CN118378071B - Mass spectrum imaging data processing method, device, equipment and storage medium - Google Patents

Abstract

The present application discloses a method, device, equipment and storage medium for processing mass spectrometry imaging data, and relates to the field of data processing technology, including: dividing the plane space corresponding to the mass spectrometry imaging data of each tissue sample into a number of compartment spaces based on a preset spatial threshold, and determining the corresponding representative spectra; wherein the plane space is constructed by the spatial position information of the scanning detection points of the mass spectrometry imaging data; the compartment space includes a number of scanning detection points; the mass spectrometry imaging spectra of each scanning detection point are processed using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectra, and the peak intensity matrix corresponding to the peak mass-to-charge ratio of the detected object in the processed mass spectrometry imaging data, the peak mass-to-charge ratio vector of the detected object and the spatial position information associated with each scanning detection point are determined as the mass spectrometry imaging detected object peak data set. In this way, the present application can apply the parameters corresponding to the representative spectra of the compartment space to all data, and can ensure the fluency and reliability of data processing.

Description

A mass spectrometry imaging data processing method, device, equipment and storage medium

Technical Field

The present invention relates to the field of data processing technology, and in particular to a mass spectrometry imaging data processing method, device, equipment and storage medium.

Background Art

In mass spectrometry imaging data analysis, if a spectrum is sampled with a step length of 100um, a typical tissue of about 1 square centimeter will have 10,000 scanning detection points and 10,000 spectra. If there are two groups of samples, each with 10 tissues, there will be 200,000 spectra for 20 tissues, and each spectrum also contains hundreds of thousands of sampling data information, which is an extremely large amount of data. In order to maintain the comparability of samples and spectra during data preprocessing, the mass-to-charge ratio of the signal peaks of all spectra of each tissue sample must be aligned, and the peak intensity of all spectra must be normalized. Conventional automatic peak alignment algorithms and TIC (Total Ion Chromatography) total ion flow normalization both directly align or normalize all spectra at the same time. If 200,000 spectra of 20 tissues are compared at the same time, the hardware performance will be unable to process or the processing will be extremely slow due to the large amount of data.

It can be seen that how to improve the processing efficiency of mass spectrometry imaging data is a problem to be solved in this field.

Summary of the invention

In view of this, the purpose of the present invention is to provide a mass spectrometry imaging data processing method, device, equipment and storage medium, which can apply the parameters corresponding to the representative spectrum of the partition space to all data, and can ensure the smoothness and reliability of data processing. The specific scheme is as follows:

In a first aspect, the present application provides a mass spectrometry imaging data processing method, comprising:

Based on a preset spatial threshold, the plane space corresponding to the mass spectrometry imaging data of each tissue sample is divided into a plurality of compartment spaces, and a representative spectrum of each compartment space is determined; wherein the plane space is constructed by the spatial position information of the scanning detection points of the mass spectrometry imaging data; and the compartment space includes a plurality of scanning detection points;

The mass spectrometry imaging spectrum of each scanning detection point is processed using the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum, and the peak intensity matrix corresponding to the mass-to-charge ratio of the detection object peak in the processed mass spectrometry imaging data, the mass-to-charge ratio vector of the detection object peak and the spatial position information associated with each scanning detection point are determined as the mass spectrometry imaging detection object peak data set.

Optionally, the planar space corresponding to the mass spectrometry imaging data of each tissue sample is divided into a plurality of compartment spaces based on a preset spatial threshold, including:

Establishing a plane space according to the spatial position information of the scanning detection point of the mass spectrometry imaging data of each tissue sample;

The plane space is divided into a plurality of continuous and adjacent partition spaces based on a preset space threshold.

Optionally, determining a representative spectrum of each of the compartment spaces comprises:

Averaging the mass spectrometry imaging data of all scanning detection points in a single compartment space, so as to use the obtained average mass spectrometry data as a representative spectrum of the compartment space;

Alternatively, the mass spectrometry imaging data corresponding to the scanning detection point closest to the center position of a single compartment space is determined as the representative spectrum of the compartment space.

Optionally, determining a representative spectrum of each of the compartment spaces comprises:

Perform peak alignment operations on representative spectra of all compartment spaces corresponding to a number of tissue samples, and save corresponding peak alignment coefficients;

A peak intensity normalization operation is performed on all representative spectra after the peak alignment operation, and the corresponding normalization coefficients are saved, so that the mass spectrometry imaging spectrum of each scanning detection point can be processed using the peak alignment coefficients and normalization coefficients corresponding to the representative spectra.

