R language-based metabonomics full-spectrum quality control report generation system

Abstract

The invention discloses a metabonomics full spectrum quality control report generation system based on R language, and the system comprises a data importing module which is used for importing experimental data and a mass spectrum injection sample information table of a sample; the data processing module is used for performing format verification and data preprocessing on the experimental data and extracting key metabolite features; the data visualization module is used for analyzing according to the key metabolite characteristics, generating one or more visualization charts, and smoothing the chromatographic signal data of the quality control samples in the visualization charts; the report generation module is used for generating a quality control report according to the mass spectrum injection sample information table and the visual chart; and the data export module is used for exporting and downloading data of the quality control report. Through an automatic process based on the R code, the quality control efficiency and the data processing capacity are remarkably improved, a visual and detailed quality control report is provided, and a user can conveniently and rapidly evaluate the data quality and the experiment stability.

Description

Technical Field

[0001] This application relates to the fields of bioanalysis and mass spectrometry technology. More specifically, it relates to a metabolomics full-spectrum quality control report generation system based on the R language. Background Art

[0002] Metabolomics is the science of studying metabolites in living organisms and their changing patterns, and is widely used in disease diagnosis and drug research and development. LC-MS is an efficient metabolite analysis technology, while FIA is a fast and automated sample introduction method. However, existing systems have problems in quality control, such as complex quality control processes, low data processing efficiency, and unintuitive quality control results. The present invention aims to solve these problems through a system written in the R language, and generate a comprehensive quality control report in HTML format. Summary of the Invention

[0003] In view of the above problems, the object of the present invention is to provide a metabolomics full-spectrum quality control report generation system based on the R language, which can automatically generate an HTML format report containing detailed quality control information. Through an automated process based on R code, the quality control efficiency and data processing ability are significantly improved, and an intuitive and detailed quality control report is provided, which is convenient for users to quickly evaluate the data quality and experimental stability.

[0004] In particular, the smoothing algorithm developed for chromatographic quality control of QC samples has the following features: 1. Superior smoothing effect: The moving average method can quickly eliminate random noise, the Savitzky-Golay filter retains the local characteristics of the signal, and the weight fusion algorithm realizes the complementarity of the advantages of both, taking into account both smoothness and signal fidelity; 2. Accurate calculation of peak height and FWHM: The noise of the smoothed signal is significantly reduced, and the result of feature extraction is more accurate, which is beneficial to the quantitative analysis of complex samples; 3. Strong versatility: The algorithm can be applied to chromatographic data generated by different instruments (such as gas chromatography, liquid chromatography, and mass spectrometry), and has a wide application prospect.

[0005] The present invention provides a metabolomics full-spectrum quality control report generation system based on the R language, including:

[0006] A data import module for importing experimental data and a mass spectrometry injection sample information table of samples; the mass spectrometry injection sample information table includes, but is not limited to, sample number, type, injection time, sample source, and treatment method;

[0007] A data processing module for performing format verification and data preprocessing on the experimental data, and extracting key metabolite features;

[0008] A data visualization module, which is used to analyze according to the key metabolite characteristics, generate one or more visualization charts, and smooth the chromatographic signal data of the quality control samples in the visualization charts; the visualization charts include but are not limited to the TIC curve of the sample, the TIC curve fluctuation chart, the TIC quality control out-of-control sample display chart, and the signal intensity fluctuation chart of the user-specified compound;

[0009] A report generation module, which is used to generate a quality control report according to the mass spectrometry injection sample information table and the visualization charts;

[0010] A data export module, which is used to export and download the data of the quality control report.

[0011] In this solution, it also includes:

[0012] The data preprocessing includes error data identification and correction, missing value processing, and outlier processing.

