Data registration method and device for space transcriptome and space metabolome, electronic equipment and storage medium

Abstract

The invention provides a data registration method and device for a space transcriptome and a space metabolome, electronic equipment and a storage medium. The method comprises the steps of obtaining a space transcriptome image and a space metabolome image to be registered; performing image registration on the space transcriptome image and the space metabolome image through an image registration model to obtain an optimal registration parameter; and performing space coordinate registration on the data points of the space transcriptome image and the space metabolome image after image registration by using the optimal registration parameter. According to the method, the tedious process of manual registration can be eliminated, and the unification of the space coordinates of the two groups of data points can be quickly and accurately completed. In addition, the data registration method provided by the invention is low in cost and extremely short in time consumption, does not depend on any closed source business software, reduces the use cost, provides space and freedom for subsequent algorithm upgrading and iteration, and can flexibly adapt to continuously changing research requirements.

Description

Technical Field

[0001] The present invention relates to a method, device, electronic equipment and storage medium for data registration of spatial transcriptome and spatial metabolome in biological information analysis. Background Art

[0002] Spatial metabolomics uses mass spectrometry technology to obtain high-throughput qualitative and quantitative information on small-molecule metabolites, while spatial transcriptomics uses sequencing technology to obtain high-throughput information on gene expression in samples. Transcribed mRNA is the direct product of gene expression and is processed, modified, and translated into protein. Metabolites are the products or intermediates of protein (enzyme)-catalyzed reactions in biological substances and are the ultimate outcome of gene expression and the material basis of an organism's phenotype. Spatial transcriptomics and spatial metabolomics respectively examine different stages of the central principle. Joint analysis of spatial transcriptomics and spatial metabolomics data can cross-validate the conclusions of the two groups, helping researchers explore the biological functional changes caused by changes in conditions (environment, disease, drugs, development) through differences in gene expression and metabolite phenotypes.

[0003] In the practical application process of the joint analysis of spatial transcriptomics and spatial metabolomics, data registration is an indispensable technical link. However, in the process of realizing the present invention, the inventors found that the existing data registration technology has the following technical defects in its specific implementation: on the one hand, it is currently possible to obtain high-resolution images for data registration with the help of image upsampling technology. However, although the spatial transcriptomics slice data can have high-resolution optical acquisition images, the spatial metabolomics images can only rely on mass spectrometry imaging of raw data with lower resolution. This data configuration method not only fails to effectively improve the shortcomings of the raw data, but further amplifies the morphological differences that originally existed between adjacent slices, resulting in a significant reduction in registration accuracy, thereby seriously affecting the accuracy of subsequent analysis. On the other hand, the data registration process is highly dependent on manual operation, the operation process is complicated and tedious, time-consuming, and requires a high level of professional experience and skill level from the operator, which not only greatly increases the labor cost, but also makes batch processing extremely difficult, and also makes it difficult to accurately quantify the accuracy, which cannot meet the research needs. Therefore, how to improve the data registration accuracy and efficiency of spatial transcriptomics and spatial metabolomics is a technical problem that needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, electronic device and storage medium for spatial transcriptomics and spatial metabolomics data registration to improve the efficiency and accuracy of spatial transcriptomics and spatial metabolomics data registration. To achieve the above purpose, the scheme adopted by the present invention is as follows: In a first aspect, the present invention provides a method for data registration of a spatial transcriptome and a spatial metabolome, the method comprising: obtaining a spatial transcriptome image and a spatial metabolome image to be registered; performing image registration on the spatial transcriptome image and the spatial metabolome image using an image registration model to obtain optimal registration parameters; and using the optimal registration parameters, performing spatial coordinate registration on the data points of the spatial transcriptome image and the spatial transcriptome image after image registration.

[0005] In a second aspect, the present invention provides a data registration device for a spatial transcriptome and a spatial metabolome, comprising: an acquisition module and a registration module; the acquisition module is used to obtain a spatial transcriptome image and a spatial metabolome image to be registered; the registration module is used to perform image registration on the spatial transcriptome image and the spatial metabolome image through an image registration model to obtain optimal registration parameters; the registration module is also used to use the optimal registration parameters to perform spatial coordinate registration on the data points of the spatial transcriptome image and the spatial transcriptome image after image registration.

[0006] In a third aspect, the present invention provides an electronic device comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the method for data registration of the spatial transcriptome and the spatial metabolome as described in the first aspect.

[0007] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for data registration of the spatial transcriptome and the spatial metabolome as described in the first aspect is implemented.

[0008] The embodiments of the present invention provide a method, device, electronic device, and storage medium for data registration of the spatial transcriptome and the spatial metabolome. This method uses an image registration model to achieve accurate and automatic image registration of the spatial transcriptome image and the spatial metabolome image, thereby obtaining the optimal registration parameters. Subsequently, using these optimal registration parameters, the spatial coordinates of the data points in the registered spatial transcriptome image and the spatial metabolome image are aligned to complete the spatial unification of the two-omics data. Compared with traditional data registration technology, the embodiments of the present invention have significant advantages: on the one hand, by applying the image registration model, the tedious process of manual registration is eliminated and automated operation is achieved. The entire registration process does not rely on any marker identification or manual intervention, and can quickly and accurately complete the spatial coordinate unification of the two-omics data points, providing a solid and reliable data foundation for subsequent data integration. On the other hand, the data registration method independently developed by the embodiments of the present invention is low-cost, extremely time-consuming, and does not rely on any closed-source commercial software. This not only reduces the cost of use, but also provides space and freedom for subsequent algorithm upgrades and iterations, and can flexibly adapt to changing research needs.