Optionally, the processing of the mass spectrometry imaging spectrum of each scanning detection point by using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum includes:

Processing the mass spectrometry imaging spectra of each scanning detection point of all tissue samples using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectra to obtain processed mass spectrometry imaging data;

Alternatively, a preset selected area of each tissue sample is determined, and the mass spectrometry imaging spectrum of each scanning detection point in the preset selected area is processed using the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum of the target compartment space matching the preset selected area to obtain processed mass spectrometry imaging data.

Optionally, the processing of the mass spectrometry imaging spectrum of each scanning detection point by using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum includes:

Performing a peak alignment operation on the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient corresponding to the representative spectrum to obtain first processed mass spectrometry imaging data;

The peak intensity normalization operation is performed on the first processed mass spectrometry imaging data using the normalization coefficient corresponding to the representative spectrum to obtain the second processed mass spectrometry imaging data.

Optionally, before determining the peak intensity matrix corresponding to the peak mass-to-charge ratio of the detected object in the processed mass spectrometry imaging data, the peak mass-to-charge ratio vector of the detected object, and the spatial position information associated with each scanning detection point as the mass spectrometry imaging detected object peak data set, the method further includes:

According to the list of aligned peak mass-to-charge ratios, the detection object peak vectors for several detection objects are established, so that the peak intensity matrix corresponding to the detection object peak mass-to-charge ratio in the processed mass spectrometry imaging data, the detection object peak mass-to-charge ratio vector and the spatial position information associated with each scanning detection point are determined as the mass spectrometry imaging detection object peak data set.

In a second aspect, the present application provides a mass spectrometry imaging data processing device, comprising:

A representative spectrum determination module is used to divide the plane space corresponding to the mass spectrometry imaging data of each tissue sample into a plurality of compartment spaces based on a preset spatial threshold, and determine a representative spectrum of each compartment space; wherein the plane space is constructed by the spatial position information of the scanning detection points of the mass spectrometry imaging data; and the compartment space includes a plurality of scanning detection points;

The data processing module is used to process the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum, and determine the peak intensity matrix corresponding to the mass-to-charge ratio of the detection object peak in the processed mass spectrometry imaging data, the mass-to-charge ratio vector of the detection object peak and the spatial position information associated with each scanning detection point as the mass spectrometry imaging detection object peak data set.

In a third aspect, the present application provides an electronic device, including:

Memory, used to store computer programs;

A processor is used to execute the computer program to implement the mass spectrometry imaging data processing method as described above.

In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the mass spectrometry imaging data processing method as described above.

It can be seen that in this application, the plane space corresponding to the mass spectrometry imaging data of each tissue sample is first divided into several compartment spaces based on a preset spatial threshold, and the representative spectrum of each compartment space is determined; wherein, the plane space is constructed by the spatial position information of the scanning detection point of the mass spectrometry imaging data; the compartment space includes several scanning detection points; then the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum are used to process the mass spectrometry imaging spectrum of each scanning detection point, and the peak intensity matrix corresponding to the peak mass-to-charge ratio of the detected object in the processed mass spectrometry imaging data, the peak mass-to-charge ratio vector of the detected object and the spatial position information associated with each scanning detection point are determined as the mass spectrometry imaging detected object peak data set. In this way, the application first delimits the compartment space, and uses the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum of the corresponding compartment space to process the mass spectrometry imaging spectrum of each scanning detection point separately; in this way, the representative spectrum data of the compartment space can be quickly processed, and the corresponding processing parameters are applied to all data one by one, which can ensure the fluency and reliability of data processing.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

FIG1 is a flow chart of a mass spectrometry imaging data processing method disclosed in the present application;

FIG2 is a flow chart of a specific mass spectrometry imaging data processing method disclosed in the present application;

FIG3 is a schematic diagram of a tissue sample and a compartmentalized space position disclosed in the present application;

FIG4 is a schematic diagram of a partition space and representative spectrogram positions disclosed in the present application;

FIG5 is a schematic diagram of the structure of a mass spectrometry imaging data processing device disclosed in the present application;

FIG. 6 is a structural diagram of an electronic device disclosed in this application.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

As shown in FIG1 , an embodiment of the present invention discloses a mass spectrometry imaging data processing method, comprising:

Step S11, based on a preset spatial threshold, the plane space corresponding to the mass spectrometry imaging data of each tissue sample is divided into a number of compartment spaces, and a representative spectrum of each compartment space is determined; wherein the plane space is constructed by the spatial position information of the scanning detection points of the mass spectrometry imaging data; and the compartment space includes a number of scanning detection points.

