine, and has strong practical value and promotion significance. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a controlled cell death sequencing method provided in an embodiment of this application; Figure 2 yes Figure 1 A flowchart illustrating step S3 in the process; Figure 3 yes Figure 1 A flowchart illustrating step S6 in the process; Figure 4 This is a visual schematic diagram of the controlled cell death pathway priority assessment results provided in the embodiments of this application; Figure 5 This is a schematic diagram of a controlled cell death sequencing method provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0022] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0023] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0025] Before providing a detailed description of the embodiments of this application, some of the nouns and terms used in the embodiments of this application will be explained first. The nouns and terms used in the embodiments of this application shall be interpreted as follows: Controlled cell death or regulated cell death (RCD) refers to programmed cell death processes that occur under the action of specific molecular regulatory mechanisms, including various forms such as apoptosis, pyroptosis, and ferroptosis. It plays an important role in biological processes such as tumorigenesis, immune regulation, and drug response. Transcriptomic expression data refers to gene expression information obtained through high-throughput sequencing technologies (such as RNA-seq). It typically includes gene expression matrix data and differentially expressed gene list data, which are used to characterize the expression level of genes under different samples or conditions. A gene expression matrix is ​​a two-dimensional data structure with genes as rows and samples as columns. The matrix elements represent the expression level of the corresponding gene in a specific sample, such as FPKM, TPM, or counts. Differentially expressed genes (DEGs) are a list of genes whose expression levels differ significantly between different experimental conditions or sample groups. They are usually selected based on statistical methods (such as DESeq2, edgeR, etc.). Enrichment analysis refers to the use of statistical methods to assess the enrichment of a target gene list in a predefined gene set (such as a pathway gene set), thereby determining the significance of related biological pathways or functional categories. Over-representation analysis (ORA) is a method that calculates pathway significance based on the overlap between the target gene list and the background gene set using statistical tests (such as hypergeometric test or Fisher's exact test). Functional Class Scoring (FCS) is a class of methods that evaluate the overall expression trend of a predefined gene set based on whole-gene expression ranking information or expression profiles, including GSEA, ssGSEA, GSVA, etc. Gene Set Enrichment Analysis (GSEA) is a statistical method that assesses the enrichment of gene sets in the overall expression distribution based on gene sorting lists, and is used to identify functional pathways that change significantly under different biological states. Single-sample gene set enrichment analysis (ssGSEA) is a method that calculates the enrichment score of each gene set for a single sample, and is used to assess the pathway activity level at the single-sample level. Gene Set Variation Analysis (GSVA) ​​is a non-parametric, unsupervised gene set enrichment method that converts gene expression data into continuous scores at the pathway level to reflect differences in pathway activity among different samples. The GOAT method (Gene set enrichment analysis with Optimal thresholding) is a gene set enrichment analysis method based on expression profiles or sequencing information. It improves the stability and sensitivity of enrichment analysis by optimizing threshold selection. The p-value is a probability index used in statistical hypothesis testing to measure the significance of observed results; the smaller the p-value, the more significant the result. The p-value combination method refers to a method for statistically integrating multiple p-values ​​of the same object. It includes combination statistical methods based on the independence assumption or weighted fusion methods based on rank statistics, which are used to improve the stability of significance assessment. Rank aggregation refers to the process of merging multiple ranking results from different analysis methods to obtain a unified ranking result. Robust Rank Aggregation (RRA) is a statistical method for integrating multi-source ranking information. It generates a stable comprehensive ranking result by evaluating the consistent position of elements in multiple ranking lists. The consistency score is a quantitative indicator used to characterize the degree of consistency in ranking the same pathway across multiple ranking results. The higher the score, the stronger the consistency between the results of different methods. A comprehensive evaluation metric is a quantitative indicator used to assess pathway priority, constructed based on the pooled p-value and consistency score. GMT (Gene Matrix Transposed Format) is a text format used to store gene set information. Each line represents a gene set and contains the gene set name and its corresponding gene list.

[0026] This application provides a controlled cell death sequencing method and system. This method acquires and analyzes transcriptomic expression data of target samples to construct a list of target genes related to controlled cell death. It then combines multiple enrichment analysis methods to comprehensively evaluate preset pathways, thereby achieving a multi-dimensional characterization of different controlled cell death pathways. Compared to traditional techniques relying solely on a single enrichment analysis strategy, this method, by introducing a multi-method parallel analysis mechanism, effectively reduces the bias of a single algorithm on the results, significantly improving the comprehensiveness and accuracy of pathway identification. Furthermore, by merging the p-values ​​obtained from multiple enrichment analysis methods, the stability of statistical significance evaluation is enhanced. Simultaneously, by combining the consistency score obtained from sequencing aggregation, the sequencing information of each pathway under different analysis methods is effectively integrated, thus constructing a more reliable comprehensive evaluation index. Based on this, prioritizing controlled cell death pathways allows for more precise screening of key pathways, improving the discriminative power and interpretability of the results. This method can efficiently identify key controlled cell death pathways in complex biological data contexts, providing strong data support for disease mechanism research, target screening, and precision medicine, and has significant practical value and promotional significance.

[0027] This application provides a controlled cell death ranking method, relating to the field of bioinformatics. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited thereto. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the controlled cell death ranking method, but is not limited to the above forms.

