Method and device for processing single-cell gene expression profile data of different platforms

Abstract

The invention discloses a method and a device for processing single cell gene expression profile data of different platforms, electronic equipment and a storage medium, which are used for converting the single cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, a first column in the gene expression matrix file is a gene name, reading the gene expression matrix file of a single sample, and converting the gene expression matrix file of the single sample into a single sample data object; performing principal component analysis on all the single-sample data objects to obtain a preset number of principal components of a gene expression profile in each single-sample data object; correcting a preset number of principal components of the gene expression profile by taking samples of different platforms and single cell experimental methods as covariables to obtain a corrected first principal component and a corrected second principal component; and drawing a double plot of the corrected first principal component and the second principal component.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for processing single-cell gene expression profile data on different platforms. Background Art

[0002] With the rapid development of sequencing technology, a large amount of single-cell expression profile data has been accumulated, which comes from different experimental technologies and sequencing platforms. These platforms mainly use microfluidic chips to quickly partition single-cell suspension samples, add sequence tags, and construct high-throughput sequencing libraries containing connected fragments to achieve large-scale single-cell separation and single-cell library construction. Due to the differences in the length and base composition of sequence identification codes and molecular identifiers (Unique Molecular Identifiers, UMI) used by different platforms, the data generated by different platforms have certain technical deviations. When analyzing data from different platforms together, it is necessary to consider eliminating the technical errors introduced by the platform.

[0003] Canonical correlation analysis is a multivariate statistical method for studying the correlation between two groups of variables. It is used to identify common variations between different single-cell datasets and is suitable for identifying anchors when cell types are conservative. However, there are differences in gene expression data produced by different experimental platforms. Methods based on canonical correlation analysis may lead to over-correction. When multiple datasets are integrated based on interactive principal component analysis, obvious boundaries are easily observed between different datasets. Summary of the invention

[0004] The present disclosure provides a method and device for processing single-cell gene expression profile data from different platforms, the main purpose of which is to solve the problem of transition correction caused by integrating gene expression data produced by different experimental platforms in related technologies.

[0005] According to a first aspect of the present disclosure, a method for processing single-cell gene expression profile data on different platforms is provided, comprising:

[0006] Converting single-cell gene expression profile data from different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is the gene name;

[0007] Reading the gene expression matrix file of the single sample, and converting the gene expression matrix file of the single sample into a single sample data object;

[0008] Performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object;

[0009] Using samples from different platforms and single-cell experimental methods as covariates, correcting a preset number of principal components of the gene expression profile to obtain a corrected first principal component and a second principal component;

[0010] Draw a biplot of the corrected first principal component and the second principal component.

[0011] Optionally, before performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object, the method further comprises:

[0012] Merging all the single sample data objects on different platforms to generate a target data object;

[0013] The performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object comprises:

[0014] A principal component analysis is performed on the target data object to obtain a preset number of principal components of the gene expression profile in the target data object.

[0015] Optionally, after converting the gene expression matrix file of the single sample into a single sample data object, the method further comprises:

[0016] Performing normalization processing on the single sample data object by using a first preset function to obtain a single sample data object after first processing;

[0017] The data quality of the single sample data object after the first processing is controlled based on preset parameters to obtain a final single sample data object.

[0018] Optionally, converting the gene expression matrix file of the single sample into a single sample data object comprises:

[0019] All final single sample data objects are used as elements to generate an R object of list type, and the second preset function is used to obtain the characteristic genes of the R object of list type.

[0020] Optionally, merging all the single sample data objects of different platforms to generate a target data object includes:

[0021] The third preset function is called to merge the final single sample data objects to generate the target data object, where the target data object is an R object of type S4.

[0022] Optionally, before reading the gene expression matrix file of the single sample, the method further comprises:

[0023] According to the human gene naming database HGNC, the gene names that do not conform to the preset naming standards are modified;

[0024] If there are gene names that are not included in the HGNC, the species classification database NCBI is used to modify the gene names that do not conform to the preset naming specifications.