[0013] In this solution, the analysis according to the key metabolite characteristics to generate one or more visualization charts includes:

[0014] Generate total ion current diagrams in positive and negative modes according to the key metabolite characteristics, and determine the TIC curve of the sample;

[0015] Statistically analyze the fluctuation conditions of the TIC curves of different categories of samples to generate a TIC curve fluctuation chart; the different categories of samples include quality control samples, standard samples, and test samples;

[0016] Perform quality control detection on the samples through preset quality control standards and algorithms, and mark the out-of-control samples that deviate from the normal range.

[0017] In this solution, it also includes:

[0018] Obtain the user-specified compound; the specified compound includes specific compounds and internal standard compounds;

[0019] Analyze the fluctuation conditions of the signal intensity of the specified compound to generate a signal intensity fluctuation chart of the specified compound.

[0020] In this solution, the smoothing of the chromatographic signal data of the quality control samples in the visualization charts includes:

[0021] Smooth the chromatographic signal data of the quality control samples through the moving average smoothing algorithm and the Savitzky-Golay filter smoothing algorithm respectively to determine the moving average smoothing signal data y MA (i) and the filtered smoothing signal data y SG (i);

[0022] Multiply the moving average smoothed signal data y MA (i) and the filtered smoothed signal data y SG (i) by their respective influence weights, accumulate the calculation results, and determine the smoothed chromatographic signal data y combined (i);

[0023] y combincd (i) = αy MA (i) + βy SG (i);

[0024] where α is the influence weight of the moving average smoothed signal data y MA (i), and β is the influence weight of the filtered smoothed signal data y SG (i).

[0025] In this solution, the chromatographic signal data of the quality control sample is smoothed respectively by the moving average smoothing algorithm and the Savitzky-Golay filtering smoothing algorithm to determine the moving average smoothed signal data y MA (i) and the filtered smoothed signal data y SG (i), including:

[0026] Set the first window;

[0027] Determine the first input signal x(j) according to the chromatographic signal data i to be smoothed and the window size N of the first window;

[0028] Input the first input signal x(j) into the moving average smoothing formula, and output the signal y MA (i) after moving average smoothing;

[0029]

[0030] where N is the window size of the first window, i is the chromatographic signal data to be smoothed, and j is the chromatographic signal data for assisting in smoothing;

[0031] Set the second window;

[0032] Determine the second input signal x(i + j) according to the chromatographic signal data i to be smoothed and the window size M of the second window;

[0033] Input the second input signal x(i + j) into the filtering smoothing formula, and output the signal y SG (i) after filtering smoothing;

[0034]

[0035] Wherein, M is the window size of the second window, i is the chromatographic signal data to be smoothed, j is the chromatographic signal data assisting in the smoothing process, x(i + j) is the chromatographic signal data between the chromatographic signal data i and the chromatographic signal data j, and c j is the polynomial coefficient, and the value of c j is determined according to the chromatographic signal data j.

[0036] This solution further includes:

[0037] Calculating the peak height and the full width at half maximum of the smoothed chromatographic signal data y combined (i);

[0038] Calculating the ratio of the peak height to the historical average peak height to determine the first ratio;

[0039] Calculating the ratio of the full width at half maximum to the historical average full width at half maximum to determine the second ratio;

[0040] When both the first ratio and the second ratio satisfy the corresponding preset ratio value ranges, the smoothed chromatographic signal data y combined (i) meets the quality control requirements; otherwise, it does not meet the quality control requirements.

[0041] This solution further includes:

[0042] Visualizing the change of the signal intensity over time to generate a time series graph.

[0043] This solution further includes:

[0044] Performing quality analysis based on the signal intensity of the quality control samples to determine multiple statistical indicators of the quality control samples and generating a quality analysis report for the quality control samples; the multiple statistical indicators at least include the mean, standard deviation, and coefficient of variation of the signal intensity.