[0009] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0011] Figure 1 is a schematic flow chart of a method for data registration of spatial transcriptome and spatial metabolome provided in an embodiment of the present invention; Figure 2A This is a display diagram of the spatial transcriptome optical image provided by an embodiment of the present invention; Figure 2B This is an effect diagram of the preprocessing of the spatial transcriptome optical image provided by an embodiment of the present invention; Figure 3A This is an effect diagram of the spatial metabolomics optical image preprocessing provided by an embodiment of the present invention; Figure 3B This is a display diagram of the spatial metabolome mass spectrometry image provided by an embodiment of the present invention; Figure 3C This is an effect diagram of the spatial metabolomics mass spectrometry image preprocessing provided by an embodiment of the present invention; Figure 4 A functional structure diagram of the image registration model provided by an embodiment of the present invention; Figure 5 A workflow diagram of the image registration model provided by an embodiment of the present invention; Figure 6 The superimposed image and the local magnified image of the spatial transcriptome optical image and the spatial metabolome optical image after image registration in an embodiment of the present invention are shown; Figure 7A A schematic diagram of the distribution of spots collected by a 15μm spatial transcriptome chip provided in an embodiment of the present invention; Figure 7B This is a schematic diagram of the distribution of pixel points collected as a basic detection unit for 15 μm spatial metabolomics provided by an embodiment of the present invention; Figure 8A This is a schematic diagram of the geometric relationship between the data points of the first spatial metabolome and spatial transcriptome provided by an embodiment of the present invention; Figure 8B Schematic diagram of the geometric relationship between the data points of the second spatial metabolome and spatial transcriptome provided by an embodiment of the present invention; Figure 8C Schematic diagram of the geometric relationship between the data points of the third spatial metabolome and spatial transcriptome provided by an embodiment of the present invention; Figure 9 A functional module diagram of a data registration device for spatial transcriptome and spatial metabolome provided in an embodiment of the present invention; Figure 10 This is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0013] Considering the shortcomings and deficiencies of existing spatial transcriptomics and spatial metabolomics data registration technologies in terms of cost, efficiency, and accuracy, the present invention provides a method for spatial transcriptomics and spatial metabolomics data registration. Figure 1 , Figure 1 : is a schematic flow chart of a method for data registration of a spatial transcriptome and a spatial metabolome provided by an embodiment of the present invention. The method may be performed by an electronic device with data processing capabilities, and mainly includes steps S101 to S103, which are described as follows: S101: Obtaining the spatial transcriptome image and the spatial metabolome image to be registered; S102: performing image registration on the spatial transcriptome image and the spatial metabolome image using an image registration model to obtain optimal registration parameters.

[0014] In the embodiment of the present invention, the optimal registration parameter refers to a combination of transformation parameters that achieves the optimal alignment of two images in spatial position; S103: Using the optimal registration parameters, spatial coordinate registration is performed on the data points of the spatial transcriptome image and the spatial metabolome image after image registration.

[0015] In the embodiment of the present invention, the data point of the spatial transcriptome image is the collection center of the spatial transcriptome data, that is, the spot point, and the data point of the spatial metabolome image is the collection center of the spatial metabolome data, that is, the pixel point.

[0016] The data registration method of steps S101 to S103 provided by the present invention first uses an image registration model to complete the precise automatic image registration of the spatial transcriptome image and the spatial metabolome image, thereby obtaining the optimal registration parameters. Then, using the optimal registration parameters, the spatial coordinates of the registered spatial transcriptome image and the data points in the spatial metabolome image are aligned to complete the spatial unification of the two-omics data. The embodiment of the present invention realizes automated operation by applying the image registration model, and without relying on any marker identification or manual intervention, it is possible to quickly and accurately complete the spatial coordinate unification of the two-omics data, providing a solid and reliable data foundation for subsequent data integration. At the same time, the data registration method independently developed by the embodiment of the present invention is low in cost, extremely time-consuming, does not rely on any closed-source commercial software, reduces the cost of use, provides space and freedom for subsequent algorithm upgrades and iterations, and can flexibly adapt to changing research needs.

[0017] Next, the embodiment of the present invention will provide a detailed and clear description of the above steps S101 to S103 with reference to the relevant drawings.

[0018] In step S101, data registration of the spatial transcriptome image and the spatial metabolome image may be performed in the following manner: In the first embodiment, the spatial transcriptome optical image and the spatial metabolome optical image are directly used for data registration; in the second embodiment, the spatial transcriptome optical image and the spatial metabolome mass spectrometry image are directly used for data registration; in the third embodiment, the spatial metabolome optical image is used as a "bridge" to achieve data registration between the spatial transcriptome optical image and the spatial metabolome mass spectrometry image, making the registration effect more reliable and accurate.

[0019] Next, the present embodiment will use the third embodiment as an example to describe the process of obtaining the spatial transcriptome image and the spatial metabolome image to be registered in S101. It should be understood that the process of obtaining the images to be registered in the first and second embodiments is similar to the process of obtaining the images in the third embodiment, and will not be further described in this embodiment.

[0020] In the third embodiment described above, step S101 of the embodiment of the present invention may include the following steps: Step a1: obtaining a spatial metabolome mass spectrometry image, a spatial metabolome optical image, and a spatial transcriptome optical image; Step a2: The spatial metabolome mass spectrometry image and the spatial metabolome optical image are used as a set of images to be registered, and the spatial metabolome optical image and the spatial transcriptome optical image are used as another set of images to be registered.

