Pathological data processing method and device and electronic equipment

Abstract

The invention relates to a pathological data processing method and device and electronic equipment, and relates to the technical field of biological information, and the method comprises the steps: obtaining first gene expression data of a pathological tissue section based on a space transcriptome; performing cell annotation according to the first gene expression data, and determining a cell annotation result of the pathological tissue section; second gene expression data of target cells are extracted from the first gene expression data on the basis of the cell annotation result, and the target cells are cells to be subjected to DNA mismatch repair detection; and according to the second gene expression data of the target cell, determining a mismatch repair result of the pathological tissue section. The present application can use spatio-temporal transcriptome data to assist morphology, typical molecular marker staining (Hamp; e, IHC), determining a mismatch repair result of the pathological tissue section according to the gene expression data, thereby providing a basis for subsequent diagnosis and treatment, and reducing misdiagnosis in the aspect of pathology.

Description

Pathology data processing method, device and electronic equipment Technical Field

[0001] The present application relates to the field of bioinformatics, and in particular to a pathology data processing method, device, and electronic device. Background Art

[0002] Pathological diagnosis studies the causes and pathogenesis of disease, as well as the morphological structure, functional metabolic changes, and disease outcomes of the diseased organism during the course of the disease, thereby providing the necessary theoretical and practical basis for diagnosis, treatment, and prevention. Currently, tumor diagnosis and differential diagnosis are achieved through morphology and typical molecular marker staining (H&E, IHC). However, due to the technical experience and high sample heterogeneity, the processing of pathological data is ineffective, leading to frequent misdiagnosis and missed diagnosis in clinical practice, resulting in certain errors in pathological diagnosis.

[0003] Summary of the Invention

[0004] The present disclosure proposes a pathology data processing method, device, and electronic device that can use spatiotemporal transcriptome data to assist in morphology and typical molecular marker staining (H&E, IHC) to reduce pathology misdiagnosis.

[0005] A first embodiment of the present disclosure provides a pathology data processing method, the method comprising:

[0006] Obtaining the first gene expression data of pathological tissue sections based on spatial transcriptome;

[0007] Performing cell annotation based on the first gene expression data to determine the cell annotation result of the pathological tissue section;

[0008] Extracting second gene expression data of target cells from the first gene expression data based on the cell annotation result, wherein the target cells are cells to be tested for DNA mismatch repair;

[0009] The mismatch repair result of the pathological tissue section is determined based on the second gene expression data of the target cell.

[0010] In some embodiments of the present disclosure, obtaining first gene expression data of a pathological tissue section based on the spatial transcriptome includes:

[0011] Using spatiotemporal microarrays to capture mRNA from pathological tissue sections;

[0012] cDNA is synthesized using the mRNA as a template, and a library is constructed and then sequenced to obtain first gene expression data of the pathological tissue section.

[0013] In some embodiments of the present disclosure, performing cell annotation based on the first gene expression data to determine the cell annotation result of the pathological tissue section includes:

[0014] The following correlation calculation process is performed on each cell in the pathological tissue section until a cell annotation result for each cell in the pathological tissue section is determined:

[0015] Iteratively calculate the correlation between the first gene expression data corresponding to the cell and the preset gene expression data corresponding to each cell type until the cell type with the greatest correlation with the first gene expression data is screened out, and determine the cell type with the greatest correlation as the cell annotation result of the cell.

[0016] In some embodiments of the present disclosure, extracting second gene expression data of target cells from the first gene expression data based on the cell annotation result includes:

[0017] Determining cells annotated as epithelial cells and expressing target genes in the cell annotation results as target cells for DNA mismatch repair;

[0018] The second gene expression data of the target cell is extracted from the first gene expression data.

[0019] In some embodiments of the present disclosure, determining the mismatch repair result of the pathological tissue section based on the second gene expression data of the target cell includes:

[0020] calculating the mismatch repair deficiency score of the pathological tissue section according to the second gene expression data of the target cell;

[0021] The mismatch repair deficiency score is compared with a preset mismatch repair deficiency score to determine the mismatch repair result of the pathological tissue section.

[0022] In some embodiments of the present disclosure, calculating the mismatch repair deficiency score of the pathological tissue section based on the second gene expression data of the target cell comprises:

[0023] Extracting, based on the second gene expression data of the target cells, four genes corresponding to MMR proteins annotated as the average gene expression of the target cells;

[0024] Calculating the Z-Score value of each of the four genes corresponding to the MMR protein based on the average gene expression;

[0025] The lowest Z-Score value among the four MMR proteins was used as the mismatch repair deficiency score of the pathological tissue section.