[0045] The present invention discloses a metabolomics full-spectrum quality control report generation system based on R language. The system includes: a data import module for importing experimental data and a mass spectrometry injection sample information table of samples; a data processing module for performing format verification and data preprocessing on the experimental data and extracting key metabolite features; a data visualization module for analyzing based on the key metabolite features, generating one or more visualization charts, and smoothing the chromatographic signal data of the quality control samples in the visualization charts; a report generation module for generating a quality control report based on the mass spectrometry injection sample information table and the visualization charts; a data export module for exporting and downloading the data of the quality control report. The present invention significantly improves the quality control efficiency and data processing ability through an automated process based on R code, provides an intuitive and detailed quality control report, and facilitates users to quickly evaluate the data quality and experimental stability. Description of the Drawings

[0046] Figure 1 Shows a block diagram of a metabolomics full-spectrum quality control report generation system provided by the present invention based on the R language.

[0047] Figure 2 Shows a flowchart of a method for generating a metabolomics full-spectrum quality control report provided by the present invention based on the R language;

[0048] Figure 3 Shows a schematic diagram of a mass spectrometry injection sample information table provided by the present invention;

[0049] Figure 4 Shows a schematic diagram of a positive and negative TIC diagram provided by the present invention;

[0050] Figure 5 Shows a schematic diagram of QC sample chromatographic quality control provided by the present invention;

[0051] Figure 6 Shows a schematic diagram of TIC fluctuation analysis of various types of samples provided by the present invention;

[0052] Figure 7 Shows a schematic diagram of a TIC quality control out-of-control sample provided by the present invention;

[0053] Figure 8 Shows a schematic diagram of a specific compound signal intensity fluctuation chart provided by the present invention;

[0054] Figure 9 Shows a schematic diagram of an internal standard compound signal intensity fluctuation chart provided by the present invention. Detailed implementation manners

[0055] In order to be able to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0056] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0057] Figure 1 Shows a block diagram of a metabolomics full-spectrum quality control report generation system provided by the present invention based on the R language.

[0058] As Figure 1 shown, the present invention discloses a metabolomics full-spectrum quality control report generation system based on the R language, including:

[0059] A data import module for importing experimental data and the mass spectrometry injection sample information table of samples; the mass spectrometry injection sample information table includes, but is not limited to, sample numbers, types, injection times, sample sources, and processing methods.

[0060] A data processing module for performing format verification and data preprocessing on experimental data and extracting key metabolite features.

[0061] A data visualization module for analyzing based on key metabolite features, generating one or more visualization charts, and smoothing the chromatographic signal data of quality control samples in the visualization charts; the visualization charts include, but are not limited to, the TIC curves of samples, TIC curve fluctuation charts, TIC quality control out-of-control sample display charts, and signal intensity fluctuation charts of user-specified compounds.

[0062] A report generation module for generating a quality control report based on the mass spectrometry injection sample information table and the visualization charts.

[0063] A data export module for data export and data download of the quality control report.

[0064] Figure 2 Shows the flowchart of the method for generating a metabolomics full-spectrum quality control report based on the R language provided by the present invention.

[0065] As Figure 2 shown, the present invention discloses a method for generating a metabolomics full-spectrum quality control report based on the R language, including:

[0066] S202, importing experimental data and the mass spectrometry injection sample information table of samples; the mass spectrometry injection sample information table includes, but is not limited to, sample numbers, types, injection times, sample sources, and processing methods.

[0067] S204, performing format verification and data preprocessing on experimental data and extracting key metabolite features.

[0068] S206, analyzing based on key metabolite features, generating one or more visualization charts, and smoothing the chromatographic signal data of quality control samples in the visualization charts; the visualization charts include, but are not limited to, the TIC curves of samples, TIC curve fluctuation charts, TIC quality control out-of-control sample display charts, and signal intensity fluctuation charts of user-specified compounds.

[0069] S208, generating a quality control report based on the mass spectrometry injection sample information table and the visualization charts.

[0070] S210, performing data export and data download of the quality control report.