[0021] In step a1, the embodiment of the present invention can obtain the above three images based on the biological tissue slice sample, and only one image of each type can be obtained. Step a1 can be implemented according to the following process: Step 1: Use a pathology section scanner to perform bright field scanning on two adjacent biological tissue sections, one stained and one unstained, to obtain spatial transcriptome optical images and spatial metabolome optical images; In an embodiment of the present invention, relevant personnel can select two biological tissue slice samples for slide preparation. One biological tissue slice sample is used to obtain a spatial transcriptome optical image, denoted as sample A; the other biological tissue slice sample is used to obtain a spatial metabolome optical image and a spatial metabolome mass spectrometry image, denoted as sample B. To ensure that the two biological tissue slice samples have a high degree of morphological similarity, sample A and sample B can preferably be two adjacent biological tissue slices.

[0022] The process for obtaining the spatial transcriptome optical image based on the two prepared samples is as follows: Sample A is stained and then scanned using a pathology slide scanner under brightfield conditions to acquire a high-quality optical image, namely the spatial transcriptome optical image. The process for obtaining the spatial metabolome optical image is: Sample B is mounted and dried, and then directly scanned using a pathology slide scanner under brightfield mode to acquire its optical image, thus obtaining the spatial metabolome optical image.

[0023] Step 2: Perform mass spectrometry imaging on unstained biological tissue sections to obtain spatial metabolome mass spectrometry images; In the embodiment of the present invention, based on the above-mentioned sample B, the spatial coordinate information in the spatial metabolomics data is converted into a spatial metabolomics mass spectrometry image in the form of a single-channel grayscale image at a ratio of 1:1.

[0024] Step 3: Preprocess the spatial transcriptome optical images, spatial metabolome optical images and spatial metabolome mass spectrometry images.

[0025] In the embodiment of the present invention, the pre-processing operations that can be performed on all three images include but are not limited to: cropping the blank areas around the image so that the sample in the image is located at the center of the image; and removing impurities and dirt from the surrounding area of ​​the sample.

[0026] For the optical images of the spatial transcriptome and the spatial metabolome, the embodiment of the present invention can also convert them into single-channel grayscale images, and then downsample them according to the original image size. The downsampling multiple can be 2 times, 4 times, or 8 times, etc.

[0027] For the spatial metabolome mass spectrometry image, considering that its low resolution will affect the registration effect, the embodiment of the present invention can also refer to the downsampling multiple of the spatial metabolome optical image to upsample the spatial metabolome mass spectrometry image. For example, if the downsampling multiple of the spatial metabolome optical image is 3 times, 5 times, 7 times, or 9 times, then the upsampling multiple of the spatial metabolome mass spectrometry image is correspondingly 3 times, 5 times, 7 times, or 9 times.

[0028] The benefits of the above preprocessing operations are that they can reduce the size of image data, improve image quality, and ensure improved operational efficiency and accuracy of the image registration model. Furthermore, considering that the image registration model in the embodiments of the present invention may fall into a "saddle point" during the optimization process, causing the model to mistakenly regard it as the optimal solution, resulting in registration accuracy failing to achieve the expected effect, the above preprocessing operations can effectively avoid this risk, ensuring that the image registration model can accurately achieve the expected accuracy.

[0029] In an optional embodiment, the various pre-processed images may be stored in, but not limited to, image formats such as png and tiff for subsequent registration.

[0030] In order to intuitively display the three images in the embodiment of the present invention, please see Figures 2A to 2B as well as Figures 3A to 3C , Figure 2A This is a display diagram of the spatial transcriptome optical image provided by an embodiment of the present invention; Figure 2B This is an effect diagram of the preprocessing of the spatial transcriptome optical image provided by an embodiment of the present invention. Figure 3A This is the effect diagram after the spatial metabolomics optical image preprocessing provided by the embodiment of the present invention. Figure 3B is a display diagram of the spatial metabolome mass spectrometry image provided by an embodiment of the present invention, Figure 3C This is an effect diagram of the spatial metabolomics mass spectrometry image preprocessing provided by an embodiment of the present invention.

[0031] After the images to be registered are obtained through step a1 above, in step a2, the embodiment of the present invention uses the spatial metabolomics mass spectrometry image and the spatial metabolomics optical image as a group of images to be registered, and uses the spatial metabolomics optical image and the spatial transcriptomics optical image as another group of images to be registered.

[0032] In one embodiment of the present invention, before performing image registration, the resolution of the spatial transcriptome image and the spatial metabolome image to be registered can be unified. This can be understood as follows: if the resolutions of the two images are different, the high-resolution image can be downsampled, or the low-resolution image can be upsampled, to bring the resolutions of the two images to the same level.

[0033] In an embodiment of the present invention, for the two sets of images to be registered obtained in step a1 and step a2, each set of images must first complete a unified resolution process. For the convenience of subsequent explanation, the embodiment of the present invention collectively refers to the spatial transcriptome optical image and the spatial metabolome optical image in step a2 as the "first image group", and the spatial metabolome optical image and the spatial metabolome mass spectrometry image as the "second image group". It should be understood that this naming method is only for the sake of simplicity in language expression and is not a limitation on the relevant images or technical solutions.

[0034] In the first image set, if the resolution of the spatial transcriptome optical image is greater than the resolution of the spatial metabolome optical image, then the spatial transcriptome optical image is downsampled, or the spatial metabolome optical image is upsampled to make the resolution of the two equal. In the second image set, the resolution of the spatial metabolome mass spectrometry image is significantly lower than that of the spatial transcriptome optical image, so the spatial metabolome mass spectrometry image can be upsampled with reference to the resolution of the spatial metabolome optical image in the first image set.

