PDX model-based spatial transcriptome data analysis method, device and storage medium

Abstract

The application provides a PDX model-based spatial transcriptome data analysis method and device and a storage medium, and relates to the technical field of biological information analysis. The method comprises the following steps: extracting coordinate identifiers, molecular identifiers and sample transcript sequences in sequencing sequences; analyzing the sample transcript sequences, and preliminarily classifying the sequencing sequences into first sequences, second sequences, uncertain sequences and discarded sequences; calculating a first ratio under each bin; according to the first ratio, classifying the bins into first bins and second bins; reclassifying the uncertain sequences in the first bins as the first sequences, and reclassifying the uncertain sequences in the second bins as the second sequences; and S104, combining and analyzing all the first sequences and the second sequences respectively to obtain first spatial gene expression profiles and second spatial gene expression profiles. The method provided by the application improves the accuracy of spatial transcriptome gene expression distribution and the utilization rate of sequencing sequences.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bio-information analysis, and in particular, to a spatial transcriptome data analysis method based on a PDX model, a device and a storage medium. BACKGROUND

[0002] The birth of spatial transcriptome technology effectively makes up for the defects of traditional transcriptome and single-cell transcriptome in losing spatial information, enabling researchers to study the spatial characteristics of gene expression while preserving the spatial structure of the tissue. At present, a variety of spatial transcriptome technologies have emerged, such as 10X Visium and Stereo-seq. These technologies achieve spatial localization of gene expression through different ways, such as using oligonucleotide arrays with spatial barcodes to capture mRNA. However, they differ in resolution, for example, the minimum distance between the center points of the minimum spatial capture units of 10X Visium is 100 μm, which is much larger than the size of a single cell, while the minimum distance between the minimum spatial capture units of Stereo-seq is 0.5 μm, which is much smaller than the size of a single cell, so it can be called high-definition spatial transcriptome.

[0003] Patient-Derived Xenograft (PDX) model is a tumor research method that transplants tumor tissue or primary cells from patients directly into immunodeficient mice, and uses the microenvironment provided by mice to make the tumor grow and pass on. Since this step does not require in vitro culture, it avoids genetic and phenotypic changes that occur when cell lines adapt to the culture environment in vitro. In terms of data analysis, although PDX models can generate a large amount of tumor-related data, and when analyzing, additional labels can be added to the genes to construct a reference genome that can distinguish between two species, but due to the presence of homologous genes in the genomes of humans and mice, it can lead to inaccurate allocation of gene expression, or loss of expression due to multiple alignments. SUMMARY

[0004] The present application provides a spatial transcriptome data analysis method based on a PDX model, a device and a storage medium to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present application, a spatial transcriptome data analysis method based on a PDX model is provided, comprising the following steps:

[0006] S100, extracting coordinate identifiers for recording coordinates, molecular identifiers for distinguishing different transcripts, and captured sample transcript sequences in the sequencing sequences;

[0007] S101, analyzing the sample transcript sequence, and preliminarily classifying the sequencing sequence into a first sequence, a second sequence, an uncertain sequence and a discarded sequence;

[0008] S102, analyzing the coordinate identifier, and calculating a first ratio under each bin, the first ratio being a ratio of the number of sequencing sequences classified as the first sequence to the total number of sequencing sequences classified as the first sequence and the second sequence;

[0009] S103, according to the first ratio, classifying the bin into a first bin and a second bin; reclassifying the sequencing sequence classified as the uncertain sequence in the first bin as the first sequence, and reclassifying the sequencing sequence classified as the uncertain sequence in the second bin as the second sequence;

[0010] S104, combining and analyzing all the first sequences to obtain a first spatial gene expression profile, and combining and analyzing all the second sequences to obtain a second spatial gene expression profile.

[0011] In some embodiments of the first aspect of the application, in S101, the method for analyzing the sample transcript sequence and preliminarily classifying the sequencing sequence into a first sequence, a second sequence, an uncertain sequence and a discarded sequence is as follows:

[0012] For each sequencing sequence, the sample transcript sequence is used to align with the first reference genome and the second reference genome, and according to the alignment result, the sequencing sequence is classified into the following cases:

[0013] If the sequencing sequence aligns to the first reference genome but does not align to the second reference genome, the sequencing sequence is classified as the first sequence;

[0014] If the sequencing sequence does not align to the first reference genome but aligns to the second reference genome, the sequencing sequence is classified as the second sequence;

[0015] If the sequencing sequence aligns to both the first reference genome and the second reference genome, the sequencing sequence is classified as the uncertain sequence;

[0016] If the sequencing sequence does not align to both the first reference genome and the second reference genome, the sequencing sequence is classified as the discarded sequence.

