Data comparison method and device based on space transcriptome and program product

Abstract

The invention discloses a data comparison method and device based on a space transcriptome and a program product. The method comprises the steps of obtaining a reference position bar code; acquiring spatial transcription sequencing data, wherein the spatial transcription sequencing data comprises a sequencing position bar code; based on each reference position bar code and each sequencing position bar code, dividing the spatial transcription sequencing data into a plurality of groups of sequencing data; comparing each group of sequencing data with a reference genome sequence to obtain a comparison result corresponding to each group of sequencing data; and combining the comparison results corresponding to each group of sequencing data to obtain a combined result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene, in particular to a data alignment method based on spatial transcriptome, a computing device and a computer program product. BACKGROUND

[0002] Spatial transcriptome technology is an emerging research method that can simultaneously obtain spatial information of gene expression in tissue or cell samples. In spatial transcriptome experiments, specific marker sequences are usually added to the sequencing fragments to record the spatial position information of each fragment. The sequencing data of spatial transcriptome contains nucleotide sequences with spatial position information and cDNA base sequences obtained by reverse transcription. Spatial transcriptome technology not only provides gene expression data, but also retains the spatial positioning information of cells in the tissue. This feature enables researchers to explore the spatial relationship between cells, gene expression patterns in the tissue microenvironment, and cell functional states.

[0003] In order to obtain accurate quantitative expression, it is necessary to first align the spatial transcriptome sequencing data to the whole genome, and then construct an expression matrix according to the alignment results and the marker sequence recording the position information, wherein the rows represent genes, the columns represent different spatial positions, and the elements in the matrix represent the expression of the corresponding gene in the corresponding position.

[0004] Because the amount of spatial transcriptome sequencing data is much larger than that of single-cell transcriptome data, the required memory of the computing device using the data analysis scheme of single-cell transcriptome data is much larger than that required for analyzing single-cell data. Unless running in a large memory computing node, it will not be able to run due to insufficient memory of the conventional computing device. It is necessary to configure a large computing device with 256G or even larger memory to complete the calculation. Spaceranger launched by 10X company is a data analysis tool customized for 10X data, which can only be applied to 10x sequencing data, cannot be applied to data produced by other sequencing platforms, and also needs to be equipped with a large computer with 512G memory. This makes it difficult to popularize spatial transcriptome data analysis, especially for data with higher spatial resolution and higher sequencing depth, which can only be implemented in large memory computing devices, increasing hardware costs. SUMMARY

[0005] In order to solve the existing technical problems, the present application provides a data alignment method based on spatial transcriptome, a computing device and a computer program product, which can save peak memory during data analysis by data splitting, thereby reducing the cost required for data analysis.

[0006] In a first aspect, a spatial transcriptome-based data alignment method is provided, including: obtaining reference position barcodes; obtaining spatial transcript sequencing data, wherein the spatial transcript sequencing data includes sequencing position barcodes; dividing the spatial transcript sequencing data into multiple groups of sequencing data based on each of the reference position barcodes and each of the sequencing position barcodes; for each group of sequencing data, respectively aligning with a reference genome sequence to obtain an alignment result corresponding to each group of sequencing data; and merging the alignment results corresponding to each group of sequencing data to obtain a merged result.

[0007] In a second aspect, a computing device is provided, including a memory and a processor, the memory storing a computer program, and the computer program, when executed by the processor, causes the processor to perform the steps of the spatial transcriptome-based data alignment method provided by the embodiments.

[0008] In a third aspect, a computer program product is provided, including a computer program, and the computer program, when executed by a processor, implements the steps of the spatial transcriptome-based data alignment method provided by the embodiments.

[0009] In the present application, the reference position barcodes are obtained, the sequencing position barcodes in the spatial transcript sequencing data are compared with the reference position barcodes, the spatial transcript sequencing data is uniformly divided into multiple parts, the sequencing data is divided, then respectively aligned with the reference genome to obtain the alignment result of each group, and the alignment results of each group are merged to obtain the merged result. In the present application, the sequencing position barcodes are compared with the reference position barcodes, and data splitting is introduced in the error correction process, so that the peak memory during data analysis can be saved through data splitting, thereby reducing the cost required for data analysis. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 FIG. 1 is a diagram of an application environment of the spatial transcriptome-based data alignment method in an embodiment;

[0011] Figure 2 FIG. 2 is a flowchart of the spatial transcriptome-based data alignment method in an embodiment;

[0012] Figure 3 FIG. 3 is a flowchart of dividing the spatial transcript sequencing data into multiple groups of sequencing data in an embodiment;

[0013] Figure 4 FIG. 4 is a schematic diagram of generating a group of sequencing data in an embodiment;

[0014] Figure 5 FIG. 5 is a flowchart of obtaining a gene merging file in an embodiment;

[0015] Figure 6 FIG. 6 is a schematic diagram of obtaining a sparse matrix merging file in an embodiment;

[0016] Figure 7 A schematic diagram of a method for spatial transcriptome-based data alignment in an embodiment;

[0017] Figure 8 A schematic diagram of a device for spatial transcriptome-based data alignment in an embodiment;

[0018] Figure 9 A structural schematic diagram of a computing device in an embodiment. DETAILED DESCRIPTION

[0019] The technical solutions of the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments of the present application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0021] In the following description, expressions relating to "some embodiments" describe a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset of all possible embodiments or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0022] As Figure 1 shown, Figure 1 A schematic diagram of an application environment of a method for spatial transcriptome-based data alignment in an embodiment; a gene sequencer 10 communicates with a computing device 20, the gene sequencer 10 uses a sequencing chip to sequence sequencing fragments to obtain spatial transcript sequencing data. The spatial transcript sequencing data includes sequencing sequences obtained by sequencing the sequencing fragments, sequencing position barcodes corresponding to the sequencing sequences, and the like. The computing device 20 groups the spatial transcript sequencing data to obtain multiple groups of sequencing data, compares each group of sequencing data with a reference genome respectively to obtain an alignment result of each group, and then merges the alignment results of each group to obtain a merged result.