[0026] In some embodiments of the present disclosure, comparing the mismatch repair deficiency score with a preset mismatch repair deficiency score to determine the mismatch repair result of the pathological tissue section includes:

[0027] Obtaining a first preset mismatch repair deficiency score for the mismatch repair normal sample and a second preset mismatch repair deficiency score for the mismatch repair abnormal sample;

[0028] Among the first preset mismatch repair deficiency score and the second preset mismatch repair deficiency score, selecting a target mismatch repair deficiency score having the smallest difference with the mismatch repair deficiency score of the pathological tissue section;

[0029] If the target mismatch repair deficiency score is the first preset mismatch repair deficiency score, determining that the mismatch repair result of the pathological tissue section is normal mismatch repair;

[0030] If the target mismatch repair deficiency score is the second preset mismatch repair deficiency score, the mismatch repair result of the pathological tissue section is determined to be mismatch repair abnormality.

[0031] A second aspect of the present disclosure provides a pathology data processing device, the device comprising:

[0032] A first acquisition module is used to acquire first gene expression data of a pathological tissue section based on the spatial transcriptome;

[0033] a first determining module, configured to perform cell annotation based on the first gene expression data to determine a cell annotation result of the pathological tissue section;

[0034] an extraction module, configured to extract second gene expression data of target cells from the first gene expression data based on the cell annotation result, wherein the target cells are cells to be tested for DNA mismatch repair;

[0035] The second determining module is used to determine the mismatch repair result of the pathological tissue section according to the second gene expression data of the target cell.

[0036] An embodiment of the third aspect of the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the embodiment of the first aspect of the present disclosure.

[0037] The fourth embodiment of the present disclosure provides a computer storage medium, wherein the computer storage medium stores computer-executable instructions; after the computer-executable instructions are executed by a processor, the method described in the first or second embodiment of the present disclosure can be implemented.

[0038] The fifth embodiment of the present disclosure provides a computer program product, including a computer program, which implements the method described in the first embodiment of the present disclosure when executed by a processor.

[0039] An embodiment of the sixth aspect of the present disclosure provides a chip, comprising one or more interface circuits and one or more processors; the interface circuit is used to receive signals from a memory of an electronic device and send signals to the processor, the signals including computer instructions stored in the memory, and when the processor executes the computer instructions, the electronic device executes the method described in the embodiment of the first aspect of the present disclosure.

[0040] According to the pathological data processing method, device, and electronic device disclosed herein, the first gene expression data of the pathological tissue section can be first obtained based on the spatial transcriptome, and then cell annotation can be performed based on the first gene expression data to determine the cell annotation result of the pathological tissue section; further, based on the cell annotation result, the second gene expression data of the target cell to be tested for DNA mismatch repair can be extracted from the first gene expression data; finally, the mismatch repair result of the pathological tissue section can be determined based on the second gene expression data of the target cell. The present disclosure uses spatiotemporal transcriptome data to assist in morphological and typical molecular marker staining (H&E, IHC), and determines the mismatch repair result of the pathological tissue section based on the gene expression data, thereby providing a basis for subsequent diagnosis and treatment, and can reduce pathological misdiagnosis.

[0041] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0043] FIG1 is a schematic flow chart of a pathology data processing method according to an embodiment of the present disclosure;

[0044] FIG2 is a flow chart of a pathology data processing method according to an embodiment of the present disclosure;

[0045] FIG3 is a schematic block diagram of a pathology data processing device according to an embodiment of the present disclosure;

[0046] FIG4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0048] Pathological diagnosis studies the causes and pathogenesis of disease, as well as the morphological structure, functional metabolic changes, and disease outcomes of the diseased organism during the course of the disease, thereby providing the necessary theoretical and practical basis for diagnosis, treatment, and prevention. Currently, tumor diagnosis and differential diagnosis are achieved through morphology and typical molecular marker staining (H&E, IHC). However, due to the technical experience and high sample heterogeneity, the processing of pathological data is ineffective, leading to frequent misdiagnosis and missed diagnosis in clinical practice, resulting in certain errors in pathological diagnosis.

[0049] In view of this, the present disclosure provides a pathology data processing method, device and electronic device, which can use spatiotemporal transcriptome data to assist morphology and typical molecular marker staining (H&E, IHC) to reduce pathology misdiagnosis.