[0028] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0029] Figure 1 This is an optional flowchart of a controlled cell death sequencing method provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S1 to S6: S1: Obtain transcriptome expression data of the target sample; Transcriptome expression data includes gene expression matrix data and differentially expressed gene list data; This gene expression matrix data was used to perform expression profile-based enrichment analysis; This differentially expressed gene list data is used to perform enrichment analysis based on the gene list, and the gene list supports either a single-column gene identifier format or a two-column data format containing gene identifiers and corresponding numerical indicators.

[0030] In this embodiment, transcriptome expression data of the target sample is obtained. Specifically, the target sample can be a cell or tissue sample from different biological conditions, such as a disease group and a control group, a drug-treated group and an untreated group, or dynamic samples collected at different time points. Preferably, the transcriptome data is derived from RNA sequencing (RNA-seq) analysis results, and the raw data is processed through standard bioinformatics workflows, including sequencing quality control, adapter removal, sequence alignment, and expression quantification calculation, ultimately forming gene expression matrix data. The gene expression matrix data is formatted with genes as rows and samples as columns. Matrix elements can be represented as expression level indicators such as FPKM, TPM, or counts, and sample grouping information is included to support subsequent enrichment analysis methods based on expression profiles.

[0031] Furthermore, to accommodate the input requirements of different types of enrichment analysis methods, this step can also derive a list of differentially expressed genes from the gene expression matrix data. Specifically, differential analysis can be performed between the experimental and control groups based on preset statistical methods (such as limma, DESeq2, or edgeR) to obtain a list of differentially expressed genes containing gene identifiers and their corresponding statistical indicators (such as log2FoldChange, p-value, and corrected p-value). The list of differentially expressed genes can be in the form of a single-column gene identifier, or in a two-column or multi-column format, and must contain at least a gene identifier and a numerical indicator for ranking or screening, thus meeting the input ranking information requirements of methods such as GSEA prerank or GOAT. By simultaneously constructing the expression matrix data and the list of differentially expressed genes, this step provides a unified and flexible data foundation for subsequent multi-type enrichment analyses.

[0032] S2: Determine the list of target genes based on transcriptome expression data; The list of target genes determined based on transcriptome expression data includes: S21: Gene identification and preprocessing are performed on transcriptome expression data to obtain standardized gene data; S22: Based on standardized gene data, obtain a list of target genes according to preset screening rules.

[0033] In this embodiment, the obtained transcriptome expression data is first standardized to eliminate differences between different data sources or annotation systems. Specifically, based on a preset gene annotation mapping relationship, different types of gene identifiers (such as Ensembl ID, RefSeq ID, or Uniprot ID) in the input data are uniformly converted into standard gene symbols (such as HGNC symbols), and duplicate gene entries are merged, for example, by taking the average expression value or retaining records with higher significance to achieve uniqueness. At the same time, missing values, low-expression genes, or abnormal genes that cannot be mapped are filtered to obtain a high-quality standardized gene dataset. For specific analysis methods (such as GOAT), gene identifiers can be further converted into Entrez IDs to ensure algorithm compatibility.

[0034] After standardization, a target gene list is determined from the standardized gene data based on preset screening rules. Specifically, when the input is a list of differentially expressed genes, differentially expressed candidate genes can be screened based on significance thresholds (e.g., FDR < 0.05) and expression change thresholds (e.g., |log2FC| > 1). When the input is expression matrix data, all genes that have passed quality control can be used as a candidate set for subsequent enrichment analysis based on expression profiles. Subsequently, the candidate gene list is matched and screened with a preset set of genes for controlled cell death pathways. The pathway gene set is preferably stored in GMT format and covers multiple types of controlled cell death (e.g., apoptosis, pyroptosis, ferroptosis, etc.). By calculating the intersection relationship between candidate genes and each pathway gene set, pathways with effective overlap and their corresponding genes are screened to form the target gene list. This step effectively limits the scope of subsequent analysis, allowing the enrichment analysis to focus on a list of key genes related to controlled cell death mechanisms, thereby improving the biological relevance and analytical efficiency of the sorting results.

[0035] S3: Based on the target gene list, use multiple enrichment analysis methods to perform enrichment analysis on the pre-set controlled cell death pathway gene set, and obtain the p-value and ranking results for each pathway; Among them, reference Figure 2 As shown, based on the target gene list, various enrichment analysis methods were used to perform enrichment analysis on the pre-defined controlled cell death pathway gene set, obtaining the p-values ​​and ranking results for each pathway, including: S31: Based on the target gene list and the pre-defined controlled cell death pathway gene set, enrichment analysis is performed on each pathway using both overrepresentation analysis and functional category scoring methods. The overrepresentation analysis method is used to calculate the significance p-value of each pathway based on the gene overlap relationship between the target gene list and the background gene set. The functional category scoring method is used to calculate the enrichment score and corresponding significance index of each pathway based on gene expression ranking information or sample expression profile. S32: Calculate the significance p-value of each controlled cell death pathway based on each enrichment analysis method, and rank each pathway to obtain the corresponding pathway ranking results; S33: Summarize the p-values ​​and pathway ranking results obtained from various enrichment analysis methods.

[0036] In this embodiment, the obtained target gene list is used as the analysis object, and the pre-constructed controlled cell death pathway gene set is used as the reference gene set. Multi-method parallel enrichment analysis is performed on each controlled cell death pathway. The controlled cell death pathway gene set is preferably stored in the standard GMT format, with each pathway record containing the pathway name and its corresponding member genes. The enrichment analysis methods include overrepresentation analysis (ORA) and functional category scoring (FCS). The overrepresentation analysis methods preferably include hypergeometric test and Enrichr, and the functional category scoring methods preferably include GSEA, GSEAPrerank, ssGSEA, GOAT, and GSVA.

