Children asthma development risk prediction method, device and equipment and storage medium

Abstract

The invention provides a children asthma development risk prediction method and device, equipment and a storage medium, and relates to the technical field of quasi-timing analysis. The method comprises the following steps: acquiring single cell sequencing data of a to-be-detected sample of a target person, and performing quasi-timing analysis to obtain a quasi-timing analysis result; constructing to obtain a cell development quasi-time sequence span interval; dividing into different to-be-time-sequence unit sections, and obtaining average expression quantity data of each to-be-time-sequence unit section; and determining a state section in the quasi-time sequence unit section, and obtaining a risk prediction result. According to the method provided by the invention, a brand-new data-driven asthma risk assessment and management mode is provided through highly refined cellular level analysis, the accuracy of prediction, assessment and data analysis of asthma is remarkably improved, and the method has a profound clinical application prospect.

Description

Technical Field

[0001] The present application relates to the technical field of pseudo-time series analysis, and in particular to a method, device, equipment and storage medium for predicting the risk of childhood asthma development. Background Art

[0002] Asthma is a complex and common chronic respiratory disease. Its pathological basis involves chronic inflammatory response in the airways, which shows high individual differences and heterogeneity. The diversity of this disease is reflected in its clinical manifestations, pathogenesis, and response to treatment. Therefore, it poses great challenges to the diagnosis and treatment of asthma. Although scientific and technological advances have brought new diagnostic tools and treatment methods, the management of asthma still faces the need for individualization and precision.

[0003] At present, the diagnosis of asthma mainly relies on the patient's clinical symptoms and a series of physiological function tests, such as pulmonary function tests and allergen tests. Although these methods provide necessary diagnostic information, they still have obvious limitations. For example, pulmonary function tests may not fully reveal the extent of airway inflammation or predict the long-term trend of the disease. In addition, the currently widely used blood biomarkers, such as IgE levels and eosinophil counts in serum, can reflect a certain inflammatory state, but they are still insufficient in determining disease subtypes and providing precise treatment guidance.

[0004] Although current technologies are able to provide disease monitoring based on traditional biomarkers, the sensitivity and specificity of these indicators are still limited. For example, although the eosinophil count is higher in some asthma patients, the difference between different subtypes is not obvious, and the predictive ability of treatment response is limited. In addition, current technologies cannot effectively monitor the real-time progression of the disease and the immediate response to treatment, so that asthma management often cannot be adjusted and optimized in real time.

[0005] Therefore, the shortcomings of existing technologies in personalized treatment and precise management of asthma are obvious. Single or traditional diagnostic methods are difficult to cope with the complexity of asthma pathology, and there is a lack of diagnostic tools that can reflect the dynamic changes of the disease in real time, which greatly limits the timeliness and effectiveness of treatment strategies. Future research needs to develop more advanced technologies to achieve a deeper understanding of asthma and more effective management. Summary of the invention

[0006] The purpose of the present invention is to provide a method, device, equipment and storage medium for predicting the risk of childhood asthma development. The method is for predicting and evaluating the risk of childhood asthma development for non-diagnostic purposes, and has the advantages of being able to effectively obtain a probabilistic prediction of the risk of childhood asthma development, accurate prediction, and being able to effectively analyze the differentiation trajectory and extract useful information.

[0007] In order to achieve the above-mentioned purpose of the present invention, the following technical solutions are particularly adopted:

[0008] In a first aspect, the present invention provides a method for predicting the risk of childhood asthma development, comprising:

[0009] Acquire single-cell sequencing data of the sample to be tested of the target person, and perform pseudo-time series analysis to obtain a pseudo-time series analysis result including a pseudo-time series trajectory;

[0010] According to the time series trajectory of the pseudo-time series analysis result, constructing a pseudo-time series span interval of cell development;

[0011] The pseudo-time span of cell development is divided into different pseudo-time unit segments; and the gene expression value of each pseudo-time unit segment is calculated based on the marker gene to obtain the average expression data of each pseudo-time unit segment;

[0012] Confirm the state segment in the pseudo-sequential unit segment; and obtain the risk prediction result corresponding to the target person based on the state segment and the average expression data.

[0013] In an optional embodiment, constructing a pseudo-time series span interval of cell development based on the time series trajectory of the pseudo-time series analysis result includes:

[0014] Extracting the pseudo-time series value of each cell in the pseudo-time series analysis result;

[0015] Based on the timing trajectory, the pseudo-timing value of each cell is counted to determine the maximum pseudo-timing value and the minimum pseudo-timing value of the cell development; and the segment between the maximum pseudo-timing value and the minimum pseudo-timing value is used as the pseudo-timing span interval of the cell development.

[0016] In an optional embodiment, the pseudo-time span interval of cell development is divided into different pseudo-time unit segments; and the gene expression value of each pseudo-time unit segment is calculated based on the marker gene to obtain the average expression data of each pseudo-time unit segment, including:

[0017] According to the preset number of units, the pseudo-time-series span interval of cell development is evenly divided to obtain a plurality of pseudo-time-series unit segments with equal segment ranges and arranged in order;

[0018] Obtaining gene expression values ​​based on marker genes of all cells in the pseudo-time series analysis results as marker gene expression values;

[0019] Performing logarithmic transformation on the marker gene expression values ​​to obtain a transformed expression value corresponding to each marker gene expression value;

[0020] The transformed expression values ​​of the pseudo-sequential unit segments of all cells are averaged to obtain the average expression data of each pseudo-sequential unit segment.

[0021] In an optional embodiment, the calculation method of the transformed expression value is:

[0022] LEL = log2(REL);

[0023] Wherein, LEL represents the transformed expression value; REL represents the marker gene expression value;

[0024] The calculation method of the average expression data is:

[0025]

[0026] Among them, LEL n is the average expression data of the marker gene of the nth pseudo-sequential unit segment; N is the number of cells in the nth pseudo-sequential unit segment based on the marker gene and whose expression value of the marker gene is not 0; LELC i Represents the gene expression value of the ith cell in the pseudo-temporal unit segment.

[0027] In an optional embodiment, the confirming the state segment in the pseudo-sequential unit segment; and obtaining the risk prediction result corresponding to the target person according to the state segment and the average expression data, includes:

[0028] Determining a state segment in the pseudo-time-sequential unit segment according to an arrangement position of the pseudo-time-sequential unit segment in the pseudo-time-sequential span of cell development;

[0029] Calculate the change rates in different periods according to the average expression data corresponding to different state segments;

[0030] Data analysis is performed according to the change rates in different periods to obtain the risk prediction results.

