Functional genome visualization analysis method, electronic device and medium

Abstract

The application discloses a functional genome visual analysis method, which first filters genes according to information such as expression levels to improve the proportion of effective genes, then generates a gene co-expression correlation coefficient matrix based on filtered gene data to obtain a co-expression network, and performs data conversion based on the gene co-expression correlation coefficient matrix to obtain a distance matrix of the genes, and finally performs dimension reduction and clustering analysis based on the distance matrix to obtain visual gene expression according to an analysis result. The application realizes better visual effect of the co-expression gene network and can more efficiently obtain a co-expression gene cluster.

Description

Technical Field

[0001] This invention relates to the field of RNA sequencing technology, and in particular to a method, electronic device and medium for visualizing and analyzing functional genomics. Background Technology

[0002] RNA sequencing technology, also known as transcriptome sequencing technology, refers to the use of high-throughput sequencing technology to analyze and reflect the expression levels of mRNA, small RNA, and noncoding RNA. The transcriptome is the collection of all transcripts produced by a species or a specific cell type. Transcriptome sequencing technology can study gene function and structure at the holistic level, revealing the molecular mechanisms of specific biological processes and disease development, and has been widely used in basic research, clinical diagnosis, and drug development.

[0003] However, RNA data contains a vast amount of information, and how to fully mine and interpret this information and present it intuitively is an important problem that the industry needs to solve. Summary of the Invention

[0004] To address some or all of the problems in the prior art, the first aspect of this invention provides a method for functional genomic imaging (FGI), comprising:

[0005] Filter genes based on expression levels to increase the proportion of effective genes;

[0006] Gene co-expression correlation coefficient matrix is ​​generated based on the filtered gene data to obtain the co-expression network;

[0007] Data transformation is performed based on the gene co-expression correlation coefficient matrix to obtain the gene distance matrix; and

[0008] Dimensionality reduction and clustering analysis are performed based on the distance matrix, and the gene expression status is visualized based on the analysis results.

[0009] Furthermore, filtering genetic data includes removing non-coding genes and specific housekeeping genes from the genetic data.

[0010] Furthermore, the specific housekeeping gene data includes mitochondrial and ribosome genes.

[0011] Furthermore, the gene co-expression correlation coefficient matrix was obtained using the Weighted Gene Co-expression Network Analysis (WGCNA) method.

[0012] Furthermore, dimensionality reduction and clustering include:

[0013] Based on the distance matrix, the tSNE dimensionality reduction method is used to reduce the dimensionality of the co-expression network; and

[0014] An improved PhenoGraph clustering algorithm was used to perform cluster analysis on the distance matrix to obtain co-expressed gene clusters for subsequent functional annotation.

[0015] Furthermore, the functional genomics visualization analysis method also includes:

[0016] Before filtering, the sample to be analyzed is calibrated.

[0017] Furthermore, the calibration includes:

[0018] The samples to be analyzed were calibrated batch-to-batch using ComBat-seq; and

[0019] CPM data is obtained by calibrating the library size of the batch-calibrated samples using EdgeR.

[0020] Furthermore, the visualized gene expression data obtained from the analysis results include:

[0021] Calculate the Z-Score values ​​of each gene in normal and tumor tissues to form an FGI gene scatter plot.

[0022] Furthermore, the visualized gene expression data obtained from the analysis results also includes:

[0023] Local statistical analysis was performed on the expression data of each gene in the FGI gene scatter plot to form an FGI gene cloud map.

[0024] Furthermore, the visualized gene expression data obtained from the analysis results also includes:

[0025] The FGI scatter plot highlights gene sets related to cell function in public databases.

[0026] Based on the functional genomics visualization analysis method described above, a second aspect of the present invention provides an electronic device for functional genomics visualization analysis, comprising a memory and a processor, wherein the memory is configured to store a computer program that executes the functional genomics visualization analysis method described above when the processor is running.

[0027] A third aspect of the present invention also provides a computer-readable storage medium for functional genomics visualization analysis, which stores a computer program that, when run on a processor, executes the functional genomics visualization analysis method as described above.

[0028] This invention provides a functional genomics visualization analysis method. First, it increases the number of effective genes in the co-expression network through filtering. Then, it introduces relevant dimensionality reduction and clustering algorithms to perform dimensionality reduction and clustering analysis on the correlation network, thereby achieving better visualization of the co-expression gene network, obtaining co-expression gene clusters more efficiently, and thus improving the efficiency of gene sequencing. Furthermore, the method includes an effective data calibration process to calibrate batch effects between sequencing samples and public databases, ensuring the stability and accuracy of data interpretation. Attached Figure Description

[0029] To further illustrate the above and other advantages and features of the various embodiments of the present invention, a more specific description of the various embodiments of the present invention will be presented with reference to the accompanying drawings. It is to be understood that these drawings depict only typical embodiments of the invention and are therefore not intended to limit its scope. In the drawings, identical or corresponding parts will be indicated by identical or similar reference numerals for clarity.