[0037] In practice, the analysis path is automatically matched according to the data type of the input: when the input is a list of differentially expressed genes, the overrepresentation analysis method based on the gene list is preferred; when the input is a gene expression matrix or a pre-sorted gene list, the functional category scoring method based on expression profiles or sorting information is preferred, thereby achieving unified adaptation to different data forms.

[0038] Furthermore, enrichment analyses were performed on each controlled cell death pathway using overrepresentation analysis and functional category scoring methods, respectively. Overrepresentation analysis calculated the significance level of each pathway based on the overlap between the target gene list and the background gene set. Functional category scoring, on the other hand, assessed the enrichment trend of each pathway within the overall expression background based on gene expression ranking information or sample expression profiles, and obtained corresponding significance indicators by combining inter-group differences or randomization tests. Through the synergistic analysis of these two methods, each controlled cell death pathway was comprehensively characterized from two dimensions: gene overlap and expression change trends.

[0039] After calculating the values ​​for each enrichment analysis method, the p-values ​​or corrected significance indices for each pathway are extracted, and the pathways are ranked according to preset rules to obtain the pathway ranking results for each method. Preferably, the pathways are ranked from high to low significance to reflect their enrichment degree in the current analysis method. Subsequently, the p-value results and ranking results obtained from each enrichment analysis method are uniformly summarized to construct a standardized intermediate result data structure, so that each controlled cell death pathway corresponds to multiple significance indices and ranking positions under multiple methods, thereby providing a data foundation for p-value merging and ranking aggregation in subsequent steps.

[0040] Specifically, the computational process of the overrepresentation analysis method includes: Based on the target gene list, the pre-defined controlled cell death pathway gene set, and the background gene set, the number of genes in each controlled cell death pathway and the number of genes overlapping with the target gene list are determined. Based on the number of genes and their overlap, the significance p-values ​​of each pathway were calculated using the hypergeometric test or Fisher's exact test. Multiple tests were performed to correct the p-values, and the pathways were ranked based on the corrected p-values ​​to obtain the corresponding pathway ranking results.

[0041] In this embodiment, the overrepresentation analysis method takes a target gene list as input and combines it with a pre-defined controlled cell death pathway gene set and a background gene set to perform enrichment significance analysis on each controlled cell death pathway. Specifically, firstly, the target gene list is read. This target gene list can be a list of differentially expressed genes after screening, supporting either a single-column gene identifier format or a two-column data format containing gene identifiers and corresponding numerical indicators. Simultaneously, the controlled cell death pathway gene set file stored in GMT format is read, where each record corresponds to a controlled cell death pathway and its member genes. Then, the background gene set is read, which is preferably a list of all human genes extracted from the hg38.refGene.gtf.gz annotation file. A unified mapping and format adaptation are performed based on the ID type of the input genes, ensuring that the target gene list, the controlled cell death pathway gene set, and the background gene set are under a consistent gene identifier system. Subsequently, for each controlled cell death pathway, the number of genes corresponding to that pathway and the number of overlapping genes between the pathway genes and the target gene list are counted, thus forming the basic counting information required for subsequent significance testing. The inputs for overrepresentation analysis include a list of target genes, a custom RCD pathway gene set file, and a global background gene set, with the number of pathway genes and the number of overlapping genes used as the basis for enrichment testing.

[0042] Further, after obtaining the gene count and overlap relationships for each pathway, significance tests are performed on each controlled cell death pathway. Preferably, hypergeometric tests or Fisher's exact tests can be used. When using hypergeometric tests, the target gene list is considered as a target set extracted from the background gene set. The probability that a gene from a controlled cell death pathway appears with the current overlap or a higher overlap in this target set is calculated to determine whether the pathway exhibits non-random enrichment in the target gene list. When using Fisher's exact test, the corresponding enrichment significance p-value is calculated based on the difference between the proportion of pathway genes in the target gene list and the proportion of such genes in the background gene set. Specifically, hypergeometric tests can be performed using the hypergeom module in the scipy.stats library in the Python environment to complete a one-sided enrichment test, while Fisher's exact test can be implemented using the enrichr function in the gseapy library, with corresponding proportionality test logic integrated at the underlying level. Through these methods, the system obtains the original significance p-value for each controlled cell death pathway, which characterizes the enrichment intensity of that pathway in the target gene list. In this embodiment, the hypergeometric test uses a one-sided enrichment test method, and the Enrichr uses Fisher's exact test logic. Both are used to quickly screen for significantly enriched RCD pathways.

[0043] After calculating the original p-values ​​for each controlled cell death pathway, the p-values ​​are corrected using multiple tests to reduce the risk of false positives from parallel testing of multiple pathways. Preferably, the hypergeometric test branch uses the Benjamini-Hochberg method for false discovery rate control, and the Enrichr branch uses its built-in FDR correction mechanism, both yielding corrected p-values. Subsequently, the controlled cell death pathways are sorted based on the corrected p-values, preferably in ascending order of corrected p-values. The smaller the corrected p-value, the stronger the enrichment significance of the corresponding pathway in the target gene list, and the higher its ranking position. Finally, the corresponding pathway ranking results are output. These results may include the names of all controlled cell death pathways and their corrected p-values, or they may further output a result file that only retains significant pathways, providing standardized input data for subsequent p-value merging and ranking aggregation. In this embodiment, the hypergeometric test outputs all RCD pathway names and FDR-corrected p-values, and the Enrichr outputs all RCD pathway names and built-in FDR-corrected p-values, with a corrected p-value less than 0.05 used as the significance criterion.