[0023] Before the embodiments of the present application are further described in detail, the terms and phrases involved in the embodiments of the present application are explained, and the terms and phrases involved in the embodiments of the present application are applicable to the following explanations.

[0024] Barcode, a unique nucleotide sequence (DNA or RNA), is used to label and identify individual molecules or cells in a biological sample. Barcode is usually a string of barcodes of a certain length, which plays a crucial role in spatial transcriptomics, enabling RNA molecules to be mapped to the original location in the tissue sample.

[0025] RNA, ribonucleic acid, is a linear macromolecule polymerized by 3', 5'-phosphodiester bond of ribonucleotides. RNA in nature is usually single-stranded, and the four basic bases in RNA are adenine (A), uracil (U), guanine (G), and cytosine (C). In contrast, DNA, which is also a nucleic acid, is usually a double-stranded molecule, and its nitrogenous base contains thymine (T) instead of uracil (U) in RNA.

[0026] Spatial transcriptomics is a technique for analyzing RNA-Seq data from a spatial perspective, analyzing all mRNAs in a single tissue section to locate and distinguish the active expression of functional genes in a specific tissue region. An optional application scenario of the sequencing method of spatial transcriptomics. The sequencing process of spatial transcriptomics mainly includes: 1. Designing a spatial transcriptome chip; 2. Sequencing to determine the reference barcode; 3. Sample preparation and quality control; 4. Tissue sectioning, staining and imaging, etc.; 5. Tissue permeation and cDNA synthesis, mainly including placing the tissue section on a slide containing RNA-binding capture probes (in the embodiments of the present application, it is called a spatial transcriptome chip, and the capture probes all have unique barcodes, thereby retaining the spatial location information of RNA); and fixing and permeabilizing (releasing mRNA in cells and binding to corresponding capture probes to obtain gene expression information); 6. Library construction, after cDNA synthesis using captured RNA as a template, construct a sequencing library for the cDNA sample; 7. Sequencing the prepared library, such as using high-throughput sequencing platform NGS (Next Generation Sequencing, second-generation sequencing technology) sequencing method for sequencing; 8. Data analysis and visualization (quality control, analysis, and obtaining gene information expressed in spatial location, etc.).

[0027] Spatial transcriptome data usually has a high noise level, and gene expression data can be very sparse, and the coverage of different regions can also differ. These characteristics bring challenges to accurate analysis of data. Compared with single-cell data, the most prominent feature is that it contains up to millions of spatial sequences, compared with tens of thousands of cells at most, the sparsity of data is more obvious. In order to obtain detection accuracy comparable to single-cell data, the amount of sequencing can be increased to improve the amount of capture, thereby obtaining more accurate expression atlas.

[0028] The existing spatial transcriptome data processing scheme usually uses the analysis software developed for single-cell transcriptome data to align the spatial transcriptome sequencing data. Commonly used alignment tools include alignment software STAR and HISAT2. There are also customized analysis tools for spatial transcriptome, such as spaceranger developed by 10X company. The data analysis of spatial transcriptome data in the prior art is very limited, especially for data with higher spatial resolution and higher sequencing depth, which can only be implemented in a large memory computing device, increasing the hardware cost.

[0029] Referring to Figure 2 A flowchart of a spatial transcriptome-based data alignment method provided by an embodiment of the present application is shown. The spatial transcriptome-based data alignment method is applied to a computing device. The spatial transcriptome-based data alignment method includes the following steps:

[0030] S10, obtaining a reference position barcode.

[0031] In this embodiment, in spatial transcript sequencing, a nucleotide sequence representing spatial information, i.e., a position barcode, is added before the sequencing fragment, for example, a 30 bp position barcode. According to the above-mentioned spatial transcriptome sequencing process, the position barcode can be sequenced first. The physical coordinates are pre-encoded as a unique DNA sequence (barcode), and this position barcode is labeled to the RNA molecule from the position through in-situ biochemical reaction (reverse transcription). Finally, the position barcode and gene sequence are read out simultaneously through in-situ sequencing, and are associated with the physical coordinates under the microscope. The position barcode sequenced first is not sequenced for the sequencing fragment, so that the sequenced position barcode sequence is more accurate, serving as a reference position barcode. The reference position barcode is used for alignment of the sequencing position barcode in the subsequent spatial transcript data. The position barcode is used to distinguish the UMI sequence of PCR and the corresponding cDNA base sequence. Each sequencing fragment corresponds to a position barcode. The coordinates of each position barcode on the x-axis and y-axis positions are stored in an independently saved list.

[0032] S11, obtaining spatial transcript sequencing data, wherein the spatial transcript sequencing data includes a sequencing position barcode.

[0033] In the embodiment, the spatial transcriptome sequencing data represents the sequencing file obtained after sequencing the prepared library. The spatial transcriptome sequencing data includes, but is not limited to, a base sequencing file, a molecular barcode file, and a sequencing position barcode file. The base sequencing file includes the sequencing sequence corresponding to each sequencing fragment. The molecular barcode file includes the sequencing UMI sequence corresponding to each sequencing fragment. The sequencing position barcode file includes the sequencing position barcode corresponding to each sequencing fragment. The sequencing position barcode is obtained in the process of sequencing the prepared library. Due to the interference of the sequencing fragment in the sequencing process, there may be certain errors in the sequencing position barcode obtained by sequencing. Therefore, the reference position barcode is more accurate. Each sequencing fragment has a sequence identifier, which uniquely identifies the sequencing fragment. The sequence identifier can be a number or the like.

[0034] S12, based on each reference position barcode and each sequencing position barcode, the spatial transcriptome sequencing data is divided into multiple groups of sequencing data.

[0035] In the embodiment, the sequencing position barcode is compared with the reference position barcode, and the spatial transcriptome sequencing data is uniformly split into multiple parts. In the grouping process, based on the reference position barcode, the sequencing error of the sequencing position barcode can be corrected in the splitting process. In the prior art, the reference genome is split into multiple segments with a length of 100M, for example. However, this method will cause differences between the results and before splitting, and cannot be applied to transcriptome analysis.