[0030] Figure 1 A flowchart illustrating a functional genomics visualization analysis method according to an embodiment of the present invention is shown.

[0031] Figure 2 This diagram shows a scatter plot of FGI genes in a certain cancer obtained using a functional genomics visualization analysis method according to an embodiment of the present invention.

[0032] Figures 3a-3c This shows the highlighted markers, gene counts, and gene cluster locations of gene sets from public databases in the FGI gene scatter plot; and...

[0033] Figures 4a-4d The images show scatter plots and cloud maps of FGI genes from two different samples obtained using a functional genomics visualization analysis method according to an embodiment of the present invention. Detailed Implementation

[0034] In the following description, the invention is described with reference to various embodiments. However, those skilled in the art will recognize that the embodiments may be practiced without one or more specific details or in conjunction with other alternatives and / or additional methods or components. In other instances, well-known structures or operations are not shown or described in detail so as not to obscure the inventive points of the invention. Similarly, for illustrative purposes, specific numbers and configurations are set forth to provide a comprehensive understanding of embodiments of the invention. However, the invention is not limited to these specific details. Furthermore, it should be understood that the embodiments shown in the drawings are illustrative representations and are not necessarily drawn to scale.

[0035] In this specification, references to "an embodiment" or "this embodiment" mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. The phrase "in one embodiment" appearing throughout this specification does not necessarily refer to the same embodiment in all instances.

[0036] It should be noted that the embodiments of the present invention describe the method steps in a specific order; however, this is only for illustrating the specific embodiment and not for limiting the order of the steps. On the contrary, in different embodiments of the present invention, the order of the steps can be adjusted according to actual needs.

[0037] In this invention, the modules of the system according to the invention can be implemented using software, hardware, firmware, or a combination thereof. When a module is implemented using software, its function can be implemented through computer program flow. For example, the module can be implemented using code segments (such as code segments in languages ​​like C and C++) stored in a storage device (such as a hard disk, memory, etc.), wherein the corresponding function of the module can be implemented when the code segment is executed by a processor. When a module is implemented using hardware, its function can be implemented by setting a corresponding hardware structure. For example, the module's function can be implemented by hardware programming a programmable device such as a field-programmable gate array (FPGA), or by designing an application-specific integrated circuit (ASIC) that includes multiple transistors, resistors, capacitors, and other electronic devices. When a module is implemented using firmware, the module's function can be written into a read-only memory such as an EPROM or EEPROM in the form of program code, and the corresponding function of the module can be implemented when the program code is executed by a processor. In addition, some functions of the module may need to be implemented by separate hardware or by working in cooperation with the hardware. For example, the detection function is implemented by the corresponding sensor (such as a proximity sensor, accelerometer, gyroscope, etc.), the signal transmission function is implemented by the corresponding communication device (such as a Bluetooth device, infrared communication device, baseband communication device, Wi-Fi communication device, etc.), the output function is implemented by the corresponding output device (such as a display, speaker, etc.), and so on.

[0038] To provide a basis for cancer subtyping and single-sample sequencing data analysis, gene co-expression networks can be constructed based on public databases such as TCGA and visualized to obtain a series of co-expressed gene clusters among specific cancer samples, which are then annotated according to their gene functional enrichment. Simultaneously, data such as the expression levels and ranges of each gene in cancer sample populations and normal tissue sample populations are integrated. To improve the detection efficiency and flexibility of co-expression networks, this invention optimizes existing functional genomics imaging (FGI) methods. First, an improved algorithm is used to perform dimensionality reduction and clustering analysis on the co-expression neural network, thereby achieving better visualization of the co-expressed gene network and obtaining co-expressed gene clusters more efficiently. Second, an effective data calibration process is established to calibrate batch effects between sequencing samples and public databases, ensuring the stability and accuracy of data interpretation.

[0039] The present invention will be further described below with reference to the accompanying drawings of the embodiments.