[0044] Specifically, the calculation process for the functional category scoring method includes: Based on the gene expression ranking information or expression matrix data corresponding to the target gene list, construct a whole gene ranking list or sample expression profile; For a pre-defined set of genes for controlled cell death pathways, enrichment scores were calculated on the sorted list or expression profile using gene set enrichment analysis methods to obtain the enrichment scores and significance indices for each pathway. The gene set enrichment analysis methods include at least one of the following: phenotypic grouping-based enrichment analysis methods, pre-sorted gene lists-based enrichment analysis methods, and single-sample enrichment analysis methods. Based on the permutation test, the significance of the enrichment score was evaluated to obtain the corresponding p-value, and the controlled cell death pathways were ranked to obtain the corresponding pathway ranking results.

[0045] In this embodiment, the functional category scoring method first constructs a whole-gene sorting list or sample expression profile based on the gene expression sorting information or expression matrix data corresponding to the target gene list. Specifically, when the input data is a gene expression matrix with phenotypic grouping, the expression matrix is ​​organized into a standard structure of "behavioral genes, columns as samples," and the sample grouping information is read simultaneously to construct a sample expression profile suitable for phenotypic grouping enrichment analysis and single-sample enrichment analysis. When the input data is differential expression results or a pre-sorted gene list, gene identifiers and their corresponding sorting indices are extracted to form a whole-gene sorting list. This sorting index can be a numerical value that comprehensively reflects the direction and significance of expression differences, used to characterize the relative position of genes in the overall trend of change. For input gene identifiers, HGNC symbol, Ensembl gene ID, and UniprotKB accession are preferably supported, and unified mapping and format standardization are completed before entering the analysis. Through the above data construction, the functional category scoring method can adapt to both phenotypic grouping-based analysis scenarios and analysis scenarios based on pre-sorted gene lists and single-sample expression profiles, thereby ensuring compatibility with different transcriptome input formats. GSEA-adapted gene expression matrix with phenotypic grouping, GSEA prerank-adapted gene list with ranking scores, ssGSEA and GSVA-adapted expression matrix, and GOAT-adapted pre-ranked gene list.

[0046] Furthermore, for the pre-defined controlled cell death pathway gene set, enrichment analysis methods are used to calculate enrichment scores on the sorted lists or expression profiles to obtain the enrichment scores and significance indicators for each pathway. Specifically, if an enrichment analysis method based on phenotypic grouping is used, GSEA is preferred. After sorting the entire gene set according to phenotypic-related sorting indicators, the degree of clustering of each controlled cell death pathway at the front or back of the sorted list is calculated to obtain the enrichment score for the corresponding pathway. If an enrichment analysis method based on a pre-sorted gene list is used, GSEAprerank or GOAT is preferred. The former directly performs pathway enrichment testing based on the pre-sorted gene list, while the latter assigns higher weights to high-ranking genes to enhance the contribution of key change genes to the pathway score. If a single-sample enrichment analysis method is used, ssGSEA or GSVA is preferred. ssGSEA calculates the activity score of each controlled cell death pathway for a single sample and then combines the sample group differences to obtain significance indicators. GSVA converts the gene expression matrix into a pathway activity matrix and assesses functional differences between different samples based on pathway-level expression changes. The controlled cell death pathway gene set is preferably stored in GMT format, with each pathway record including the pathway name and corresponding member genes. Each analysis method outputs the enrichment score and significance index of the corresponding pathway to reflect the activation trend and statistical support strength of the pathway in the current expression context. In this embodiment, the functional category scoring methods include GSEA, GSEA prerank, ssGSEA, GOAT, and GSVA. Each method outputs the enrichment results of the RCD pathway from the perspectives of phenotypic differences, preranking trends, single-sample activity, and pathway variation. After obtaining the enrichment scores of each controlled cell death pathway, the significance of the enrichment scores is further evaluated based on the permutation test to obtain the corresponding p-values. The controlled cell death pathways are then ranked to obtain the corresponding pathway ranking results. Specifically, for the GSEA method, a phenotypic permutation approach is preferred. This involves repeatedly performing random permutations on the sample labels, repeatedly calculating the enrichment scores of each controlled cell death pathway under the permutation background, and calculating the significance index based on the deviation between the observed enrichment scores and the permutation null distribution. For the GSEA prerank method, an enrichment null distribution is established through a preset number of random permutations to obtain the FDR q value for each pathway. For the ssGSEA and GSVA methods, the pathway activity score or pathway variation score for each sample is obtained first, and then the statistical comparison results between the experimental and control groups are combined to obtain the significance p-value for the corresponding pathway. For the GOAT method, the pathway scores are tested for significance using the bootstrap method or a pre-calculated null distribution. Subsequently, the p-values ​​obtained for each pathway are corrected using multiple tests, and the pathways are ranked from smallest to largest according to the corrected p-values ​​to obtain the pathway ranking results under the current functional category scoring method. The smaller the corrected p-value, the higher the enrichment significance of the corresponding controlled cell death pathway under this analysis method, and the higher its ranking position. Finally, a standardized result table containing the controlled cell death pathway name, enrichment score, corrected p-value, and ranking position can be output, providing a unified input for p-value merging and ranking aggregation in subsequent steps. In this embodiment, both GSEA and GSEA prerank use 1000 permutations and output the FDR q-value as the significance index. Simultaneously, the results of each method can be uniformly organized into a standard result list containing the RCD name and p-value / FDR value.

