CellRanger single-cell RNA (Ribonucleic Acid) sequencing data analysis method

Abstract

The invention relates to the technical field of single-cell RNA (Ribonucleic Acid) sequencing, and discloses a CellRanger single-cell RNA sequencing data analysis method, which comprises a grading treatment for cell quality screening and a tolerance mode for gene expression screening. The method provided by the invention not only strictly ensures the integrity of each functional index in the cells which are finally put in storage, but also retains part of cell information which possibly has biological significance and causes candidate marker deletion due to low overall transcriptional activity by further judging the low expression state in the to-be-determined cells. Through the method, the reliability of subsequent analysis data can be ensured, and omission of information of low-expression or rare cells due to a strict one-step standard can be reduced as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of single-cell RNA sequencing, and specifically to a CellRanger single-cell RNA sequencing data analysis method. Background Art

[0002] The primary goal of single-cell RNA sequencing is to analyze gene expression at the individual cell level to reveal heterogeneity and dynamic changes within cell populations. By analyzing each cell independently, this technology not only identifies distinct cell subtypes and rare cells, but also provides a deeper understanding of the role of cells in development, differentiation, immune response, and disease. Compared to population RNA sequencing, single-cell RNA sequencing provides more detailed information.

[0003] CellRanger is a comprehensive suite of single-cell RNA sequencing data processing and analysis software designed to automate the conversion of raw sequencing data into single-cell expression matrices. The tool first uses mkfastq to convert BCL files into FASTQ files. The tool then uses the count module for alignment, cell barcode identification, and UMI correction, accurately isolating and quantifying gene transcripts within each cell.

[0004] Although CellRanger enables one-click, automated single-cell RNA sequencing data processing, strict quality control and filtering strategies may lose information on some low-expressing or rare cells, thereby affecting the discovery and in-depth analysis of certain cell subpopulations. Summary of the Invention

[0005] In response to the problems existing in the above-mentioned prior art, the object of the present invention is to provide a CellRanger single-cell RNA sequencing data analysis method to improve the quality control and filtering strategies of CellRanger.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a CellRanger single-cell RNA sequencing data analysis method, comprising: step 1, converting the raw BCL data into FASTQ format and splitting the sequencing data. After splitting, identifying cell barcodes and unique molecular markers to filter low-quality reads. After filtering, aggregating multiple reads of the same gene to obtain a gene and cell expression matrix. After obtaining the expression matrix, using cell calling to identify a valid cell barcode; step 2, obtaining the UMI number of all genes and the UMI number of mitochondrial genes for each cell, dividing the mitochondrial gene UMI number by the UMI number of all genes to obtain the mitochondrial gene ratio, setting a mitochondrial gene ratio range threshold, comparing the mitochondrial gene ratio with the mitochondrial gene ratio range threshold, and determining high-quality cells and low-quality cells based on the comparison result; step 3, for high-quality cells, selecting five candidate genes based on defined annotations, detecting whether the five candidate genes are present in each high-quality cell and marking them. After marking, screening is performed. For a single high-quality cell, all markers one to five must be present simultaneously to be judged as a qualified high-quality cell. All other cases are judged as non-qualified high-quality cells.

[0007] In some embodiments, the specific process of deriving high-quality cells and low-quality cells based on the comparison results is: if the mitochondrial gene ratio falls within the mitochondrial gene ratio range threshold or is less than the minimum value of the mitochondrial gene ratio range threshold, it means that the RNA comes from nuclear genes. In this case, the cell has normal transcriptional activity and is marked as a high-quality cell; if the mitochondrial gene ratio is greater than the maximum value of the mitochondrial gene ratio range threshold, it means that the cell is in a stress state or the cell is undergoing apoptosis. In this case, it is marked as a low-quality cell.

[0008] In some embodiments, for low-quality cells, a second mitochondrial gene ratio threshold is set that is greater than the maximum value of the mitochondrial gene ratio range threshold. Under this condition, the mitochondrial gene ratio of the low-quality cells is compared again with the second mitochondrial gene ratio threshold, and different responses are obtained based on the comparison results.

[0009] In some embodiments, if the mitochondrial gene ratio is less than or equal to the second threshold of the mitochondrial gene ratio, a re-judgment is performed; if the mitochondrial gene ratio is greater than the second threshold of the mitochondrial gene ratio, the judgment that it is a low-quality cell is maintained.