[0031] In an optional embodiment, the state segment includes an initial state node, an intermediate state node, and a final state node;

[0032] The method for confirming the status section includes:

[0033] The number of preset units is defined as n; wherein n is an odd number; and satisfies n=2k+1; k is a non-negative integer; and k+1 is the midpoint index value;

[0034] Among all the quasi-sequential unit segments, the quasi-sequential unit segment corresponding to the initial state node is Node1, and the corresponding average expression data is LEL 初始 ;

[0035] The pseudo-sequential unit segment corresponding to the intermediate state node is Node k+1 , the corresponding average expression data is LEL 中间 ;

[0036] The pseudo-sequential unit segment corresponding to the final state node is Node n , the corresponding average expression data is LEL 最终 .

[0037] In an optional embodiment, the change rates in different periods include:

[0038] Base rate of change, early rate of change, and late rate of change;

[0039] The calculation method of the basic change rate is:

[0040]

[0041] The calculation method of the previous change rate is:

[0042]

[0043] The calculation method of the late change rate is:

[0044]

[0045] Among them, BRV, ERV and LRV are the basic change rate, the early change rate and the late change rate respectively.

[0046] In an optional embodiment, the data analysis is performed according to the change rate in different periods to obtain the risk prediction result, including:

[0047] When ERV>BRV and LRV<BRV, the risk prediction result is that the target person is predicted to be in a high probability severe asthma state;

[0048] When ERV≤BRV and LRV≥BRV, the risk prediction result is that the target person is predicted to be in a low-probability severe asthma state.

[0049] In an optional embodiment, the method for predicting the risk of childhood asthma development further comprises: determining the marker gene;

[0050] Determining the marker gene comprises:

[0051] The RNA sequencing data based on single-cell sequencing were obtained from patients with severe acute asthma and patients without severe acute asthma, and quality-controlled sequencing data were obtained after library splitting, quality control and dimensionality reduction.

[0052] Performing cell clustering on the quality control sequencing data, screening out the gene with the largest expression change in each cell cluster that meets the preset number of changes as a marker gene; and determining the cell type corresponding to the cell cluster according to the marker gene;

[0053] A differential expression analysis is performed on each of the cell clusters, and genes with a significance level not greater than a threshold are screened out as the marker genes.

[0054] In a second aspect, the present invention provides a device for predicting the risk of childhood asthma development, comprising:

[0055] A time series analysis module is used to obtain single-cell sequencing data of the target person's sample to be tested, and perform pseudo-time series analysis to obtain a pseudo-time series analysis result containing a pseudo-time series trajectory;

[0056] An interval construction module, used to construct a pseudo-time series span interval of cell development according to the time series trajectory of the pseudo-time series analysis result;

[0057] An expression calculation module is used to divide the pseudo-time span of cell development into different pseudo-time unit segments; and calculate the gene expression value of each pseudo-time unit segment based on the marker gene to obtain the average expression data of each pseudo-time unit segment;

[0058] The risk prediction module is used to confirm the state segment in the pseudo-sequential unit segment; and obtain the risk prediction result corresponding to the target person based on the state segment and the average expression data.

[0059] In a third aspect, the present invention provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method for predicting the risk of development of asthma in children described in any one of the aforementioned embodiments.

[0060] In a fourth aspect, the present invention provides a computer storage medium storing a computer program, which, when executed on a processor, implements the method for predicting the risk of developing asthma in children according to any one of the aforementioned embodiments.

[0061] The method, device, equipment and storage medium for predicting the development risk of childhood asthma provided by the present application relate to the field of pseudo-sequential analysis technology. The method for predicting the development risk of childhood asthma can accurately depict the expression profile of each cell by acquiring single-cell sequencing data, which is crucial for understanding the heterogeneity and complexity of asthma. By conducting a detailed analysis of the pseudo-sequential span interval of cell development, the specific path and risk of the development of different childhood asthma conditions can be revealed, thereby achieving more personalized disease prediction and diagnosis; using the results of pseudo-sequential analysis, the temporal trajectory of cell development can be constructed, which not only helps to understand the time dimension changes of asthma pathology, but also provides a new perspective and tool for dynamically monitoring the progression of the disease. This dynamic disease monitoring method is of great value for timely adjusting treatment plans and preventing the deterioration of the disease; by detailed division of the pseudo-sequential span interval of cell development and analysis of the expression values ​​based on marker genes, specific disease state segments can be identified, thereby improving the sensitivity and specificity of the prediction. This is of great significance for distinguishing different subtypes of asthma, predicting the development trend of the disease and providing a reference for formulating targeted treatment plans. Based on the obtained average expression data and status segment analysis, the patient's responsiveness to specific treatments can be predicted more accurately, thereby assisting the clinician in formulating more effective treatment strategies. This treatment decision-making method based on precise biomarkers can significantly improve the success rate of treatment and reduce unnecessary medical interventions.

[0062] In summary, the method provided in this application, through highly refined cellular-level analysis, provides a new, data-driven approach to asthma risk assessment and management, significantly improves the accuracy of asthma prediction, assessment, and data analysis, and has far-reaching prospects for clinical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. For ordinary technicians in this field, other related drawings can also be obtained based on these drawings without creative work.

[0064] Figure 1 It is a structural diagram of the hardware operating environment involved in the embodiment of the method for predicting the risk of childhood asthma development of the present invention;

[0065] Figure 2 This is a schematic diagram of the process of Example 1 of the method for predicting the risk of childhood asthma development of the present invention;

[0066] Figure 3 This is a schematic diagram of a detailed process of step S200 in Example 2 of the method for predicting the risk of childhood asthma development of the present invention;

[0067] Figure 4 This is a schematic diagram of a detailed process of step S300 in Example 2 of the method for predicting the risk of childhood asthma development of the present invention;

[0068] Figure 5 This is a schematic diagram of a detailed process of step S400 in Example 3 of the method for predicting the risk of childhood asthma development of the present invention;

[0069] Figure 6 This is a flow chart of the refinement of step S400 (including step S431 and step S432) in Example 3 of the method for predicting the development risk of childhood asthma of the present invention;

[0070] Figure 7 Schematic diagram of the process of Example 4 of the method for predicting the risk of childhood asthma development of the present invention (including step S500);

[0071] Figure 8 This is a schematic diagram of a detailed process of step S500 in Example 4 of the method for predicting the risk of childhood asthma development of the present invention;

[0072] Fig. 9 This is a schematic diagram of the distribution of the expression levels of the specific gene (SA100A8) in all cells of different target persons in Example 5 of the method for predicting the risk of childhood asthma development of the present invention;

[0073] Fig.10 This is a schematic diagram of the distribution of the expression levels of the specific gene (SA100A9) in all cells of different target persons in Example 5 of the method for predicting the risk of childhood asthma development of the present invention;

[0074] Fig.11 This is a schematic diagram of the expression level changes of all state node characteristic genes of different target persons in Example 5 of the method for predicting the development risk of childhood asthma of the present invention;

[0075] Fig.12 Schematic diagram of module connection of a device for predicting the risk of childhood asthma development in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The embodiments of the present invention are described in detail below, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions.