[0017] In some embodiments of the first aspect of the application, in S102, the method for analyzing the coordinate identifier and calculating the first ratio under each bin is as follows:

[0018] S1021, according to the coordinate identifier, attaching a coordinate to the sequencing sequence;

[0019] S1022, in combination with the coordinate, obtaining the bin corresponding to the sequencing sequence;

[0020] S1023, counting the number of sequencing sequences classified as the first sequence in each bin, denoted as the first number; and the number of sequencing sequences classified as the second sequence, denoted as the second number;

[0021] S1024, dividing the first number by the sum of the first number and the second number to obtain the first ratio of the bin.

[0022] In some embodiments of the first aspect of the present application, in S102, in each bin, the sequencing sequences are de-duplicated.

[0023] In some embodiments of the first aspect of the present application, the de-duplication method of the sequencing sequences is as follows:

[0024] When the coordinate identifier, the molecular identifier, and the sample transcript sequence of a plurality of sequencing sequences are all repeated, it is determined that the plurality of sequencing sequences are repeated, and in S1023, the number of sequencing sequences is counted as only one.

[0025] In some embodiments of the first aspect of the present application, in S103, according to the first ratio, the method of classifying the bins into the first bin and the second bin is as follows:

[0026] The first ratio is compared with a set threshold value, if the first ratio is greater than or equal to the threshold value, the bin is classified as the first bin; if the first ratio is less than the threshold value, the bin is classified as the second bin.

[0027] In some embodiments of the first aspect of the present application, the threshold value is 0.5.

[0028] In some embodiments of the first aspect of the present application, in S104, after the genes of the first spatial gene expression profile and the second spatial expression profile are respectively labeled with species labels, the bins are combined into a comprehensive spatial gene expression profile.

[0029] According to the second aspect of the present application, a spatial transcriptome data analysis system based on a PDX model is provided, comprising:

[0030] An extraction module extracts a coordinate identifier used to record coordinates, a molecular identifier used to distinguish different transcripts, and a captured sample transcript sequence in a sequencing sequence;

[0031] A preliminary classification module analyzes the sample transcript sequence and classifies the sequencing sequence into a first sequence, a second sequence, an uncertain sequence, and a discarded sequence;

[0032] a proportion calculation module, analyzing the coordinate identifier, calculating a first proportion under each bin, the first proportion being a ratio of a number of sequencing sequences classified as the first sequence and a total number of sequencing sequences classified as the first sequence and the second sequence;

[0033] a final classification module, according to the first proportion, classifying the bins as first bins and second bins; reclassifying sequencing sequences classified as the uncertain sequence in the first bins as the first sequence, and reclassifying sequencing sequences classified as the uncertain sequence in the second bins as the second sequence;

[0034] a profile generation module, combining and analyzing all the first sequences to obtain a first spatial gene expression profile, and combining and analyzing all the second sequences to obtain a second spatial gene expression profile.

[0035] In some embodiments of the second aspect of the application, the method of preliminarily classifying sequencing sequences into the first sequence, the second sequence, the uncertain sequence and the discarded sequence is as follows:

[0036] For each sequencing sequence, the sample transcript sequence therein is used to align with the first reference genome and the second reference genome, and according to the alignment result, the sequencing sequence is classified into the following cases:

[0037] If the sequencing sequence aligns to the first reference genome but does not align to the second reference genome, the sequencing sequence is classified as the first sequence;

[0038] If the sequencing sequence does not align to the first reference genome but aligns to the second reference genome, the sequencing sequence is classified as the second sequence;

[0039] If the sequencing sequence aligns to both the first reference genome and the second reference genome, the sequencing sequence is classified as the uncertain sequence;

[0040] If the sequencing sequence does not align to both the first reference genome and the second reference genome, the sequencing sequence is classified as the discarded sequence.