In an embodiment of the present application, the plane space corresponding to the mass spectrometry imaging data of each tissue sample can be divided into several compartment spaces based on a preset spatial threshold, and a single compartment space can include several scanning detection points; then a representative spectrum characterizing the compartment space can be determined based on the mass spectrometry data in each compartment space. In a specific embodiment, the plane space corresponding to the mass spectrometry imaging data of each tissue sample is divided into several compartment spaces based on a preset spatial threshold, which can include: establishing a plane space according to the spatial position information of the scanning detection points of the mass spectrometry imaging data of each tissue sample; dividing the plane space into several continuous and adjacent compartment spaces based on a preset spatial threshold. Specifically, in the process of dividing the compartment space, first, for the mass spectrometry imaging data of each tissue sample, a plane space for a single tissue sample is established according to the spatial position information of the scanning detection points therein; then, based on the preset spatial threshold, the plane space can be divided into several continuous and adjacent compartment spaces.

Further, the determination of the representative spectrum of each of the compartmented spaces may include: averaging the mass spectrum imaging data of all scanning detection points in a single compartmented space, so as to use the obtained average mass spectrum data as the representative spectrum of the compartmented space; or, determining the mass spectrum imaging data corresponding to the scanning detection point closest to the center position of a single compartmented space as the representative spectrum of the compartmented space. Specifically, in the process of determining the representative spectrum of each compartmented space, since a single compartmented space contains several scanning detection points, the mass spectrum imaging data of all scanning detection points in the single compartmented space may be averaged to obtain the average mass spectrum data, and the average mass spectrum data may be used as the representative spectrum of the relevant compartmented space; accordingly, in some embodiments, the mass spectrum imaging data corresponding to the scanning detection point at the center position of a single compartmented space may also be directly determined as the representative spectrum of the compartmented space, and the spatial position information of the representative spectrum at this time is assigned by the spatial position information carried by the scanning detection point at the center of the compartmented space.

In another specific embodiment, the determination of the representative spectrum of each of the compartment spaces may include: performing peak alignment operations on the representative spectra of all compartment spaces corresponding to a number of tissue samples, and saving the corresponding peak alignment coefficients; performing peak intensity normalization operations on all representative spectra after the peak alignment operation, and saving the corresponding normalization coefficients, so as to use the peak alignment coefficients and normalization coefficients corresponding to the representative spectra to process the mass spectrometry imaging spectrum of each scanning detection point. Specifically, in the process of obtaining the representative spectrum corresponding to the compartment space, the peak alignment operation can be performed on all representative spectra corresponding to each tissue sample; first, baseline correction is performed on all representative spectra, and spectrum peak search is performed, and then the peak alignment operation is performed on all representative spectrum mass-to-charge ratio peaks together; the corresponding peak alignment coefficients can be saved here; accordingly, the peak intensities of the representative spectra after all peak alignment are normalized, and the corresponding normalization coefficients can be saved. In this way, the corresponding processing parameters, i.e., the peak alignment coefficients and the normalization coefficients, can be obtained according to the representative spectrum data of the compartment space, and the peak alignment coefficients may include a reference peak list and an offset threshold. In this way, the present application can quickly process the spectral intensity and signal peaks of multiple tissue samples by spatially segmenting.

Step S12, using the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum to process the mass spectrometry imaging spectrum of each scanning detection point, and determining the peak intensity matrix corresponding to the mass-to-charge ratio of the detection object peak in the processed mass spectrometry imaging data, the mass-to-charge ratio vector of the detection object peak and the spatial position information associated with each scanning detection point as the mass spectrometry imaging detection object peak data set.

In this embodiment, the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum of the compartment space can be obtained through the above steps, and then the peak alignment coefficient and normalization coefficient can be used to process the mass spectrum imaging spectrum of each scanning detection point in the mass spectrum imaging data of the tissue sample to obtain the processed mass spectrum imaging data; further, the peak intensity matrix corresponding to the peak mass-to-charge ratio of the detection object in the processed mass spectrum imaging data, the peak mass-to-charge ratio vector of the detection object and the spatial position information associated with each scanning detection point can constitute a mass spectrum imaging detection object peak data set.