[0077] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0078] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0079] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0080] like Figure 1 , which is a schematic diagram of the structure of the hardware operating environment of the terminal involved in the embodiment of the present invention.

[0081] The childhood asthma development risk prediction system provided by the embodiment of the present invention can be a PC, or a mobile terminal device such as a smart phone, a tablet computer or a portable computer. The childhood asthma development risk prediction system may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005 and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen, an input unit such as a keyboard, and a remote control. The optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory, such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001. Optionally, the childhood asthma development risk prediction system may also include an RF (Radio Frequency) circuit, an audio circuit, a WiFi module, and the like.

[0082] Those skilled in the art will understand that Figure 1 The childhood asthma development risk prediction system shown in the figure does not constitute a limitation thereof, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a data interface control program, a network connection program, and a child asthma development risk prediction program.

[0083] The present application provides a method for predicting the risk of childhood asthma development. By combining single-cell sequencing data with pseudo-time series analysis technology, accurate prediction and personalized diagnosis of the risk of childhood asthma development can be achieved. This method analyzes the pseudo-time series span interval of cell development in detail and constructs a cell development time series trajectory, thereby revealing the specific path of asthma development, providing dynamic disease monitoring, and helping to adjust treatment plans in a timely manner. In addition, by analyzing the expression values ​​of marker genes, the method can identify disease state segments, improve the sensitivity and specificity of predictions, and assist in the clinical formulation of effective treatment strategies. Overall, this treatment decision-making method based on precise biomarkers can significantly improve the success rate of asthma treatment and has important clinical application value.

[0084] Example 1

[0085] Reference Figure 2 Embodiment 1 of the present invention provides a method for predicting the risk of childhood asthma development, comprising:

[0086] Step S100, obtaining single-cell sequencing data of a sample to be tested of a target person, and performing a pseudo-time series analysis to obtain a pseudo-time series analysis result including a pseudo-time series trajectory;

[0087] It should be noted that the method for predicting the risk of childhood asthma development provided in the embodiments of the present application is used for non-diagnostic purposes and cannot directly derive the disease diagnosis results or health status of the target person or patient; the prediction results obtained are also probabilistic risk prediction results, but at the basic research level, through the relevant indicators and information of the target person, the method in this embodiment is used to explore the correlation between asthma at different stages and single-cell sequencing data, and through the evaluation results, provide qualitative and quantitative data support and theoretical support for the in-depth research of scientific researchers, and provide theoretical and data support for further clinical decision-making.

[0088] As mentioned above, single-cell sequencing is a high-throughput gene sequencing technology that measures gene expression at the level of a single cell. This technology can reveal the heterogeneity and complexity in cell populations, providing a powerful tool for understanding cell functions, developmental processes, and disease mechanisms.

[0089] The target persons mentioned above are samples corresponding to the persons who need to be evaluated and predicted, namely children with severe acute asthma.

[0090] It should be noted that the target persons in the method provided in this embodiment are mainly children's asthma and pediatric asthma, which are different from adult asthma. However, it is not excluded that the overall method or some of the method steps can be applied to the risk prediction of asthma in adults, but such applications are within the scope of protection of this application.

[0091] The sample to be tested may be a sample collected from a person who needs to be evaluated and predicted, for example, a peripheral blood sample, or a peripheral blood sample before treatment.

[0092] The single-cell sequencing data mentioned above can be: raw data obtained by single-cell sequencing technology, and the raw data can be subjected to pseudo-time series analysis after being subjected to standardization, quality control, data dimension reduction and visualization.

[0093] Through single-cell sequencing technology, single-cell sequencing data can be obtained for the sample to be tested through the following methods:

[0094] A. Sample collection and preparation:

[0095] (1) Collect peripheral blood samples from the target individuals before treatment as test samples, and record the clinical information, disease status, and diagnosis results of the children in detail.

[0096] (2) Peripheral blood mononuclear cells (PBMCs) were isolated from the collected blood (target sample) using Ficoll-Paque medium and density gradient centrifugation.

[0097] The viability of isolated PBMCs was assessed using trypan blue staining, and only samples with viability exceeding 90% were selected for further analysis. The number of cells contained in each sample was estimated based on the calculated volume of the cell suspension (more than 12,000 cells were considered qualified) to ensure that the number of cells for the subsequent capture and sequencing process was sufficient.

[0098] B. Single-cell sequencing:

[0099] (1) Capture and lysis of single cells: Single cells were isolated and captured from blood samples using the Chromium Next GEM Single Cell V(D)J Reagent Kit (v1.1) from 10x Genomics. Gel beads and partitioning oil containing barcodes were mixed with the cell suspension and loaded onto a 10x Genomics Chromium chip to form a single-cell gel bead-emulsion (GEM). In the GEM, cells were lysed and their RNA transcripts were barcoded by reverse transcription, thereby achieving specific labeling of the transcriptome of a single cell.

[0100] (2) cDNA amplification and library construction: After reverse transcription, cDNA carrying the cell barcode is amplified by PCR to ensure sufficient quantity for sequencing. Subsequently, the scRNA-seq library is constructed using the 5-cell library kit to prepare for subsequent sequencing.

[0101] (3) High-throughput sequencing: The constructed scRNA-seq library was subjected to high-throughput sequencing on the Illumina NovaSeq platform to generate paired-end reads of 2 × 150 base pairs in length. The sequencing results contain the RNA sequence information of each cell, providing a basis for subsequent data analysis.

[0102] C. Single-cell raw data processing: The raw sequencing data containing unique cell barcodes are split and assigned to the corresponding samples. The split data are then aligned with the GRCh38 reference genome using the Cell Ranger software suite to obtain the single-cell raw data required in the subsequent analysis process.

[0103] D. Single-cell data analysis: Various analysis steps were performed on the gene-cell barcode matrix using the Seurat package (v3.0.2) in R, including quality control, data normalization, dimensionality reduction, batch effect correction, clustering, and data visualization.

[0104] (1) Cell quality control:

[0105] Quality control was performed on most samples according to the following criteria to filter out low-quality cell data that did not meet the requirements:

[0106] 1) The total UMI count (nFeature_RNA) is between 200 and 6000;

[0107] 2) The proportion of mitochondrial genes, hemoglobin genes and ribosomal genes is limited to less than 10%, less than 0.1% and less than 3% respectively;

[0108] (2) Data dimensionality reduction and visualization:

[0109] The integrated data set was standardized using the ScaleData function, and the principal component analysis (PCA) dimensionality reduction (npcs = 30) was performed using the RunPCA function. Subsequently, the RunUMAP function (dims = 1:30) based on the unified manifold approximation and projection (UMAP) technology was used to perform nonlinear dimensionality reduction on the data after PCA dimensionality reduction and visualized in two-dimensional space to obtain single-cell sequencing data.