[0041] In some embodiments of the second aspect of the application, the method of calculating the first proportion under each bin is as follows:

[0042] According to the coordinate identifier, a coordinate is attached to the sequencing sequence;

[0043] In combination with the coordinate, a bin corresponding to the sequencing sequence is obtained;

[0044] In units of bins, a number of sequencing sequences classified as the first sequence under each bin is counted and marked as a first number, and a number of sequencing sequences classified as the second sequence under each bin is counted and marked as a second number;

[0045] The first ratio under the bin is obtained by dividing the first number by the sum of the first number and the second number.

[0046] In some embodiments of the second aspect of the application, under each bin, the sequencing sequences are de-duplicated.

[0047] In some embodiments of the second aspect of the application, the de-duplication method of the sequencing sequences is as follows:

[0048] When the coordinate identifier, the molecular identifier, and the sample transcript sequence of a plurality of sequencing sequences are all repeated, it is determined that the plurality of sequencing sequences are repeated, and in the counting of the number of sequencing sequences, the plurality of sequencing sequences are only counted as one.

[0049] In some embodiments of the second aspect of the application, according to the first ratio, the method of classifying the bins into first bins and second bins is as follows:

[0050] The first ratio is compared with a set threshold value, if the first ratio is greater than or equal to the threshold value, the bin is classified as a first bin; if the first ratio is less than the threshold value, the bin is classified as a second bin.

[0051] According to a third aspect of the application, an electronic device is provided, comprising:

[0052] at least one processor; and

[0053] a memory in communication with the at least one processor; wherein

[0054] 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 perform the method described in the application.

[0055] According to a fourth aspect of the application, a non-transitory computer readable storage medium storing computer instructions is provided, the computer instructions being used to enable the computer to perform the method described in the application.

[0056] Compared with the prior art, the application has the following beneficial effects:

[0057] By classifying all sequencing sequences and calculating the first ratio of different transcripts under each bin, the bins are further classified, and then the transcripts of homologous genes are classified again according to the classification results of the bins, so as to realize accurate classification of sequencing sequences. Finally, according to the classification results, the analysis is combined to obtain the corresponding spatial gene expression profile. Compared with the prior art, the method provided by the application improves the accuracy of spatial transcriptome gene expression distribution and the utilization rate of sequencing sequences.

[0058] It is to be understood that the description of the contents described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0059] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description read in conjunction with the accompanying drawings, in which:

[0060] In the drawings, identical or corresponding reference signs indicate identical or corresponding parts.

[0061] Figure 1 An implementation flowchart of the embodiment one of the present application is shown.

[0062] Figure 2 A first ratio calculation method flowchart of the embodiment one of the present application is shown.

[0063] Figure 3 A system structure schematic diagram of the embodiment two of the present application is shown.

[0064] Figure 4 A component structure schematic diagram of an electronic device of the embodiment three of the present application is shown. DETAILED DESCRIPTION

[0065] In order to make the purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0066] Embodiment one: spatial transcriptome data analysis method based on PDX model

[0067] The embodiment one provides a spatial transcriptome data analysis method based on PDX model, please refer to Figure 1 , comprising the following steps:

[0068] S100, extracting coordinate identifiers for recording coordinates, molecular identifiers for distinguishing different transcripts and captured sample transcript sequences in sequencing sequences.

[0069] Stereo-seq technology: In the Stereo-seq spatial transcriptome technology platform, single-stranded linear spherical DNA nanoballs (DNA NanoBall, DNB) arranged in a regular array are used to capture mRNA - each DNB has a coordinate identifier CID (Coordinate ID) and a molecular identifier MID (Molecular ID), which are used to record coordinates and distinguish different transcripts, respectively. When the tissue section is placed on the chip, the sample transcript sequence (mRNA) in the tissue can bind to the capture probe or random primer with poly-T on the chip, and form cDNA through reverse transcription, so as to be captured and carry coordinate information. Through subsequent sequencing and data analysis, the gene expression information can be accurately restored to the original coordinates in the tissue.

[0070] Stereo-seq also performs nuclear staining on the section, which is aligned with the gene expression information in subsequent analysis, and can be located to the gene expression amount on the area of a single cell. The analysis method in units of gene expression of a single cell is called cellbin. Of course, the chip can also be cut into consecutive square regions according to a certain diameter, and the gene expression amount in each region can be combined into a square bin. The diameter of the square region is usually referred to as Bin*, such as Bin20, which is an analysis unit combined by 20*20 consecutive DNBs.