[0010] In some embodiments, when making a second judgment, the number of gene types in the low-quality cells is obtained, and at the same time, a gene type number threshold is set, and the number of gene types is compared with the gene type number threshold. If the number of gene types is greater than or equal to the gene type number threshold, it means that the gene expression of the cell is rich and it is a healthy cell. In this case, the low-quality cell is re-labeled as a high-quality cell; if the number of gene types is less than the gene type number threshold, it means that the gene expression of the cell is not rich and it is a cell in the process of degradation. In this case, the judgment that it is a low-quality cell is maintained.

[0011] In some embodiments, a gene type number proximity threshold value that is less than a gene type number threshold value is set. When the gene type number of a cell is less than the gene type number threshold value, the gene type number of the cell is again compared with the gene type number proximity threshold value. If the gene type number of the cell is greater than or equal to the gene type number proximity threshold value, it means that the gene expression of the cell is not rich at all. In this case, further judgment is made. If the gene type number of the cell is less than the gene type number proximity threshold value, it means that the gene expression of the cell is not rich at all. In this case, the judgment that the cell is a low-quality cell is maintained.

[0012] In some embodiments, during further judgment, the number of ribosomal gene UMIs in the cell is obtained, and the ribosomal gene ratio is obtained by dividing the number of ribosomal gene UMIs by the number of UMIs of all genes. At the same time, a ribosomal gene ratio range threshold is set, and the ribosomal gene ratio of the cell is compared with the ribosomal gene ratio range threshold. If the ribosomal gene ratio of the cell falls within the ribosomal gene ratio range threshold, it indicates that the cell is in a normal state. In this case, the low-quality cell is re-labeled as a high-quality cell. If the ribosomal gene ratio of the cell is greater than the maximum value of the ribosomal gene ratio range threshold or less than the minimum value of the ribosomal gene ratio range threshold, it indicates that the cell has a transcriptional or translational imbalance. In this case, the judgment of the cell as a low-quality cell is maintained.

[0013] In some embodiments, among all non-compliant high-quality cells, high-quality cells with at least one marker are obtained and recorded as pending high-quality cells. High-quality cells without a marker are recorded as non-pending high-quality cells. Non-pending high-quality cells are directly excluded. For pending high-quality cells, the method for determining whether they are low-expression cells is as follows: randomly extract multiple compliant high-quality cells, obtain the gene type in each of the multiple compliant high-quality cells extracted, average them, and obtain the average value of the gene types of the compliant high-quality cells. At the same time, obtain the gene type of each pending high-quality cell, compare the gene type of each pending high-quality cell with the average value of the gene types of the compliant high-quality cells, and obtain different responses based on the comparison results.

[0014] In some embodiments, if the gene type of the pending high-quality cell is less than the average value of the gene types of the qualified high-quality cells, it means that the gene type of the pending high-quality cell is lower than the average value. In this case, the marker loss is the result of low expression, and the pending high-quality cell is marked as a low-expression cell; if the gene type of the pending high-quality cell is greater than or equal to the average value of the gene types of the qualified high-quality cells, it means that the gene type of the pending high-quality cell is not lower than the average value. In this case, the marker loss is not the result of low expression, and the pending high-quality cell is directly excluded.

[0015] The present invention further provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the CellRanger single-cell RNA sequencing data analysis method described above.

[0016] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0017] The method provided by this invention not only strictly ensures the integrity of various functional indicators in the final banked cells, but also, by further evaluating the low expression status of the cells under investigation, retains information on cells that may be biologically significant despite missing candidate markers due to low overall transcriptional activity. This approach ensures the reliability of subsequent analysis data while minimizing the omission of information on low-expressing or rare cells due to strict, one-size-fits-all criteria. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0021] The CellRanger single-cell RNA sequencing data analysis method provided by the present invention is as follows: Figure 1 As shown, including:

[0022] In the first step, the raw BCL data obtained from the laboratory is converted to FASTQ format using the mkfastq template, and the sequencing data of different samples are automatically split according to the preset sample table. Afterwards, the STAR algorithm is used to align the reads in FASTQ to the reference genome, identifying the cell barcode and unique molecular marker corresponding to each read. The cell barcode is used to distinguish each cell, and the unique molecular marker is used to distinguish the redundant information generated by repeated amplification, achieving molecular-level deduplication counting to filter low-quality reads. The cell barcode is corrected using a mismatch tolerance mechanism to reduce the impact of sequencing errors on downstream counts. After the reads are aligned, multiple reads of the same gene are aggregated based on the unique molecular marker to avoid counting errors caused by PCR amplification bias, ensure that the true molecules expressed by each gene in each cell are accurately counted, and generate a gene × cell expression matrix. After obtaining the expression matrix, cell calling is used to identify true cells and background noise to obtain valid cell barcodes.