[0025] According to a second aspect of the present disclosure, a device for processing single-cell gene expression profile data on different platforms is provided, comprising:

[0026] A first conversion unit is used to convert the single-cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is a gene name;

[0027] A reading unit, used for reading the gene expression matrix file of the single sample;

[0028] A second conversion unit, used for converting the gene expression matrix file of the single sample into a single sample data object;

[0029] An analysis unit, configured to perform principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object;

[0030] A correction unit, used to correct a preset number of principal components of the gene expression profile by taking samples and single-cell experimental devices of different platforms as covariates, to obtain a corrected first principal component and a second principal component;

[0031] A drawing unit is used to draw a biplot of the first principal component and the second principal component after correction.

[0032] Optionally, the device further comprises:

[0033] a merging unit, configured to merge all the single sample data objects on different platforms to generate a target data object before performing principal component analysis on all the single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object;

[0034] The analysis unit is further used to perform principal component analysis on the target data object to obtain a preset number of principal components of the gene expression profile in the target data object.

[0035] Optionally, the device further comprises:

[0036] A processing unit, configured to, after converting the gene expression matrix file of the single sample into a single sample data object, perform normalization processing on the single sample data object by a first preset function to obtain a first processed single sample data object;

[0037] The filtering unit is used to perform data quality control on the single sample data object after the first processing based on preset parameters to obtain a final single sample data object.

[0038] Optionally, the second conversion unit is further used to use all final single sample data objects as elements to generate an R object of list type, and use a second preset function to obtain characteristic genes of the R object of list type.

[0039] Optionally, the merging unit is further configured to call a third preset function to merge the final single sample data objects to generate the target data object, where the target data object is an R object of type S4.

[0040] Optionally, the device further comprises:

[0041] A modification unit, used for modifying the gene names that do not conform to the preset naming specification in the gene names according to the human gene naming database HGNC before reading the gene expression matrix file of the single sample;

[0042] If there are gene names that are not included in the HGNC, the species classification database NCBI is used to modify the gene names that do not conform to the preset naming specifications.

[0043] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0044] at least one processor; and

[0045] a memory communicatively connected to the at least one processor; wherein,

[0046] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0047] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0048] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0049] The present disclosure provides a method and device for processing single-cell gene expression profile data of different platforms, converting single-cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is a gene name; reading the gene expression matrix file of the single sample, and converting the gene expression matrix file of the single sample into a single sample data object; performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object; using samples of different platforms and single-cell experimental methods as covariates, correcting a preset number of principal components of the gene expression profile to obtain a corrected first principal component and a second principal component; and drawing a biplot of the corrected first principal component and the second principal component. Compared with the related art, the embodiment of the present application utilizes data from a public database to organize it into a gene expression matrix file of a single sample, converts the gene expression matrix file of the single sample into a single sample data object, runs principal component analysis on the data object, and corrects the result of the principal component analysis to improve the accuracy of integrating single-cell gene expression profile data of different platforms.

[0050] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0052] Figure 1 A schematic diagram of a process for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure;

[0053] Figure 2 A schematic diagram of a process flow of another method for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure;

[0054] Figure 3 A schematic diagram of a biplot of a dataset GSE134355 provided in an embodiment of the present disclosure after principal component analysis without data integration;

[0055] Figure 4 A schematic diagram of a biplot of principal components drawn after data integration of a dataset GSE134355 provided in an embodiment of the present disclosure;

[0056] Figure 5 A schematic diagram of a process flow of another method for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure;

[0057] Figure 6 A schematic diagram of the structure of a device for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure;

[0058] Figure 7 A schematic diagram of the structure of another device for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure;

[0059] Figure 8 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0061] The following describes the processing methods, devices, electronic devices and storage media of single-cell gene expression profile data on different platforms of the embodiments of the present disclosure with reference to the accompanying drawings.

[0062] Figure 1 A schematic diagram of a process for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure.