[0071] S101, analyze the sample transcript sequence, and preliminarily classify the sequencing sequence into a first sequence, a second sequence, an uncertain sequence, and a discarded sequence.

[0072] The specific method of this step S101 is as follows:

[0073] For each sequencing sequence, the sample transcript sequence therein is aligned with the first reference genome and the second reference genome, and is classified into the following cases according to the alignment result:

[0074] If the sequencing sequence is aligned to the first reference genome but not to the second reference genome, the sequencing sequence is classified as the first sequence;

[0075] If the sequencing sequence is not aligned to the first reference genome but is aligned to the second reference genome, the sequencing sequence is classified as the second sequence;

[0076] If the sequencing sequence is aligned to both the first reference genome and the second reference genome, the sequencing sequence is classified as the uncertain sequence;

[0077] If the sequencing sequence is not aligned to both the first reference genome and the second reference genome, the sequencing sequence is classified as the discarded sequence.

[0078] By the specific method of S101, the species of the sequencing sequence is preliminarily distinguished, that is, the first sequence corresponds to the first species, and the second sequence corresponds to the second species. The uncertain sequence will be further distinguished in the subsequent steps. It is worth mentioning that the generation of the uncertain sequence is often due to the existence of homologous genes between two species.

[0079] The present embodiment is illustrated by taking two species of human and mouse as examples.

[0080] The sequencing sequence is respectively aligned with the human reference genome and the mouse reference genome, and the sequence is divided into four categories: only aligned to human Rhuman, only aligned to mouse Rmouse, aligned to both reference genomes Rboth, and neither aligned to both reference genomes Rneither. Specifically, on the Stereo-seq platform, each sequencing sequence is composed of three sequences, namely the coordinate identifier CID (Coordinate ID) for recording the coordinate position, the molecular identifier MID (Molecular ID) for distinguishing different transcripts, and the captured sample transcript sequence (mRNA).

[0081] For each sequencing sequence, only the mRNA is used to align the human reference genome and the mouse reference genome - if the sequencing sequence aligns to the human reference genome but does not align to the mouse reference genome, the sequence is classified as a "human sequence" and is denoted as Rhuman; if it aligns to the mouse reference genome but does not align to the human reference genome, the sequence is classified as a "mouse sequence" and is denoted as Rmouse; if it aligns to both the human reference genome and the mouse reference genome, the sequence is classified as an "uncertain sequence" and is denoted as Rboth; if it does not align to both the human reference genome and the mouse reference genome, the sequence is classified as a "discarded sequence" and is denoted as Rneither. Table 1 shows the sequencing sequence and its classification results.

[0082] Table 1: Sequencing sequence and its classification results

[0083]

[0084] S102, analyze the coordinate identifier, and calculate the first ratio under each bin, which is the ratio of the number of sequencing sequences classified as the first sequence to the total number of sequencing sequences classified as the first sequence and the second sequence.

[0085] Please refer to Figure 2 The method for calculating the first ratio is as follows:

[0086] S1021, according to the coordinate identifier, the sequencing sequence is attached with coordinates;

[0087] S1022, in combination with the coordinates, obtaining the corresponding bin of the sequencing sequence;

[0088] S1023, in units of bins, counting the number of sequencing sequences classified as the first sequence under each bin, marked as the first number; and the number of sequencing sequences classified as the second sequence, marked as the second number;

[0089] S1024, dividing the first number by the sum of the first number and the second number to obtain the first ratio of the bin.

[0090] Still taking the two species of humans and mice as examples.

[0091] Using the official software SAW to analyze the coordinate identifier CID in the sequencing sequence, attaching coordinates (x, y) to each CID, and further obtaining the corresponding bin in combination with the coordinates.

[0092] In the Stereo-seq spatial transcriptome technology platform, three types of information are obtained after the experiment, one is the sequencing sequence information (recorded as reads file), the second is the corresponding information of the coordinate identifier CID region sequence and the chip spatial coordinates (recorded as mask file, each coordinate identifier CID sequence corresponds to a pair of x and y, the corresponding relationship is determined when the experimental consumables are generated), and the third is the high-definition photo of the experimental tissue section cell nucleus staining (recorded as image file). In the official software SAW analysis process, the coordinate identifier CID of the sequencing sequence is first compared with the mask file to record the spatial coordinates (x and y coordinates) of each sequence, then the transcript region in the sequencing sequence is compared to the reference genome to obtain the gene expression, and the MID sequence is used to filter the repeated captured transcripts.