[0050] The pathology data processing solution provided in this application is described in detail below with reference to the accompanying drawings.

[0051] As shown in FIG1 , an embodiment of the present disclosure provides a pathology data processing method, comprising:

[0052] Step 101: Acquire first gene expression data of a pathological tissue section based on the spatial transcriptome.

[0053] Spatial transcriptome sequencing is a sequencing technology that measures the total mRNA in intact tissue sections, combines the spatial information of total mRNA with morphological content, and maps the locations where all gene expression occurs, thereby obtaining a complex and complete gene expression map of biological processes. Compared to single-cell sequencing technology, spatial transcriptome records the spatial location of cells, which can better explore the spatial heterogeneity of cells.

[0054] In the disclosed embodiments, a pathological tissue section can be placed in the capture area of ​​a stereo chip. After HE staining and imaging, the tissue section is permeabilized to release the mRNA in the cells, which is then captured by oligo-dT probes on the chip. Each probe has a specific address sequence, and cDNA is then synthesized using the mRNA as a template. After a library is constructed, sequencing is performed to obtain the first gene expression data.

[0055] Step 102: Perform cell annotation based on the first gene expression data to determine the cell annotation result of the pathological tissue section.

[0056] For the disclosed embodiments, SingleR can be used for cell annotation. SingleR is a software that uses the Spearman correlation of the expression levels of the cells to be identified with known cells in a reference library to determine the cell type. Its principle is to first establish a set of marker genes, then obtain the correlation between the first round of cells and the cell types in the reference library, then exclude the cell type with the least correlation in the reference library, re-establish the marker gene set, and then calculate the correlation. The above process is repeated until the most similar cell type is left for each cell. In this way, the cell annotation results of each cell in the pathological tissue section can be determined.

[0057] Step 103: extracting second gene expression data of target cells from the first gene expression data based on the cell annotation results, wherein the target cells are cells to be tested for DNA mismatch repair.

[0058] For example, taking the target cells as malignant tumor cells, for the disclosed embodiments, first gene expression data annotated as epithelial cells with Ki67 gene expression can be extracted as the second gene expression data for the target cells. The second gene expression data can be used to view the average gene expression of four genes corresponding to MMR proteins (MLH1, MSH2, MSH6, and PMS2) in the malignant tumor cells annotated as such.

[0059] Step 104: Determine the mismatch repair result of the pathological tissue section based on the second gene expression data of the target cell.

[0060] The mismatch repair results of the pathological tissue sections may include normal mismatch repair and abnormal mismatch repair.

[0061] For the embodiments of the present disclosure, a mismatch repair loss score (MMR loss score) can be calculated based on the second gene expression data of the target cells, and then the mismatch repair loss score can be compared with the mismatch repair loss score in the database. If the score is closer to the sample with normal mismatch repair, the mismatch repair result of the pathological tissue section is judged to be normal mismatch repair. If the score is closer to the sample with mismatch repair deficiency, the mismatch repair result of the pathological tissue section is judged to be abnormal mismatch repair.

[0062] In summary, according to the pathological data processing method provided by the embodiment of the present disclosure, the first gene expression data of the pathological tissue section can be first obtained based on the spatial transcriptome, and then cell annotation can be performed based on the first gene expression data to determine the cell annotation result of the pathological tissue section; further based on the cell annotation result, the second gene expression data of the target cell to be tested for DNA mismatch repair is extracted from the first gene expression data; finally, the mismatch repair result of the pathological tissue section is determined based on the second gene expression data of the target cell. The present disclosure uses spatiotemporal transcriptome data to assist morphology and typical molecular marker staining (H&E, IHC), and determines the mismatch repair result of the pathological tissue section based on gene expression data, thereby providing a basis for subsequent diagnosis and treatment, and can reduce misdiagnosis in pathology.

[0063] Figure 2 shows a schematic flow chart of a pathology data processing method according to an embodiment of the present disclosure. Based on the embodiment shown in Figure 1 , as shown in Figure 2 , the method may include the following steps.

[0064] In this disclosure, in order to facilitate understanding of the scheme, an implementation method, process and effect of the method are also described in detail, and the implementation method of each step will be specifically described. It should be understood that this example is not a limitation of the method.

[0065] Step 201: Acquire first gene expression data of a pathological tissue section based on the spatial transcriptome.