[0110] In this step, single-cell sequencing data is first collected from the children to be tested. These data contain gene expression information of individual cells extracted from the children's samples. Then, these data are processed using pseudo-time series analysis technology. Pseudo-time series analysis is a computational method that can infer the time trajectory of cell development based on single-cell gene expression data, even if these data were originally collected independently at different time points.

[0111] The result of this step is an analysis containing a pseudo-time-series trajectory of cell development, which can help researchers understand how cells change over time. This has the advantage of providing insights into how diseases progress at the cellular level, especially when time-series samples are not available.

[0112] This step can be implemented using a variety of existing software and algorithms, such as Seurat, Monocle, or other bioinformatics tools that support pseudo-time series analysis.

[0113] Step S200, constructing a pseudo-time series span interval of cell development according to the time series trajectory of the pseudo-time series analysis result;

[0114] Based on the pseudo-time series analysis results of step S100, the pseudo-time series span interval of cell development is further analyzed and constructed in this step, which means that the cells are segmented according to the time sequence of their development, and each segment represents a specific stage in the cell development process.

[0115] In this step, by defining specific span intervals, the development process of cells can be divided more finely, providing a basis for subsequent detailed analysis. Its advantage is that it can more accurately understand and predict how cells behave at different development stages, which is crucial for early diagnosis and prevention of diseases.

[0116] Step S300, dividing the pseudo-time span interval of cell development into different pseudo-time unit segments; and calculating the gene expression value of each pseudo-time unit segment based on the marker gene to obtain the average expression data of each pseudo-time unit segment;

[0117] As described above, the determined pseudo-chronological span of cell development is further subdivided into smaller units, namely pseudo-chronological unit segments. Then, the gene expression value of the marker gene in each segment is calculated to obtain the average expression data.

[0118] The above steps provide detailed gene expression information for each cell unit segment, which is crucial for determining disease status and understanding pathological mechanisms. The advantage is that the functional status of cells at each stage of development can be accurately grasped, and diseases can be more accurately classified and predicted.

[0119] This can be achieved using bioinformatics software, such as R packages or Python libraries, to calculate statistical descriptions of gene expression data, such as mean, variance, etc.

[0120] Step S400, confirming the state segment in the pseudo-sequential unit segment; and obtaining the risk prediction result corresponding to the target person according to the state segment and the average expression data.

[0121] As described above, based on the data in step S300, key status segments are identified, which may be closely related to the development of the disease. Then, based on these status segments and their average expression data, the risk of asthma development in the children to be tested is predicted. This step can provide a specific risk assessment result for each child to be tested, helping doctors and parents understand the health status and potential risks of the children. The advantage is that it provides personalized risk assessment through precise molecular level analysis, providing a scientific basis for formulating personalized treatment plans and preventive measures.

[0122] This step can be achieved through data mining and machine learning methods, such as using logistic regression, random forests, or deep learning models to predict asthma risk based on gene expression data.

[0123] In this embodiment, single-cell analysis technology is used to provide more detailed cellular-level information, reveal the heterogeneity of asthma, and identify new biomarkers related to the occurrence and development of asthma; bioinformatics tools are used to integrate and analyze single-cell data to improve the specificity and sensitivity of biomarkers; an asthma disease development risk warning model based on biomarker expression levels is established to predict treatment response and guide personalized treatment; through model validation research, biomarkers that can be continuously monitored are provided to achieve real-time tracking and management of asthma disease status.

[0124] Example 2

[0125] Reference Figure 3 Embodiment 2 of the present invention provides a method for predicting the risk of childhood asthma development. Based on the above embodiment 1, step S200, according to the time series trajectory of the pseudo-time series analysis result, constructs a pseudo-time series span interval of cell development, including:

[0126] Step S210, extracting the pseudo-time series value of each cell in the pseudo-time series analysis result;

[0127] In this step, a pseudo-time axis track is established based on the pseudo-time analysis. The pseudo-time values ​​(Pseudotime) of all cells in the sample are obtained, and the pseudo-time values ​​of each cell are extracted from the results of the pseudo-time analysis. Pseudo-time values ​​are obtained by analyzing the gene expression data of individual cells, and these values ​​reflect the relative position or time point of each cell in its development process.

[0128] The obtained pseudo-time series values ​​provide a data basis for subsequent steps, allowing researchers to conduct more in-depth analysis based on the development stage of each cell.

[0129] Pseudo-time series values ​​can be calculated using software tools such as Monocle and Seurat, which can estimate the development time of cells based on complex statistical models.

[0130] Step S220, based on the timing trajectory, the pseudo-timing value of each cell is counted to determine the maximum pseudo-timing value and the minimum pseudo-timing value of the cell development; and the segment between the maximum pseudo-timing value and the minimum pseudo-timing value is used as the pseudo-timing span interval of the cell development.

[0131] This step involves statistical analysis of the pseudo-time series values ​​obtained in the previous step for all cells and determining the range of these values, i.e. the maximum and minimum values. These values ​​define the time span of the developmental states of all cells, from the earliest to the most advanced developmental stages.

[0132] The determined maximum and minimum pseudo-time values ​​define the overall time frame of cell development, which is crucial for understanding the beginning and end stages of cell population development. The advantage is that it provides a clear time window for further analysis of how cells express different genes at different time stages.

[0133] For example, this step can be implemented using relevant functions in statistical software packages such as R or Python, such as using the min() and max() functions to obtain the minimum and maximum values ​​of a series of values.

[0134] For further reference, Figure 4 , the step S300, dividing the cell development pseudo-time span interval into different pseudo-time unit segments; and calculating the gene expression value of each pseudo-time unit segment based on the marker gene to obtain the average expression data of each pseudo-time unit segment, including:

[0135] Step S310, dividing the pseudo-time-series span interval of cell development evenly according to a preset number of units to obtain a plurality of pseudo-time-series unit segments with equal segment ranges and arranged in order;

[0136] In the above steps, the pseudo-time span interval of cell development obtained from the pseudo-time analysis is evenly divided according to the preset number of units. This means that the entire time trajectory is divided into multiple units of equal length, each unit representing a specific time period in the cell development process.

[0137] This division allows subsequent analysis to be performed for each specific time period, thereby more accurately identifying and comparing cell expression characteristics at different developmental stages. The advantage is that it provides a structured method to analyze and compare time series data, making data processing and result interpretation more systematic and standardized.

[0138] This can be done mathematically by determining the length of each cell by dividing the difference between the maximum and minimum pseudo-timing values ​​of cell development by the number of cells.

[0139] The above-mentioned preset number of units is the number of divided pseudo-sequential unit segments, which can be preset and can be adjusted accordingly based on the specific time periods and time points for clinical classification and staging of the development of childhood asthma.

[0140] Step S320, obtaining the gene expression values ​​based on the marker genes of all cells in the pseudo-time series analysis result as the marker gene expression values;

[0141] As described above, the gene expression values ​​of all cells for specific marker genes were extracted from the pseudo-time series analysis results.