[0071] According to an embodiment of the present invention, the system uses R language to write the quality control process, automatically processes experimental data and generates a report in HTML format. Through a series of automated scripts, the system can automatically read the original data file, perform data preprocessing, and evaluate according to the preset quality control standards. The finally generated HTML report not only contains detailed quality control analysis results, but also presents them in the form of intuitive charts and annotations, reducing the time for users to manually process data and operation errors. The system mainly includes the following modules:

[0072] (1) Data import module. The data import module is responsible for importing experimental data. It supports multiple data formats such as CSV, Excel, Rdata, etc., ensuring compatibility and flexibility. Through a simple user interface, users can easily select and import the required data files. During the import process, the system will perform data format verification and preprocessing to ensure the integrity and consistency of the data. Among them, as Figure 3 shown, the mass spectrometry injection sample information table contains detailed information on all mass spectrometry injection samples, including sample number, type, injection time, sample source, processing method, etc. The detailed records in the sample information table not only facilitate sample management and traceability, but also provide necessary metadata support for data analysis. By comprehensively recording the specific information of each sample, the integrity and traceability of experimental data are ensured, providing a basis for the accurate interpretation of experimental results.

[0073] (2) Data processing module. The data processing module is the core of the system and processes experimental data based on analysis scripts written in R language. The system integrates a variety of statistical and machine learning algorithms, which can automatically identify and correct outliers in the data and extract key metabolite features. Through efficient parallel computing technology, the system can quickly process large-scale data, significantly improving the efficiency and accuracy of data processing.

[0074] (3) Report generation module. The report generation module automatically generates a quality control report in HTML format. The report content includes basic information of the experiment, detailed sample information, TIC diagram, fluctuation analysis, etc. The system generates a quality control report with clear structure and rich content through predefined templates and styles. The charts and statistical results included in the report are presented in an intuitive form to help users quickly understand and evaluate experimental results. The generated report can not only be viewed online, but also be exported to PDF format for easy archiving and sharing. Among them, the beginning part of the report contains basic information of the experiment, such as experiment date, instrument model, experimenter, experimental conditions, etc. These information provide complete background materials for the report, making the experiment transparent and helping with the repetition and verification of subsequent experiments. In addition, these basic information can also be used for comparison and data integration between experiments, improving the systematicness and scientificity of research.

[0075] (4) Data visualization module. The data visualization module is responsible for generating various charts to visually present the analysis results. The system integrates a variety of data visualization tools, such as ggplot2, plotly, etc., and can generate high-quality TIC charts, time series charts, fluctuation analysis charts, etc. Users can select and customize the types and styles of charts through an interactive interface. The visualization results can not only be embedded in reports but also be saved and shared separately, facilitating in-depth analysis and discussion by users.

[0076] (5) Data export module. The data export module provides the function of downloading data and reports. Users can download raw data, processed data, analysis results, and complete quality control reports. The system supports multiple export formats, such as CSV, Excel, etc., to meet the needs of different users. The exported data and reports are automatically sorted and annotated by the system to ensure their readability and usability. Users can also directly export data to other data analysis platforms through the API interface to achieve seamless data integration and sharing.

[0077] According to an embodiment of the present invention, it further includes:

[0078] Data preprocessing includes error data identification and correction, missing value processing, and outlier processing.

[0079] It should be noted that error data identification and correction include discovering errors and inconsistencies in the data and making corrections. For example, logical errors, syntax errors, etc. Missing value processing is to handle missing values in the data, which can be processed by deleting, filling, or predicting missing values. Missing value processing methods include, but are not limited to, mean imputation, maximum / minimum imputation, and regression imputation. Outlier processing is to identify and process outliers in the data and process them by deleting, smoothing, or normalizing, etc. Data preprocessing can also include data deduplication, which is to delete duplicate data records to ensure the uniqueness of the data.