[0047] S4: For each controlled cell death pathway, the p-values ​​obtained from multiple enrichment analysis methods are merged to obtain the merged p-value; Specifically, for each controlled cell death pathway, the p-values ​​obtained from multiple enrichment analysis methods were merged to obtain a combined p-value, including: S41: Based on the significance p-values ​​obtained by each enrichment analysis method, construct a multi-source p-value set corresponding to the same controlled cell death pathway; S42: Based on a multi-source p-value set, a p-value merging algorithm is used to statistically integrate each p-value to obtain the merged p-value of each controlled cell death pathway; the p-value merging algorithm includes at least one of a combination statistical method based on the independence test hypothesis or a weighted fusion method based on rank statistics.

[0048] In this embodiment, for each controlled cell death pathway, the significance p-values ​​obtained from multiple enrichment analysis methods are merged to obtain the corresponding merged p-value. Specifically, the output data of each enrichment analysis method is first acquired. This data is preferably stored in a unified format, including at least the name of the controlled cell death pathway and its corresponding significance p-value or corrected significance index. Since the output formats of different enrichment analysis methods may differ—for example, some methods output corrected p-values ​​while others output FDR values—various significance indices can be uniformly converted to p-value format before proceeding to this step to ensure consistency and comparability in the subsequent statistical integration process.

[0049] Furthermore, for each controlled cell death pathway, the significance results from different enrichment analysis methods were merged to construct a corresponding multi-source p-value set. Specifically, for any controlled cell death pathway, its significance p-values ​​in each enrichment analysis method were extracted and integrated into a set of data records according to a preset order, thus forming a multi-source p-value vector for that pathway. This multi-source p-value set reflects the statistical significance of the same pathway under different analysis methods, providing basic data for subsequent joint evaluation.

[0050] After obtaining the multi-source p-value set, a p-value merging algorithm is used to statistically integrate the p-values ​​to obtain the merged p-value for each controlled cell death pathway. Preferably, this embodiment uses a combined statistical method based on the independence test hypothesis, such as the Tippett method, to fuse multiple p-values. This method can identify pathways with significant support from multiple analysis methods, ensuring that even when a pathway shows a strong enrichment trend in at least one method, the merged result still reflects this significant characteristic. Through this processing, each controlled cell death pathway corresponds to a unique merged p-value, used to characterize its overall enrichment significance under multi-method joint analysis.

[0051] In engineering implementation, the calculated pooled p-values ​​can be compiled into a standardized results table. This table should at least include the name of the controlled cell death pathway and its corresponding pooled p-value, and may also include information on the number of times the pathway reached the significance threshold in different methods, as needed. It should be noted that the pooled p-value is independent of the consistency score calculated based on the ranking results in subsequent steps. The pooled p-value primarily reflects the enrichment significance strength of the pathway; the smaller the value, the stronger the statistical support for the pathway in multi-method comprehensive analysis. Through this step, the dispersed significance results from multiple enrichment analysis methods can be unified and integrated into a single evaluation index, providing a reliable data foundation for subsequent ranking aggregation and priority assessment.

[0052] S5: Based on the ranking results corresponding to various enrichment analysis methods, perform ranking aggregation processing to obtain a consistency score; Among them, based on the ranking results corresponding to various enrichment analysis methods, ranking aggregation processing is performed to obtain a consistency score, including: S51: Based on the pathway ranking results obtained by each enrichment analysis method, construct a multi-source ranking list corresponding to the same controlled cell death pathway; S52: Perform unified standardization processing on the multi-source sorted list to generate a standardized multi-source sorted list; S53: Based on the standardized multi-source sorting list, a robust sorting aggregation algorithm is used to fuse the sorting information of each controlled cell death pathway to obtain the consistent sorting value corresponding to each pathway. S54: Calculate and generate consistency scores for each controlled cell death pathway based on consistency ranking values.

[0053] In this embodiment, based on the pathway ranking results obtained from multiple enrichment analysis methods, each controlled cell death pathway is ranked and aggregated to obtain a consistency score. Specifically, firstly, the pathway ranking results output by each enrichment analysis method are obtained. These ranking results preferably include the name of the controlled cell death pathway and its ranking position in the corresponding analysis method. For each controlled cell death pathway, its ranking in different enrichment analysis methods is extracted and merged according to a unified order to construct a multi-source ranking list corresponding to that pathway. This multi-source ranking list reflects the differences in ranking performance of the same pathway under multiple analysis methods, providing basic data for subsequent consistency assessment.

[0054] After constructing the multi-source ordination list, the ordination data is standardized to eliminate differences in ordination direction, ordination scale, and result completeness among different enrichment analysis methods. Specifically, the ordination rules for each method can be unified, ensuring that a higher ranking position indicates stronger pathway enrichment significance. For cases with tied rankings, the data can be expanded sequentially according to preset rules or assigned the same rank. For pathway results missing in individual methods, default completion or filling with the highest-ranking result can be used. Through this standardization process, each controlled cell death pathway forms a multi-source ordination vector of consistent length and uniform structure, ensuring the comparability of ordination information between different methods and enabling fusion within the same statistical framework.

