Single cell sequencing gene drawing tool

Abstract

The invention discloses a single cell sequencing gene drawing tool, and belongs to the technical field of sequencing gene visualization, the single cell sequencing gene drawing tool comprises R packages of TidyGenePlot, R packages of TidyGenePlot comprise tidy.VlnPlot and tidy.FeaturePlot, and R packages of TidyGenePlot comprise tidy.VlnPlot and tidy.FeaturePlot; wherein the tidy.VlnPlot is used for drawing a violin chart, and the tidy.FeaturePlot function is used for drawing a feature map. In the invention, a TidyGenePlot drawing tool is developed in an R language environment, and is used for analyzing single-cell RNA sequencing data. When the tool is used for drawing a violin chart, the Identity element and the Express Level element are automatically removed, so that the chart is more concise. Meanwhile, when the feature map is drawn, the coordinate axis is automatically removed, and the definition of the map is further improved. In addition, the TidyGenePlot can automatically perform typesetting on a plurality of generated gene maps, so that the simplicity and the attractiveness of a drawing result are enhanced, and the subsequent typesetting workload of the user is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of sequencing gene visualization, and in particular to a single-cell sequencing gene mapping tool. Background Art

[0002] Single-cell sequencing is a high-throughput sequencing technology that can sequence and analyze genomes, transcriptomes, epigenomes, etc. at the single-cell level. It allows researchers to explore genetic information and molecular characteristics within individual cells, thereby revealing cell-to-cell heterogeneity, the composition and functional status of cell populations.

[0003] When single-cell sequencing is currently performed, Vlnplot() and FeaturePlot() functions are often used to draw violin plots and feature plots to display gene expression levels and expression patterns. However, these plot results often come with some unnecessary elements such as "Identity" and "Expression Level" labels, as well as redundant coordinate axes, which not only affects the intuitiveness of the analysis results, but also brings inconvenience to subsequent typesetting. To this end, a single-cell sequencing gene plotting tool is proposed. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, that the graphics generated by the existing tools often contain redundant elements. To this end, one object of the present invention is to provide a single cell sequencing gene mapping tool.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A single-cell sequencing gene plotting tool, comprising: an R package TidyGenePlot, wherein the R package TidyGenePlot comprises tidy.VlnPlot and tidy.FeaturePlot;

[0007] Among them, tidy.VlnPlot is used to draw violin plots, and tidy.FeaturePlot function is used to draw feature plots.

[0008] As a further optimization scheme of the present invention, the parameters of tidy.VlnPlot include:

[0009] seu_obj: Seurat object created using the Seurat package; features: a vector containing genes; pt.size: point size for plotting; ncol: number of permutation plots per row; cols: colors used for plotting; split.by: variable used to split the data; Seurat: used to draw violin plots; ggplot2: used for figure labels and theme settings; patchwork: used to merge multiple plots.

[0010] As a further optimization scheme of the present invention, the parameters of tidy.FeaturePlot include:

[0011] seu_obj: Seurat object created using the Seurat package; features: vector containing genes; pt.size: point size used for plotting; ncol: number of permutation plots per row; cols: colors used for plotting, default is gray and red; Legend: logical value for whether to show the legend.

[0012] As a further optimization scheme of the present invention, the implementation of tidy.VlnPlot includes the following steps:

[0013] Step 1: Input Seurat object and gene list;

[0014] Step 2: Set the default data slot of the Seurat object to RNA;

[0015] Step 3: Convert the gene list into a character vector;

[0016] Step 4: Use the VlnPlot function of the Seurat package to draw a violin plot and set the parameters to remove the Identity and Expression Level elements;

[0017] Step 5: Automatically set the number of graphs per row based on the number of genes;

[0018] Step 6: Use the patchwork package to merge all the images into one graphic;

[0019] Step 7: Output the merged graphics. The generated graphics are exported as files in at least ".png" and ".pdf" formats.

[0020] As a further optimization scheme of the present invention, the implementation of tidy.FeaturePlot includes the following steps:

[0021] Step 1: Input Seurat object and gene list;

[0022] Step 2: Set the default data slot of the Seurat object to RNA;

[0023] Step 3: Convert the gene list into a character vector;

[0024] Step 4: Use the FeaturePlot function of the Seurat package to draw the feature map and set the parameters to remove the coordinate axis;

[0025] Step 5: Automatically set the number of graphs per row based on the number of genes;

[0026] Step 6: Use the patchwork package to merge all the images into one graphic;

[0027] Step 7: Output the merged graphics. The generated graphics are exported as files in at least ".png" and ".pdf" formats.