[0035] In the image after sampling processing, the conversion formula of the data point coordinates can be expressed as: (1) Where t is the multiple of upsampling (or downsampling); ( , ) and (x, y) are the coordinates of the data points in the image after upsampling (or downsampling) and before upsampling (or downsampling), respectively.

[0036] Next, based on the images to be registered with unified resolution, the image registration model provided by the embodiment of the present invention can be used to automatically complete image registration and obtain optimal registration parameters, see step S102.

[0037] In step S102, the spatial transcriptome image and the spatial metabolome image are registered using an image registration model. The process of obtaining the optimal registration parameters includes steps b1 to b3, which are described as follows: Step b1: determining the transformed image and the fixed image in the spatial transcriptome image and the spatial metabolome image; In the embodiments of the present invention, a transformable image refers to an image that requires a transform operation, and a fixed image refers to an image that does not require a transform operation. For example, continuing with the above-mentioned first and second groups of images, in the first group of images, the spatial transcriptome optical image is a fixed image, and the spatial metabolome optical image is a transformable image; in the second group of images, the spatial metabolome optical image is a fixed image, and the spatial metabolome mass spectrometry image is a transformable image. Through this configuration, the spatial metabolome optical image will serve as a "bridge" in the data alignment process, assisting in the spatial coordinate alignment of data points in the spatial transcriptome optical image and the spatial metabolome mass spectrometry image.

[0038] Step b2: The transformed image is transformed multiple times by the image registration model; In an embodiment of the present invention, the transformation can be any one of the following and their combinations: translation transformation, rotation transformation, isotropic / anisotropic scaling, affine transformation, rigid transformation (Euclidean transformation), etc., to meet the diverse needs of image transformation in different scenarios.

[0039] For a transformed image, there is a clear equation relationship between the coordinates of the data points before and after the transformation. For example, as an example, this equation relationship can be expressed as: (2) in, is the coordinate matrix of the data point after transformation; x is the coordinate matrix of the data point before transformation; M is the transformation coefficient matrix; c is the coordinate matrix of the transformation center point; t is the initial translation vector, M, c and t constitute the transformation parameters in the embodiment of the present invention.

[0040] It should be understood that formula (2) and its corresponding transformation parameters are merely examples, and the specific transformation parameters output in the embodiment of the present invention depend on the specific transformation algorithm used.

[0041] Step b3: When it is determined that the similarity between the transformed image and the fixed image reaches a preset convergence threshold, the image registration is completed and the optimal registration parameters are output.

[0042] In an embodiment of the present invention, the image registration model can not only perform multiple transformations on the transformed image, but also evaluate the similarity between the fixed image and the transformed image after each transformation to determine whether the image registration is completed. For example, taking the above-mentioned first image group as an example, after the spatial transcriptome optical image and the spatial metabolome optical image are input into the image registration model, the model performs multiple transformation operations on the spatial metabolome optical image. After each transformation, the model will evaluate the similarity between the spatial transcriptome optical image and the transformed spatial metabolome optical image, and determine whether the preset convergence threshold is reached. If the convergence threshold is not reached, the model will transform the spatial metabolome optical image again, and iterate repeatedly until the convergence threshold is reached. At this point, the model stops performing the transformation on the transformed image and can output the current transformation parameters.

[0043] Alternatively, the convergence threshold can be flexibly set by relevant personnel based on experience. For example, when the convergence threshold is set to 0.8, if the similarity is less than 0.8, the transformation of the transformed image continues. Once the convergence threshold reaches 0.8, the transformation can be stopped and the current transformation parameters can be output.

[0044] Based on the inventive concept of the above-mentioned image registration process, the embodiment of the present invention also provides a functional structure diagram of an optional image registration model. Figure 4 , Figure 4 The functional structure diagram of the image registration model provided by the embodiment of the present invention includes a registrator, an evaluator and an optimizer. The functions of these three components are introduced in turn below.

[0045] The aligner has a built-in specific transformation algorithm, based on which the aligner can perform corresponding transformation operations on the transformed image.

[0046] The evaluator has a built-in similarity evaluation algorithm that can accurately evaluate the similarity between the fixed image and the transformed image after the transformation operation.

[0047] Optionally, the similarity evaluation algorithm may include, but is not limited to, a matrix similarity algorithm (Correlation), a least mean square (MeanSquares), a Demons algorithm, etc. Relevant personnel may flexibly select an algorithm based on specific application scenarios and image characteristics to achieve the best similarity evaluation effect.

[0048] The optimizer, a key iterative framework for the image registration model, incorporates a specific optimization algorithm. Based on pre-defined parameters such as the learning rate, convergence threshold, and number of iterations, it monitors the model's convergence in real time during each iteration. Specifically, it monitors whether the similarity reaches the preset convergence threshold. This ensures that the image registration model optimally aligns fixed and transformed images within a limited number of iterations, providing data support for the subsequent unification of data point spatial coordinates.

[0049] In the context of the optimizer, the "learning rate" refers to the magnitude or degree of transformation that the aligner applies to the transformed image. For example, when rotating an image, the learning rate might correspond to the angle of rotation; or when scaling an image, the learning rate might refer to the degree of scaling.

