Skip to main content
Glama

pl_highly_variable_genes

Identify and visualize highly variable genes in single-cell RNA-seq data by plotting gene dispersions or normalized variance versus means to highlight biologically relevant features.

Instructions

plot highly variable genes; Plot dispersions or normalized variance versus means for genes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
figsizeNoFigure size. Format is (width, height).
color_mapNoColor map to use for continuous variables.
paletteNoColors to use for plotting categorical annotation groups.
vmaxNoThe value representing the upper limit of the color scale.
vminNoThe value representing the lower limit of the color scale.
vcenterNoThe value representing the center of the color scale.
legend_fontsizeNoNumeric size in pt or string describing the size.
legend_fontweightNoLegend font weight. A numeric value in range 0-1000 or a string.bold
legend_locNoLocation of legend, either 'on data', 'right margin' or a valid keyword for the loc parameter.right margin
legend_fontoutlineNoLine width of the legend font outline in pt.
logNoPlot on logarithmic axes.
highly_variable_genesNoWhether to plot highly variable genes or all genes.

Implementation Reference

  • Handler function that executes pl_highly_variable_genes by calling sc.pl.highly_variable_genes(adata, **validated_kwargs) and handles figure saving.
    def run_pl_func(ads, func, arguments): """ Execute a Scanpy plotting function with the given arguments. Parameters ---------- adata : AnnData Annotated data matrix. func : str Name of the plotting function to execute. arguments : dict Arguments to pass to the plotting function. Returns ------- The result of the plotting function. """ adata = ads.adata_dic[ads.active] if func not in pl_func: raise ValueError(f"Unsupported function: {func}") run_func = pl_func[func] parameters = inspect.signature(run_func).parameters kwargs = {k: arguments.get(k) for k in parameters if k in arguments} if "title" not in parameters: kwargs.pop("title", False) kwargs.pop("return_fig", True) kwargs["show"] = False kwargs["save"] = ".png" try: fig = run_func(adata, **kwargs) fig_path = set_fig_path(func, **kwargs) add_op_log(adata, run_func, kwargs) return fig_path except Exception as e: raise e return fig_path
  • Pydantic input schema model used by the tool for validation.
    class HighlyVariableGenesModel(BaseVisualizationModel): """Input schema for the highly_variable_genes plotting tool.""" log: bool = Field( default=False, description="Plot on logarithmic axes." ) highly_variable_genes: bool = Field( default=True, description="Whether to plot highly variable genes or all genes." )
  • Definition of the MCP Tool object.
    pl_highly_variable_genes = types.Tool( name="pl_highly_variable_genes", description="plot highly variable genes; Plot dispersions or normalized variance versus means for genes.", inputSchema=HighlyVariableGenesModel.model_json_schema(), )
  • Mapping of tool name to the underlying Scanpy plotting function.
    pl_func = { "pl_pca": sc.pl.pca, "pl_embedding": sc.pl.embedding, # Add the new embedding function "diffmap": sc.pl.diffmap, "pl_violin": sc.pl.violin, "pl_stacked_violin": sc.pl.stacked_violin, "pl_heatmap": sc.pl.heatmap, "pl_dotplot": sc.pl.dotplot, "pl_matrixplot": sc.pl.matrixplot, "pl_tracksplot": sc.pl.tracksplot, "pl_scatter": sc.pl.scatter, "embedding_density": sc.pl.embedding_density, "rank_genes_groups": sc.pl.rank_genes_groups, "pl_rank_genes_groups_dotplot": sc.pl.rank_genes_groups_dotplot, # Add function mapping "pl_clustermap": sc.pl.clustermap, "pl_highly_variable_genes": sc.pl.highly_variable_genes, "pl_pca_variance_ratio": sc.pl.pca_variance_ratio, }
  • Registration of the tool in the pl_tools dictionary used by the server.
    pl_tools = { "pl_pca": pl_pca_tool, "pl_embedding": pl_embedding, # Add the new embedding tool # "diffmap": diffmap, "pl_violin": pl_violin, "pl_stacked_violin": pl_stacked_violin, "pl_heatmap": pl_heatmap, "pl_dotplot": pl_dotplot, "pl_matrixplot": pl_matrixplot, "pl_tracksplot": pl_tracksplot, "pl_scatter": pl_scatter, # "embedding_density": embedding_density, # "spatial": spatial, # "rank_genes_groups": rank_genes_groups, "pl_rank_genes_groups_dotplot": pl_rank_genes_groups_dotplot, # Add tool mapping # "pl_clustermap": pl_clustermap, "pl_highly_variable_genes": pl_highly_variable_genes, "pl_pca_variance_ratio": pl_pca_variance_ratio, }

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/huang-sh/scmcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server