[0066] In a specific application scenario, for the embodiments of the present disclosure, the embodiment steps may include: using a spatiotemporal chip to capture the mRNA of pathological tissue sections; synthesizing cDNA using mRNA as a template, constructing a library, and then sequencing to obtain the first gene expression data of the pathological tissue sections.

[0067] Step 202: Perform cell annotation based on the first gene expression data to determine the cell annotation result of the pathological tissue section.

[0068] In a specific application scenario, before executing the steps of this embodiment, it is also necessary to perform normalization processing on the first gene expression data, and then use the cell type database of SingleR to iteratively calculate the similarity between the cell and the cell type in the database to obtain the cell annotation result of the cell. The preset gene expression data corresponding to each cell type is stored in the cell type database. Specifically, the correlation between the first gene expression data corresponding to the cell and the preset gene expression data corresponding to each cell type in the cell type database can be iteratively calculated until the cell type with the greatest correlation with the first gene expression data is screened out, and the cell type with the greatest correlation is determined as the cell annotation result of the cell. Among them, the cell types contained in the cell type database can be determined by gene network community division. Specifically, after obtaining the sample gene expression data, the Leiden method can be used for unsupervised clustering. After normalization preprocessing and PCA dimensionality reduction processing, the output is the cell clustering situation in the slice, that is, each gene network community and the gene expression data corresponding to each gene network community can be obtained. Among them, Leiden clustering measures and optimizes the community division of nodes in the graph by Modularity, which can solve the problem of poor internal connection of communities in Louvain results. Accordingly, the steps of the embodiment may further include: obtaining sample gene expression data; performing clustering processing on the sample gene expression data to obtain cell types corresponding to each gene network community.

[0069] Accordingly, for this embodiment, the embodiment steps may include: performing the following correlation calculation process on each cell in the pathological tissue section until the cell annotation result of each cell in the pathological tissue section is determined: iteratively calculating the correlation between the first gene expression data corresponding to the cell and the preset gene expression data corresponding to each cell type until the cell type with the greatest correlation with the first gene expression data is screened out, and determining the cell type with the greatest correlation as the cell annotation result of the cell.

[0070] Step 203: Determine the cells annotated as epithelial cells in the cell annotation results and expressing the target gene as target cells to be subjected to DNA mismatch repair.

[0071] Among them, taking the target cells as malignant proliferating tumor cells as an example, the target gene expression can be ki67 gene expression.

[0072] Step 204: extract the second gene expression data of the target cell from the first gene expression data.

[0073] Step 205: Calculate the mismatch repair deficiency score of the pathological tissue section based on the second gene expression data of the target cell.

[0074] In a specific application scenario, taking the target cells as malignant tumor cells as an example, cells annotated as epithelial cells and expressing the ki67 gene can be extracted as malignant tumor cells. Then, based on the second gene expression data of the target cells, the average gene expression of the four genes corresponding to the MMR protein (MLH1, MSH2, MSH6, PMS2) in the annotated malignant tumor cells can be viewed. Then, based on the average gene expression, the Z-Score value of each gene is calculated. First, based on unsupervised model-based clustering, the expression of the four genes of the patient is clustered (1 or 2), and then the class with the highest average value is calculated and selected, and the variance is calculated therein. Finally, the Z-Score of each gene in each sample = gene expression minus the average value / variance. For each patient, the gene with the lowest Z-Score among the four genes is selected as the mismatch repair loss score (MMR Lose Score).

[0075] Accordingly, for the embodiment of the present disclosure, the embodiment steps may include: extracting the average gene expression of four genes corresponding to the MMR protein that are annotated as target cells based on the second gene expression data of the target cells; calculating the Z-Score value of each of the four genes corresponding to the MMR protein based on the average gene expression; and using the lowest Z-Score value of the four MMR proteins as the mismatch repair deficiency score of the pathological tissue section.

[0076] Step 206: Compare the mismatch repair deficiency score with a preset mismatch repair deficiency score to determine the mismatch repair result of the pathological tissue section.

[0077] In specific application scenarios, for the disclosed embodiments, the mismatch repair deficiency score can be compared with a preset mismatch repair deficiency score in a database. If the score is closer to that of a sample with normal mismatch repair, the mismatch repair result of the pathological tissue section is determined to be normal. If the score is closer to that of a sample with mismatch repair deficiency, the mismatch repair result of the pathological tissue section is determined to be abnormal. By determining the mismatch repair result of the pathological tissue section, a basis for subsequent diagnosis and treatment can be provided.