[0093] In the analysis process, there are two binning methods:

[0094] One is to bin by square region, that is, to split all DNBs into square bins according to a set diameter size (such as 20 DNBs), that is, each bin contains the gene expression of adjacent 20*20 DNBs, simply referred to as Bin20.

[0095] The other is to analyze by cell region, automatically identify and predict the cell boundary through the high-definition photo of the cell nucleus staining obtained by the experiment, and combine all DNBs and their gene expressions in a single cell boundary into a cell bin, simply referred to as cellbin.

[0096] In the embodiment, the binning is one of square binning and cell binning. In either square binning or cell binning, the median of x and y of DNBs in the same bin is taken as the coordinate of the bin. Through the analysis process of SAW, on the one hand, the expression profile matrix of bin20 (or cell bin) * gene can be obtained, and on the other hand, the spatial coordinates of each bin can be obtained.

[0097] Table 2 shows the sequencing sequences and their corresponding bins in the case of cell binning.

[0098] Table 2: Sequencing sequences and their corresponding bins

[0099]

[0100] In units of bins, the number of sequences marked as human sequences Rhuman and mouse sequences Rmouse under each bin is counted. In this process, the sequencing sequences are de-duplicated, and when the CID+MID+mRNA complete sequence of multiple sequencing sequences is repeated, the number of sequencing sequences is only recorded as 1.

[0101] The number of de-duplicated human sequences and mouse sequences is marked as Nhuman (i.e., the first number) and Nmouse (i.e., the second number), respectively. The first ratio under the bin is calculated based on the following formula:

[0102] Phuman=Nhuman / (Nhuman+Nmouse)

[0103] Wherein, Phuman is the first ratio, representing the proportion of human transcripts; Table 3 shows the proportion of human transcripts of the bins in the embodiment.

[0104] Table 3: Proportion of human transcripts of bins

[0105]

[0106] S103, according to the first ratio, the bin is classified into a first bin and a second bin; the sequencing sequence classified as an uncertain sequence in the first bin is reclassified as a first sequence, and the sequencing sequence classified as an uncertain sequence in the second bin is reclassified as a second sequence.

[0107] Specifically, the first ratio is compared with a set threshold, and the threshold is preferably 0.5; if the first ratio is greater than or equal to the threshold, the bin is classified as a first bin; if the first ratio is less than the threshold, the bin is classified as a second bin.

[0108] In the case of both human and mouse species, according to the first ratio Phuman (i.e. the proportion of human transcripts), as shown in Table 3, the species to which the bins belong are classified into two categories, human and mouse, and the sequencing sequences classified as uncertain sequences Rboth are divided into two categories (Rboth-human, Rboth-mouse) according to the bins. If the Phuman of a sequencing sequence is not less than 0.5, the species to which the bin belongs is marked as human; on the contrary, if the Phuman of the sequencing sequence is less than 0.5, the species to which the bin belongs is marked as mouse. The uncertain sequences Rboth belonging to human in the bin are reclassified as Rboth-human; similarly, the uncertain sequences Rboth belonging to mouse are reclassified as Rboth-mouse.

[0109] S104, all the first sequences are combined and analyzed, such as directly using the official software SAW and the human reference genome for analysis, to obtain the first spatial gene expression profile (i.e. the spatial gene expression profile of human) as shown in Table 4; similarly, all the second sequences are combined and analyzed to obtain the second spatial gene expression profile (i.e. the spatial gene expression profile of mouse) as shown in Table 5.

[0110] Table 4: Spatial gene expression profile of human

[0111]

[0112] Table 5: Spatial gene expression profile of mouse

[0113]

[0114] Finally, after labeling the genes of the first and second spatial gene expression profiles with species tags, the bins are combined to obtain a comprehensive spatial gene expression profile as shown in Table 6, i.e. the spatial gene expression profile of PDX model, for further analysis.

[0115] Table 6: Comprehensive spatial gene expression profile

[0116]

[0117] The multiple classification steps of the embodiment can accurately allocate gene expression even if homologous genes exist in two species.