[0050] The optimization method built into the optimizer can be, but is not limited to, the gradient descent algorithm. Different optimization methods set different model parameters. The optimizer completes the next iteration by adjusting the model parameters after each iteration until the iteration ends.

[0051] For example, in the case of the gradient algorithm, the preset model parameters may include: initial learning rate, maximum iteration coefficient, convergence threshold, etc. These parameters can be set by relevant personnel based on actual experience until the image registration model automatically completes image registration. For example, suppose the initial value of the learning rate is set to 3.0, the maximum number of iterations is set to 300 times, and the convergence threshold is set to 0.8. During each round of iteration, the optimizer will continue to monitor whether the similarity output by the evaluator reaches 0.8. If not, the optimizer will adjust the initial value of the learning rate, and then the registrant will perform the corresponding transformation operation on the transformed image again based on the adjusted learning rate. Through continuous iterations until the similarity reaches 0.8, the image registration is completed, and the current transformation parameters are output as the optimal registration parameters.

[0052] In an optional embodiment, the optimal registration parameters can be output in the form of a file, which records in detail the optimal parameter combination such as the transformation coefficient matrix, transformation center, rotation angle, scaling ratio, etc. involved in formula (2). The specific parameter combination depends on the specific registration algorithm selected in the aligner.

[0053] based on Figure 4 For an overall understanding of the image registration model, see Figure 5 , Figure 5 The workflow diagram of the image registration model provided in the embodiment of the present invention includes the following process: S1: Input the transformed image and the fixed image into the image registration model; S2: The register uses a preset transformation algorithm to transform the transformation image; S3: The evaluator calculates the similarity between the transformed image and the fixed image using a preset similarity evaluation algorithm; S4: The optimizer detects whether the similarity reaches the preset convergence threshold; If yes, execute S5; otherwise, adjust the preset learning rate and return to S2; S5: Outputting the optimal registration parameters, the transformed image under the optimal registration parameters, and the superimposed image of the transformed image under the optimal registration parameters and the fixed image.

[0054] In order to intuitively understand the image registration effect of the embodiment of the present invention, the first set of images in step a2 (spatial metabolome optical image and spatial transcriptome optical image) is still taken as an example. Figure 6 , Figure 6 The superimposed image of the spatial transcriptome optical image and the spatial metabolome optical image after image registration in the embodiment of the present invention and its local magnification are displayed. The superimposed image of the spatial transcriptome image and the spatial metabolome image can intuitively present the effect of image registration, providing relevant personnel with a convenient visualization analysis method.

[0055] After automatically completing the image registration of the spatial transcriptome image and the spatial metabolome image in step S102, embodiments of the present invention can perform spatial coordinate system alignment on the data points in the spatial transcriptome image and the spatial metabolome image based on the optimal registration parameters output by the image registration model to complete the spatial coordinate registration of the data points in the two images (see step S103).

[0056] In step S103, the data points of the spatial transcriptome image and the spatial metabolome image after image registration are spatially aligned using the optimal registration parameters. This means that the coordinates of the data points in one image are converted to the coordinate system of the data points in the other image using the optimal registration parameters. For example, the coordinates of the data points in the spatial metabolome mass spectrometry image are converted to the coordinate system of the data points in the spatial metabolome optical image, thereby achieving spatial transcriptome and spatial metabolome data point coordinate alignment.

[0057] For example, as one embodiment, for the first image group (spatial transcriptome optical image and spatial metabolome optical image) and the second image group (spatial metabolome optical image and spatial metabolome mass spectrometry image), step S103 may be implemented as follows: Step c1: performing spatial coordinate registration on the data points of the spatial metabolome mass spectrometry image and the data points of the spatial metabolome optical image according to the optimal registration parameters of the spatial metabolome mass spectrometry image and the spatial metabolome optical image; Step c2: performing spatial coordinate registration on the data points of the spatial metabolome mass spectrometry image and the spatial transcriptome optical image after spatial coordinate registration according to the optimal registration parameters corresponding to the spatial metabolome optical image and the spatial transcriptome optical image.

[0058] Through the above implementation, the data point coordinates of spatial metabolomics and spatial transcriptomics can be accurately unified into the same spatial plane coordinate system. At this point, the embodiment of the present invention completes the spatial coordinate alignment of the data points of spatial transcriptomics and spatial metabolomics.

[0059] In summary, the data registration method provided by the embodiment of the present invention has the following significant advantages: First, the image registration model designed by the embodiment of the present invention realizes automatic image registration, and there is no need to manually add any marks throughout the process, thereby greatly improving the registration efficiency; second, in the process of automatic image registration, the image registration model can accurately obtain the optimal transformation parameters through multiple rounds of iteration and optimization. Using these optimal image registration parameters for spatial position registration can significantly improve the registration accuracy. In addition, the registration algorithm adopted by the image registration model can be flexibly adjusted and can take into account the global and local features of the image. After the registration is completed, the model can automatically output the registration result image and key graphic transformation parameters. The entire registration process does not require manual intervention, and the registration results and effects can be quantitatively evaluated by similarity.

[0060] In one embodiment of the present invention, data integration can be performed on the spatial metabolomics data points and the spatial transcriptomics data after spatial coordinate registration to achieve joint analysis of the two omics data.

[0061] Optionally, the method for integrating the spatial metabolome and spatial transcriptome data may refer to existing data integration technologies, which will not be described in detail in the embodiment of the present invention.