[0023] In the second step, for each cell, the UMI counts for all genes and the mitochondrial gene UMI counts are counted. The mitochondrial gene UMI count is divided by the total gene UMI count to obtain the mitochondrial gene ratio. A mitochondrial gene ratio range threshold is also set. The mitochondrial gene ratio is compared with the mitochondrial gene ratio range threshold, and different responses are determined based on the comparison results. If the mitochondrial gene ratio falls within the mitochondrial gene ratio range threshold or is less than the minimum value of the mitochondrial gene ratio range threshold, it indicates that the majority of the RNA comes from nuclear genes. In this case, the cell has normal transcriptional activity and is labeled as a high-quality cell. If the mitochondrial gene ratio is greater than the maximum value of the mitochondrial gene ratio range threshold, it indicates that the cell is under stress or undergoing apoptosis. In this case, the cell is labeled as a low-quality cell. For low-quality cells, a second mitochondrial gene ratio threshold is set that is slightly greater than the maximum value of the mitochondrial gene ratio range threshold. For example, the mitochondrial gene ratio range threshold is 5%-10%, and the second mitochondrial gene ratio threshold is 15%. Under this condition, the mitochondrial gene ratio of the low-quality cell is compared again with the second mitochondrial gene ratio threshold, and different responses are determined based on the comparison results. If the mitochondrial gene ratio is less than or equal to the second mitochondrial gene ratio threshold, a further assessment is performed. If the mitochondrial gene ratio is greater than the second threshold of the mitochondrial gene ratio, the judgment that it is a low-quality cell is maintained. During the second judgment process, the number of gene types in the low-quality cell is obtained. At the same time, a gene type number threshold is set, and the number of gene types is compared with the gene type number threshold. Different responses are obtained based on the comparison results. If the number of gene types is greater than or equal to the gene type number threshold, it means that the cell has rich gene expression and is a healthy cell. In this case, the low-quality cell is re-labeled as a high-quality cell. If the number of gene types is less than the gene type number threshold, it means that the cell has poor gene expression and is a cell in the process of degradation. In this case, the judgment that it is a low-quality cell is maintained.

[0024] The third step, after obtaining high-quality cells, is to select five candidate genes based on their defined gene annotations. Each high-quality cell is tested for the presence of each of these five candidate genes. For each high-quality cell, the expression matrix is checked to see if the candidate gene is expressed. Candidate genes 1, 2, 3, 4, and 5 are scanned separately. High-quality cells with candidate gene 1 are labeled as high-quality cells 1, 2, 3, 4, and 5, respectively. High-quality cells with candidate gene 2 are labeled as high-quality cells 2, 3, 4, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, Because if a cell only expresses one, two, three, or four of these markers, although it may perform well in some indicators, it still does not meet the strict standard of "full marker" because the lack of any one may indicate a deficiency or imbalance in the corresponding biological function. This strict requirement is based on comprehensive control of the overall state of the cell, with the aim of excluding samples that may affect the accuracy and reliability of subsequent analysis due to abnormalities in individual indicators. Each marker represents an important checkpoint of the cell in specific transcription, translation, metabolism, or organelle function, and therefore none of them can be missing. Only by meeting the requirements of all markers simultaneously can we ensure that the cell is in an ideal state for all key functions. It is worth mentioning that when selecting candidate genes, we first select key genes that are commonly and stably expressed in various cell types based on the extensively experimentally verified gene sets provided by authoritative gene annotation databases. Secondly, the sequencing data are accurately mapped to the reference genome using high-precision alignment tools to ensure that reads in these candidate gene regions can be reliably captured, and UMI correction technology is used to eliminate redundant errors caused by PCR amplification, thereby improving the accuracy of counting. In addition, during the data preprocessing stage, strict cell calling and quality screening are carried out to retain only cells with stable expression and high-quality signals, so that the low-noise expression of the candidate genes can be truly reflected. Finally, through preliminary data exploration and multiple biological validations, it was confirmed that these five candidate genes have detectable expression in the vast majority of high-quality cells. Even if there are expression differences between individuals, their existence is still highly reliable, and thus they can be used as indicators for subsequent cell identity identification and functional analysis.