[0036] Optionally, the number of parts S indicated by the multiple groups of sequencing data can be calculated according to the total number M of sequencing fragments and the number N of reference position barcodes. For example, S = int(N / (M / preset value)), where int is a function of rounding down the decimal.

[0037] S13, for each group of sequencing data, respectively compared with the reference genome sequence to obtain the alignment result corresponding to each group of sequencing data.

[0038] S14, merging the alignment results corresponding to each group of sequencing data to obtain a merged result.

[0039] In the above embodiment, the reference position barcode is obtained, the sequencing position barcode in the spatial transcriptome sequencing data is compared with the reference position barcode, the spatial transcriptome sequencing data is uniformly split into multiple parts, the sequencing data is split, then respectively compared with the reference genome to obtain the alignment result of each group, and then the alignment results of each group are merged to obtain a merged result. In the present application, the sequencing position barcode is compared with the reference position barcode, and data splitting is introduced in the error correction process, so that the peak memory during data analysis can be saved through data splitting, thereby reducing the cost required for data analysis.

[0040] In some embodiments, as shown in Figure 3 , Figure 3 FIG. 12 is a flowchart illustrating a process of dividing spatial transcript sequencing data into groups of sequencing data in an embodiment, S12 can further include:

[0041] S121, generating a position index file based on the reference position barcodes, wherein the position index file includes an index order of each of the reference position barcodes in the position index file.

[0042] In this embodiment, after obtaining the reference position barcodes by sequencing, the duplicates in the reference position barcodes can be removed to avoid interference caused by different spatial positions. The reference position barcodes in the reference position barcode file are randomly rearranged, and a data processing tool is called to generate a position index file. The data processing tool includes but is not limited to: bwa, bwa2, bowtie2, snap, etc. Each reference position barcode in the position index file corresponds to a base sequence. For example, a sequencing sequence includes 500,000 reference position barcodes of 30 bp, and after deduplication, there are 490,000 left. Randomly rearranging these reference position barcodes can obtain a position index file for alignment. The index order indicates the index number of the reference position barcode in the position index file.

[0043] S122, for any sequencing position barcode, matching the sequencing position barcode with each reference position barcode in the position index file, when there is a first reference position barcode identical to the sequencing position barcode, grouping the sequencing position barcode based on the first reference position barcode.

[0044] In this embodiment, according to the expected length B of the reference position barcode, the sequencing position barcode is matched with each reference position barcode in the position index file, and when there is a first reference position barcode identical to the sequencing position barcode, it means that the first reference position barcode is completely identical to the sequencing position barcode.

[0045] Optionally, the spatial transcript sequencing data further includes a base sequencing file, and the grouping the sequencing position barcode based on the first reference position barcode includes:

[0046] determining, according to the index order of the first reference position barcode, a sequencing group file corresponding to the first reference position barcode;

[0047] obtaining, according to the sequence identifier corresponding to the sequencing position barcode, a sequencing sequence corresponding to the sequencing position barcode from the base sequencing file;

[0048] writing the sequence identifier corresponding to the sequencing position barcode into the sequence identifier group file corresponding to the first reference position barcode and storing the sequencing position barcode, the sequence identifier corresponding to the sequencing position barcode and the sequencing sequence corresponding to the sequencing position barcode in the sequencing group file corresponding to the first reference position barcode.

[0049] In the embodiment, each reference position barcode has an index order, and the corresponding sequencing group file and the corresponding sequence identifier group file can be determined according to the index order. That is, a group of files includes a plurality of continuous index orders. For example, the sequencing group file corresponding to the index order of 1 to int(M / S) belongs to the first sequencing group file, for example, No. 1 fastq file; the sequencing group file corresponding to the index order of int(M / S)+1 to int(M / S)*2 belongs to the second sequencing group file, for example, No. 2 fastq file, and the like. The sequence identifier group file is processed in a similar manner. The fastq file is used to store the file format of the sequencing sequence.

[0050] Using the established position index file, the reference position barcode is matched with the sequencing position barcode by establishing a hash table. For a sequencing position barcode, if the first reference position barcode is matched, the sequencing position barcode is grouped according to the index order of the first reference position barcode. For example, if the index order belongs to 1 to int(M / S), the sequence identifier, sequence information and quality control information corresponding to the sequencing position barcode are output to the corresponding No. 1 fastq file. If the index order of the first reference position barcode belongs to int(M / S)+1 to int(M / S)*2, the sequence identifier, sequence information and quality value information corresponding to the sequencing position barcode are output to the corresponding No. 2 fastq file, and the like.

[0051] S123, when there is no first reference position barcode identical to the sequencing position barcode, the sequencing position barcode is put into a sequence different group.

[0052] In the embodiment, the sequencing position barcode that is not matched with the same reference position barcode is then output to the sequence different group, for example, No. L fastq file. For the sequencing position barcode in the sequence different group, a fault tolerance method can be used for matching grouping.

[0053] Optionally, the method further comprises:

[0054] obtaining a target sequencing position barcode in the sequence different group, determining a second reference position barcode matched with the target sequencing position barcode, and the mismatch value of the second reference position barcode being lower than a preset fault tolerance threshold;

[0055] replacing the target sequencing position barcode with the second reference position barcode to obtain an updated sequencing position barcode;

[0056] determining, according to an index order of the second reference position barcode, a sequencing group file corresponding to the second reference position barcode;

[0057] obtaining, according to a sequence identifier corresponding to the target sequencing position barcode, a sequencing sequence corresponding to the target sequencing position barcode from the base sequencing file;

[0058] writing the sequence identifier corresponding to the target sequencing position barcode into a sequence identifier group file corresponding to the second reference position barcode, and storing the updated sequencing position barcode, the sequence identifier corresponding to the target sequencing position barcode, and the sequencing sequence corresponding to the target sequencing position barcode in the sequencing group file corresponding to the second reference position barcode.