[0142] The marker genes mentioned above can be predefined because they have important biological functions or indicative effects in the development of asthma or cell development. In this embodiment, S100A8 and S100A9 are used as marker genes.

[0143] By collecting expression data of marker genes, it is possible to deeply analyze the expression changes of these key genes at different developmental stages. This helps to identify possible disease markers or developmental key points. Advantages include the ability to directly associate gene expression changes with disease risk, enhancing the accuracy and relevance of predictions.

[0144] Step S330, performing logarithmic transformation on the marker gene expression values ​​to obtain a transformed expression value corresponding to each marker gene expression value;

[0145] As mentioned above, logarithmic transformation of gene expression values ​​is a data preprocessing step, the purpose of which is to normalize data distribution, reduce the impact of extreme values, and improve the stability and effect of statistical analysis. Logarithmic transformed data are usually closer to normal distribution, making subsequent statistical analysis more reliable. The advantage is that it improves the robustness of data processing, making the results less sensitive to extreme values ​​and more suitable for average calculation.

[0146] Step S340, calculating the mean of the transformed expression values ​​of the pseudo-sequential unit segments of all cells to obtain the average expression data of each pseudo-sequential unit segment.

[0147] As mentioned above, the logarithmically transformed expression values ​​of all cells in each pseudo-chronological unit segment are averaged to obtain the average expression level of the marker gene in that time period. The average expression data thus obtained provides a quantitative expression overview for each time period, which helps to identify the stage where the expression level changes significantly, which may be closely related to the increase or decrease of disease risk. Averaging reduces the impact of individual differences and provides clearer trends and patterns.

[0148] Furthermore, the calculation method of the transformed expression value is:

[0149] LEL = log2(REL); (Formula 1)

[0150] Wherein, LEL represents the transformed expression value; REL represents the marker gene expression value;

[0151] The calculation method of the average expression data is:

[0152]

[0153] Among them, LEL n is the average expression data of the marker gene of the nth pseudo-sequential unit segment; N is the number of cells in the nth pseudo-sequential unit segment based on the marker gene and whose expression value of the marker gene is not 0; LELC i Represents the gene expression value of the ith cell in the pseudo-temporal unit segment.

[0154] As mentioned above, the calculation of the transformed expression value involves logarithmic transformation of the expression value of the marker gene, using a logarithmic transformation with base 2. Wherein LEL represents the transformed expression value, and REL represents the original marker gene expression value.

[0155] In the above, the calculation of the average transformed expression value of each pseudo-chronological unit segment can obtain a single value reflecting the expression trend of all cells in the segment by calculating the average transformed expression value of each segment. Such processing simplifies the data and facilitates the observation of the overall expression trend of marker genes at different developmental stages.

[0156] Example 3

[0157] Reference Figure 5 Embodiment 3 of the present invention provides a method for predicting the risk of childhood asthma development. Based on the above embodiment 1, the step S400 confirms the state segment in the pseudo-sequential unit segment; and obtains the risk prediction result corresponding to the target person according to the state segment and the average expression data, including:

[0158] Step S410, determining the state segment in the pseudo-chronological unit segment according to the arrangement position of the pseudo-chronological unit segment in the pseudo-chronological span of cell development.

[0159] In the above steps, the key state segments are determined according to the arrangement positions of the pseudo-temporal unit segments in the pseudo-temporal span of cell development.

[0160] As mentioned above, "state segment" refers to a specific time point or time period within the pseudo-time span of cell development, which is considered to be a critical biological state change in the course of the disease.

[0161] These state segments are defined by dividing the pseudo-temporal trajectory of cell development into multiple nodes. They are based on the divided pseudo-temporal unit segments, and the segments in the pseudo-temporal unit segments are defined as corresponding state segments according to special attention to several key nodes therein, for example, the initial, intermediate and final state nodes, which correspond to different key stages of the pseudo-timing, respectively.

[0162] "State segments" can help identify and explain key biological stages in the disease process in single-cell pseudo-time series analysis, providing new strategies and targets for early diagnosis and treatment of the disease.

[0163] Step S420, calculating the change rates in different periods according to the average expression data corresponding to the different state segments;

[0164] After the state segments are determined, the rate of change of gene expression within these state segments is then calculated. Specifically, the average expression data of adjacent state segments can be compared to evaluate how gene expression changes rapidly over time.

[0165] By calculating the rate of change, the magnitude and speed of expression changes within each state segment can be quantified, providing a numerical basis for evaluating the dynamics of disease development.

[0166] Step S430, performing data analysis according to the change rates in different periods to obtain the risk prediction result.

[0167] In the above, data analysis is performed based on the change rate calculated in the previous step to predict the risk of asthma development. The change rate can be combined with a known disease development model or risk scoring system to assess the likelihood of an individual developing a disease.

[0168] Furthermore, the state segment includes an initial state node, an intermediate state node and a final state node.

[0169] The key nodes in the pseudo-time sequence unit segment may include: initial state nodes, intermediate state nodes and final state nodes. By dividing the pseudo-time sequence trajectory, key biological state nodes are determined, which represent key transition points in disease development or cell behavior.

[0170] The method for confirming the status section includes:

[0171] The number of the preset units is defined as n; wherein n is an odd number; and satisfies n=2k+1; k is a non-negative integer; and k+1 is the midpoint index value;

[0172] Among all the quasi-sequential unit segments, the quasi-sequential unit segment corresponding to the initial state node is Node1, and the corresponding average expression data is LEL 初始 ;

[0173] The pseudo-sequential unit segment corresponding to the intermediate state node is Node k+1 , the corresponding average expression data is LEL 中间 ;

[0174] The pseudo-sequential unit segment corresponding to the final state node is Node n , the corresponding average expression data is LEL 最终 .

[0175] As mentioned above, n is the total number of quasi-sequential units and k is the number of nodes before the midpoint, ensuring that there is a clear midpoint.

[0176] In the pseudo-time series analysis, the preset number of cells is set to an odd number. Setting the number of cells in the pseudo-time series analysis to an odd number can correspond well to the common classification or staging methods of childhood asthma. In clinical practice, childhood asthma can usually be divided into several major development stages, such as mild, moderate and severe, or classified according to the degree of symptom control. Such classification helps guide treatment plans and management strategies.

[0177] For example, in childhood asthma management, there are often several main categories or stages:

[0178] Mild asthma: Symptoms are mild, attacks are infrequent, there are fewer symptoms at night, and lung function is basically normal.

[0179] Moderate asthma: Symptoms are more frequent and may affect daily activities. Symptoms increase at night and lung function may be slightly affected.

[0180] Severe asthma: Daily symptoms are frequent, often occur at night, lung function is significantly affected, and quality of life is significantly reduced.