In a specific embodiment, the processing of the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectra may include: processing the mass spectrometry imaging spectrum of each scanning detection point of all tissue samples using the peak alignment coefficient and the normalization coefficient corresponding to all the representative spectra to obtain processed mass spectrometry imaging data; or, determining a preset selected area for each tissue sample, and processing the mass spectrometry imaging spectrum of each scanning detection point in the preset selected area using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum of the target compartment space matching the preset selected area to obtain processed mass spectrometry imaging data. Specifically, in the process of processing the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient and normalization coefficient obtained in the above steps, it is possible to choose, according to actual conditions, to use the peak alignment coefficient and normalization coefficient corresponding to all representative spectra to process the mass spectrometry imaging spectrum of each scanning detection point in the corresponding compartment space; or to choose to analyze the mass spectrum data of a selected area, to determine the target compartment space corresponding to the preset selected area, and then use the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum of the target compartment space to process the mass spectrometry imaging spectrum of each scanning detection point in the target compartment space.

In another specific embodiment, the processing of the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum may include: performing a peak alignment operation on the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient corresponding to the representative spectrum to obtain first processed mass spectrometry imaging data; performing a peak intensity normalization operation on the first processed mass spectrometry imaging data using the normalization coefficient corresponding to the representative spectrum to obtain second processed mass spectrometry imaging data. Specifically, in the process of processing the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum, the peak alignment operation is first performed on the mass spectrometry imaging spectrum of each scanning detection point in each compartment space using the peak alignment coefficient. Within the offset threshold range, the peaks in the spectrum closest to each reference peak mass-to-charge ratio are corrected to the corresponding reference peak mass-to-charge ratio according to the mass-to-charge ratio calibration curve, and the mass-to-charge ratio calibration curve is applied to all peaks of the entire spectrum to complete peak alignment, thereby obtaining the first processed mass spectrometry imaging data; correspondingly, the first processed mass spectrometry imaging data can be subjected to a peak intensity normalization operation using the normalization coefficient corresponding to the representative spectrum, and the ratio of the normalization coefficient to the normalized calculated value corresponding to the peak intensity of the mass spectrometry imaging spectrum is used as the peak intensity correction coefficient of the spectrum. All peak intensity values of the mass spectrometry imaging spectrum are multiplied by the correction coefficient to obtain the mass spectrometry imaging spectrum after peak intensity normalization; each mass spectrometry imaging spectrum is normalized one by one according to this step. In this way, the present application can apply the processing parameters corresponding to the representative spectra of the segmented space to all data, thereby ensuring the smoothness and reliability of data processing.

In another specific embodiment, before determining the peak intensity matrix corresponding to the peak mass-to-charge ratio of the detected object in the processed mass spectrometry imaging data, the peak mass-to-charge ratio vector of the detected object, and the spatial position information associated with each scanning detection point as the mass spectrometry imaging detected object peak data set, it can also include: establishing detected object peak vectors for several detected objects according to the list of aligned peak mass-to-charge ratios, so as to determine the peak intensity matrix corresponding to the peak mass-to-charge ratio of the detected object in the processed mass spectrometry imaging data, the peak mass-to-charge ratio vector of the detected object, and the spatial position information associated with each scanning detection point as the mass spectrometry imaging detected object peak data set. Specifically, according to the relevant steps of the peak alignment operation mentioned above, a list of aligned peak mass-to-charge ratios can be obtained, and according to the list, a detected object peak (mass-to-charge ratio) vector for several detected objects can be established, and the detected object can be a metabolite, a peptide, a protein, a drug or other signal substance, etc., and the detected object peak vector contains mass-to-charge ratio information of several peaks. Furthermore, a corresponding peak intensity matrix can be formed according to the peak intensity corresponding to the peak mass-to-charge ratio of the detected object in the processed mass spectrometry imaging data; then the peak intensity matrix, the peak mass-to-charge ratio vector of the detected object and the spatial position information associated with each scanning detection point can be used to form a final mass spectrometry imaging detected object peak data set. Then, any detected object peak mass-to-charge ratio can be selected in the detected object peak mass-to-charge ratio list, and the intensity data of the selected mass-to-charge ratio at all scanning detection points of each tissue sample can be converted into color information, which is displayed at its corresponding spatial position to form a mass spectrometry imaging plane color image.