[0078] Accordingly, the steps of the embodiment may include: obtaining a first preset mismatch repair deficiency score for a sample with normal mismatch repair, and a second preset mismatch repair deficiency score for a sample with abnormal mismatch repair; screening a target mismatch repair deficiency score with the smallest difference between the first preset mismatch repair deficiency score and the second preset mismatch repair deficiency score and the mismatch repair deficiency score of the pathological tissue section; if the target mismatch repair deficiency score is the first preset mismatch repair deficiency score, determining that the mismatch repair result of the pathological tissue section is normal mismatch repair; if the target mismatch repair deficiency score is the second preset mismatch repair deficiency score, determining that the mismatch repair result of the pathological tissue section is abnormal mismatch repair.

[0079] In summary, according to the pathological data processing method provided by the present disclosure, the first gene expression data of the pathological tissue section can be first obtained based on the spatial transcriptome, and then cell annotation can be performed based on the first gene expression data to determine the cell annotation result of the pathological tissue section; further based on the cell annotation result, the second gene expression data of the target cell to be tested for DNA mismatch repair is extracted from the first gene expression data; finally, the mismatch repair result of the pathological tissue section is determined based on the second gene expression data of the target cell. The present disclosure uses spatiotemporal transcriptome data to assist morphology and typical molecular marker staining (H&E, IHC), and determines the mismatch repair result of the pathological tissue section based on the gene expression data, thereby providing a basis for subsequent diagnosis and treatment, and can reduce misdiagnosis in pathology.

[0080] To implement the various functions of the methods provided in the embodiments of the present application, the network devices and terminal devices may include hardware structures and software modules, and implement the aforementioned functions in the form of hardware structures, software modules, or hardware structures and software modules. Certain of the aforementioned functions may be implemented in the form of hardware structures, software modules, or hardware structures and software modules.

[0081] Corresponding to the pathological data processing methods provided in the above-mentioned embodiments, the present disclosure also provides a pathological data processing device. Since the pathological data processing device provided in the embodiment of the present disclosure corresponds to the pathological data processing methods provided in the above-mentioned embodiments, the implementation method of the pathological data processing method is also applicable to the pathological data processing device provided in this embodiment and will not be described in detail in this embodiment.

[0082] FIG3 is a schematic structural diagram of a pathology data processing device provided by an embodiment of the present disclosure. As shown in FIG3 , the device may include: a first acquisition module 31 , a first determination module 32 , an extraction module 33 and a second determination module 34 .

[0083] A first acquisition module 31 is configured to acquire first gene expression data of a pathological tissue section based on a spatial transcriptome;

[0084] A first determination module 32 may be configured to perform cell annotation based on the first gene expression data to determine a cell annotation result of the pathological tissue section;

[0085] An extraction module 33 is configured to extract second gene expression data of target cells from the first gene expression data based on the cell annotation results, wherein the target cells are cells to be subjected to DNA mismatch repair testing;

[0086] The second determination module 34 can be used to determine the mismatch repair result of the pathological tissue section according to the second gene expression data of the target cell.

[0087] In a specific application scenario, the acquisition module 31 can be used to capture mRNA of pathological tissue sections using the spatiotemporal chip; synthesize cDNA using mRNA as a template, construct a library, and then sequence to obtain the first gene expression data of the pathological tissue sections.

[0088] In a specific application scenario, the first determination module 32 can be used to perform the following correlation calculation process on each cell in the pathological tissue section until the cell annotation result of each cell in the pathological tissue section is determined: iteratively calculate the correlation between the first gene expression data corresponding to the cell and the preset gene expression data corresponding to each cell type until the cell type with the greatest correlation with the first gene expression data is screened out, and the cell type with the greatest correlation is determined as the cell annotation result of the cell.

[0089] In a specific application scenario, the extraction module 33 can be used to determine cells annotated as epithelial cells and expressing target genes in the cell annotation results as target cells for DNA mismatch repair; and extract the second gene expression data of the target cells from the first gene expression data.

[0090] In a specific application scenario, the second determination module 34 can be used to calculate the mismatch repair deficiency score of the pathological tissue section based on the second gene expression data of the target cell; compare the mismatch repair deficiency score with the preset mismatch repair deficiency score to determine the mismatch repair result of the pathological tissue section.