[0025] When the number of gene types in a cell is less than the gene type number threshold, a gene type number proximity threshold is set that is slightly less than the gene type number threshold. For example, the gene type number threshold is 100 and the gene type number proximity threshold is 90. Under this condition, the cell's gene type number is again compared with the gene type number proximity threshold, and different responses are determined based on the comparison results. If the cell's gene type number is greater than or equal to the gene type number proximity threshold, it indicates that the cell has low gene expression richness, in which case further judgment is made. If the cell's gene type number is less than the gene type proximity threshold, it indicates that the cell has high gene expression richness, in which case the judgment of the cell as a low-quality cell is maintained. During the further judgment process, the number of UMIs of ribosomal genes in the cell is obtained, and the ribosomal gene ratio is calculated by dividing the number of UMIs of ribosomal genes by the number of UMIs of all genes. Simultaneously, a ribosomal gene ratio range threshold is set, and the cell's ribosomal gene ratio is compared with the ribosomal gene ratio range threshold. Different responses are determined based on the comparison results. If the cell's ribosomal gene ratio falls within the ribosomal gene ratio range threshold, it indicates that the cell is in a normal state, in which case the low-quality cell is relabeled as a high-quality cell. If the ribosomal gene ratio in a cell is greater than the maximum or less than the minimum threshold of the ribosomal gene ratio range, it indicates a transcriptional or translational imbalance. For example, under certain stress conditions, cells tend to oversynthesize ribosomal proteins to cope with adversity, or due to biological reasons such as cell cycle arrest and abnormal proliferation, ribosomal RNA is over-enriched. In these cases, cells are considered to be of low quality. This is because ribosomes play a crucial role in protein synthesis within the cell, and maintaining an appropriate ribosomal gene ratio helps maintain cellular homeostasis. An abnormally high ribosomal gene ratio may indicate that cells, under stress or abnormal proliferation, attempt to compensate for transcriptional or translational imbalances by synthesizing excessive amounts of ribosomal proteins. Conversely, an abnormally low ratio may reflect suppressed overall protein synthesis or decreased metabolic activity. Both conditions, whether excessively high or low, indicate abnormalities in transcriptional or translational regulation, deviating from a normal equilibrium, and can therefore be considered signs of transcriptional or translational imbalance. This is different from the fact that only when the mitochondrial gene ratio is greater than the maximum value of the mitochondrial gene ratio range threshold does the cell indicate a state of stress or apoptosis. This is because an excessively high mitochondrial gene ratio often indicates that the cell may be experiencing stress, apoptosis, or damage. In these cases, due to the loss of more cytoplasmic RNA, the mitochondrial RNA ratio is relatively increased. Healthy cells generally maintain a stable level of mitochondrial gene expression, so the ratio is within a reasonable range.On the other hand, a low mitochondrial gene ratio does not necessarily mean that the cell is abnormal, because different cell types or differences in capture technology may lead to a low mitochondrial RNA fraction, which does not constitute a bad signal in itself.

[0026] In the above process, a single high-quality cell is considered a qualified high-quality cell only if all markers one through five are present. This quality control approach may miss information about low-expressing or rare cells. To preserve information about low-expressing or rare cells, in addition to qualified high-quality cells that present all markers one through five, all high-quality cells that do not meet the criteria are identified as high-quality cells that present at least one marker. These cells are designated as pending high-quality cells. These pending high-quality cells may present one, two, three, or four markers. High-quality cells that do not present a single marker are designated as non-pending high-quality cells. This divides high-quality cells into qualified high-quality cells and non-qualified high-quality cells, where non-qualified high-quality cells further include pending high-quality cells and non-pending high-quality cells. Non-pending high-quality cells are directly excluded. For pending high-quality cells, whether they are low-expressing cells is determined by randomly sampling multiple qualified high-quality cells, obtaining the gene types in each of these selected qualified high-quality cells, and averaging these to obtain the average gene type value for the qualified high-quality cells. At the same time, the gene type of each pending high-quality cell is obtained, and the gene type of each pending high-quality cell is compared with the average gene type of the qualified high-quality cells. Different responses are obtained based on the comparison results. If the gene type of the pending high-quality cell is less than the average gene type of the qualified high-quality cells, it means that the gene type of this pending high-quality cell is lower than the average. In this case, the marker loss is the result of low expression, and this pending high-quality cell is marked as a low-expression cell. If the gene type of the pending high-quality cell is greater than or equal to the average gene type of the qualified high-quality cells, it means that the gene type of this pending high-quality cell is not lower than the average. In this case, the marker loss is not the result of low expression, and this pending high-quality cell is directly excluded.

