voyagerpy.plotting.plot_pca#

voyagerpy.plotting.plot_pca(adata: AnnData, ndim: int = 5, cmap: str | None = None, color_by: str = 'cluster', obsm='X_pca', legend_kwargs: Dict[str, Any] | None = None, subplot_kwargs: Dict[str, Any] | None = None, **kwargs) → ndarray[Any, dtype[Axes]]#

Create a PCA plot of the fist ndim dimensions.

Each component is plotted againts the other components. The plots on the diagonals represent the density of the points. The color of each point is determined by the color_by parameter.

Parameters:

  • adata (AnnData) – The annotated data matrix.

  • ndim (int, optional) – The number of components to plot, by default 5

  • cmap (Optional[str], optional) – The colormap to use, by default None

  • color_by (str, optional) – The column name in adata.obs to color the points by, by default “cluster”

  • obsm (str, optional) – The obsm to use for plotting the dimension, by default “X_pca”

  • legend_kwargs (Optional[Dict[str, Any]], optional) – The keywords to use for the legend, by default None

  • subplot_kwargs (Optional[Dict[str, Any]], optional) – The keywords to use for subplots_single_colorbar(), by default None

Returns:

The axes containing the plots.

Return type:

npt.NDArray[Axes]