[0080] According to an embodiment of the present invention, analysis is performed based on key metabolite characteristics to generate one or more visualization charts, including:

[0081] Generate a total ion current chart in positive and negative modes based on key metabolite characteristics to determine the TIC curve of the sample;

[0082] Statistically analyze the fluctuation of the TIC curves of different categories of samples to generate a TIC curve fluctuation chart; different categories of samples include quality control samples, standard samples, and test samples;

[0083] Perform quality control detection on the samples through preset quality control standards and algorithms, and mark the samples that deviate from the normal range as out-of-control.

[0084] It should be noted that, such as Figure 4As shown, the system automatically generates total ion chromatogram (TIC) graphs in positive and negative ion modes based on the key metabolite features extracted from experimental data, showing the TIC curves of each sample. The TIC graphs can visually display the ion current intensities of samples in different polarity modes, helping users quickly identify abnormal samples and key characteristic peaks in the experiment. By comparing the TIC curves in positive and negative ion modes, the performance of samples under different analysis conditions can be comprehensively evaluated, providing richer experimental information.

[0085] As Figure 6 shown, the system evaluates the consistency and stability of the experiment by statistically analyzing the fluctuations of the TIC curves of various types of samples. The generated charts not only show the overall fluctuation trends of different types of samples, but also highlight abnormal samples and potential quality control problems, facilitating users to quickly locate and solve problems in the experiment.

[0086] As Figure 7 shown, the system automatically identifies samples that deviate from the normal range by presetting quality control standards and algorithms, and marks them in the report in a highlighted or other prominent way. In this way, users can clearly discover quality control problems in the experiment and take timely measures for adjustment and improvement to ensure the reliability and effectiveness of subsequent experiments.

[0087] According to an embodiment of the present invention, it further includes:

[0088] Obtain the user-specified compound; the specified compound includes a specific compound and an internal standard compound;

[0089] Analyze the fluctuation of the signal intensity of the specified compound to generate a signal intensity fluctuation chart of the specified compound.

[0090] It should be noted that, as Figure 8 shown, the user can specify a specific compound, and the system will analyze the fluctuation of its signal intensity and generate a corresponding chart. This function can help users focus on the performance of a specific target compound during the experiment and evaluate the stability and fluctuation of its signal intensity. The generated charts and analysis results help users deeply understand the behavioral characteristics of the target compound and provide valuable information for subsequent research and applications.

[0091] As Figure 9 shown, the stability of the internal standard compound can also be ensured by analyzing the signal fluctuations of the user-specified internal standard compound. The internal standard compound is used to correct the systematic errors during the experiment. By monitoring the fluctuations of its signal intensity, the repeatability and accuracy of the experiment can be verified. The charts and analysis results generated by the system help users evaluate the performance of the internal standard compound and ensure the reliability and consistency of the experimental data.

[0092] According to an embodiment of the present invention, smoothing the chromatographic signal data of the quality control sample in the visualization chart includes:

[0093] Smoothing the chromatographic signal data of the quality control sample through a moving average smoothing algorithm and a Savitzky-Golay filtering smoothing algorithm respectively to determine the moving average smoothing signal data y MA (i) and the filtered smoothing signal data y SG (i);

[0094] Multiplying the moving average smoothing signal data y MA (i) and the filtered smoothing signal data y SG (i) by their corresponding influence weights respectively, and accumulating the calculation results to determine the smoothed chromatographic signal data y combined (i);

[0095] y combincd (i) = αy MA (i) + βy SG (i);

[0096] Wherein, α is the influence weight of the moving average smoothing signal data y MA (i), and β is the influence weight of the filtered smoothing signal data y SG (i).

[0097] It should be noted that, as Figure 5 shown, the system provides an efficient and reliable chromatographic smoothing algorithm. By combining the moving average algorithm and the Savitzky-Golay filtering smoothing algorithm, the signal smoothing effect of the chromatographic signal data of the quality control sample is optimized. The moving average algorithm can quickly eliminate random noise, the Savitzky-Golay filtering smoothing algorithm retains the local characteristics of the signal, and the weight fusion algorithm realizes the complementary advantages of the two, taking into account both smoothness and signal fidelity, and improving the measurement accuracy of key parameters such as peak height and full width at half maximum (FWHM).