[0059] In the embodiment, for distinction, the sequencing position barcode in the sequence different group is called target sequencing position barcode. According to the expected length B of the reference position barcode, the target sequencing position barcode is matched with each reference position barcode in the position index file, and a second reference position barcode matching the target sequencing position barcode with a mismatch value lower than a preset fault tolerance threshold is searched. For example, the fault tolerance threshold is set to int(B / 10). The target sequencing position barcode in the sequence different group is aligned with the reference position barcode, and the fault tolerance threshold is int(B / 10) mismatches during alignment. When the alignment is successful, it indicates that the target sequencing position barcode deviates during sequencing, the second reference position barcode is used to replace the target sequencing position barcode, an updated sequencing position barcode is obtained, and the updated sequencing position barcode, the sequence identifier corresponding to the target sequencing position barcode, and the sequencing sequence corresponding to the target sequencing position barcode are stored in the sequencing group file corresponding to the second reference position barcode. When there is no successful alignment, it indicates that the target sequencing position barcode may be sequencing error, and the sequencing sequence can be filtered out. According to the above method, each target sequencing position barcode in the sequence different group is processed. For example, there is a target sequencing position barcode X in the file L. The target sequencing position barcode X is matched with the reference position barcode Y through the alignment software, and the matching distance between the target sequencing position barcode X and the reference position barcode Y is 1, that is, there is 1 mismatch. Since 1 is less than int(B / 10), the matched reference position barcode Y is ranked at the A position in the position index file, A is greater than 1+int(M / S), and less than 2*int(M / S), the target sequencing position barcode X and the sequence identifier corresponding thereto are split into the No. 2 fastq file, and the read number corresponding to the target sequencing position barcode X is added after the existing No. 2 fastq file. Each target sequencing position barcode in the file L is processed in turn until the end of the file L. That is, the sequencing data corresponding to the target sequencing position barcode based on fault tolerance matching is merged into the corresponding sequencing group file based on the same matching. Fault tolerance matching is directly performed on all target sequencing position barcodes instead of unmatched target sequencing position barcodes, and data splitting can also be completed, so that spatial transcriptome data processing is performed in a low-cost computing device, so as to reduce the operation time.

[0060] As Figure 4 shown, Figure 4For the schematic diagram of generating a set of sequencing data in an embodiment, for any sequencing position barcode, if there is the same match, grouping is performed based on the first reference position barcode, if the matching is not successful, the matching is performed in a fault-tolerant manner, and grouping is performed based on the second reference position barcode, and finally the sequencing data of each group is obtained. The sequencing group file includes but is not limited to one or more of the following: position barcode group file, base group file. For example, for any sequencing data, the sequencing data corresponding to the sequencing sequence obtained based on the same matching manner and the fault-tolerant matching manner is merged. For example, for a piece of sequencing data, the position barcode group file obtained based on the same matching manner is BC1-1, and the base group file is cDNA1-1; the position barcode group file obtained based on the fault-tolerant matching is BC1-2, and the base group file is cDNA1-2; BC1-1 and BC1-2 are merged as the position barcode group file of the piece of sequencing data; cDNA1-1 and cDNA1-2 are merged as the base group file of the piece of sequencing data, thereby obtaining the final sequencing group file of the group.

[0061] In the embodiment, after two matching comparisons, the input sequencing position barcode is uniformly split into multiple parts according to the position index file, each group corresponds to a compressed file containing sequencing data, and a sequence identification group file. According to the above grouping, S pieces of sequence identification group files can be obtained, and the sequence identification of each group is stored in the sequence identification group file. According to each sequence identification group file, in the sequencing down-machine fastq file containing the cDNA base sequence, i.e. the base sequencing file, the sequencing sequence with the same sequence identification as the sequence identification group file is matched in the base sequencing file recording the cDNA according to each sequence identification group file in turn, and after matching, the sequencing position barcode sequence under the sequence identification, the sequencing sequence, and the quality information are sequentially written into the corresponding sequencing group file, such as D1, D2, …, DS, to obtain the first to Sth split sequencing group files.

[0062] In some embodiments, according to the sequence identification group file, the UMI sequence corresponding to each sequence identification group file is extracted from the sequencing down-machine fastq file containing the UMI in S parts, and S pieces of split UMI sequence group files are obtained. Each UMI sequence group file and the sequencing group file are merged into a sequencing file for STAR alignment.

[0063] In the above embodiment, through twice matching and comparison, the sequencing position barcodes in the spatial transcriptome data can be evenly divided into multiple parts. In the grouping process, possible sequencing errors in the sequencing position barcodes are corrected at the same time, thereby improving the utilization rate of the sequencing data. For the massive data brought by the improvement of sequencing depth and spatial resolution of spatial transcriptome sequencing data, the data is split and processed according to the sequence representing the spatial coordinates, and error correction is introduced in the splitting process, thereby improving the proportion of useful data in the sequencing data, increasing the data utilization rate of spatial organization sequencing, and reducing the sequencing cost.

[0064] In some embodiments, as shown in Figure 5 Figure 5 FIG. 1 is a flowchart of a process for obtaining a gene merging file in an embodiment. The alignment results include a sequencing position barcode file, an expression file and a gene file. The alignment results corresponding to each group of sequencing data are merged to obtain a merging result, which includes:

[0065] S51, obtaining a sequencing position barcode file corresponding to each group of sequencing data, and sequentially merging each sequencing position barcode file to obtain a position barcode merging file.

[0066] In this embodiment, for S parts of sequencing group files containing sequencing position barcodes and corresponding sequencing sequences, STAR is used for alignment pair by pair. Since the size of the sequence used for alignment is only 1 / S of the sequencing data, and the sequencing position barcode identifying the spatial position is also only 1 / S of the original file during each alignment, the required memory for running is reduced, thereby enabling data processing to run on a home computer. If acceleration is required, this step can be run in parallel on the cloud in a mutually independent manner, thereby reducing the time required for data processing. After all S parts are compared separately, the expression sparse matrix file of each part can be read in order, wherein each sparse matrix file includes a sequencing position barcode file, an expression file and a gene file. The sequencing position barcode file stores the sequencing position barcode, the expression file stores the expression value, and the gene file stores the gene.