[0055] Furthermore, based on the standardized multi-source ranking list, a robust ranking aggregation algorithm (RRA) is used to fuse the ranking information of each controlled cell death pathway to obtain a consistency ranking value. Preferably, this embodiment uses the RRA algorithm to statistically evaluate the rank distribution of the same pathway in multiple ranking lists to determine whether its ranking position is more stable than random. This method can effectively integrate multi-source ranking information and can still identify pathways that rank highly in most methods even in the presence of noise or method differences. Subsequently, the obtained consistency ranking value is used as a consistency score, or further standardized and mapped to generate a unified scoring index. The lower the consistency score, the more concentrated and stable the ranking of the pathway is in multiple enrichment analysis methods, and the stronger its credibility as a priority controlled cell death pathway. The final output includes result data containing pathway names and consistency scores, providing a basis for subsequent comprehensive priority evaluation by combining and merging p-values.

[0056] S6: Based on the merged p-value and consistency score, prioritize each controlled cell death pathway, determine the set of controlled cell death pathways with higher priority, and generate the final ranking result.

[0057] Among them, reference Figure 3 As shown, based on the pooled p-value and consistency score, the controlled cell death pathways are prioritized to determine the set of controlled cell death pathways with higher priority, generating the final ranking results, including: S61: Construct a comprehensive evaluation index for each controlled cell death pathway based on the merged p-value and consistency score; S62: Standardize the comprehensive evaluation indicators to generate standardized comprehensive evaluation indicators; S63: Based on preset weights, the standardized comprehensive evaluation indicators are weighted and fused to obtain the comprehensive priority score of each controlled cell death pathway; S64: Based on the comprehensive priority score, each controlled cell death pathway is ranked, and a set of controlled cell death pathways with higher priority is selected to generate the final ranking result.

[0058] In this embodiment, the controlled cell death pathways are prioritized based on the pooled p-value and consistency score to generate a final ranking result. Specifically, the pooled p-value and consistency score of each pathway are first integrated using the name of the controlled cell death pathway as an index to construct a comprehensive evaluation index. The pooled p-value reflects the overall significance level of the pathway under multiple enrichment analysis methods; a smaller value indicates stronger enrichment evidence. The consistency score reflects the stability of the pathway across multiple ranking results; a lower value indicates higher ranking consistency. By jointly representing these two dimensions, the controlled cell death pathways can be comprehensively evaluated from both significance and stability perspectives.

[0059] Furthermore, to eliminate differences in numerical range and units among different evaluation indicators, the comprehensive evaluation indicators are standardized. Specifically, the merged p-value and consistency score can be uniformly directionally transformed to ensure they both meet the consistency rule that "a higher numerical value indicates higher priority," and then normalized to map them to a unified numerical range. Subsequently, the standardized indicators are weighted and fused based on preset weights to obtain the comprehensive priority score for each controlled cell death pathway. Preferably, under normal circumstances, the two indicators can be fused using an equal-weight method to balance enrichment significance and ranking consistency; in specific application scenarios, the weight allocation can be adjusted according to needs to highlight the importance of a particular evaluation dimension.

[0060] After obtaining the overall priority score, the controlled cell death pathways are ranked, and a set of pathways with higher priority is selected. Specifically, pathways can be ranked from best to worst according to their overall priority score, with pathways scoring higher being prioritized as candidate key controlled cell death pathways. Simultaneously, a two-dimensional distribution relationship can be constructed by combining the pooled p-value and consistency score to assist in the judgment of the results, where pathways with both lower pooled p-values ​​and lower consistency scores have higher priority. Finally, the output includes result data containing pathway names, pooled p-values, consistency scores, and overall priority scores, generating a set of high-priority controlled cell death pathways to provide a basis for subsequent biological analysis or application decisions.

[0061] Specifically, for each RCD pathway, there is a pooled p-value and a consistency score. The consistency score is an index calculated based on the RRA algorithm and is used to measure the consistency of the pathway's ranking among the seven enrichment methods. The lower the score, the higher the ranking consistency. The pooled p-value is obtained by combining the p-values ​​of the seven enrichment methods using the Tippett method, reflecting the enrichment significance of the pathway itself; the smaller the value, the stronger the enrichment intensity.

[0062] refer to Figure 4 As shown, Figure 4This is a visualization of the controlled cell death pathway priority assessment results in this embodiment. The horizontal axis represents the consistency score obtained by aggregating the ranking results based on multiple enrichment analysis methods, and the vertical axis represents the combined p-value obtained by merging multi-source p-values. The color of each data point is determined by the pathway enrichment p-value. The color bar on the right transitions from dark blue to dark red, representing p-values ​​decreasing from large to small. The size of the point represents the number of hits (i.e., the number of methods that detected significant pathway enrichment). The magenta dashed line marks the p-value threshold of 0.05. The closer the point corresponding to a pathway is to the lower left area of ​​the chart, the lower its consistency score and combined p-value. This means that the pathway has higher ranking consistency and stronger enrichment significance, and therefore has a higher priority. Each scatter point in the figure corresponds to a controlled cell death pathway, and the position of different pathways in the two-dimensional space reflects their comprehensive performance in both the "enrichment significance" and "ranking consistency" dimensions.