[0062] In the embodiment of the present invention, the inventors found during the research process that there are significant differences in the spatial structure distribution between the sequencing spots of the spatial transcriptomics sequencing chip and the basic detection units in the spatial metabolomics detection - pixels. This difference is as follows Figure 7A and Figure 7B As shown, Figure 7A A schematic diagram of the distribution of spots collected by a 15μm spatial transcriptome chip provided in an embodiment of the present invention; Figure 7B This is a schematic diagram of the distribution of basic detection unit pixels of 15 μm spatial metabolomics provided by an embodiment of the present invention. Figure 7A In the figure, the green squares represent the spots of spatial transcriptomics. Figure 7B In the figure, the red squares represent the pixels of the spatial metabolome.

[0063] Observations show that the pixels of the spatial metabolomics group exhibit a dense distribution in the image, while the spots of the spatial transcriptomics group exhibit a relatively sparse distribution, forming a sharp contrast with the pixel distribution of the spatial metabolomics group. This mismatch in spatial structure reduces the reliability of the integration results of the two omics data. In addition, the differences in the structure of spatial transcriptomics chips—that is, the arrangement of sequencing spots on the chip—also make existing integration technologies incompatible with different sequencing chip structures.

[0064] In order to solve the limitations of existing spatial transcriptomics and spatial metabolomics data integration technologies in terms of reliability and compatibility, the present invention first analyzes the geometric relationship between the spatial transcriptomics data points and the spatial metabolomics data points after unifying the spatial metabolomics data points and the spatial transcriptomics data points into the same spatial coordinate system based on the spatial transcriptomics data points and the spatial metabolomics data points obtained above. Figures 8A to 8C As shown, Figure 8A This is a first effect diagram of the geometric relationship diagram of the data points of the first spatial metabolome and spatial transcriptome provided by an embodiment of the present invention. Figure 8B : is a schematic diagram of the geometric relationship between the data points of the second spatial metabolome and spatial transcriptome provided by an embodiment of the present invention, Figure 8C Schematic diagram of the geometric relationship between the data points of the third spatial metabolome and spatial transcriptome provided by an embodiment of the present invention.

[0065] observe Figures 8A to 8C It can be seen that each spatial transcriptome spot (green square) is constantly surrounded by some spatial metabolome pixels (red squares), and these spatial metabolome pixels together form a regular quadrilateral structure. Figure 8A What is presented in the figure is that the spatial transcriptome spot points and the spatial metabolome pixel points are independent of each other and do not overlap. Figure 8B It depicts the scene where the spatial transcriptome spot coincides with one of the spatial metabolome pixels; Figure 8C It shows the special layout of the spatial transcriptome spot point located exactly on one edge of a regular polygon composed of multiple adjacent spatial metabolome pixel points.

[0066] It should be made clear that in Figure 8B In the figure, the red square drawn in the lower left corner overlaps with the green square and part of the red square exceeds the image boundary. This is only to more intuitively show the state of overlap of the two pixel positions, and is not a strict limitation on the relationship between the two pixel positions.

[0067] Based on the geometric relationship between the spatial transcriptome spot points and the spatial metabolome pixel points, the present invention proposes a data integration method from step d1 to step d2, which is described as follows: Step d1: Determine the nearest neighboring spatial metabolome data point corresponding to each spatial transcriptome data point; Step d2: Using the distance between the spatial transcriptome data point and the nearest neighboring spatial metabolome data point, the original data at the nearest neighboring spatial metabolome data point is fitted and associated with the spatial transcriptome data point.

[0068] By step d1 to step d2, the embodiment of the present invention utilizes the distance between the spatial transcriptome data point and the nearest neighbor spatial metabolome data point to fit the original data at the nearest neighbor spatial metabolome data point and associate it with the spatial transcriptome data point. This fitting process can adjust and optimize the data according to the actual spatial distance relationship, so that the association between the two data points is more reasonable and accurate. By this distance-based fitting method, the data mismatch problem caused by spatial structure differences can be effectively compensated, thereby significantly improving the reliability of data integration results. At the same time, the flexibility of this method enables it to adapt to the data characteristics under different chip structures, enhancing the compatibility of the scheme.

[0069] In the embodiment of the present invention, for step d1, Figures 8A to 8C As shown in the geometric relationship diagram, each spot point is surrounded by a number of pixels that are closest to it, namely the "nearest neighbor spatial metabolome data points." Based on this distribution pattern, in order to quickly find the nearest neighbor spatial metabolome data points, the present invention provides an implementation method for determining the nearest neighbor spatial metabolome data points corresponding to each spatial transcriptome data point using a sliding window strategy, including the following process: Step 1: Determine the length of the sliding window; In an embodiment of the present invention, the distance between the spatial transcriptome data points determines the sliding window side length. For example, as an example, the sliding window side length can be set to twice the spot point spacing. Such a setting can ensure that the pixel points corresponding to each spot point can be fully captured within the area covered by the sliding window, thereby improving the accuracy of data integration.

[0070] Step 2: Traverse each spatial transcriptome data point according to the sliding window side length to obtain several candidate spatial metabolome pixel points within the sliding window side length; It can be understood that during the sliding window operation, as the sliding window gradually moves, each spot point can obtain a pixel point set, which includes several adjacent pixel points. The coordinates of all pixel points are within the range defined by the sliding window, so that the nearest neighbor pixel point of each spot point will be found from the pixel point set based on distance in the future.

[0071] Step 3: Calculate the distance between each candidate spatial metabolome data point and the spatial transcriptome data point; In the embodiment of the present invention, the distance may be, but is not limited to, the Euclidean distance. For example, the Euclidean distance between each candidate pixel and the spot point can be expressed by formula (3): (3) In formula (3), (x, y) is the coordinate of the spot point; ( , ) is the coordinate of the i-th candidate pixel.