It can be seen that the present application first defines the segmentation space, and uses the peak alignment coefficient and normalization coefficient corresponding to the corresponding representative spectrum to process the mass spectrometry imaging spectrum of each scanning detection point; in this way, the representative spectrum data of the segmentation space can be quickly processed, and the corresponding peak alignment coefficient and normalization coefficient can be applied to all the data one by one, which can ensure the smoothness and reliability of data processing.

As shown in FIG. 2 , the following embodiment will specifically introduce the steps of processing the representative spectrum of the compartment space and applying the processing parameters of the representative spectrum to all data, specifically including:

First, a segmentation space threshold is set for the original mass spectrometry imaging data, such as using the area of 10*10 points as the segmentation space threshold. In the original data set of tissue sample mass spectrometry imaging, the plane space established according to the spatial position information of the scanning detection point for the mass spectrometry imaging data of each tissue sample is divided into continuous adjacent segmentation spaces according to the segmentation space threshold, and each segmentation space contains <=10*10 scanning detection points. For each segmentation space, a segmentation space representative spectrum is established. The mass spectrum data of all scanning detection points in the segmentation space can be averaged to establish the average mass spectrum data as the segmentation space representative spectrum, or the mass spectrum data of the scanning detection point at the center of the segmentation space can be specified as the segmentation space representative spectrum. The spatial position information of the segmentation space representative spectrum is assigned by the spatial position information carried by the scanning detection point at the center of the segmentation space. The segmentation space representative spectrum of each tissue sample is summarized to establish a segmentation space representative spectrum data set. In this way, the segmentation space representative spectrum data set will be the size of 1/segmentation space threshold (such as 1/100) of the original data set, and 200,000 original mass spectrometry data will be transformed into a segmentation space representative spectrum data set of about 2,000 spectra.

Then, all the representative spectrum data sets of the compartment space containing all tissue samples are preprocessed, and the preprocessing includes: 1. Perform TIC (or XIC, AUC, signal median, noise level, etc., reference ref) normalization on the intensity of all spectra in the data set after baseline correction, and save the normalization coefficient of the representative spectrum data set of the compartment space (multiple normalization coefficients can be saved). 2. After peak search for all spectra in the data set, peak alignment is performed in all samples for the peak mass-to-charge ratio. The peak alignment can apply the peak automatic alignment algorithm and align to the standard peak (if any). Save the peak alignment coefficient of the representative spectrum data set of the compartment space. It should be pointed out that in a specific embodiment, imaging display can be selected based on the representative spectrum data set; specifically, the detected object peak vector corresponding to the detected metabolite, peptide, protein, drug or other signal substance can be established according to the list of aligned peak mass-to-charge ratios. The detected object peak vector often contains only the mass-to-charge ratio information of hundreds or thousands of peaks. On each preprocessed compartment space representative spectrum data, the peak intensity corresponding to the mass-to-charge ratio of each detected object peak in the detected object peak vector is extracted. After this step of processing, the hundreds of thousands of sampling data information contained in each spectrum are further converted into the mass-to-charge ratio information and intensity information of hundreds or thousands of peaks corresponding to the detection object peak vector. The data is further reduced by about 100 times. The peak intensity matrix composed of the peak intensity corresponding to the mass-to-charge ratio of the detection object peak in all preprocessed segmented space representative spectrum data, the detection object peak mass-to-charge ratio vector, and the spatial position information of all segmented space representative spectra, these three together constitute the segmented space representative spectrum detection object peak data set. The segmented space representative spectrum detection object peak data set is stored. Afterwards, imaging display can be performed based on the segmented space representative spectrum detection object peak data set.