[0027] The entire process of this application first uses mkfastq to convert the original BCL data into a FASTQ file, and then uses the STAR alignment algorithm to map the reads to the reference genome, and uses cell barcodes and UMIs to identify each cell and perform molecular-level deduplication counting, finally constructing a gene × cell expression matrix, and using cell calling to eliminate background noise to obtain an effective cell barcode; next, the number of UMIs of all genes and mitochondrial genes is calculated for each cell to obtain the mitochondrial gene ratio, and a suitable threshold range is set to distinguish high-quality cells whose RNA mainly comes from nuclear genes from low-quality cells in a state of stress or apoptosis; for cells initially marked as low-quality, their status is re-judged by setting a second threshold for the mitochondrial ratio and comparing the number of gene types in the cells, and then combining indicators such as the ribosomal gene ratio, and some cells with rich overall expression are re-marked as high-quality cells; after obtaining high-quality cells, five candidate marker genes are screened based on the pre-defined gene annotations, and each Each cell is scanned for the presence of these five markers. Only cells that detect all five candidate genes are considered high-quality cells, as each marker represents a key checkpoint in transcription, translation, metabolism, and specific organelle function. However, this strict standard for full marker expression may miss some cells that only display a subset of markers due to low expression or rarity. Therefore, high-quality cells that do not meet the full marker expression requirement are further divided into two categories: pending high-quality cells and non-pending high-quality cells. Cells that detect at least one candidate gene are classified as pending, while cells that completely lack the candidate gene are excluded. Subsequently, a random sample of high-quality cells that meet the criteria is selected, and the number of gene types in these cells is counted and averaged. The number of gene types in each pending cell is then compared to this average. If the number of gene types in a pending cell is lower than the average, the absence of the candidate marker is likely due to low expression, and the cell is labeled as low-expressing. If the number of gene types is at least the average, the absence is not considered to be due to low expression and is excluded. This process ensures that high-quality cells are ideal for all key functions while maintaining data rigor while preserving information on low-expressing or rare cells.

[0028] In the embodiments disclosed herein, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed herein include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the method illustrated in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component and / or installed from removable media. When the computer program is executed by a central processing unit, the functions defined in the methods of this application are performed. It should be noted that the computer-readable medium referred to herein can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, electrical, optical, RF, or any suitable combination thereof.

[0029] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or portion of code that contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as combinations of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0030] Those skilled in the art should understand that the above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered by the scope of protection of the present application.

Claims

1. CellRanger single-cell RNA sequencing data analysis method, characterized by: include: Step 1: Convert the raw BCL data to FASTQ format and split the sequencing data. After splitting, identify cell barcodes and unique molecular markers to filter low-quality reads. After filtering, aggregate multiple reads of the same gene to obtain a gene and cell expression matrix. After obtaining the expression matrix, use cell call recognition to obtain a valid cell barcode. Step 2: Obtain the UMI counts of all genes and mitochondrial genes for each cell. Divide the mitochondrial gene UMI count by the UMI count of all genes to obtain the mitochondrial gene ratio. At the same time, set a mitochondrial gene ratio range threshold, compare the mitochondrial gene ratio with the mitochondrial gene ratio range threshold, and determine high-quality and low-quality cells based on the comparison results. Step 3: For high-quality cells, five candidate genes are selected based on the defined annotations. The five candidate genes are detected and marked in each high-quality cell. After marking, screening is performed. For a single high-quality cell, all markers one to five must be present at the same time to be judged as a qualified high-quality cell. All other cases are judged as non-qualified high-quality cells.

2. The CellRanger single-cell RNA sequencing data analysis method according to claim 1, characterized in that: The specific process of deriving high-quality cells and low-quality cells based on the comparison results is: if the mitochondrial gene ratio falls within the mitochondrial gene ratio range threshold or is less than the minimum value of the mitochondrial gene ratio range threshold, it means that the RNA comes from nuclear genes. In this case, the cell has normal transcriptional activity and is marked as a high-quality cell; if the mitochondrial gene ratio is greater than the maximum value of the mitochondrial gene ratio range threshold, it means that the cell is in a stress state or the cell is undergoing apoptosis. In this case, it is marked as a low-quality cell.