[0072] Step 4: The candidate spatial metabolome data point with the smallest distance is used as the nearest neighbor spatial metabolome data point. The smallest distance here does not refer only to the spatial metabolome data point with the smallest distance to the spatial transcriptome data point, but rather to the spatial metabolome data points surrounding the spatial transcriptome. Thus, for a spatial metabolome data point with the smallest distance to a spatial transcriptome data point, the spatial metabolome data point is associated with the spatial transcriptome data point with the smallest distance. In this way, when spatial transcriptome data points are sparse and spatial metabolome data points are dense, a spatial transcriptome data point will generally be associated with multiple spatial metabolome data points.

[0073] It should be understood that using a sliding window strategy to find metabolome pixels in the target space that satisfy the aforementioned specific geometric distribution is only one example of many possible approaches. In practical applications, researchers can flexibly adopt a variety of other methods, such as clustering, image segmentation, or machine learning to achieve this effect, thereby accurately screening the nearest neighbor pixels corresponding to each spot.

[0074] Next, the original data at the nearest neighbor spatial metabolome data point are fitted and associated using the nearest neighbor pixel points corresponding to each spot point and the distance between them, that is, step d2 is executed.

[0075] In step d2, for each nearest neighbor spatial metabolome data point, an embodiment of the present invention can use the distance between it and the spatial transcriptome data point to quantify the weight of the original data on the nearest neighbor spatial metabolome data point. Wherein, the original data refers to all mass spectrometry response values. The greater the weight, the more important the mass spectrometry response value at the nearest neighbor spatial metabolome data point, and the stronger the influence on the final integration result. Furthermore, the embodiment of the present invention uses the obtained weight coefficient to fit the original data and complete the data association. Therefore, the embodiment of the present invention provides the following implementation method for implementing step d2, which may include the following process: In the first step, the weight coefficient of the original data is determined using the distance corresponding to the metabolomics data points in the nearest neighbor space; In the embodiment of the present invention, the weight of the original data at each nearest neighbor pixel is determined by converting the distance into a Gaussian distance and then normalizing it. Specifically: In the embodiment of the present invention, the distances corresponding to the adjacent spatial metabolome data points are first converted into Gaussian distance weights according to the following formula (4): (4) In formula (4), is the height of the normal distribution curve, the default value is 1; b is the offset of the center line of the normal distribution curve on the x-axis, the default value is 0; The half-peak width of the normal distribution curve is 2.0 by default.

[0076] Next, the Gaussian distance weights obtained by formula (4) are converted to relative values ​​to obtain the weight coefficient. The calculation formula is as follows: (5) In formula (5), represents the Gaussian distance weight corresponding to the kth nearest neighbor spatial metabolome data point; K represents the number of nearest neighbor spatial metabolome data points.

[0077] The second step is to weight the original data according to the weight coefficient; In the embodiment of the present invention, weighting the original data according to the weight coefficient can be understood as: multiplying the mass spectrum response value at the nearest neighbor spatial metabolome data point by the weight coefficient.

[0078] In the third step, the weighted raw data are added together as the fitting result and associated with the spatial transcriptome data points.

[0079] By gradually implementing the above steps, the embodiment of the present invention can not only effectively improve the reliability of data integration results, but also be compatible with scenarios with different sequencing chip structures, thereby meeting diverse integration needs.

[0080] Based on Figure 1 With the same inventive concept, the present invention also provides a data registration device 90 for spatial transcriptome and spatial metabolome. Figure 9 , Figure 9 The functional module diagram of the data registration device of the spatial transcriptome and spatial metabolome provided in the embodiment of the present invention includes: an acquisition module 901 and a registration module 902; An acquisition module 901 is used to obtain a spatial transcriptome image and a spatial metabolome image to be registered; The registration module 902 is used to perform image registration on the spatial transcriptome image and the spatial metabolome image using an image registration model to obtain optimal registration parameters; and use the optimal registration parameters to perform spatial coordinate registration on the data points of the spatial transcriptome image and the spatial metabolome image after image registration.

[0081] It should be noted that the device for the data alignment and integration of the spatial transcriptome and the spatial metabolome provided in the embodiment of the present invention can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brief description, for parts not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0082] Optionally, the above modules can be stored in the form of software or firmware. Figure 10 The memory shown in FIG. 1 or the operating system (OS) of the electronic device 100 may be fixed in the operating system (OS) and may be Figure 10 Meanwhile, the data and program codes required to execute the above modules may be stored in the memory.

[0083] See Figure 10 , Figure 10 An electronic device 100 provided in an embodiment of the present invention includes a memory 1001, a processor 1002, and a communication interface 1003. The memory 1001, processor 1002, and communication interface 1003 are electrically connected to each other, directly or indirectly, to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines.

[0084] Optionally, the bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0085] In an embodiment of the present invention, the processor 1002 may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor. The software module may be located in the memory 1001, and the processor 1002 reads the program instructions in the memory 1001 and performs the steps of the above method in conjunction with its hardware.

[0086] In an embodiment of the present invention, the memory 1001 may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory (Volatile Memory), such as RAM. The memory may also be any other medium that can be used to carry or store the desired program executable code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in an embodiment of the present invention may also be a circuit or any other device that can implement a storage function, for storing instructions and / or data.