Furthermore, the peak alignment coefficients and normalization coefficients of several representative spectra corresponding to the compartment space can be obtained through the above steps, and the peak alignment coefficients and normalization coefficients obtained through the representative spectra can be applied to all mass spectrometry data later; specifically, for the original data set of mass spectrometry imaging of tissue samples, or for the mass spectrometry spectrum of each scanning detection point in the original data set of the selected analysis area, preprocessing can be performed separately. The preprocessing here can be performed one by one on a single spectrum, or in parallel, so there will be no situation where insufficient computer resources are caused by reading or processing hundreds of thousands of data at the same time. The preprocessing process includes: reading the normalization coefficients (multiple optional) of the representative spectrum data set of the compartment space and the peak alignment coefficients of the representative spectrum data set of the compartment space; after baseline correction of the above mass spectrometry imaging original data set or the mass spectrometry spectrum corresponding to the selected analysis area, the normalization coefficients corresponding to the representative spectrum of the compartment space are applied for normalization; after spectrum peak search of the above mass spectrometry imaging original data set or the mass spectrometry spectrum corresponding to the selected analysis area, the peak alignment coefficients corresponding to the representative spectrum of the compartment space are applied for peak alignment. Afterwards, the peak intensity corresponding to each detection object peak mass-to-charge ratio in the detection object peak vector in the preprocessed mass spectrometry imaging data can be extracted. The peak intensity matrix composed of the peak intensities corresponding to the detection object peak mass-to-charge ratios in all preprocessed mass spectrometry imaging data, the detection object peak mass-to-charge ratio vector, and the spatial position information associated with all scanning detection points, these three together constitute the mass spectrometry imaging detection object peak data set. The mass spectrometry imaging detection object peak data set is stored. It should be pointed out that in a specific embodiment, in the subsequent mass spectrometry imaging detection object peak display process, any detection object peak mass-to-charge ratio can be selected in the detection object peak list, and the intensity data of the selected mass-to-charge ratio at all scanning detection points of each tissue sample can be converted into color information, and displayed at its corresponding spatial position to form a mass spectrometry imaging plane color image.

In a specific embodiment, the tissue sample is a circular tissue with a diameter of 1 cm, and the imaging is scanned and detected at a step length of 100um, and a total of 7854 scanning detection points (dots) are detected, as shown in FIG3, which includes a schematic diagram of the plane position of the scanning detection points, the compartment space and the representative spectrum of the entire tissue, and the red straight line is the boundary of the compartment space with a threshold of 5. Then, the compartment space is established with a threshold of 5, and each compartment space has a total of 25 points. 330 compartment spaces can be established, and the center of each compartment space has a representative spectrum (diamond point); as shown in FIG4, a partial enlarged schematic diagram of the plane position of the scanning detection points, the compartment space and the representative spectrum; the dots are scanning detection points, and the diamonds are representative spectrum plane positions. A total of 330 representative spectra. Each representative spectrum is the average spectrum of the non-empty detection points in the compartment space or the spectrum of the point closest to the center position. Further, this embodiment can perform peak alignment and normalization on the 330 representative spectra and save the corresponding processing parameters. Then, for the 7854 scanning detection points of the entire tissue sample, the mass spectrum data of each scanning detection point are processed one by one using the saved peak alignment coefficient and normalization coefficient to complete the mass spectrum data preprocessing process.

It can be seen that the present application can quickly process the intensity and signal peaks of mass spectrometry imaging spectra of multiple tissue samples based on a preset compartmental space threshold, and can save the peak alignment coefficients and normalization coefficients corresponding to all representative spectra during the processing; subsequently, the peak alignment coefficients and normalization coefficients corresponding to the compartmental space representative spectra can be applied one by one to all mass spectrometry data, which can ensure the smoothness and reliability of data processing.

As shown in FIG5 , the embodiment of the present application discloses a mass spectrometry imaging data processing device, comprising:

The representative spectrum determination module 11 is used to divide the plane space corresponding to the mass spectrometry imaging data of each tissue sample into a plurality of compartment spaces based on a preset spatial threshold, and determine the representative spectrum of each compartment space; wherein the plane space is constructed by the spatial position information of the scanning detection points of the mass spectrometry imaging data; and the compartment space includes a plurality of scanning detection points;

The data processing module 12 is used to process the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum, and determine the peak intensity matrix corresponding to the mass-to-charge ratio of the detection object peak in the processed mass spectrometry imaging data, the mass-to-charge ratio vector of the detection object peak and the spatial position information associated with each scanning detection point as the mass spectrometry imaging detection object peak data set.

It can be seen that the present application first defines the compartment space, and uses the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum of the corresponding compartment space to process the mass spectrometry imaging spectrum of each scanning detection point; in this way, the representative spectrum data of the compartment space can be processed quickly, and the corresponding processing parameters can be applied to all the data one by one, which can ensure the smoothness and reliability of data processing.

In a specific embodiment, the representative spectrum determination module 11 may include:

A plane space establishing unit, used to establish a plane space according to the spatial position information of the scanning detection point of the mass spectrometry imaging data of each tissue sample;

The partition space division unit is used to divide the plane space into a plurality of continuous and adjacent partition spaces based on a preset space threshold.