[0118] Embodiment two: Spatial transcriptome data analysis system based on PDX model

[0119] The second embodiment provides a spatial transcriptome data analysis system based on PDX model, please refer to Figure 3 , which comprises the following functional modules: extraction module, preliminary classification module, proportion calculation module, final classification module, and expression profile generation module.

[0120] the extraction module extracts a coordinate identifier for recording coordinates, a molecular identifier for distinguishing different transcripts, and a captured sample transcript sequence in the sequencing sequence.

[0121] The preliminary classification module analyzes the sample transcript sequence and preliminarily classifies the sequencing sequence into a first sequence, a second sequence, an uncertain sequence, and a discarded sequence.

[0122] Specifically, the method of preliminarily classifying the sequencing sequence into the first sequence, the second sequence, the uncertain sequence, and the discarded sequence is as follows:

[0123] For each sequencing sequence, the sample transcript sequence therein is aligned with the first reference genome and the second reference genome, and according to the alignment result, the sequencing sequence is classified into the following cases:

[0124] If the sequencing sequence is aligned to the first reference genome but not to the second reference genome, the sequencing sequence is classified as the first sequence;

[0125] If the sequencing sequence is not aligned to the first reference genome but is aligned to the second reference genome, the sequencing sequence is classified as the second sequence;

[0126] If the sequencing sequence is aligned to both the first reference genome and the second reference genome, the sequencing sequence is classified as the uncertain sequence;

[0127] If the sequencing sequence is not aligned to both the first reference genome and the second reference genome, the sequencing sequence is classified as the discarded sequence.

[0128] The proportion calculation module analyzes the coordinate identifier and calculates a first ratio under each bin, wherein the first ratio is a ratio of the number of sequencing sequences classified as the first sequence to the total number of sequencing sequences classified as the first sequence and the second sequence.

[0129] Specifically, the method of calculating the first ratio under each bin is as follows:

[0130] According to the coordinate identifier, the sequencing sequence is attached with a coordinate;

[0131] In combination with the coordinate, the bin corresponding to the sequencing sequence is obtained;

[0132] In units of bins, the number of sequencing sequences classified as the first sequence under each bin is counted and marked as a first number, and the number of sequencing sequences classified as the second sequence is counted and marked as a second number;

[0133] The first ratio under the bin is obtained by dividing the first number by the sum of the first number and the second number.

[0134] It is worth mentioning that under each bin, the sequencing sequences are de-duplicated.

[0135] The de-duplication method of the sequencing sequences is as follows:

[0136] When the coordinate identifiers, molecular identifiers, and sample transcript sequences of a plurality of sequencing sequences are all repeated, it is determined that the plurality of sequencing sequences are repeated, and in the counting of the number of sequencing sequences, the plurality of sequencing sequences are only counted as one.

[0137] The final classification module classifies the bins into first bins and second bins according to the first ratio, and reclassifies the sequencing sequences classified as uncertain sequences in the first bins as first sequences and reclassifies the sequencing sequences classified as uncertain sequences in the second bins as second sequences.

[0138] The method of classifying the bins into first bins and second bins according to the first ratio is as follows:

[0139] The first ratio is compared with a set threshold value, if the first ratio is greater than or equal to the threshold value, the bin is classified as a first bin, if the first ratio is less than the threshold value, the bin is classified as a second bin, and the threshold value is preferably 0.5.

[0140] The expression profile generation module combines and analyzes all the first sequences to obtain a first spatial gene expression profile, and combines and analyzes all the second sequences to obtain a second spatial gene expression profile.

[0141] Finally, the genes of the first spatial gene expression profile and the second spatial expression profile are labeled with species tags, and the bins are combined into a comprehensive spatial gene expression profile.

[0142] The specific execution methods and principles of the various functional modules in this embodiment two correspond to steps S100 to S104 in embodiment one, and will not be repeated again.

[0143] Embodiment three: electronic device and readable storage medium

[0144] This embodiment three also provides an electronic device and a readable storage medium.