[0181] By setting the number of pseudo-sequential units to an odd number, for example, including but not limited to: 3, 5, 7, 9 units, etc., it can be naturally mapped to these three stages.

[0182] The above description satisfies the condition that n is an odd number and n=2k+1 in order to define the intermediate state node k+1.

[0183] For example, if n=5, then k=2, and the midpoint index value k+1=3. Then the five pseudo-time sequence unit segments are: Pseudotime_node1, Pseudotime_node2, Pseudotime_node3, Pseudotime_node4, Pseudotime_node5, where Pseudotime_node1 is the initial state node, and the corresponding average expression data is LEL 初始 ; Pseudotime_node3 is an intermediate state node, and the corresponding average expression data is LEL 中间 ; Pseudotime_node5 is the final state node, and the corresponding average expression data is LEL 最终 .

[0184] Furthermore, the change rates in different periods include:

[0185] Base rate of change, early rate of change, and late rate of change;

[0186] The calculation method of the basic change rate is:

[0187]

[0188] In formula 3, BRV is the geometric mean of the overall rate of change from the beginning to the end of the disease. This rate reflects the speed of change of the overall expression from the initial to the final state. It can provide an overview of the entire disease process, showing the average degree of change from the beginning to the end of the disease.

[0189] The calculation method of the previous change rate is:

[0190]

[0191] In Formula 4, ERV measures the rate of change from the onset of the disease to the intermediate stage. This rate helps understand how fast the disease progresses in the early stages.

[0192] The calculation method of the late change rate is:

[0193]

[0194] Among them, BRV, ERV and LRV are the basic change rate, the early change rate and the late change rate respectively.

[0195] In Formula 5, LRV measures the rate of change from the intermediate state to the final state. This rate shows how quickly the disease progresses in the later stages.

[0196] The above involves calculating the basic change rate (BRV), early change rate (ERV), and late change rate (LRV) to analyze the development risk and disease progression rate of childhood asthma. These rates help understand the speed of disease progression and its stage characteristics.

[0197] For further reference, Figure 6 The step S430, performing data analysis according to the change rate in different periods to obtain the risk prediction result, includes:

[0198] Step S431, when ERV>BRV and LRV<BRV, the risk prediction result is that the target person is predicted to be in a high probability severe asthma state;

[0199] Step S432, when ERV≤BRV and LRV≥BRV, the risk prediction result is that the target person is predicted to be in a low probability severe asthma state.

[0200] After calculating BRV, ERV and LRV, further analysis is required based on the rate of change in different periods to obtain risk prediction results.

[0201] If the rate of change in the early stage is higher than the overall average rate, and the rate of change in the later stage is lower than the average rate, this may indicate that the disease rapidly worsens in the early stage, but then progresses more slowly. This pattern prediction is consistent with the development of severe asthma, in which a sharp initial deterioration may be relieved or controlled. Therefore, the risk prediction result is that the target person is in a high probability severe asthma state. Among them, the high probability severe asthma state can be interpreted as having a high probability that the target person is currently in a severe asthma state.

[0202] If the rate of change in the early stage is lower than or equal to the overall average rate, and the rate of change in the later stage is higher than or equal to the average rate, this may indicate that the disease develops slowly in the early stage and accelerates in the later stage, but the overall change amplitude is small. This pattern may correspond to milder symptoms or well-controlled asthma. Therefore, the risk prediction result can be: the target person is in a low-probability severe asthma state, that is, the target person has a low probability of being in a severe asthma state.

[0203] In this embodiment, a method based on statistical data analysis is provided to predict the risk level of childhood asthma. This method is based on pseudo-time series data of cell development and is achieved by calculating the rate of change of gene expression at different stages. The application of this method can help improve the management efficiency of asthma, especially in identifying high-risk patients in advance and formulating personalized treatment plans.

[0204] Example 4

[0205] Reference Figure 7 Embodiment 4 of the present invention provides a method for predicting the risk of childhood asthma development. Based on the above embodiment 1, the method for predicting the risk of childhood asthma development also includes:

[0206] Step S500, determining the marker gene;

[0207] Reference Figure 8 The step S500, determining the marker gene, comprises:

[0208] Step S510, obtaining RNA sequencing data based on single-cell sequencing from patients with severe acute asthma and patients without severe acute asthma, and performing library splitting, quality control and dimensionality reduction processing to obtain quality-controlled sequencing data;

[0209] In this embodiment, a method for determining marker genes is provided, which play a key role in predicting the risk of developing asthma in children. This process involves multiple steps of bioinformatics analysis, including data processing, cell clustering and differential expression analysis, and ultimately determines the key genes that can distinguish different asthma severities.

[0210] In step S510, single cell RNA sequencing data is obtained from patients with severe acute asthma and patients with non-severe acute asthma. Library splitting, quality control and dimensionality reduction are performed to optimize the data quality for subsequent analysis. This step ensures that the data obtained is of high quality and suitable for accurate bioinformatics analysis. High-quality data processing can significantly improve the reliability and accuracy of the analysis results.

[0211] The specific steps may include the following processing methods:

[0212] 1. Sample collection and preparation

[0213] (1) Peripheral blood samples were collected from children with non-severe acute asthma (asthma1, asthma2) and severe acute asthma (asthma3) before treatment, and the clinical information, disease status, and diagnosis of the children were recorded in detail;

[0214] (2) Peripheral blood mononuclear cells (PBMCs) were isolated from the collected blood using Ficoll-Paque medium and density gradient centrifugation. The viability of the isolated PBMCs was assessed using trypan blue staining, and only samples with a viability of more than 90% were selected for further analysis. The number of cells contained in each sample was estimated based on the calculated volume of the cell suspension (more than 12,000 cells were qualified) to ensure that the number of cells for the subsequent capture and sequencing process was sufficient.

[0215] 2. Single-cell sequencing:

[0216] (1) Single cell capture and lysis: Single cells were isolated and captured from blood samples using the Chromium Next GEM Single Cell V(D)J Reagent Kit (v1.1) from 10x Genomics. Gel beads and partitioning oil containing barcodes were mixed with the cell suspension and loaded onto a 10x Genomics Chromium chip to form a single cell gel bead-emulsion (GEM). In the GEM, cells were lysed and their RNA transcripts were barcoded by reverse transcription, thereby achieving specific labeling of the transcriptome of a single cell.

[0217] (2) cDNA amplification and library construction: After reverse transcription, cDNA carrying the cell barcode is amplified by PCR to ensure sufficient quantity for sequencing. Subsequently, a 5' library kit is used to construct a scRNA-seq library in preparation for subsequent sequencing.

[0218] (3) High-throughput sequencing: The constructed scRNA-seq library was subjected to high-throughput sequencing on the Illumina NovaSeq platform to generate paired-end reads of 2 × 150 base pairs in length. The sequencing results contain the RNA sequence information of each cell, providing a basis for subsequent data analysis.