[0040] Figure 1 This diagram illustrates a flowchart of a functional genomics visualization analysis method according to an embodiment of the present invention. Figure 1 As shown, a functional genomics visualization analysis method includes:

[0041] First, in step 101, data filtering. Before constructing the co-expression network, genes are filtered based on information such as expression levels to maximize the number of effective genes in the co-expression network. Before constructing the co-expression network, non-coding genes and some housekeeping genes are filtered out, increasing the number of effective genes included in the co-expression network and making the resulting FGI gene co-expression model more representative. In one embodiment of the present invention, the non-coding genes include pseudogenes. In another embodiment of the present invention, the filtered housekeeping genes mainly refer to mitochondrial and ribosome-related genes;

[0042] Next, in step 102, a co-expression network is constructed. A gene co-expression correlation coefficient matrix is ​​generated based on the filtered gene data to obtain the co-expression network. In one embodiment of the present invention, the gene co-expression correlation coefficient matrix is ​​obtained using Weighted Gene Co-expression Network Analysis (WGCNA). WGCNA is a method for analyzing gene expression patterns in multiple samples. It can cluster genes with similar expression patterns and analyze the association between modules and specific traits or phenotypes. It first assumes that the gene network follows a scale-free distribution and defines the gene co-expression correlation matrix and the adjacency function for forming the gene network. Then, it calculates the dissimilarity coefficients of different nodes and constructs a hierarchical clustering tree accordingly. Different branches of the hierarchical clustering tree represent different gene modules, which include genes with similar expression profiles.

[0043] Next, in step 103, data transformation is performed. Based on the gene co-expression correlation coefficient matrix obtained in step 102, data transformation is performed to obtain the gene distance matrix; and

[0044] Finally, in step 104, dimensionality reduction and clustering analysis are performed. Dimensionality reduction and clustering analysis are conducted based on the distance matrix, and the gene expression status is visualized based on the analysis results. In one embodiment of the present invention, an improved Phenograph clustering algorithm is introduced to perform clustering analysis on the distance matrix, improving the detection efficiency and flexibility of the co-expression module, effectively increasing the number of effective genes in the co-expression network, and visualizing the co-expression network through an optimized dimensionality reduction analysis algorithm, making the display of gene clusters / co-expression modules more intuitive. Specifically, in one embodiment of the present invention, based on the distance matrix, the tSNE dimensionality reduction method is used to reduce the dimensionality of the co-expression network, and the improved Phenograph clustering algorithm is used to perform clustering analysis on the distance expression to obtain co-expressed gene clusters for subsequent functional annotation, i.e., obtaining FGI gene scatter plots and gene clusters related to specific cancer types. In one embodiment of the present invention, FGI gene scatter plots and FGI gene cloud maps are used to visually display the relative expression levels of genes in the co-expression network and the associated gene sets. The FGI gene scatter plot is used to display the calculated Z-Score values ​​of each gene in normal and tumor tissues. The z-Score values ​​visually demonstrate the expression level of each functional cluster of genes. Figure 2 This figure illustrates an FGI gene scatter plot of a cancer obtained using a functional genomics visualization analysis method according to an embodiment of the present invention. As shown, different grayscale points represent gene clusters obtained through clustering, involving aspects such as proliferation, immunity, stroma, and differentiation. The FGI gene cloud map is a local statistical analysis of the expression data of each gene in the FGI gene scatter plot, eliminating the influence of mutual masking caused by scatter points in the gene scatter plot. To visually demonstrate the co-expression relationships of genes in a gene set and their relationship with existing model gene clusters, in one embodiment of the present invention, gene sets related to cell function from public databases such as MSigDB and Reactome can also be highlighted in the FGI gene scatter plot. Figures 3a-3c The image shows highlighted markers, gene counts, and gene cluster locations of gene sets from public databases in an FGI gene scatter plot. Figure 3a The dark dots represent the genes contained in this Genest. Figure 3b It shows the statistical count of the number of genes falling into each gene cluster, and Figure 3c The location of the gene clusters involved is shown, and it can be seen that there is a great correlation between the gene clusters and Genest. Figures 4a-4d The following figures illustrate FGI gene scatter plots and FGI gene cloud plots obtained from two different samples using a functional genomics visualization analysis method according to an embodiment of the present invention. Figure 4a This is a cloud map of the FGI gene in sample 1. Figure 4b This is a scatter plot of the FGI gene in sample 1. Figure 4c For sample 2, the FGI gene cloud map, and Figure 4d The image shows a scatter plot of FGI genes for Sample 2. The plot indicates that Sample 2 exhibits higher expression levels of proliferation-related gene clusters, while Sample 1 shows higher expression levels of stroma-related and angiogenesis-related gene clusters.