In a specific embodiment, the representative spectrum determination module 11 may include:

A first representative spectrum determination unit is used to average the mass spectrum imaging data of all scanning detection points in a single compartment space, so as to use the obtained average mass spectrum data as the representative spectrum of the compartment space;

The second representative spectrum determination unit is used to determine the mass spectrum imaging data corresponding to the scanning detection point closest to the center position of a single partition space as the representative spectrum of the partition space.

In a specific embodiment, the representative spectrum determination module 11 may include:

A normalization coefficient storage unit is used to perform peak alignment operations on representative spectra of all compartment spaces corresponding to a number of tissue samples, and store corresponding peak alignment coefficients;

The peak alignment coefficient storage unit is used to perform peak intensity normalization operation on all representative spectra after the peak alignment operation and save the corresponding normalization coefficients so as to process the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficients and normalization coefficients corresponding to the representative spectra.

In a specific embodiment, the data processing module 12 may include:

A first data processing unit is used to process the mass spectrometry imaging spectra of each scanning detection point of all tissue samples using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectra to obtain processed mass spectrometry imaging data;

The second data processing unit is used to determine the preset selected area of each tissue sample, and use the peak alignment coefficient and normalization coefficient corresponding to the representative spectrum of the target compartment space matching the preset selected area to process the mass spectrometry imaging spectrum of each scanning detection point in the preset selected area to obtain processed mass spectrometry imaging data.

In a specific embodiment, the data processing module 12 may include:

A peak alignment processing unit, used to perform a peak alignment operation on the mass spectrometry imaging spectrum of each scanning detection point using the peak alignment coefficient corresponding to the representative spectrum to obtain first processed mass spectrometry imaging data;

The normalization processing unit is used to perform a peak intensity normalization operation on the first processed mass spectrometry imaging data using the normalization coefficient corresponding to the representative spectrum to obtain the second processed mass spectrometry imaging data.

In a specific embodiment, the device may further include:

A peak vector establishment module is used to establish a detection object peak vector for a number of detection objects based on a list of aligned peak mass-to-charge ratios, so as to determine the peak intensity matrix corresponding to the detection object peak mass-to-charge ratio in the processed mass spectrometry imaging data, the detection object peak mass-to-charge ratio vector and the spatial position information associated with each scanning detection point as a mass spectrometry imaging detection object peak data set.

Furthermore, an embodiment of the present application also discloses an electronic device. FIG6 is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.

FIG6 is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input/output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the mass spectrometry imaging data processing method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the mass spectrometry imaging data processing method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs that can be used to complete other specific tasks.

Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the mass spectrometry imaging data processing method disclosed above is implemented. For the specific steps of the method, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, and no further description will be given here.

In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims (8)

1. A method of mass spectrometry imaging data processing comprising:

dividing a plane space corresponding to mass spectrum imaging data of each tissue sample into a plurality of partition spaces based on a preset space threshold, and determining a representative spectrogram of each partition space; the plane space is constructed by the space position information of a scanning detection point of mass spectrum imaging data; the partition space comprises a plurality of scanning detection points;

Processing the mass spectrum imaging spectrogram of each scanning detection point by using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrogram, and determining a peak intensity matrix corresponding to the peak-to-charge ratio of the detected object, a peak-to-charge ratio vector of the detected object and space position information associated with each scanning detection point in the processed mass spectrum imaging data as a mass spectrum imaging detected object peak data set;

Wherein said determining a representative spectrogram for each of said compartmental spaces comprises:

Averaging mass spectrum imaging data of all scanning detection points in a single partition space to obtain average mass spectrum data serving as a representative spectrogram of the partition space;

Or, determining mass spectrum imaging data corresponding to a scanning detection point closest to the central position of a single partition space as a representative spectrogram of the partition space;

Wherein said determining a representative spectrogram for each of said compartmental spaces comprises:

Respectively carrying out peak alignment operation on representative spectrograms of all the partition spaces corresponding to a plurality of tissue samples, and storing corresponding peak alignment coefficients;

And carrying out peak intensity normalization operation on all the representative spectrograms after the peak alignment operation, and storing corresponding normalization coefficients so as to process the mass spectrum imaging spectrograms of each scanning detection point by utilizing the peak alignment coefficients and the normalization coefficients corresponding to the representative spectrograms.