[0063] like Figure 1 As shown, the method comprises the following steps:

[0064] Step 101, converting single-cell gene expression profile data from different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is the gene name;

[0065] The single-cell gene expression profile data of different platforms were converted into a gene expression matrix for a single sample. For those merged into a gene expression profile matrix file, the gene expression profile matrix was split into single samples according to the sample name information in the cell attribute field of the file, or the sample name information in the cell number, so that one sample had one gene expression profile matrix file. The data in bam format were converted into FASTQ format using cellranger and continued to run to generate the gene expression profile matrix.

[0066] Step 102: read the gene expression matrix file of the single sample, and convert the gene expression matrix file of the single sample into a single sample data object.

[0067] In the specific application process, the gene expression matrix file of a single sample is used to generate a single sample data object using the CreateSeuratObject function of the Seurat program.

[0068] Step 103: perform principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object.

[0069] Run the RunPCA function on all single sample data objects in sequence to obtain the principal components (Principal Component Analysis, PCA) of the data set. The number of principal components calculated and stored is set between 30 and 50. The specific number of principal components can be set according to different scenarios. The embodiment of the present application does not limit the number of principal components.

[0070] Step 104 , using samples from different platforms and single-cell experimental methods as covariates, calibrating a preset number of principal components of the gene expression profile to obtain a calibrated first principal component and a calibrated second principal component.

[0071] The principal component data in step 103 is used as input, and the RunHarmony function is used to cluster the low-dimensional coordinate values ​​(embedding) after PCA dimensionality reduction using k-means and the Lloyd algorithm is used to integrate the data using samples and single-cell experimental methods as covariates.

[0072] The technical method for integrating multiple single-cell gene expression profile datasets is mainly to use the merge function in R language to implement two or more data frames through common gene names. On this basis, Seurat software provides three methods for integrating multiple datasets based on canonical correlation analysis (CCA), interactive principal component analysis (RPCA) and constructing a subset of the dataset (or a single dataset) as a "reference".

[0073] Step 105 , drawing a biplot of the corrected first principal component and the second principal component.

[0074] Use the DimPlot function to draw biplots of the PCA and Harmony first and second principal components, respectively.

[0075] The present disclosure provides a method for processing single-cell gene expression profile data of different platforms, which converts single-cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is a gene name; reads the gene expression matrix file of the single sample, and converts the gene expression matrix file of the single sample into a single sample data object; performs principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object; uses samples of different platforms and single-cell experimental methods as covariates, corrects the preset number of principal components of the gene expression profile, and obtains a corrected first principal component and a second principal component; draws a biplot of the corrected first principal component and the second principal component. Compared with the related art, the embodiment of the present application uses data from a public database to organize it into a gene expression matrix file of a single sample, converts the gene expression matrix file of the single sample into a single sample data object, runs principal component analysis on the data object, and corrects the result of the principal component analysis to improve the accuracy of integrating single-cell gene expression profile data of different platforms.

[0076] In order to facilitate the analysis of all single sample data objects, before performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object, the method also includes: merging all the single sample data objects on different platforms to generate a target data object; and performing principal component analysis on the target data object to obtain a preset number of principal components of the gene expression profile in the target data object.

[0077] In an embodiment of the present application, in order to obtain a higher quality single sample data object, after the gene expression matrix file of the single sample is converted into a single sample data object, the single sample data object is normalized by a first preset function to obtain a first processed single sample data object; data quality control is performed on the first processed single sample data object based on preset parameters to obtain a final single sample data object.

[0078] As an implementable method, the single sample data object is normalized using the positive negative binomial regression statistical method through the SCTransform function, and the data quality is controlled through the three parameters of min.cells, min.features and vars variable.to.regress regression. According to the source and data characteristics of the original data set, the value interval of min.cells is set to 3-30, the value interval of min.features is set to 200-500, and vars.to.regress sets the mitochondrial gene ratio as the regression variable; the above specific numerical values ​​are only illustrative examples and are not specifically limited.