[0091] In a specific application scenario, the second determination module 34 can be used to extract the average gene expression of the four genes corresponding to the MMR protein that are annotated as target cells based on the second gene expression data of the target cells; calculate the Z-Score value of each of the four genes corresponding to the MMR protein based on the average gene expression; and use the lowest Z-Score value of the four MMR proteins as the mismatch repair deficiency score of the pathological tissue section.

[0092] In a specific application scenario, the second determination module 34 can be used to obtain a first preset mismatch repair missing score for a sample with normal mismatch repair, and a second preset mismatch repair missing score for a sample with abnormal mismatch repair; among the first preset mismatch repair missing score and the second preset mismatch repair missing score, a target mismatch repair missing score with the smallest difference corresponding to the mismatch repair missing score of the pathological tissue section is screened; if the target mismatch repair missing score is the first preset mismatch repair missing score, the mismatch repair result of the pathological tissue section is determined to be normal mismatch repair; if the target mismatch repair missing score is the second preset mismatch repair missing score, the mismatch repair result of the pathological tissue section is determined to be abnormal mismatch repair.

[0093] It should be noted that for other corresponding descriptions of the functional units involved in the pathology data processing device provided in this embodiment, reference can be made to the corresponding descriptions in FIG1 and FIG2 , which will not be repeated here.

[0094] In the embodiments provided above, the methods and devices provided in the embodiments of the present application are introduced. In order to implement the various functions of the methods provided in the embodiments of the present application, the electronic device may include a hardware structure and a software module, and implement the aforementioned functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. One of the aforementioned functions may be executed in the form of a hardware structure, a software module, or a hardware structure plus a software module.

[0095] FIG4 is a block diagram of an electronic device 400 for implementing the above-mentioned pathology data processing method according to an exemplary embodiment. For example, the electronic device 400 may be a mobile phone, a computer, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0096] 4 , electronic device 400 may include one or more of the following components: a processing component 402 , a memory 404 , a power component 405 , a multimedia component 406 , an audio component 410 , an input / output (I / O) interface 412 , a sensor component 414 , and a communication component 416 .

[0097] The processing component 402 generally controls the overall operation of the electronic device 400, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 402 may include one or more modules to facilitate interaction between the processing component 402 and other components. For example, the processing component 402 may include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.

[0098] The memory 404 is configured to store various types of data to support operations on the electronic device 400. Examples of such data include instructions for any application or method operating on the electronic device 400, contact data, phone book data, messages, pictures, videos, etc. The memory 404 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0099] The power supply assembly 405 provides power to the various components of the electronic device 400. The power supply assembly 405 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 400.

[0100] The multimedia component 406 includes a screen that provides an output interface between the electronic device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front camera and / or a rear camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0101] The audio component 410 is configured to output and / or input audio signals. For example, the audio component 410 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 400 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 404 or transmitted via the communication component 416. In some embodiments, the audio component 410 also includes a speaker for outputting audio signals.

[0102] I / O interface 412 provides an interface between processing component 402 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0103] The sensor assembly 414 includes one or more sensors for providing various aspects of status assessment for the electronic device 400. For example, the sensor assembly 414 can detect the open / closed state of the electronic device 400, the relative positioning of components, such as the display and keypad of the electronic device 400. The sensor assembly 414 can also detect changes in the position of the electronic device 400 or a component of the electronic device 400, the presence or absence of user contact with the electronic device 400, the orientation or acceleration / deceleration of the electronic device 400, and temperature changes of the electronic device 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0104] The communication component 416 is configured to facilitate wired or wireless communication between the electronic device 400 and other devices. The electronic device 400 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, 4G LTE, 5G NR (New Radio) or a combination thereof. In an exemplary embodiment, the communication component 415 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0105] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described methods.

[0106] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by the processor 420 of the electronic device 400 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0107] The embodiments of the present disclosure further provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the pathology data processing method described in the above embodiments of the present disclosure.

[0108] An embodiment of the present disclosure further provides a computer program product, including a computer program, which executes the pathology data processing method described in the above embodiment of the present disclosure when a processor is used.

[0109] An embodiment of the present disclosure also proposes a chip, which includes one or more interface circuits and one or more processors; the interface circuit is used to receive signals from the memory of the electronic device and send signals to the processor, the signals including computer instructions stored in the memory, and when the processor executes the computer instructions, the electronic device executes the pathology data processing method described in the above embodiments of the present disclosure.