[0219] 3. Single-cell raw data processing: The raw sequencing data containing unique cell barcodes are split and assigned to the corresponding samples. The split data are then aligned with the GRCh38 reference genome using the Cell Ranger software suite to obtain the single-cell raw data required in the subsequent analysis process.

[0220] Step S520, clustering the quality control sequencing data, screening out the gene with the largest expression change in each cell cluster that meets the preset change number as a marker gene; and determining the cell type corresponding to the cell cluster based on the marker gene.

[0221] As mentioned above, the processed data is clustered, and the genes with the largest expression changes in each cell cluster are screened as marker genes. Through cluster analysis, cell populations with similar phenotypes can be identified; marker genes help further define and distinguish these cell populations.

[0222] Identification of marker genes is crucial for understanding the biological functions of each cell type and helps to reveal the underlying mechanisms of asthma.

[0223] Step S520 may include the following steps:

[0224] 4. Single-cell data analysis: Various analysis steps were performed on the gene-cell barcode matrix using the Seurat package (v3.0.2) in R, including quality control, data normalization, dimensionality reduction, batch effect correction, clustering, and data visualization.

[0225] (1) Cell quality control: Quality control was performed on most samples according to the following standards, and low-quality cell data that did not meet the requirements were filtered out:

[0226] 1) The total UMI count (nFeature_RNA) is between 200 and 6000;

[0227] 2) The proportion of mitochondrial genes, hemoglobin genes and ribosomal genes is limited to less than 10%, less than 0.1% and less than 3% respectively;

[0228] (2) Data dimensionality reduction and visualization: The integrated data set was standardized using the ScaleData function, and the principal component analysis (PCA) dimensionality reduction (npcs = 30) was performed using the RunPCA function. Subsequently, the RunUMAP function (dims = 1:30) based on the unified manifold approximation and projection (UMAP) technique was used to perform nonlinear dimensionality reduction on the data after PCA dimensionality reduction and visualized in two-dimensional space.

[0229] (3) Cell clustering and subpopulation identification: The FindNeighbors function (dims=1:30) was used to construct a shared nearest neighbor graph on the data after PCA dimensionality reduction, and the FindClusters function (resolution=1.2) was used to perform cell clustering to identify the main cell types in PBMCs.

[0230] The FindAllMarkers function (only.pos=TRUE,min.pct=0.25,logfc.threshold=0.25) was used to screen the 50 genes with the largest expression changes in each cluster as the marker genes of the corresponding cluster, and the cell annotation was refined according to the marker genes of the corresponding cluster. The SeuratDisk package was used for multimodal reference mapping, the SCTransform function was used to perform single-cell transcriptomics transformation based on multimodal reference data, and the FindTransferAnchors function was used to find transfer anchors (normalization.method="SCT",reference.reduction="spca",dims=1:50), and reference mapping was performed on clusters with ambiguous cell types to determine the cell type.

[0231] Step S530, performing differential expression analysis on each of the cell clusters, and screening out genes with a significance level not greater than a threshold as the marker genes.

[0232] As mentioned above, differential expression analysis was performed on each cell cluster, and genes within the significance level threshold were screened out as marker genes. Differential expression analysis is used to identify genes with significant expression differences between severe and non-severe patients, which may be directly related to the severity of asthma.

[0233] As mentioned above, the significance level threshold is a concept in statistical analysis, which is used to determine whether the statistical test results are statistically significant. In biomedical research and other scientific research, the significance level is usually set to a predetermined probability value to evaluate whether the difference between the research results and the accidental results is large enough so that it can be considered that the observed effect or difference is real and not caused by random variation. For example, in this embodiment, it is defined as 0.01.

[0234] Specifically, the monocle2 package in R can be used to perform pseudo-time analysis of cell differentiation trajectories for each sample data set: the differentialGeneTest function based on the built-in "dpFeature" method of Monocle is used to perform differential expression analysis on each cell cluster, and genes with qval < 0.01 are screened out as marker genes.

[0235] Example 5

[0236] In this embodiment, a specific risk prediction experiment was carried out using the methods in Embodiments 1 to 4.

[0237] Experimental methods:

[0238] Refer to the methods in Examples 1 to 4.

[0239] Among them, the preset number of units is 5; the marker genes include S100A8 and S100A9.

[0240] Experimental samples: Test samples (peripheral blood samples) were collected from three target persons (children), namely: Asthma1, Asthma2 and Asthma3.

[0241] Experimental results:

[0242] refer to Figure 9-10 , which is the distribution of specific gene expression levels in all cells of the three target persons. It is used to preliminarily analyze and compare the changing trends of specific gene expression levels in cells of different types of target persons.

[0243] refer to Fig.11 , which is the expression level change of characteristic genes of all state nodes of the three target personnel. The average situation of the five units (state nodes) is used to compare the expression level change trend of specific genes of different types of target personnel in different units. The data in the figure are used to calculate related indicators (BRV, ERV and LRV), which can be used to intuitively supplement the results of Table 1.

[0244] The severity of asthma in children was classified based on changes in the expression levels of marker genes (S100A8, S100A9) in different cell development states.

[0245] Table 1. Risk prediction results for different children

[0246] Patient ID BRV ERV LRV Risk prediction results Actual clinical results Asthma1 2.90 1.25 6.72 Low probability of severe asthma state Non-severe asthma Asthma2 3.24 0.98 10.67 Low probability of severe asthma state Non-severe asthma Asthma3 2.74 3.83 1.95 High probability of severe asthma state Severe asthma

[0247] According to the risk prediction results in Table 1, Asthma1 meets ERV≤BRV and LRV≥BRV; therefore, it means that the specific gene in the child's body is in a relatively stable rising expression state for a long time, and the probability of developing severe asthma is low;

[0248] The data analysis of Asthma2 is similar to that of Asthma1, and the risk prediction result is also a low probability of severe asthma state.

[0249] As for Asthma3, the results are consistent with ERV>BRV and LRV<BRV, which means that the specific genes in the children's bodies are in a high expression state as a whole for a long time, and the probability of developing severe asthma is high.

[0250] In addition, reference Fig.12 The present invention also provides a device for predicting the risk of childhood asthma development, comprising:

[0251] The time series analysis module 10 is used to obtain the single-cell sequencing data of the sample to be tested of the target person, and perform pseudo-time series analysis to obtain a pseudo-time series analysis result including a pseudo-time series trajectory;

[0252] An interval construction module 20, configured to construct a pseudo-time series span interval of cell development according to the time series trajectory of the pseudo-time series analysis result;

[0253] The expression calculation module 30 is used to divide the pseudo-time span of cell development into different pseudo-time unit segments; and calculate the gene expression value of each pseudo-time unit segment based on the marker gene to obtain the average expression data of each pseudo-time unit segment;

[0254] The risk prediction module 40 is used to confirm the state segment in the pseudo-time-sequential unit segment; and obtain the risk prediction result corresponding to the target person according to the state segment and the average expression data.