2. The method for processing mass spectrum imaging data according to claim 1, wherein the dividing the planar space corresponding to the mass spectrum imaging data of each tissue sample into a plurality of separate spaces based on a preset spatial threshold comprises:

Establishing a plane space according to the space position information of the scanning detection point of the mass spectrum imaging data of each tissue sample;

The planar space is divided into consecutive and adjacent partitioned spaces based on a preset space threshold.

3. The method for processing mass spectrometry imaging data according to claim 1, wherein the processing the mass spectrometry imaging spectrum of each scan detection point by using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum comprises:

Processing the mass spectrum imaging spectrogram of each scanning detection point of all the tissue samples by using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrogram to obtain processed mass spectrum imaging data;

Or determining a preset selected area of each tissue sample, and processing the mass spectrum imaging spectrogram of each scanning detection point of the preset selected area by using a peak alignment coefficient and a normalization coefficient corresponding to a representative spectrogram of a target area space matched with the preset selected area to obtain processed mass spectrum imaging data.

4. A mass spectrometry imaging data processing method according to any one of claims 1 to 3, wherein the processing of the mass spectrometry imaging spectrum for each scan detection point using the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrum comprises:

performing peak alignment operation on the mass spectrum imaging spectrogram of each scanning detection point by using the peak alignment coefficient corresponding to the representative spectrogram to obtain first processed mass spectrum imaging data;

and carrying out peak intensity normalization operation on the first processed mass spectrum imaging data by using the normalization coefficient corresponding to the representative spectrogram to obtain second processed mass spectrum imaging data.

5. The method according to claim 4, wherein before determining, as the mass spectrum imaging detected object peak data set, a peak intensity matrix corresponding to the detected object peak-to-charge ratio, a detected object peak-to-charge ratio vector, and spatial position information associated with each scan detection point in the processed mass spectrum imaging data, further comprises:

And establishing detection object peak vectors for a plurality of detection objects according to the aligned peak-to-charge ratio list so as to determine a peak intensity matrix corresponding to the detection object peak-to-charge ratio, the detection object peak-to-charge ratio vector and spatial position information associated with each scanning detection point in the processed mass spectrum imaging data as a mass spectrum imaging detection object peak data set.

6. A mass spectrometry imaging data processing apparatus, comprising:

The representative spectrogram determining module is used for dividing a plane space corresponding to mass spectrum imaging data of each tissue sample into a plurality of partition spaces based on a preset space threshold value, and determining a representative spectrogram of each partition space; the plane space is constructed by the space position information of a scanning detection point of mass spectrum imaging data; the partition space comprises a plurality of scanning detection points;

The data processing module is used for processing the mass spectrum imaging spectrogram of each scanning detection point by utilizing the peak alignment coefficient and the normalization coefficient corresponding to the representative spectrogram, and determining a peak intensity matrix corresponding to the peak-to-charge ratio of the detected object in the processed mass spectrum imaging data, a peak-to-charge ratio vector of the detected object and space position information associated with each scanning detection point as a mass spectrum imaging detected object peak data set;

wherein the representative spectrogram determining module comprises:

A first representative spectrogram determining unit, configured to average mass spectrum imaging data of all scanning detection points in a single partition space, so as to use the obtained average mass spectrum data as a representative spectrogram of the partition space;

a second representative spectrogram determining unit, configured to determine mass spectrum imaging data corresponding to a scanning detection point closest to the center position of a single partition space as a representative spectrogram of the partition space;

wherein the representative spectrogram determining module comprises:

the peak alignment coefficient storage unit is used for respectively carrying out peak alignment operation on the representative spectrograms of all the partition spaces corresponding to the plurality of tissue samples and storing corresponding peak alignment coefficients;

And the normalization coefficient storage unit is used for carrying out peak intensity normalization operation on all the representative spectrograms after the peak alignment operation and storing corresponding normalization coefficients so as to process the mass spectrum imaging spectrograms of each scanning detection point by utilizing the peak alignment coefficients and the normalization coefficients corresponding to the representative spectrograms.

7. An electronic device, comprising:

a memory for storing a computer program;

A processor for executing the computer program to implement the mass spectrometry imaging data processing method of any of claims 1 to 5.

8. A computer readable storage medium for storing a computer program which when executed by a processor implements a mass spectrometry imaging data processing method according to any one of claims 1 to 5.