[0098] Among them, the chromatographic signal data contains a retention time (RetentionTime, RT) array and an intensity (Intensity) array.

[0099] Wherein, the values of the influence weights α and β are set by those skilled in the art according to actual needs, and their initial weights are equal, that is, α = β = 0.5.

[0100] According to an embodiment of the present invention, smoothing the chromatographic signal data of the quality control sample through a moving average smoothing algorithm and a Savitzky-Golay filtering smoothing algorithm respectively to determine the moving average smoothing signal data y MA (i) and the filtered smoothing signal data y SG(i), including:

[0101] Set the first window;

[0102] Determine the first input signal x(j) based on the chromatographic signal data i to be smoothed as needed and the window size N of the first window;

[0103] Input the first input signal x(j) into the moving average smoothing formula and output the signal y MA (i);

[0104]

[0105] Wherein, N is the window size of the first window, i is the chromatographic signal data to be smoothed, and j is the chromatographic signal data for assisting in the smoothing process;

[0106] Set the second window;

[0107] Determine the second input signal x(i + j) based on the chromatographic signal data i to be smoothed as needed and the window size M of the second window;

[0108] Input the second input signal x(i + j) into the filtering smoothing formula and output the signal y SG (i);

[0109]

[0110] Wherein, M is the window size of the second window, i is the chromatographic signal data to be smoothed, j is the chromatographic signal data for assisting in the smoothing process, x(i + j) is the chromatographic signal data between the chromatographic signal data i and the chromatographic signal data j, c j is the polynomial coefficient, c j is determined according to the chromatographic signal data j.

[0111] It should be noted that, first, a window with a fixed size, that is, the first window, is set through the moving average smoothing algorithm. The original signal of the chromatographic signal data is subjected to a moving average according to the first window, and the signal y MA (i)

[0112] after moving average smoothing of the chromatographic signal data i to be smoothed is output through the moving average smoothing algorithm formula. jIts value is determined based on the system preset rules according to the chromatographic signal data j. The smoothing value is calculated using the method of polynomial fitting, and the filtered and smoothed signal y is output SG (i).

[0113] As the window slides over the entire data set, each data point is replaced by the polynomial fitting value within its local window, thus achieving data smoothing

[0114] According to an embodiment of the present invention, it further includes

[0115] Calculate the smoothed chromatographic signal data y combined (i)'s peak height and full width at half maximum

[0116] Calculate the ratio of the peak height to the historical average peak height to determine the first ratio

[0117] Calculate the ratio of the full width at half maximum to the historical average full width at half maximum to determine the second ratio

[0118] When both the first ratio and the second ratio satisfy the corresponding preset ratio value ranges, the smoothed chromatographic signal data y combined (i) meets the quality control requirements; otherwise, it does not meet the quality control requirements

[0119] It should be noted that the peak height and full width at half maximum (FWHM) of the smoothed chromatographic signal data are calculated, and by comparing with the corresponding historical data, it is judged whether the smoothed chromatographic signal data meets the quality control requirements. The historical average peak height is the average of the historical peak heights of the same type of quality control samples as the smoothed chromatographic signal data within the system preset time interval (such as 1 month), and the historical average full width at half maximum is the average of the historical full widths at half maximum of the same type of quality control samples as the smoothed chromatographic signal data within the system preset time interval (such as 1 month). During the calculation process, the historical peak heights and historical full widths at half maximum of the quality control samples that do not meet the quality control requirements are filtered. Those skilled in the art respectively set the corresponding preset ratio value ranges for the ratio of the peak height to the historical average peak height and the ratio of the full width at half maximum to the historical average full width at half maximum. When the first ratio is within the corresponding preset ratio value range, the peak height meets the quality control requirements; when the second ratio is within the corresponding preset ratio value range, the full width at half maximum meets the quality control requirements. When both the peak height and the full width at half maximum meet the quality control requirements, the smoothed chromatographic signal data y combined (i) meets the quality control requirements