3. The CellRanger single-cell RNA sequencing data analysis method according to claim 2, characterized in that: For low-quality cells, a second mitochondrial gene ratio threshold is set that is greater than the maximum value of the mitochondrial gene ratio range threshold. Under this condition, the mitochondrial gene ratio of the low-quality cells is compared again with the second mitochondrial gene ratio threshold, and different responses are obtained based on the comparison results.

4. The CellRanger single-cell RNA sequencing data analysis method according to claim 3, characterized in that: If the mitochondrial gene ratio is less than or equal to the second threshold of the mitochondrial gene ratio, a re-judgment is performed; if the mitochondrial gene ratio is greater than the second threshold of the mitochondrial gene ratio, the judgment that it is a low-quality cell is maintained.

5. The CellRanger single-cell RNA sequencing data analysis method according to claim 4, characterized in that: When judging again, the number of gene types in the low-quality cells is obtained. At the same time, a gene type number threshold is set, and the number of gene types is compared with the gene type number threshold. If the number of gene types is greater than or equal to the gene type number threshold, it means that the cell has rich gene expression and is a healthy cell. In this case, the low-quality cell is re-labeled as a high-quality cell; if the number of gene types is less than the gene type number threshold, it means that the cell has poor gene expression and is a cell in the process of degradation. In this case, the judgment that it is a low-quality cell is maintained.

6. The CellRanger single-cell RNA sequencing data analysis method according to claim 5, characterized in that: A gene type number proximity threshold is set that is smaller than the gene type number threshold. When the gene type number of a cell is smaller than the gene type number threshold, the gene type number of the cell is compared again with the gene type number proximity threshold. If the gene type number of the cell is greater than or equal to the gene type number proximity threshold, it means that the gene expression of the cell is low and in this case, further judgment is made. If the gene type number of the cell is smaller than the gene type number proximity threshold, it means that the gene expression of the cell is high and in this case, the judgment of the cell as a low-quality cell is maintained.

7. The CellRanger single-cell RNA sequencing data analysis method according to claim 6, characterized in that: For further judgment, the number of ribosomal gene UMIs in the cell is obtained, and the ribosomal gene ratio is calculated by dividing the number of ribosomal gene UMIs by the number of UMIs of all genes. At the same time, a ribosomal gene ratio range threshold is set, and the ribosomal gene ratio of the cell is compared with the ribosomal gene ratio range threshold. If the ribosomal gene ratio of the cell falls within the ribosomal gene ratio range threshold, it indicates that the cell is in a normal state. In this case, the low-quality cell is relabeled as a high-quality cell. If the ribosomal gene ratio of the cell is greater than the maximum value of the ribosomal gene ratio range threshold or less than the minimum value of the ribosomal gene ratio range threshold, it indicates that the cell has a transcriptional or translational imbalance. In this case, the judgment of the cell as a low-quality cell is maintained.

8. The CellRanger single-cell RNA sequencing data analysis method according to claim 7, characterized in that: Obtain all non-compliant high-quality cells, and those with at least one marker are recorded as pending high-quality cells. For high-quality cells without a marker, they are recorded as non-pending high-quality cells. Non-pending high-quality cells are directly excluded. For pending high-quality cells, the method for judging whether they are low-expression cells is as follows: randomly extract multiple qualified high-quality cells, obtain the gene types in each of the multiple qualified high-quality cells extracted, average them, and obtain the average value of the gene types of qualified high-quality cells. At the same time, obtain the gene types of each pending high-quality cell, compare the gene types of each pending high-quality cell with the average value of the gene types of qualified high-quality cells, and come up with different responses based on the comparison results.

9. The CellRanger single-cell RNA sequencing data analysis method according to claim 8, characterized in that: If the number of gene types in the candidate high-quality cell is less than the average number of gene types in the qualified high-quality cells, it means that the number of gene types in the candidate high-quality cell is lower than the average. In this case, the marker loss is the result of low expression, and the candidate high-quality cell is marked as a low-expression cell. If the gene type of the candidate high-quality cell is greater than or equal to the average gene type of the qualified high-quality cells, it means that the gene type of this candidate high-quality cell is not lower than the average. In this case, the marker loss is not the result of low expression, and this candidate high-quality cell is directly excluded.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the CellRanger single-cell RNA sequencing data analysis method described in any one of claims 1 to 9.

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