[0067] Optionally, the sequentially merging each of the sequencing position barcode files to obtain a position barcode merging file includes:

[0068] sequentially writing each of the sequencing position barcode files into the tail of the position barcode merging file, calculating the writing position order of each of the sequencing position barcodes in the position barcode merging file, and taking the writing position order of each of the sequencing position barcodes as the global spatial position of the sequencing position barcode in the position barcode merging file;

[0069] ​record each of the sequencing position barcodes, a position barcode file identifier corresponding to the sequencing position barcode, a local spatial position of the sequencing position barcode in a corresponding sequencing position barcode file, and a global spatial position of the sequencing position barcode in the position barcode merged file.

[0070] In this embodiment, the sequencing position barcode, the position barcode file identifier corresponding to the sequencing position barcode, the local spatial position of the sequencing position barcode, and the global spatial position of the sequencing position barcode are recorded in the form of a quadruple. For example, S sequencing position barcode files obtained are respectively BC_1, …, BC_n, …, BC_S, and the position barcode merged file is represented by BC_ALL. When merging, the first obtained sequencing position barcode file BC_1 is read first, and this file does not need to be additionally processed and can be directly output as the merged BC_ALL. Then, the second to S sequencing position barcode files BC_2 to BC_S are read in sequence, and are written to the tail of the BC_ALL file one by one, and finally the merged BC_ALL file is obtained. Then, the order of the sequencing position barcode in each row in the BC_ALL file in the BC_n file and the order of the sequencing position barcode in the BC_ALL file are recorded in the memory. For example, the sequencing position barcode X is in the first order in the BC_2 file and is in the 235967th order in the BC_ALL file, and a quadruple (sequencing position barcode X, 2, 1, 235967) is recorded in the memory, representing that the sequencing position barcode X appears in the first row of the BC_2 file and in the 235967th row of the BC_ALL file after merging.

[0071] S52, obtaining a gene file corresponding to each group of sequencing data, and sequentially merging each gene file to obtain a gene merged file.

[0072] Optionally, the sequentially merging each gene file to obtain a gene merged file comprises:

[0073] obtaining a current gene in a current gene file;

[0074] when the current gene is not in the gene merged file, writing the current gene into the gene merged file, and calculating a writing order of the current gene in the gene merged file, and taking the writing order of the current gene in the gene merged file as a global gene position of the current gene in the gene merged file;

[0075] record the current gene, a gene file identifier corresponding to the current gene, a local gene position of the current gene in a corresponding gene file, and a global gene position of the current gene in the gene merged file.

[0076] In the embodiment, S gene files G__1, G__n, G__S obtained are respectively represented, and a gene merged file is represented by G__ALL. For a gene file recording gene information, a first obtained G_1 file is read first, and each row in the G_1 file is written into the G_ALL file, and the order of each row in the G_1 file is recorded. Then, G_2 to G_S files obtained by second to Sth alignment are read in sequence, and a current gene appearing in the G_n (n is the nth file currently processed) but not appearing in the G_ALL file is written into the G_ALL file, and the order of the current gene appearing in the G_n and the G_ALL file is recorded. For example, TP53 appears in the G_2 and is located at the 3254th order, and does not exist in the currently merged G_ALL, so TP53 is written at the end of the G_ALL, which is the 16265th order. A quadruple (TP53, 2, 3254, 16265) is recorded in the memory, representing the gene name TP53 to be merged, appearing at the 3254th row in the G_2 file, and located at the 16265th row in the G_ALL file after merging.

[0077] S53, obtaining expression quantity files corresponding to each group of sequencing data, and merging each expression quantity file in sequence based on the position barcode merged file and the gene merged file to obtain a sparse matrix merged file.

[0078] In the embodiment, the position of the gene corresponding to the expression quantity and the position of the sequencing position barcode in each output expression quantity file are only local position data relative to each expression quantity file, and the scattered local position data needs to be integrated into global position data to lay a data structure foundation for subsequent spatial transcriptome analysis (such as differential expression and spatial clustering). The key technical value lies in solving the cross-file ID unification problem through memory mapping while maintaining the efficient storage characteristics of the sparse matrix. The sparse matrix merged file represents a file after merging each sparse matrix, that is, an expression quantity sparse matrix corresponding to the entire spatial transcript sequencing data.

[0079] Optionally, the expression quantity file includes expression quantities of each gene at spatial positions indicated by each position barcode, and the merging of each expression quantity file in sequence based on the position barcode merged file and the gene merged file to obtain a sparse matrix merged file includes:

[0080] Obtaining current processing data in a current expression quantity file, the current processing data including a current gene, a local gene order of the current gene, and a local spatial position order corresponding to the current gene;

[0081] According to the gene merge file, the local gene order of the current gene is adjusted, and according to the position barcode merge file, the local spatial position order corresponding to the current gene is adjusted to obtain adjusted processing data, wherein the adjusted processing data includes the global gene order of the current gene and the global spatial position order of the current gene.

[0082] The adjusted processing data is written into the sparse matrix merge file.

[0083] Optionally, the adjusting the local spatial position order corresponding to the current gene according to the position barcode merge file comprises:

[0084] According to the local spatial position order, a target position barcode corresponding to the current gene is determined;

[0085] According to the position barcode merge file, a global spatial position of the target position barcode is obtained;

[0086] According to the global spatial position of the target position barcode, the local spatial position order is updated to obtain the global spatial position order of the current gene.

[0087] Optionally, the adjusting the local gene order of the current gene according to the gene merge file comprises:

[0088] According to the gene merge file, a global gene position of the current gene is obtained;

[0089] According to the global gene position of the current gene, the local gene order is updated to obtain the global gene order of the current gene.

[0090] In this embodiment, the local gene order represents the order of the gene relative to the corresponding gene file. The global gene order represents the order of the gene relative to the gene merge file. The local spatial position order represents the order of the sequencing position barcode of the gene in the sequencing position barcode file, and the global spatial position order represents the order of the sequencing position barcode of the gene in the position barcode merge file.