[0120] According to an embodiment of the present invention, it further includes

[0121] Visualize the change of the signal intensity over time to generate a time series plot

[0122] It should be noted that the time series graph helps users evaluate the time stability of the experiment and the time dependence of the signal. The time series graph visually shows the dynamic changes of the signal over time, facilitating the identification of abnormal fluctuations and trends. Through these visualization results, users can better understand the changes in the time dimension of the experimental data, providing a reference for experimental design and optimization.

[0123] According to an embodiment of the present invention, it further includes:

[0124] Perform quality analysis based on the signal intensity of the quality control sample, determine multiple statistical indicators of the quality control sample, and generate a quality analysis report for the quality control sample; the multiple statistical indicators at least include the mean, standard deviation, and coefficient of variation of the signal intensity.

[0125] It should be noted that a detailed quality analysis is performed on the quality control (QC) sample to evaluate the repeatability and accuracy of the experiment. The analysis of the quality control sample includes statistical indicators such as the mean, standard deviation, and coefficient of variation of the signal intensity to ensure that the results of the QC sample meet the expectations. The generated detailed quality analysis report can help users verify the quality control effect of the experiment and ensure the high quality and credibility of the experimental data.

[0126] The information involved in this application (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.), and signals (including but not limited to signals transmitted between the user terminal and other devices, etc.) are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions. For example, the "experimental data", "key metabolite features", etc. involved in this disclosure are obtained under full authorization.

[0127] The present invention discloses a metabolomics full-spectrum quality control report generation system based on R language. The system includes: a data import module for importing experimental data and a mass spectrometry injection sample information table of samples; a data processing module for performing format verification and data preprocessing on the experimental data and extracting key metabolite features; a data visualization module for analyzing based on the key metabolite features, generating one or more visualization charts, and smoothing the chromatographic signal data of the quality control samples in the visualization charts; a report generation module for generating a quality control report based on the mass spectrometry injection sample information table and the visualization charts; a data export module for exporting and downloading the data of the quality control report. The present invention significantly improves the quality control efficiency and data processing ability through an automated process based on R code, provides an intuitive and detailed quality control report, and facilitates users to quickly evaluate the data quality and experimental stability.

[0128] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the couplings, direct couplings, or communication connections between the various components shown or discussed can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0129] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately regarded as a unit, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0131] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments. The foregoing storage media include various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0132] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage media include various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

Claims

1. A metabolomics full-spectrum quality control report generation system based on R language, characterized by: include: A data import module, used to import experimental data and a mass spectrometry sample information table of a sample; the mass spectrometry sample information table includes but is not limited to sample number, type, injection time, sample source and processing method; A data processing module, used to perform format verification and data preprocessing on the experimental data and extract key metabolite features; A data visualization module, for analyzing the key metabolite characteristics, generating one or more visualization charts, and smoothing the chromatographic signal data of the quality control samples in the visualization charts; the visualization charts include but are not limited to the TIC curve of the sample, the TIC curve fluctuation chart, the TIC quality control out-of-control sample display chart, and the signal intensity fluctuation chart of the user-specified compound; A report generation module, used to generate a quality control report based on the mass spectrometry sample information table and the visual chart; The data export module is used to export and download the data of the quality control report.

2. The R language-based metabolomics full-spectrum quality control report generation system according to claim 1, characterized in that: Also includes: The data preprocessing includes erroneous data identification and correction, missing value processing and outlier processing.