[0028] As a further optimization scheme of the present invention, when split.by is empty, a violin plot is drawn for each feature, and the legend and axis labels are removed; when split.by is not empty, a violin plot is drawn according to the split variable.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] In the present invention, the TidyGenePlot drawing tool is developed in the R language environment for single-cell RNA sequencing data analysis. When drawing a violin plot, the tool automatically removes the "Identity" and "Expression Level" elements to make the graph more concise. At the same time, when drawing a feature graph, the coordinate axis is automatically removed to further improve the clarity of the graph. In addition, TidyGenePlot can also automatically typeset multiple generated gene graphs, enhance the simplicity and beauty of the drawing results, thereby reducing the user's subsequent typesetting workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] none DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] The present invention provides a technical solution:

[0034] A single-cell sequencing gene plotting tool, comprising: an R package TidyGenePlot, wherein the R package TidyGenePlot comprises tidy.VlnPlot and tidy.FeaturePlot;

[0035] Among them, tidy.VlnPlot is used to draw violin plots. The parameters of tidy.VlnPlot include:

[0036] seu_obj: Seurat object created using the Seurat package; features: a vector containing genes; pt.size: point size for plotting; ncol: number of permutation plots per row; cols: colors used for plotting; split.by: variable used to split the data; Seurat: used to draw violin plots; ggplot2: used for figure labels and theme settings; patchwork: used to merge multiple plots.

[0037] The tidy.FeaturePlot function is used to draw feature plots. The parameters of tidy.FeaturePlot include:

[0038] seu_obj: Seurat object created using the Seurat package; features: vector containing genes; pt.size: point size used for plotting; ncol: number of permutation plots per row; cols: colors used for plotting, default is gray and red; Legend: logical value for whether to show the legend.

[0039] The implementation of tidy.VlnPlot includes the following steps:

[0040] Step 1: Input Seurat object and gene list;

[0041] Step 2: Set the default data slot of the Seurat object to RNA;

[0042] Step 3: Convert the gene list into a character vector;

[0043] Step 4: Use the VlnPlot function of the Seurat package to draw a violin plot and set the parameters to remove the Identity and Expression Level elements;

[0044] Step 5: Automatically set the number of graphs per row based on the number of genes;

[0045] Step 6: Use the patchwork package to merge all the images into one graphic;

[0046] Step 7: Output the merged graphics. The generated graphics are exported as files in at least ".png" and ".pdf" formats.

[0047] Among them, when split.by is empty, a violin plot is drawn for each feature, and the legend and axis labels are removed; when split.by is not empty, a violin plot is drawn according to the split variable.

[0048] The implementation of tidy.FeaturePlot includes the following steps:

[0049] Step 1: Input Seurat object and gene list;

[0050] Step 2: Set the default data slot of the Seurat object to "RNA";

[0051] Step 3: Convert the gene list into a character vector;

[0052] Step 4: Use the FeaturePlot function of the Seurat package to draw the feature map and set the parameters to remove the coordinate axis;

[0053] Step 5: Automatically set the number of graphs per row based on the number of genes;

[0054] Step 6: Use the patchwork package to merge all the images into one graphic;

[0055] Step 7: Output the merged graphics. The generated graphics are exported as files in at least ".png" and ".pdf" formats.

[0056] Parts not involved in the present invention are the same as the prior art or can be implemented by the prior art. Although the embodiments of the present invention have been shown and described, it is understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A single-cell sequencing gene mapping tool, characterized in that: include: R package TidyGenePlot, which includes tidy.VlnPlot and tidy.FeaturePlot; Among them, tidy.VlnPlot is used to draw violin plots, and tidy.FeaturePlot function is used to draw feature plots.

2. A single-cell sequencing gene mapping tool according to claim 1, characterized in that: The parameters of tidy.VlnPlot include: seu_obj: Seurat object created using the Seurat package; features: a vector containing genes; pt.size: point size for plotting; ncol: number of permutation plots per row; cols: colors used for plotting; split.by: variable used to split the data; Seurat: used to draw violin plots; ggplot2: used for figure labels and theme settings; patchwork: used to merge multiple plots.

3. A single-cell sequencing gene mapping tool according to claim 1, characterized in that: The parameters of tidy.FeaturePlot include: seu_obj: Seurat object created using the Seurat package; features: vector containing genes; pt.size: point size used for plotting; ncol: number of permutation plots per row; cols: colors used for plotting; Legend: logical value for whether to show the legend.

4. A single-cell sequencing gene mapping tool according to claim 1, characterized in that: The implementation of tidy.VlnPlot includes the following steps: Step 1: Input Seurat object and gene list; Step 2: Set the default data slot of the Seurat object to RNA; Step 3: Convert the gene list into a character vector; Step 4: Use the VlnPlot function of the Seurat package to draw a violin plot and set the parameters to remove the Identity and Expression Level elements; Step 5: Automatically set the number of graphs per row based on the number of genes; Step 6: Use the patchwork package to merge all the images into one graphic; Step 7: Output the merged graphics.

5. A single-cell sequencing gene mapping tool according to claim 1, characterized in that: The implementation of tidy.FeaturePlot includes the following steps: Step 1: Input Seurat object and gene list; Step 2: Set the default data slot of the Seurat object to RNA; Step 3: Convert the gene list into a character vector; Step 4: Use the FeaturePlot function of the Seurat package to draw the feature map and set the parameters to remove the coordinate axis; Step 5: Automatically set the number of graphs per row based on the number of genes; Step 6: Use the patchwork package to merge all the images into one graphic; Step 7: Output the merged graphics.

6. A single-cell sequencing gene mapping tool according to claim 4, characterized in that: When split.by is empty, a violin plot is drawn for each feature and the legend and axis labels are removed; when split.by is not empty, a violin plot is drawn based on the split variable.