[0091] For example, the resulting S expression level files are represented as MTX__1, ..., MTX__n, ..., MTX__S, and the merged expression level file is represented as MTX__ALL. For the MTX file, first read the expression level file MTX_1 from the first sequencing position barcode file BC_1. This file does not require additional processing and can be directly output as the merged spatial sequence file MTX_ALL. Then, read MTX_2 to MTX_S files sequentially. Based on the quadruples of sequencing position barcode files and gene information recorded in memory, adjust the corresponding order of sequencing position barcodes and gene names in the merged file. For example, if the second file contains a line of x, y, z, this line indicates that the expression level of gene x at sequencing position y is z. According to the quadruples previously recorded in memory, we know that gene x is in order x' in G_ALL file and sequencing position y is in order y' in BC_ALL file. So, adjust the line to x', y', z and write the adjusted line to the end of MTX_ALL file. Process each line of MTX_2 to MTX_S files sequentially from the second to the Sth to obtain the merged sparse matrix MTX_ALL, resulting in the sparse matrix merged file.

[0092] like Figure 6 As shown, Figure 6 This is a schematic diagram of obtaining a sparse matrix merged file in one embodiment. The first sparse matrix file does not need to be processed and is directly written into the merged sparse matrix merged file. Subsequent sparse matrix files need to be adjusted in a similar way to obtain the sparse matrix merged file.

[0093] In some embodiments, using a GPU instead of a CPU can complete the sequencing data alignment. Since GPUs use video memory instead of system memory, the method of splitting and sequentially merging described in this application can also be used to reduce video memory consumption, thereby enabling the use of cheaper computing devices (with lower video memory requirements).

[0094] Using a GPU instead of a CPU can complete the splitting, grouping, and error correction steps in the technical solution. GPUs offer faster computation speeds, and since these steps require less memory and video memory, they do not constitute a memory bottleneck. STAR includes a GPU version, which runs even faster, but is still limited by data size, leading to a larger peak video memory usage. The spatial transcriptome-based data alignment method provided in this application can also be used to reduce the peak video memory usage when using a GPU for STAR alignment, thereby reducing hardware costs.

[0095] In the above embodiment, the split data is compared separately in each group, and then the comparison results of each group are combined, which can be completed in a computing device with smaller memory, and the computing device with smaller memory is cheaper and more popular. By adapting more affordable computing devices, the present application helps to reduce the cost and computing time of spatial transcriptome analysis, so that more research institutions such as laboratories can carry out spatial transcriptome sequencing, and promote the popularization of spatial transcriptome technology.

[0096] In some embodiments, after all S comparisons are completed, the quality control file including the quality control score output by the STAR program during each comparison can be read separately, and the overall quality control index can be calculated based on the respective quality control scores in each quality control file.

[0097] As shown in Figure 7 , Figure 7 is a schematic diagram of the data comparison method based on spatial transcriptome in an embodiment. The number of split parts S is calculated, the reference position barcode is rearranged, and the position index file is generated, that is, the reference position barcode can be divided into S parts. Based on the position index file, the sequencing position barcode in the spatial transcriptome sequencing data is uniformly divided into S parts. For any sequencing position barcode, the first reference position barcode with the same sequence is found. If the first reference position barcode exists, grouping is performed based on the first reference position barcode. If the first reference position barcode does not exist, fault-tolerant matching is performed, and the second reference position barcode meeting the fault-tolerant rate is selected until the split S parts of the sequencing position barcode file and the S parts of the sequence identification file are obtained. According to each part of the sequence identification file, the corresponding sequencing group file of each part of the sequence identification file can be extracted. Each group of sequencing group files is compared separately to obtain the comparison result of each group, which includes but is not limited to bam file, sparse matrix file, quality control file, etc. Then, the comparison results of each group are combined to obtain the combined bam file, sparse matrix combined file and combined quality control result.

[0098] In another aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the data comparison method based on spatial transcriptome according to any one of the embodiments of the present application.

[0099] In the computer program product, the optional implementation form of the program module architecture of the computer program implementing each step of the data comparison method based on spatial transcriptome can be a data comparison device based on spatial transcriptome.

[0100] Please refer to Figure 8An embodiment of the present application provides a data comparison device based on spatial transcriptome, comprising: an acquisition module 81, configured to acquire reference position barcodes; acquire spatial transcript sequencing data, wherein the spatial transcript sequencing data comprises sequencing position barcodes; a grouping module 82, configured to group the spatial transcript sequencing data into multiple groups of sequencing data based on each of the reference position barcodes and each of the sequencing position barcodes; a comparison module 83, configured to compare each group of sequencing data with a reference genome sequence respectively to obtain a comparison result corresponding to each group of sequencing data; and a merging module 84, configured to merge the comparison results corresponding to each group of sequencing data to obtain a merging result.

[0101] Optionally, the grouping module 82 is further configured to:

[0102] generate a position index file based on the reference position barcodes, wherein the position index file comprises index orders of each of the reference position barcodes in the position index file;

[0103] for any one of the sequencing position barcodes, match the sequencing position barcode with each of the reference position barcodes in the position index file, when there is a first reference position barcode identical to the sequencing position barcode, group the sequencing position barcode based on the first reference position barcode;

[0104] when there is no first reference position barcode identical to the sequencing position barcode, put the sequencing position barcode into a sequence different group.

[0105] Optionally, the spatial transcript sequencing data further comprises a base sequencing file, and the grouping module 82 is further configured to:

[0106] determine the first reference position barcode in a sequencing group file corresponding to the first reference position barcode according to the index order of the first reference position barcode;

[0107] acquire a sequencing sequence corresponding to the sequencing position barcode from the base sequencing file according to a sequence identifier corresponding to the sequencing position barcode;

[0108] write the sequence identifier corresponding to the sequencing position barcode into a sequence identifier group file corresponding to the first reference position barcode, and store the sequencing position barcode, the sequence identifier corresponding to the sequencing position barcode and the sequencing sequence corresponding to the sequencing position barcode into a sequencing group file corresponding to the first reference position barcode.