[0255] It can be understood that the device of this embodiment corresponds to the method for predicting the risk of childhood asthma development in the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described repeatedly here.

[0256] The present application also provides a computer system. Exemplarily, the computer system includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to enable the computer device to execute the functions of each module in the above-mentioned method for predicting the risk of development of childhood asthma or the above-mentioned device for predicting the risk of development of childhood asthma.

[0257] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0258] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.

[0259] The present application also provides a computer-readable storage medium for storing the computer program used in the above-mentioned computer device. For example, the computer-readable storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0260] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0261] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0262] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0263] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for predicting the risk of childhood asthma development, characterized in that: include: Acquire single-cell sequencing data of the sample to be tested of the target person, and perform pseudo-time series analysis to obtain a pseudo-time series analysis result including a pseudo-time series trajectory; According to the time series trajectory of the pseudo-time series analysis result, constructing a pseudo-time series span interval of cell development; The pseudo-time span of cell development is divided into different pseudo-time unit segments; and the gene expression value of each pseudo-time unit segment is calculated based on the marker gene to obtain the average expression data of each pseudo-time unit segment; Confirm the state segment in the pseudo-sequential unit segment; and obtain the risk prediction result corresponding to the target person based on the state segment and the average expression data.

2. The method for predicting the risk of childhood asthma development as claimed in claim 1, characterized in that: The step of constructing a pseudo-time series span interval of cell development based on the time series trajectory of the pseudo-time series analysis result includes: Extracting the pseudo-time series value of each cell in the pseudo-time series analysis result; Based on the timing trajectory, the pseudo-timing value of each cell is counted to determine the maximum pseudo-timing value and the minimum pseudo-timing value of the cell development; and the segment between the maximum pseudo-timing value and the minimum pseudo-timing value is used as the pseudo-timing span interval of the cell development.

3. The method for predicting the risk of childhood asthma development as claimed in claim 1, characterized in that: The step of dividing the pseudo-time span of cell development into different pseudo-time unit segments; and calculating the gene expression value of each pseudo-time unit segment based on the marker gene to obtain the average expression data of each pseudo-time unit segment, including: According to the preset number of units, the pseudo-time-series span interval of cell development is evenly divided to obtain a plurality of pseudo-time-series unit segments with equal segment ranges and arranged in order; Obtaining gene expression values ​​based on marker genes of all cells in the pseudo-time series analysis results as marker gene expression values; Performing logarithmic transformation on the marker gene expression values ​​to obtain a transformed expression value corresponding to each marker gene expression value; Calculating the mean of the transformed expression values ​​of the pseudo-sequential unit segments of all cells to obtain the average expression data of each pseudo-sequential unit segment; The calculation method of the transformed expression value is: LEL = log2(REL); Wherein, LEL represents the transformed expression value; REL represents the marker gene expression value; The calculation method of the average expression data is: Among them, LEL n is the average expression data of the marker gene of the nth pseudo-sequential unit segment; N is the number of cells in the nth pseudo-sequential unit segment based on the marker gene and whose expression value of the marker gene is not 0; LELC i Represents the gene expression value of the ith cell in the pseudo-temporal unit segment.

4. The method for predicting the risk of childhood asthma development as claimed in claim 3, characterized in that: The confirming the state segment in the quasi-sequential unit segment; and obtaining the risk prediction result corresponding to the target person according to the state segment and the average expression amount data, including: Determining a state segment in the pseudo-time-sequential unit segment according to an arrangement position of the pseudo-time-sequential unit segment in the pseudo-time-sequential span of cell development; Calculate the change rates in different periods according to the average expression data corresponding to different state segments; Data analysis is performed according to the change rates in different periods to obtain the risk prediction results.

5. The method for predicting the risk of childhood asthma development as claimed in claim 4, characterized in that: The state segment includes an initial state node, an intermediate state node and a final state node; The method for confirming the status section includes: The number of preset units is defined as n; wherein n is an odd number; and satisfies n=2k+1; k is a non-negative integer; and k+1 is the midpoint index value; Among all the quasi-sequential unit segments, the quasi-sequential unit segment corresponding to the initial state node is Node1, and the corresponding average expression data is LEL 初始 ; The pseudo-sequential unit segment corresponding to the intermediate state node is Node k+1 , the corresponding average expression data is LEL 中间 ; The pseudo-sequential unit segment corresponding to the final state node is Node n , the corresponding average expression data is LEL 最终 ; The rates of change in different periods include: Base rate of change, early rate of change, and late rate of change; The calculation method of the basic change rate is: The calculation method of the previous change rate is: The calculation method of the late change rate is: Among them, BRV, ERV and LRV are the basic change rate, the early change rate and the late change rate respectively.

6. The method for predicting the risk of childhood asthma development as claimed in claim 5, characterized in that: The data analysis is performed according to the change rates in different periods to obtain the risk prediction results, including: When ERV>BRV and LRV<BRV, the risk prediction result is that the target person is predicted to be in a high probability severe asthma state; When ERV≤BRV and LRV≥BRV, the risk prediction result is that the target person is predicted to be in a low-probability severe asthma state.

7. The method for predicting the risk of childhood asthma development as claimed in claim 1, characterized in that: The method for predicting the risk of childhood asthma development also includes: determining the marker gene; Determining the marker gene comprises: The RNA sequencing data based on single-cell sequencing were obtained from patients with severe acute asthma and patients without severe acute asthma, and quality-controlled sequencing data were obtained after library splitting, quality control and dimensionality reduction. Performing cell clustering on the quality control sequencing data, screening out the gene with the largest expression change in each cell cluster that meets the preset number of changes as a marker gene; and determining the cell type corresponding to the cell cluster according to the marker gene; A differential expression analysis is performed on each of the cell clusters, and genes with a significance level not greater than a threshold are screened out as the marker genes.

8. A device for predicting the risk of childhood asthma development, characterized in that: include: A time series analysis module is used to obtain single-cell sequencing data of the target person's sample to be tested, and perform pseudo-time series analysis to obtain a pseudo-time series analysis result containing a pseudo-time series trajectory; An interval construction module, used to construct a pseudo-time series span interval of cell development according to the time series trajectory of the pseudo-time series analysis result; An expression calculation module is used to divide the pseudo-time span of cell development into different pseudo-time unit segments; and calculate the gene expression value of each pseudo-time unit segment based on the marker gene to obtain the average expression data of each pseudo-time unit segment; The risk prediction module is used to confirm the state segment in the pseudo-sequential unit segment; and obtain the risk prediction result corresponding to the target person based on the state segment and the average expression data.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the method for predicting the risk of development of childhood asthma according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The device stores a computer program, which, when executed on a processor, implements the method for predicting the risk of childhood asthma development according to any one of claims 1 to 7.

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