3. The R language-based metabolomics full-spectrum quality control report generation system according to claim 1, characterized in that: The step of analyzing the key metabolite features to generate one or more visualization charts includes: Generate a total ion current graph in positive and negative electrode modes according to the key metabolite characteristics, and determine a TIC curve of the sample; Performing statistical analysis on the fluctuation of TIC curves of samples of different categories to generate a TIC curve fluctuation chart; the samples of different categories include quality control samples, standard samples and test samples; The samples are quality-controlled by using preset quality-control standards and algorithms, and samples that deviate from the normal range are marked as out-of-control.

4. The R language-based metabolomics full-spectrum quality control report generation system according to claim 3, characterized in that: Also includes: Obtaining user-specified compounds; the specified compounds include specific compounds and internal standard compounds; The fluctuation of the signal intensity of the designated compound is analyzed to generate a signal intensity fluctuation chart of the designated compound.

5. The R language-based metabolomics full-spectrum quality control report generation system according to claim 1, characterized in that: The smoothing of the chromatographic signal data of the quality control sample in the visual chart includes: The chromatographic signal data of the quality control sample are smoothed by a moving average smoothing algorithm and a Savitzky-Golay filter smoothing algorithm respectively to determine the moving average smoothed signal data yMA(i) and the filter smoothed signal data ySG(i); The moving average smoothed signal data y MA (i) and filtered smoothed signal data y SG (i) Multiply the corresponding influence weights respectively, accumulate the calculation results, and determine the smoothed chromatographic signal data y combined (i); y combincd (i)=αy MA (i)+βy sG (i); Among them, α is the influence weight of the moving average smoothed signal data yMA(i), β is the influence weight of the filtered smoothed signal data y SG (i) Impact weight.

6. The R language-based metabolomics full-spectrum quality control report generation system according to claim 5, characterized in that: The chromatographic signal data of the quality control sample are smoothed by a moving average smoothing algorithm and a Savitzky-Golay filter smoothing algorithm to determine the moving average smoothed signal data y MA (i) and filtered smoothed signal data y SG (i) including: Set the first window; Determine the first input signal x(j) according to the chromatographic signal data i to be smoothed as required and the window size N of the first window; Input the first input signal x(j) into the moving average smoothing formula, and output the moving average smoothed signal yMA(i); Wherein, N is the window size of the first window, i is the chromatographic signal data to be smoothed, and j is the chromatographic signal data to be assisted in smoothing; Set up the second window; Determine the second input signal x(i+j) according to the chromatographic signal data i to be smoothed as needed and the window size M of the second window; Input the second input signal x(i+j) into the filtering and smoothing formula, and output the filtered and smoothed signal y SG (i); Wherein, M is the window size of the second window, i is the chromatographic signal data to be smoothed, j is the chromatographic signal data to be smoothed, x(i+j) is the chromatographic signal data between chromatographic signal data i and chromatographic signal data j, c j are polynomial coefficients, c j The value of is determined according to the chromatographic signal data j.

7. The R language-based metabolomics full-spectrum quality control report generation system according to claim 5, characterized in that: Also includes: Calculate the smoothed chromatographic signal data y combined (i) Peak height and half-peak width; Calculating the ratio of the peak height to the historical average peak height to determine a first ratio; Calculating the ratio of the half-peak width to the historical average half-peak width to determine a second ratio; When the first ratio and the second ratio both satisfy the corresponding preset ratio value interval, the smoothed chromatographic signal data y combined (i) The quality control requirements are met; otherwise, the quality control requirements are not met.

8. The R language-based metabolomics full-spectrum quality control report generation system according to claim 1, characterized in that: Also includes: Visualize the change of signal strength over time and generate a time series graph.

9. The R language-based metabolomics full-spectrum quality control report generation system according to claim 1, characterized in that: Also includes: Performing quality analysis according to the signal intensity of the quality control sample, determining multiple statistical indicators of the quality control sample, and generating a quality analysis report of the quality control sample; The multiple statistical indicators include at least a mean, a standard deviation and a coefficient of variation of the signal intensity.

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