[0063] Specifically, the horizontal axis, consistency score, characterizes the ranking stability of each controlled cell death pathway across multiple enrichment analysis methods. A smaller score indicates greater consistency in ranking across different methods and higher stability. The vertical axis, pooled p-value, characterizes the enrichment significance of the pathway under combined analysis using multiple methods; a smaller p-value indicates more significant enrichment. The dashed line in the figure represents the preset significance threshold, used to distinguish between statistically significant and non-significant pathways. The color of the scatter plots indicates the pooled p-value; a redder color indicates higher significance, while a bluer color indicates weaker significance. The size of the scatter plots represents the hit count, i.e., the number of times the significance threshold is reached across multiple enrichment analysis methods; a larger value indicates that the pathway is identified as significant in more methods.

[0064] Furthermore, by analyzing the distribution of scatter points in the graph, high-priority controlled cell death pathways can be visually identified. Pathways located in the lower left region of the graph exhibit both low pooled p-values ​​and low consistency scores, indicating that these pathways not only have strong significance in various enrichment analysis methods but also show high consistency in ranking results across different methods. Therefore, they have the highest priority and should be the focus of attention. In contrast, pathways located in the upper right region typically have high pooled p-values ​​and high consistency scores, indicating weaker enrichment significance and poorer ranking stability, thus having lower priority. For pathways located in the middle region, their biological significance can be further comprehensively assessed by considering the scatter point size and specific application requirements.

[0065] therefore, Figure 4By combining the merged p-value and the consistency score in a two-dimensional display, an intuitive priority assessment of controlled cell death pathways is achieved. This reflects both the integration results of multi-source significance information in step S4 and the ranking consistency analysis results in step S5, providing a visual basis for priority screening in step S6. This helps to accurately identify key controlled cell death pathways that simultaneously possess high significance and high stability in multi-method analysis.

[0066] In one embodiment, provided that gene expression levels, differential analysis results, or gene sequencing information can be provided, this method is also applicable to proteomic data, single-cell expression data, and other processed omics data that can be mapped to the gene level, thereby achieving unified sequencing analysis of controlled cell death pathways.

[0067] Please see Figure 5 This application also provides a controlled cell death sequencing system, the system comprising: The acquisition module is used to acquire transcriptome expression data of the target sample; The gene list determination module is used to determine a list of target genes associated with controlled cell death based on transcriptome expression data. The enrichment analysis module is used to perform enrichment analysis on a pre-defined set of genes for controlled cell death pathways based on a target gene list, using various enrichment analysis methods to obtain the p-value and ranking results for each pathway. The p-value merging module is used to merge p-values ​​obtained from multiple enrichment analysis methods for each controlled cell death pathway to obtain a merged p-value. The sorting and aggregation module is used to perform sorting and aggregation processing based on the sorting results corresponding to various enrichment analysis methods to obtain a consistency score. The priority assessment module is used to prioritize each controlled cell death pathway based on the merged p-value and consistency score, determine the set of controlled cell death pathways with higher priority, and generate the final ranking result.

[0068] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0069] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0070] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0071] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0072] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0073] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0074] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0075] 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.

[0076] This application provides a controlled cell death sequencing method and system. This method acquires and analyzes transcriptomic expression data of target samples to construct a list of target genes related to controlled cell death. It then combines multiple enrichment analysis methods to comprehensively evaluate preset pathways, thereby achieving a multi-dimensional characterization of different controlled cell death pathways. Compared to traditional techniques relying solely on a single enrichment analysis strategy, this method, by introducing a multi-method parallel analysis mechanism, effectively reduces the bias of a single algorithm on the results, significantly improving the comprehensiveness and accuracy of pathway identification. Furthermore, by merging the p-values ​​obtained from multiple enrichment analysis methods, the stability of statistical significance evaluation is enhanced. Simultaneously, by combining the consistency score obtained from sequencing aggregation, the sequencing information of each pathway under different analysis methods is effectively integrated, thus constructing a more reliable comprehensive evaluation index. Based on this, prioritizing controlled cell death pathways allows for more precise screening of key pathways, improving the discriminative power and interpretability of the results. This method can efficiently identify key controlled cell death pathways in complex biological data contexts, providing strong data support for disease mechanism research, target screening, and precision medicine, and has significant practical value and promotional significance.

[0077] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0078] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0079] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0080] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. 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 comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0081] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A controlled cell death sequencing method, characterized in that, The method includes the following steps: Obtain transcriptomic expression data of the target sample; A list of target genes was determined based on the transcriptome expression data; Based on the target gene list, various enrichment analysis methods were used to perform enrichment analysis on the preset controlled cell death pathway gene set to obtain the p-value and ranking results for each pathway. For each controlled cell death pathway, the p-values ​​obtained from multiple enrichment analysis methods were merged to obtain the merged p-value; Based on the ranking results corresponding to various enrichment analysis methods, a ranking aggregation process is performed to obtain a consistency score. Based on the merged p-value and the consistency score, the controlled cell death pathways are prioritized to determine the set of controlled cell death pathways with higher priority, and the final ranking result is generated.

2. The method according to claim 1, characterized in that, The transcriptome expression data includes gene expression matrix data and differentially expressed gene list data; The gene expression matrix data is used to perform expression profile-based enrichment analysis; The differentially expressed gene list data is used to perform enrichment analysis based on the gene list, and the gene list supports a single-column gene identifier format or a two-column data format containing gene identifiers and corresponding numerical indicators.