[0110] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0111] Throughout this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" indicate that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative uses of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0112] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0113] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (control method), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0114] It should be understood that various parts of the embodiments of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0116] Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules. If the integrated modules are implemented as software functional modules and sold or used as standalone products, they may also be stored in a computer-readable storage medium. The aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0117] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A pathology data processing method, characterized in that: include: Obtaining the first gene expression data of pathological tissue sections based on spatial transcriptome; Performing cell annotation based on the first gene expression data to determine the cell annotation result of the pathological tissue section; Extracting second gene expression data of target cells from the first gene expression data based on the cell annotation result, wherein the target cells are cells to be tested for DNA mismatch repair; The mismatch repair result of the pathological tissue section is determined based on the second gene expression data of the target cell.

2. The method according to claim 1, characterized in that The obtaining of first gene expression data of pathological tissue sections based on spatial transcriptome includes: Using spatiotemporal microarrays to capture mRNA from pathological tissue sections; cDNA is synthesized using the mRNA as a template, and a library is constructed and then sequenced to obtain first gene expression data of the pathological tissue section.

3. The method according to claim 1, characterized in that Performing cell annotation according to the first gene expression data to determine the cell annotation result of the pathological tissue section includes: The following correlation calculation process is performed on each cell in the pathological tissue section until a cell annotation result for each cell in the pathological tissue section is determined: Iteratively calculate the correlation between the first gene expression data corresponding to the cell and the preset gene expression data corresponding to each cell type until the cell type with the greatest correlation with the first gene expression data is screened out, and determine the cell type with the greatest correlation as the cell annotation result of the cell.

4. The method according to claim 3, characterized in that The extracting second gene expression data of the target cell from the first gene expression data based on the cell annotation result includes: Determining cells annotated as epithelial cells and expressing target genes in the cell annotation results as target cells for DNA mismatch repair; The second gene expression data of the target cell is extracted from the first gene expression data.

5. The method according to claim 1, wherein Determining the mismatch repair result of the pathological tissue section based on the second gene expression data of the target cell includes: calculating the mismatch repair deficiency score of the pathological tissue section according to the second gene expression data of the target cell; The mismatch repair deficiency score is compared with a preset mismatch repair deficiency score to determine the mismatch repair result of the pathological tissue section.

6. The method according to claim 5, characterized in that Calculating the mismatch repair deficiency score of the pathological tissue section based on the second gene expression data of the target cell comprises: Extracting, based on the second gene expression data of the target cells, four genes corresponding to MMR proteins annotated as the average gene expression of the target cells; Calculating the Z-Score value of each of the four genes corresponding to the MMR protein based on the average gene expression; The lowest Z-Score value among the four MMR proteins was used as the mismatch repair deficiency score of the pathological tissue section.

7. The method according to claim 5, characterized in that Comparing the mismatch repair deficiency score with a preset mismatch repair deficiency score to determine the mismatch repair result of the pathological tissue section includes: Obtaining a first preset mismatch repair deficiency score for the mismatch repair normal sample and a second preset mismatch repair deficiency score for the mismatch repair abnormal sample; Among the first preset mismatch repair deficiency score and the second preset mismatch repair deficiency score, selecting a target mismatch repair deficiency score having the smallest difference with the mismatch repair deficiency score of the pathological tissue section; If the target mismatch repair deficiency score is the first preset mismatch repair deficiency score, determining that the mismatch repair result of the pathological tissue section is normal mismatch repair; If the target mismatch repair deficiency score is the second preset mismatch repair deficiency score, the mismatch repair result of the pathological tissue section is determined to be mismatch repair abnormality.

8. A pathology data processing device, characterized in that: include: A first acquisition module is used to acquire first gene expression data of a pathological tissue section based on the spatial transcriptome; a first determining module, configured to perform cell annotation based on the first gene expression data to determine a cell annotation result of the pathological tissue section; an extraction module, configured to extract second gene expression data of target cells from the first gene expression data based on the cell annotation result, wherein the target cells are cells to be tested for DNA mismatch repair; The second determining module is used to determine the mismatch repair result of the pathological tissue section according to the second gene expression data of the target cell.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

12. A chip, characterized in that: The electronic device comprises one or more interface circuits and one or more processors; the interface circuit is used to receive a signal from a memory of the electronic device and send the signal to the processor, wherein the signal includes a computer instruction stored in the memory, and when the processor executes the computer instruction, the electronic device executes the method according to any one of claims 1 to 7.

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