[0145] Figure 4A schematic block diagram of an example electronic device that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0146] As shown in Figure 4 , the device includes a computing unit that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for operation of the device can also be stored. The computing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0147] A plurality of components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, a speaker, etc.; a storage unit, such as a magnetic disk, an optical disk, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0148] The computing unit can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit performs various methods and processes described above, such as the PDX model based spatial transcriptome data analysis method described in Embodiment I. For example, in some embodiments, the PDX model based spatial transcriptome data analysis method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the computing unit, one or more steps of the PDX model based spatial transcriptome data analysis method described above can be performed. Alternatively, in other embodiments, the computing unit can be configured, by way of firmware or software, to execute the PDX model based spatial transcriptome data analysis method.

[0149] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0150] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0151] In the context of this application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable storage medium can include, but are not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0153] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0154] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0155] It should be understood that the various forms of flow shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0156] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0157] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A spatial transcriptome data analysis method based on the PDX model, characterized in that, Includes the following steps: S100, extracts coordinate identifiers used to record coordinates, molecular identifiers used to distinguish different transcripts, and captured sample transcript sequences from the sequencing sequence; S101, analyze the sample transcript sequence and preliminarily classify the sequencing sequences into first sequence, second sequence, uncertain sequence and discarded sequence; S102, Analyze the coordinate identifier and calculate the first ratio under each bin. The first ratio is the ratio of the number of sequencing sequences classified as the first sequence to the total number of sequencing sequences classified as the first sequence and the second sequence. S103, based on the first ratio, classify the bins into a first bin and a second bin; reclassify the sequencing sequences in the first bin that were classified as indeterminate sequences as first sequences, and reclassify the sequencing sequences in the second bin that were classified as indeterminate sequences as second sequences; S104, combine and analyze all first sequences to obtain the first spatial gene expression profile; combine and analyze all second sequences to obtain the second spatial gene expression profile; In S101, the sample transcript sequences were analyzed, and the sequencing sequences were initially classified into first sequences, second sequences, uncertain sequences, and discarded sequences using the following method: For each sequencing sequence, the sample transcript sequence within it is compared with the first and second reference genomes. Based on the comparison results, the following cases are identified: If a sequencing sequence aligns to the first reference genome but not to the second reference genome, the sequencing sequence is classified as the first sequence. If a sequencing sequence does not align to the first reference genome but aligns to the second reference genome, then the sequencing sequence is classified as the second sequence. If a sequencing sequence aligns to both the first and second reference genomes, the sequence is classified as an indeterminate sequence. If the sequencing sequence does not align to either the first or second reference genome, the sequencing sequence is classified as a non-discarded sequence. In step S102, the method for analyzing coordinate identifiers and calculating the first ratio under each bin is as follows: S1021, Attach coordinates to the sequencing sequence based on the coordinate identifier; S1022, Based on the coordinates, obtain the bin corresponding to the sequencing sequence; S1023, using bins as the unit, count the number of sequencing sequences classified as the first sequence in each bin and mark it as the first quantity; and count the number of sequencing sequences classified as the second sequence and mark it as the second quantity; S1024, the first ratio under the sub-box is obtained by dividing the first quantity by the sum of the first quantity and the second quantity.

2. The spatial transcriptome data analysis method based on the PDX model according to claim 1, characterized in that, In step S102, deduplication of sequencing sequences is performed in each sub-bin.

3. The spatial transcriptome data analysis method based on the PDX model according to claim 2, characterized in that, The deduplication method for the sequencing sequences is as follows: When the coordinate identifier, molecular identifier, and sample transcript sequence of multiple sequencing sequences are all duplicated, the multiple sequencing sequences are determined to be duplicated. When counting the number of sequencing sequences in S1023, the multiple sequencing sequences are counted as only 1.

4. The spatial transcriptome data analysis method based on the PDX model according to claim 1, characterized in that, In step S103, the method for classifying the sub-boxes into a first sub-box and a second sub-box based on the first ratio is as follows: The first ratio is compared with a set threshold. If the first ratio is greater than or equal to the threshold, the bin is classified as the first bin; if the first ratio is less than the threshold, the bin is classified as the second bin.

5. The spatial transcriptome data analysis method based on the PDX model according to claim 4, characterized in that, The threshold is set to 0.

5.

6. The spatial transcriptome data analysis method based on the PDX model according to claim 4, characterized in that, In step S104, after the genes of the first spatial gene expression profile and the second spatial expression profile are labeled with species tags, they are merged into a comprehensive spatial gene expression profile according to binning.

7. 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 to enable the at least one processor to perform the method of any one of claims 1-6.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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