[0079] In order to realize the conversion of data format, as a refinement of step 102, when converting the gene expression matrix file of the single sample into a single sample data object, all the final single sample data objects are used as elements to generate an R object of list type, and the characteristic genes of the R object of list type are obtained using a second preset function. The second preset function includes but is not limited to the SelectIntegrationFeatures function, and the SelectIntegrationFeatures function is used to obtain the characteristic genes of the R data object of the list type, and the number of characteristic genes is set to 2000-3000 according to the scale of the data set, and the specific scale is not limited in the embodiment of the present application.

[0080] In an embodiment of the present application, merging all the single sample data objects of different platforms to generate a target data object includes: calling a third preset function to merge the final single sample data objects to generate the target data object, and the target data object is an R object of type S4. All single sample data objects are merged into a target data object using the merge function, and the merged data object is an R object of type S4, and the attributes include sample name, single cell experimental method, and library type.

[0081] As an extension, before step 102, the gene names that do not conform to the preset naming specification are modified; based on the gene expression matrix file generated in step 101, the first column of the gene expression matrix file is the gene name, and the gene names in the first column are checked to see if they conform to the preset naming specification (such as the standard name in HGNC), and the names that do not conform to the standard name in HGNC are modified to conform to the standard name in HGNC. The purpose is to unify the gene names of different platforms.

[0082] Modifying the gene names that do not conform to the preset naming specifications includes: modifying the gene names that do not conform to the preset naming specifications according to the human gene naming database HGNC; if there are gene names that are not included in the HGNC, modifying the gene names that do not conform to the preset naming specifications using the species classification database NCBI.

[0083] The above embodiments have described in detail the processing methods of single-cell gene expression profile data on different platforms. The following two specific examples are used for supplementary explanation:

[0084] Example 1:

[0085] The GSE134355 dataset uses Microwell-seq sequencing technology and selects 5 adult livers and 2 embryonic livers, with a total of 70,000 single-cell gene expression profile data for data integration.

[0086] like Figure 2 As shown, another method for processing single-cell gene expression profile data on different platforms is provided, including:

[0087] Step 201, decompress the single-cell gene expression matrices of 7 samples Adult1A, Adult1B, Adult2, Adult4A, Adult4B, Fetal1 and Fetal2 from 5 donors in the data set with the suffix "dge.txt.gz" into one gene expression matrix file for each sample;

[0088] Step 202, checking and confirming that the first gene name is consistent with the standard name in HGNC;

[0089] Step 203: Generate data objects from the gene expression matrix files of the seven samples using the CreateSeuratObject function of the Seurat program.

[0090] The data object was normalized using the positive negative binomial regression statistical method through the SCTransform function, min.cells was set to 3, min.features was set to 200, and the proportion of mitochondrial genes was set as the regression variable;

[0091] Step 204: Generate an R object of list type from the data objects of the seven single samples.

[0092] Use the SelectIntegrationFeatures function to obtain the R object feature genes of list type, and the number of feature genes is set to 2000.

[0093] Step 205: Use the merge function to merge the seven single-sample data objects into one data object.

[0094] Step 206: Run the RunPCA function on the data objects in steps 204 and 205 to obtain the principal components of the 7 samples before integration.

[0095] Step 207: Use the principal component data as input, use the RunHarmony function, use the Lloyd algorithm when performing k-means clustering, and use the donor and single-cell platform as covariates for data integration.

[0096] Step 208: Use the DimPlot function to draw biplots of the first principal component and the second principal component of PCA and Harmony, respectively.

[0097] Figure 3 This is a biplot of the GSE134355 dataset without data integration after principal component analysis. Figure 4 Biplot of principal components after data integration for the GSE134355 dataset.

[0098] Example 2:

[0099] Five datasets GSE146115, GSE136103, GSE115469, GSE134355 and GSE156337 were selected, with 102 samples from 43 donors and 366,000 single-cell expression profile data. The single-cell platforms used for the data included 10xGenomics, Seq HT IFC and Microwell-seq.

[0100] like Figure 5 As shown, another method for processing single-cell gene expression profile data on different platforms is provided, including:

[0101] Step 301: organize the single-cell gene expression matrices of 102 samples from 43 donors in the data set into one gene expression matrix file for each sample.