[0109] Optionally, the spatial transcript sequencing data further comprises a base sequencing file, and the grouping module 82 is further configured to:

[0110] acquire a target sequencing position barcode in the sequence non-identical group, determine a second reference position barcode matched with the target sequencing position barcode and having a mismatch value lower than a preset fault tolerance threshold;

[0111] replace the target sequencing position barcode with the second reference position barcode to obtain an updated sequencing position barcode;

[0112] determine, according to an index order of the second reference position barcode, a sequencing group file corresponding to the second reference position barcode;

[0113] acquire, according to a sequence identifier corresponding to the target sequencing position barcode, a sequencing sequence corresponding to the target sequencing position barcode from the base sequencing file;

[0114] write the sequence identifier corresponding to the target sequencing position barcode into a sequence identifier group file corresponding to the second reference position barcode, and store the updated sequencing position barcode, the sequence identifier corresponding to the target sequencing position barcode and the sequencing sequence corresponding to the target sequencing position barcode in the sequencing group file corresponding to the second reference position barcode.

[0115] Optionally, the merging module 84 is further configured to:

[0116] acquire the sequencing position barcode file corresponding to each group of sequencing data, and sequentially merge each of the sequencing position barcode files to obtain a position barcode merged file;

[0117] acquire the gene file corresponding to each group of sequencing data, and sequentially merge each of the gene files to obtain a gene merged file;

[0118] acquire the expression amount file corresponding to each group of sequencing data, and sequentially merge each of the expression amount files based on the position barcode merged file and the gene merged file to obtain a sparse matrix merged file.

[0119] Optionally, the merging module 84 is further configured to:

[0120] sequentially write each of the sequencing position barcode files into a tail of the position barcode merged file, and calculate a writing order of the current gene in the gene merged file, taking the writing order of the current gene in the gene merged file as a global gene position of the current gene in the gene merged file;

[0121] record each of the sequencing position barcodes, a position barcode file identifier corresponding to the sequencing position barcode, a local spatial position of the sequencing position barcode in the corresponding sequencing position barcode file, and a global spatial position of the sequencing position barcode in the position barcode merged file.

[0122] Optionally, the merging module 84 is further configured to:

[0123] obtain a current gene in a current gene file;

[0124] when the current gene is not in the gene merging file, write the current gene into the gene merging file and calculate a write order of the current gene in the gene merging file, taking the write order of the current gene in the gene merging file as a global gene position of the current gene in the gene merging file;

[0125] record the current gene, a gene file identifier corresponding to the current gene, a local gene position of the current gene in the corresponding gene file, and a global gene position of the current gene in the gene merging file.

[0126] Optionally, the expression amount file includes expression amounts of each gene at spatial positions indicated by each position barcode, and the merging module 84 is further configured to:

[0127] obtain current processing data in a current expression amount file, the current processing data including a current gene, a local gene order of the current gene, and a local spatial position order corresponding to the current gene;

[0128] adjust the local gene order of the current gene according to the gene merging file and adjust the local spatial position order corresponding to the current gene according to the position barcode merging file to obtain adjusted processing data, wherein the adjusted processing data includes a global gene order of the current gene and a global spatial position order of the current gene;

[0129] write the adjusted processing data into the sparse matrix merging file.

[0130] Optionally, the merging module 84 is further configured to:

[0131] determine a target position barcode corresponding to the current gene according to the local spatial position order;

[0132] obtain a global spatial position of the target position barcode according to the position barcode merging file;

[0133] update the local spatial position order according to the global spatial position of the target position barcode to obtain a global spatial position order of the current gene.

[0134] Optionally, the merging module 84 is further configured to:

[0135] obtain a global gene position of the current gene according to the gene merging file;

[0136] According to the global gene position of the current gene, the local gene order is updated to obtain a global gene order of the current gene.

[0137] Those skilled in the art can understand that, Figure 8 The structure of the spatial transcriptome-based data alignment apparatus does not constitute a limitation on the spatial transcriptome-based data alignment apparatus, and each module can be realized in whole or in part by software, hardware, and a combination thereof. Each module described above can be embedded in or independent of a controller in a hardware form in a computing device, or can be stored in a memory in a software form in the computing device, so as to be called and executed by the controller to perform the operations corresponding to each module. In other embodiments, the spatial transcriptome-based data alignment apparatus can include more or fewer modules than those shown.

[0138] Please refer to Figure 9 Another aspect of the embodiments of the present application also provides a computing device 20, which includes a memory 3011 and a processor 3012, the memory 3011 stores a computer program, and the computer program is executed by the processor to make the processor 3012 perform the steps of the spatial transcriptome-based data alignment method provided by any of the embodiments of the present application. The computing device can include a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc., a mobile phone (for example, a smart phone, a wireless phone, etc.), a wearable device (for example, a pair of smart glasses or a smart watch), or the like.

[0139] The processor 3012 is a control center, which connects various parts of the entire computing device by using various interfaces and lines, performs various functions of the computing device and processes data by running or executing software programs and / or modules stored in the memory 3011, and calling data stored in the memory 3011. Optionally, the processor 3012 can include one or more processing cores; the processor 3012 includes but is not limited to a combination of one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), etc. Preferably, the processor 3012 can integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user page, and an application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 3012.

[0140] The memory 3011 can be used to store software programs and modules, and the processor 3012 executes various function applications and data processing by running the software programs and modules stored in the memory 3011. The memory 3011 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by at least one function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the computing device, etc. In addition, the memory 3011 can include a high-speed random access memory, and can also include a non-volatile memory, for example, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 3011 can also include a memory controller to provide the processor 3012 with access to the memory 3011.

[0141] In another aspect, the embodiments of the present application also provide a non-volatile storage medium storing a computer program, and the computer program is executed by a processor to make the processor execute the steps of the spatial transcriptome-based data alignment method provided by any of the above-mentioned embodiments of the present application.

[0142] Those skilled in the art can understand that all or part of the processes in the method provided by the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a non-volatile computer readable storage medium, and the program can include the processes of the above-mentioned embodiments when executed. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0143] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. The protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A spatial transcriptome-based data alignment method, characterized by, The method comprises: obtaining reference position barcodes; obtaining spatial transcriptome sequencing data, wherein the spatial transcriptome sequencing data comprises sequencing position barcodes; grouping the spatial transcriptome sequencing data into groups of sequencing data based on each of the reference position barcodes and each of the sequencing position barcodes; for each group of sequencing data, respectively comparing with a reference genome sequence to obtain an alignment result corresponding to each group of sequencing data; and merging the alignment results corresponding to each group of sequencing data to obtain a merged result.