[0087] The memory 1001 can be used to store software programs and modules, such as the instructions / modules of the spatial transcriptome and spatial metabolome data registration device 90 provided in an embodiment of the present invention. These instructions / modules can be stored in the memory 1001 in the form of software or firmware or embedded in the operating system (OS) of the electronic device 100. The processor 1002 executes the software programs and modules stored in the memory 1001 to perform various functional applications and data processing. The communication interface 1003 can be used for signaling or data communication with other node devices.

[0088] I understand. Figure 10 The structure shown is for illustration only. The electronic device 100 may also include Figure 10 More or fewer components than shown, or with Figure 10 Different configurations shown. Figure 10 The components shown may be implemented in hardware, software, or a combination thereof.

[0089] Based on the above embodiments, the present invention also provides a readable storage medium, which stores a computer program. When the computer program is executed by a computer, the computer executes the data alignment method of the spatial transcriptome and the spatial metabolome provided in the above embodiments. The specific implementation can be found in the method embodiment and will not be repeated here.

[0090] An embodiment of the present invention can also provide a computer program product for executing a method for data alignment of a spatial transcriptome and a spatial metabolome, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. For specific implementation, please refer to the method embodiment and will not be repeated here.

[0091] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0092] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0094] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

Claims

1. A method for data registration of spatial transcriptome and spatial metabolome, characterized in that: The method comprises: Obtaining the spatial transcriptome image and the spatial metabolome image to be registered; Performing image registration on the spatial transcriptome image and the spatial metabolome image using an image registration model to obtain optimal registration parameters; The optimal registration parameters are used to perform spatial coordinate registration on the data points of the spatial transcriptome image and the spatial metabolome image after image registration.

2. The method for data registration of spatial transcriptome and spatial metabolome according to claim 1, characterized in that: After obtaining the spatial transcriptome image and the spatial metabolome image to be registered, the method further includes: The resolutions of the spatial transcriptome image and the spatial metabolome image to be registered are unified.

3. The method for data registration of spatial transcriptome and spatial metabolome according to claim 1, characterized in that: Performing image registration on the spatial transcriptome image and the spatial metabolome image using an image registration model to obtain optimal registration parameters includes: determining a transformed image and a fixed image in the spatial transcriptome image and the spatial metabolome image; performing multiple transformations on the transformed image using the image registration model; When it is determined that the similarity between the transformed image and the fixed image reaches a preset convergence threshold, the image registration is completed and the optimal registration parameters are output.

4. The method for data registration of spatial transcriptome and spatial metabolome according to claim 3, characterized in that: The image registration model includes a registrant, an evaluator and an optimizer; the registrant is used to perform a transformation operation; the evaluator is used to evaluate whether the similarity reaches a preset convergence threshold; and the optimizer is used to adjust a preset learning rate to perform the next round of iteration if the similarity does not reach the preset convergence threshold.

5. The method for data registration of spatial transcriptome and spatial metabolome according to claim 1, characterized in that: Obtain the spatial transcriptome image and spatial metabolome image to be registered, including: Obtain spatial metabolome mass spectrometry images, spatial metabolome optical images, and spatial transcriptome optical images; The spatial metabolome mass spectrometry image and the spatial metabolome optical image are used as a group of images to be registered, and the spatial metabolome optical image and the spatial transcriptome optical image are used as another group of images to be registered.

6. The method for data registration of spatial transcriptome and spatial metabolome according to claim 5, characterized in that: Using the optimal registration parameters, spatial coordinate registration is performed on the data points of the spatial transcriptome image and the spatial metabolome image after image registration, including: Performing spatial coordinate registration on the data points of the spatial metabolome mass spectrometry image and the data points of the spatial metabolome optical image according to the optimal registration parameters of the spatial metabolome mass spectrometry image and the spatial metabolome optical image; According to the optimal registration parameters corresponding to the spatial metabolome optical image and the spatial transcriptome optical image, spatial coordinate registration is performed on the data points of the spatial metabolome mass spectrometry image and the spatial transcriptome optical image after spatial coordinate registration.

7. The method for data registration of spatial transcriptome and spatial metabolome according to claim 5, characterized in that: Obtain spatial metabolome mass spectrometry images, spatial metabolome optical images, and spatial transcriptome optical images, including: Performing bright field scanning on two adjacent biological tissue sections, one stained and one unstained, using a pathology section scanner to obtain the spatial transcriptome optical image and the spatial metabolome optical image; performing mass spectrometry imaging on the unstained biological tissue section to obtain the spatial metabolome mass spectrometry image; The spatial transcriptome optical image, the spatial metabolome optical image and the spatial metabolome mass spectrometry image are preprocessed.

8. The method for spatial transcriptome and spatial metabolome data registration according to any one of claims 1 to 7, characterized in that: The method further comprises: Data integration is performed on the spatial transcriptome image and the spatial metabolome image after spatial coordinate registration.

9. A data registration device for spatial transcriptome and spatial metabolome, characterized in that: include: Acquisition module and registration module; The acquisition module is used to obtain the spatial transcriptome image and the spatial metabolome image to be registered; The registration module is used to perform image registration on the spatial transcriptome image and the spatial metabolome image using an image registration model to obtain optimal registration parameters; The registration module is further configured to perform spatial coordinate registration on the data points of the spatial transcriptome image and the spatial metabolome image after image registration using the optimal registration parameters.

10. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to execute the computer program to implement the method for data registration of the spatial transcriptome and the spatial metabolome according to any one of claims 1 to 8.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for data registration of the spatial transcriptome and the spatial metabolome is implemented as claimed in any one of claims 1 to 8.

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