3. The method according to claim 1, characterized in that, The determination of the target gene list based on the transcriptome expression data includes: The transcriptome expression data were subjected to gene identification unification and preprocessing to obtain standardized gene data; Based on the standardized gene data, a list of target genes is obtained according to preset screening rules.

4. The method according to claim 1, characterized in that, Based on the target gene list, multiple enrichment analysis methods are used to perform enrichment analysis on the preset controlled cell death pathway gene set to obtain the p-value and ranking results for each pathway, including: Based on the target gene list and the pre-defined controlled cell death pathway gene set, enrichment analysis was performed on each pathway using both overrepresentation analysis and functional category scoring methods. The overrepresentation analysis method was used to calculate the significance p-value of each pathway based on the gene overlap relationship between the target gene list and the background gene set. The functional category scoring method was used to calculate the enrichment score and corresponding significance index of each pathway based on gene expression ranking information or sample expression profiles. The significance p-values ​​of each controlled cell death pathway were calculated based on each enrichment analysis method, and the pathways were ranked to obtain the corresponding pathway ranking results. Summarize the p-values ​​and pathway ranking results obtained from various enrichment analysis methods.

5. The method according to claim 4, characterized in that, The calculation process of the overrepresentation analysis method includes: Based on the target gene list, the preset controlled cell death pathway gene set, and the background gene set, determine the number of genes in each controlled cell death pathway and the number of genes overlapping with the target gene list. Based on the number of genes and their overlap, the significance p-values ​​of each pathway were calculated using the hypergeometric test or Fisher's exact test. The p-values ​​are corrected by multiple tests, and the pathways are ranked based on the corrected p-values ​​to obtain the corresponding pathway ranking results.

6. The method according to claim 4, characterized in that, The calculation process of the functional category scoring method includes: Based on the gene expression sorting information or expression matrix data corresponding to the target gene list, construct a whole gene sorting list or sample expression profile; For a pre-defined set of genes for controlled cell death pathways, enrichment scores are calculated on the sorted list or expression profile using gene set enrichment analysis methods to obtain the enrichment scores and significance indices for each pathway. The gene set enrichment analysis methods include at least one of the following: phenotypic grouping-based enrichment analysis methods, pre-sorted gene lists-based enrichment analysis methods, and single-sample enrichment analysis methods. Based on the permutation test, the enrichment score was evaluated for significance to obtain the corresponding p-value, and the controlled cell death pathways were ranked to obtain the corresponding pathway ranking results.

7. The method according to claim 1, characterized in that, For each controlled cell death pathway, the p-values ​​obtained from multiple enrichment analysis methods are merged to obtain a merged p-value, including: Based on the significance p-values ​​obtained by each of the enrichment analysis methods, a multi-source p-value set corresponding to the same controlled cell death pathway is constructed. Based on the multi-source p-value set, a p-value merging algorithm is used to statistically integrate each p-value to obtain the merged p-value of each controlled cell death pathway; the p-value merging algorithm includes at least one of a combination statistical method based on the independence test hypothesis or a weighted fusion method based on rank statistics.

8. The method according to claim 1, characterized in that, The ranking results based on multiple enrichment analysis methods are subjected to ranking aggregation processing to obtain a consistency score, including: Based on the pathway ranking results obtained by each of the enrichment analysis methods, a multi-source ranking list corresponding to the same controlled cell death pathway is constructed. The multi-source sorting list is uniformly standardized to generate a standardized multi-source sorting list; Based on the standardized multi-source sorting list, a robust sorting aggregation algorithm is used to fuse the sorting information of each controlled cell death pathway to obtain a consistent sorting value for each pathway. Based on the consistency ranking value, the consistency score of each controlled cell death pathway is calculated and generated.

9. The method according to claim 1, characterized in that, Based on the merged p-value and the consistency score, the controlled cell death pathways are prioritized to determine the set of controlled cell death pathways with higher priority, generating a final ranking result, including: A comprehensive evaluation index for each controlled cell death pathway is constructed based on the merged p-value and the consistency score. The comprehensive evaluation indicators are standardized to generate standardized comprehensive evaluation indicators; The standardized comprehensive evaluation indexes are weighted and fused based on preset weights to obtain a comprehensive priority score for each controlled cell death pathway. Based on the comprehensive priority score, each controlled cell death pathway is ranked, and a set of controlled cell death pathways with higher priority is selected to generate the final ranking result.

10. A controlled cell death sequencing system, characterized in that, The system includes: The acquisition module is used to acquire transcriptome expression data of the target sample; A gene list determination module is used to determine a list of target genes associated with controlled cell death based on the transcriptome expression data. The enrichment analysis module is used to perform enrichment analysis on the preset controlled cell death pathway gene set based on the target gene list using multiple enrichment analysis methods, and obtain the p-value and ranking results for each pathway. The p-value merging module is used to merge p-values ​​obtained from multiple enrichment analysis methods for each controlled cell death pathway to obtain a merged p-value. The sorting and aggregation module is used to perform sorting and aggregation processing based on the sorting results corresponding to multiple enrichment analysis methods to obtain a consistency score. The priority evaluation module is used to evaluate the priority of each controlled cell death pathway based on the merged p-value and the consistency score, determine the set of controlled cell death pathways with higher priority, and generate the final ranking result.

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