2. The spatial transcriptomic-based data alignment method of claim 1, wherein, The grouping the spatial transcriptome sequencing data into groups of sequencing data based on each of the reference position barcodes and each of the sequencing position barcodes comprises: generating a position index file based on the reference position barcodes, wherein the position index file comprises an index order of each of the reference position barcodes in the position index file; for any one of the sequencing position barcodes, matching the sequencing position barcode with each of the reference position barcodes in the position index file, when there is a first reference position barcode identical to the sequencing position barcode, grouping the sequencing position barcode based on the first reference position barcode; and when there is no first reference position barcode identical to the sequencing position barcode, putting the sequencing position barcode into a sequence different group.

3. The spatial transcriptomic-based data alignment method of claim 2, wherein, The spatial transcriptome sequencing data further comprises a base sequencing file, and the grouping the sequencing position barcode based on the first reference position barcode comprises: determining a sequencing group file corresponding to the first reference position barcode according to the index order of the first reference position barcode; obtaining a sequencing sequence corresponding to the sequencing position barcode from the base sequencing file according to a sequence identifier corresponding to the sequencing position barcode; writing the sequence identifier corresponding to the sequencing position barcode into a sequence identifier group file corresponding to the first reference position barcode, and storing the sequencing position barcode, the sequence identifier corresponding to the sequencing position barcode, and the sequencing sequence corresponding to the sequencing position barcode in a sequencing group file corresponding to the first reference position barcode.

4. The spatial transcriptomic-based data alignment method of claim 2, wherein, The spatial transcriptome sequencing data further comprises a base sequencing file, and the method further comprises: obtaining a target sequencing position barcode in the sequence different group, determining a second reference position barcode matching the target sequencing position barcode and having a mismatch value lower than a preset fault tolerance threshold; replacing the target sequencing position barcode with the second reference position barcode to obtain an updated sequencing position barcode; determining a sequencing group file corresponding to the second reference position barcode according to the index order of the second reference position barcode; obtaining a sequencing sequence corresponding to the target sequencing position barcode from the base sequencing file according to a sequence identifier corresponding to the target sequencing position barcode; writing the sequence identifier corresponding to the target sequencing position barcode into a sequence identifier group file corresponding to the second reference position barcode, and storing the updated sequencing position barcode, the sequence identifier corresponding to the target sequencing position barcode, and the sequencing sequence corresponding to the target sequencing position barcode in a sequencing group file corresponding to the second reference position barcode.

5. The spatial transcriptomic-based data alignment method of claim 1, wherein, The comparison result includes a sequencing position barcode file, an expression file and a gene file, the comparison results corresponding to each group of sequencing data are merged to obtain a merged result including: Obtaining the sequencing position barcode file corresponding to each group of sequencing data, and sequentially merging each sequencing position barcode file to obtain a position barcode merged file; Obtaining the gene file corresponding to each group of sequencing data, and sequentially merging each gene file to obtain a gene merged file; Obtaining the expression file corresponding to each group of sequencing data, and sequentially merging each expression file based on the position barcode merged file and the gene merged file to obtain a sparse matrix merged file.

6. The spatial transcriptomic-based data alignment method of claim 5, wherein, The sequentially merging each sequencing position barcode file to obtain a position barcode merged file includes: sequentially writing each sequencing position barcode file into the tail of the position barcode merged file, calculating the writing position order of each sequencing position barcode in the position barcode merged file, and taking the writing position order of each sequencing position barcode as the global spatial position of the sequencing position barcode in the position barcode merged file; record each sequencing position barcode, the position barcode file identifier corresponding to the sequencing position barcode, the local spatial position of the sequencing position barcode in the corresponding sequencing position barcode file, and the global spatial position of the sequencing position barcode in the position barcode merged file.

7. The spatial transcriptomic-based data alignment method of claim 5, wherein, The sequentially merging each gene file to obtain a gene merged file includes: obtaining a current gene in a current gene file; when the current gene is not in the gene merged file, writing the current gene into the gene merged file and calculating the writing order of the current gene in the gene merged file, taking the writing order of the current gene in the gene merged file as the global gene position of the current gene in the gene merged file; record the current gene, the gene file identifier corresponding to the current gene, the local gene position of the current gene in the corresponding gene file, and the global gene position of the current gene in the gene merged file.

8. The spatial transcriptomic-based data alignment method of claim 5, wherein, The expression file includes the expression of each gene at the spatial position indicated by each position barcode, and the sequentially merging each expression file based on the position barcode merged file and the gene merged file to obtain a sparse matrix merged file includes: obtaining current processing data in a current expression file, the current processing data including a current gene, a local gene order of the current gene, and a local spatial position order corresponding to the current gene; adjusting the local gene order of the current gene according to the gene merged file, and adjusting the local spatial position order corresponding to the current gene according to the position barcode merged file to obtain adjusted processing data, wherein the adjusted processing data includes a global gene order of the current gene and a global spatial position order of the current gene; writing the adjusted processing data into the sparse matrix merged file.

9. The spatial transcriptomic-based data alignment method of claim 8, wherein, The adjusting the local spatial position order of the current gene according to the position barcode merge file comprises: determining a target position barcode corresponding to the current gene according to the local spatial position order; obtaining a global spatial position of the target position barcode according to the position barcode merge file; updating the local spatial position order according to the global spatial position of the target position barcode to obtain a global spatial position order of the current gene.

10. The spatial transcriptomic-based data alignment method of claim 8, wherein, The adjusting the local gene order of the current gene according to the gene merge file comprises: obtaining a global gene position of the current gene according to the gene merge file; updating the local gene order according to the global gene position of the current gene to obtain a global gene order of the current gene.

11. A computing device, comprising: The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 10.

12. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method according to any one of claims 1 to 10.

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