[0045] Due to differences in library construction methods, sequencing platforms, etc., batch effects may exist between the test samples and public databases. The presence of batch effects can significantly affect the determination of gene expression levels. To address this issue, in one embodiment of the present invention, step 001, data calibration, can be performed before step 101:

[0046] In step 001, the samples to be analyzed are calibrated batch by batch using ComBat-seq, and the library size of the batch-calibrated samples is calibrated using EdgeR to obtain CPM data for subsequent co-expression network analysis.

[0047] Based on the functional genomics visualization analysis method described above, this invention also provides a single-sample visualization analysis method, including:

[0048] Calibrate sequencing data to correct for batch effects between samples and public databases;

[0049] The expression of each gene in the co-expression network was displayed using FGI gene scatter plots and FGI gene cloud maps.

[0050] Because single-gene expression is influenced by random factors, comprehensive analysis of the expression of various co-expressed gene clusters and clinically subtype-related gene sets can yield information on tumor proliferation, differentiation level, angiogenesis, stroma composition, and immune activity; and

[0051] Based on the immunophenotyping of samples and the target expression levels of approved drugs, the response of some drugs can be predicted.

[0052] This invention constructs a visualization analysis workflow based on co-expression networks, organically integrating information such as gene co-expression, clinical prognostic geneset, and drug targets. On the one hand, it fully displays information on tumor samples in terms of proliferation activity, differentiation level, angiogenesis, stroma composition, immune activity, and signaling pathways. On the other hand, it greatly improves the readability of data analysis reports, providing convenience for promoting the application of sequencing technology in clinical practice.

[0053] Based on the functional genomics visualization analysis method described above, a second aspect of the present invention provides an electronic device for functional genomics visualization analysis, comprising a memory and a processor, wherein the memory is configured to store a computer program that executes the functional genomics visualization analysis method described above when the processor is running.

[0054] A third aspect of the present invention also provides a computer-readable storage medium for functional genomics visualization analysis, which stores a computer program that, when run on a processor, executes the functional genomics visualization analysis method as described above.

[0055] Although various embodiments of the invention have been described above, it should be understood that they are presented by way of example only and not as limitations. It will be apparent to those skilled in the art that various combinations, modifications, and alterations can be made without departing from the spirit and scope of the invention. Therefore, the breadth and scope of the invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should be defined solely by the appended claims and their equivalents.

Claims

1. A functional genomics visualization analysis method for non-disease diagnosis, characterized in that, Including the following steps: Genes are filtered based on expression levels to increase the proportion of effective genes. The filtering includes removing non-coding genes, mitochondrial genes, and ribosomal gene data. Gene co-expression correlation coefficient matrix is ​​generated based on the filtered gene data to obtain a co-expression network, wherein the gene co-expression correlation coefficient matrix is ​​obtained by a weighted gene co-expression network analysis method. Data transformation is performed based on the gene co-expression correlation coefficient matrix to obtain the gene distance matrix; as well as The method for dimensionality reduction and clustering analysis based on the distance matrix includes the following steps: using the tSNE dimensionality reduction method to reduce the dimensionality of the co-expression network, and using the improved PhenoGraph clustering algorithm to perform clustering analysis on the distance matrix to obtain co-expressed gene clusters for subsequent functional annotation. as well as Based on the analysis results, the Z-Score values ​​of each gene in normal and tumor tissues were calculated to form an FGI gene scatter plot, thus visualizing the gene expression situation.

2. The functional genomics visualization analysis method as described in claim 1, characterized in that, The visualized gene expression data obtained from the analysis results include: Local statistical analysis was performed on the expression data of each gene in the FGI gene scatter plot to form an FGI gene cloud map.

3. The functional genomics visualization analysis method as described in claim 1, characterized in that, The visualized gene expression data obtained from the analysis results include: In the FGI scatter plot, gene sets related to cell function in public databases are highlighted.

4. The functional genomics visualization analysis method as described in claim 1, characterized in that, It also includes the following steps: Before filtering the samples to be analyzed, the samples to be analyzed are calibrated.

5. The functional genomics visualization analysis method as described in claim 4, characterized in that, The calibration includes: The samples to be analyzed were calibrated batch-to-batch using ComBat-seq; and The library size of the batch-calibrated samples is calibrated using EdgeR.

6. An electronic device for functional genomics visualization analysis, comprising a memory and a processor, characterized in that, The memory is configured to store a computer program that executes the functional genomics visualization analysis method as described in any one of claims 1 to 5 when the processor is running.

7. A computer-readable storage medium for functional genomics visualization analysis, characterized in that, The device contains a computer program that, when run on a processor, executes the functional genomics visualization analysis method as described in any one of claims 1 to 5.

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