[0102] Step 302: Modify the data set according to the standard name in HGNC.

[0103] Because the automatic correction in the data set (e.g. stored in Excel) resulted in gene names being date-type names such as 1-Dec and 1-Mar, they were modified according to the standard names in HGNC.

[0104] Step 303: Generate data objects from the gene expression matrix files of the 102 samples using the CreateSeuratObject function of the Seurat program, and normalize the data using the positive negative binomial regression statistical method through the SCTransform function.

[0105] Set min.cells to 3, min.features to 200, and set the mitochondrial gene ratio as the regression variable.

[0106] Step 304: Generate an R object of list type from the data objects of 102 single samples.

[0107] The SelectIntegrationFeatures function was used to obtain characteristic genes, and the number of characteristic genes was set to 3000.

[0108] Step 305: Use the merge function to merge the 102 single sample data objects into one data object.

[0109] Step 306: Run the RunPCA function on the data object to obtain the principal components of the 102 samples before integration.

[0110] Step 307: Use the principal component data as input, use the RunHarmony function, use the Lloyd algorithm for k-means clustering, and use the donor and single-cell platform as covariates for data integration.

[0111] Step 308: Use the DimPlot function to draw biplots of the first principal component and the second principal component of PCA and Harmony, respectively.

[0112] Corresponding to the above-mentioned method for processing single-cell gene expression profile data on different platforms, the present invention also proposes a device for processing single-cell gene expression profile data on different platforms. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0113] Figure 6 A schematic diagram of a structure of a device for processing single-cell gene expression profile data on different platforms provided in an embodiment of the present disclosure, such as Figure 6 As shown, including:

[0114] A first conversion unit 41 is used to convert the single-cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is the gene name;

[0115] A reading unit 42, used for reading the gene expression matrix file of the single sample;

[0116] A second conversion unit 43, used for converting the gene expression matrix file of the single sample into a single sample data object;

[0117] An analysis unit 44 is used to perform principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object;

[0118] A correction unit 45 is used to correct a preset number of principal components of the gene expression profile by using samples and single-cell experimental devices of different platforms as covariates to obtain a corrected first principal component and a second principal component;

[0119] The drawing unit 46 is used to draw a biplot of the corrected first principal component and the second principal component.

[0120] Furthermore, in a possible implementation of this embodiment, Figure 7As shown, the device also includes:

[0121] A merging unit 47 is used to merge all the single sample data objects on different platforms to generate a target data object before performing principal component analysis on all the single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object;

[0122] The analysis unit 44 is further configured to perform principal component analysis on the target data object to obtain a preset number of principal components of the gene expression profile in the target data object.

[0123] Furthermore, in a possible implementation of this embodiment, Figure 7 As shown, the device also includes:

[0124] A processing unit 48 is used for, after converting the gene expression matrix file of the single sample into a single sample data object, performing normalization processing on the single sample data object by a first preset function to obtain a first processed single sample data object;

[0125] The filtering unit 49 is used to perform data quality control on the single sample data object after the first processing based on preset parameters to obtain a final single sample data object.

[0126] Furthermore, in a possible implementation of this embodiment, Figure 7 As shown, the second conversion unit 43 is further used to generate a list-type R object using all final single-sample data objects as elements, and use a second preset function to obtain the characteristic genes of the list-type R object.

[0127] Furthermore, in a possible implementation of this embodiment, Figure 7 As shown, the merging unit 47 is further used to call a third preset function to merge the final single sample data objects to generate the target data object, and the target data object is an R object of type S4.

[0128] Furthermore, in a possible implementation of this embodiment, Figure 7 As shown, the device also includes:

[0129] Modification unit 410, a reading unit, modifies the gene names that do not conform to the preset naming specification in the gene names according to the human gene naming database HGNC before the reading unit 42;

[0130] If there are gene names that are not included in the HGNC, the species classification database NCBI is used to modify the gene names that do not conform to the preset naming specifications.

[0131] The present disclosure provides a processing device for single-cell gene expression profile data of different platforms, which converts single-cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is a gene name; reads the gene expression matrix file of the single sample, and converts the gene expression matrix file of the single sample into a single sample data object; performs principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object; uses samples of different platforms and single-cell experimental methods as covariates, corrects the preset number of principal components of the gene expression profile, and obtains a corrected first principal component and a second principal component; draws a biplot of the corrected first principal component and the second principal component. Compared with the related art, the embodiment of the present application uses data from a public database to organize it into a gene expression matrix file of a single sample, converts the gene expression matrix file of the single sample into a single sample data object, runs principal component analysis on the data object, and corrects the result of the principal component analysis to improve the accuracy of integrating single-cell gene expression profile data of different platforms.

[0132] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0133] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0134] Figure 8 A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0135] like Figure 8As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 502 or a computer program loaded from a storage unit 508 to a RAM (Random Access Memory) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O (Input / Output) interface 505 is also connected to the bus 504.

[0136] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0137] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a processing method for single-cell gene expression profile data on different platforms. For example, in some embodiments, a processing method for single-cell gene expression profile data on different platforms may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured in any other appropriate manner (for example, by means of firmware) to execute the aforementioned methods for processing single-cell gene expression profile data of different platforms.

[0138] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0139] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0140] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0141] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0142] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0143] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0144] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0145] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0146] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for processing single-cell gene expression profile data on different platforms, characterized in that: include: Converting single-cell gene expression profile data from different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is the gene name; Reading the gene expression matrix file of the single sample, and converting the gene expression matrix file of the single sample into a single sample data object; Performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object; Using samples from different platforms and single-cell experimental methods as covariates, correcting a preset number of principal components of the gene expression profile to obtain a corrected first principal component and a second principal component; Draw a biplot of the corrected first principal component and the second principal component.

2. The method according to claim 1, characterized in that Before performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object, the method further includes: Merging all the single sample data objects on different platforms to generate a target data object; The performing principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object comprises: A principal component analysis is performed on the target data object to obtain a preset number of principal components of the gene expression profile in the target data object.

3. The method according to claim 2, characterized in that After converting the gene expression matrix file of the single sample into a single sample data object, the method further comprises: Performing normalization processing on the single sample data object by using a first preset function to obtain a single sample data object after first processing; The data quality of the single sample data object after the first processing is controlled based on preset parameters to obtain a final single sample data object.

4. The method according to claim 3, characterized in that The step of converting the gene expression matrix file of the single sample into a single sample data object comprises: All the final single sample data objects are used as elements to generate an R object of list type, and the second preset function is used to obtain the characteristic genes of the R object of list type.

5. The method according to claim 4, characterized in that The merging of all the single sample data objects on different platforms to generate a target data object comprises: The third preset function is called to merge the final single sample data objects to generate the target data object, where the target data object is an R object of type S4.

6. The method according to claim 1, characterized in that Before reading the gene expression matrix file of the single sample, the method further comprises: According to the human gene naming database HGNC, the gene names that do not conform to the preset naming standards are modified; If there are gene names that are not included in the HGNC, the species classification database NCBI is used to modify the gene names that do not conform to the preset naming specifications.

7. A device for processing single-cell gene expression profile data on different platforms, characterized in that: include: A first conversion unit is used to convert the single-cell gene expression profile data of different platforms into a gene expression matrix file of a single sample, wherein the first column in the gene expression matrix file is a gene name; A reading unit, used for reading the gene expression matrix file of the single sample; A second conversion unit, used for converting the gene expression matrix file of the single sample into a single sample data object; An analysis unit, configured to perform principal component analysis on all single sample data objects to obtain a preset number of principal components of the gene expression profile in each single sample data object; A correction unit, used to correct a preset number of principal components of the gene expression profile by taking samples and single-cell experimental devices of different platforms as covariates, to obtain a corrected first principal component and a second principal component; A drawing unit is used to draw a biplot of the first principal component and the second principal component after correction.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

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

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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