Plotting

The plotting module ehrapy.pl.\* largely parallels the tl.\* and a few of the pp.\* functions. For most tools and for some preprocessing functions, you will find a plotting function with the same name.

Generic

plot.scatter	Scatter plot along observations or variables axes.
plot.heatmap	Heatmap of the feature values.
plot.dotplot	Makes a dot plot of the count values of var_names.
plot.tracksplot	Plots a filled line plot.
plot.violin	Violin plot.
plot.stacked_violin	Stacked violin plots.
plot.matrixplot	Creates a heatmap of the mean count per group of each var_names.
plot.clustermap	Hierarchically-clustered heatmap.
plot.ranking	Plot rankings.
plot.dendrogram	Plots a dendrogram of the categories defined in groupby.
plot.catplot	Plot categorical data.

Quality Control and missing values

plot.missing_values_matrix	A matrix visualization of the nullity of the given AnnData object.
plot.missing_values_barplot	A bar chart visualization of the nullity of the given AnnData object.
plot.missing_values_heatmap	Presents a seaborn heatmap visualization of nullity correlation in the given AnnData object.
plot.missing_values_dendrogram	Fits a scipy hierarchical clustering algorithm and visualizes the results as a scipy dendrogram.

Classes

Please refer to Scanpy’s plotting classes documentation.

Tools

Methods that extract and visualize tool-specific annotation in an AnnData object. For any method in module tl, there is a method with the same name in pl.

plot.pca	Scatter plot in PCA coordinates.
plot.pca_loadings	Rank features according to contributions to PCs.
plot.pca_variance_ratio	Plot the variance ratio.
plot.pca_overview	Plot PCA results.

Embeddings

plot.tsne	Scatter plot in tSNE basis.
plot.umap	Scatter plot in UMAP basis.
plot.diffmap	Scatter plot in Diffusion Map basis.
plot.draw_graph	Scatter plot in graph-drawing basis.
plot.embedding	Scatter plot for user specified embedding basis (e.g. umap, pca, etc).
plot.embedding_density	Plot the density of observations in an embedding (per condition).

Branching trajectories and pseudotime

plot.dpt_groups_pseudotime	Plot groups and pseudotime.
plot.dpt_timeseries	Heatmap of pseudotime series.
plot.paga	Plot the PAGA graph through thresholding low-connectivity edges.
plot.paga_path	Feature changes along paths in the abstracted graph.
plot.paga_compare	Scatter and PAGA graph side-by-side.

Feature Ranking

plot.rank_features_groups	Plot ranking of features.
plot.rank_features_groups_violin	Plot ranking of features for all tested comparisons as violin plots.
plot.rank_features_groups_stacked_violin	Plot ranking of genes using stacked_violin plot.
plot.rank_features_groups_heatmap	Plot ranking of genes using heatmap plot (see heatmap()).
plot.rank_features_groups_dotplot	Plot ranking of genes using dotplot plot (see dotplot()).
plot.rank_features_groups_matrixplot	Plot ranking of genes using matrixplot plot (see matrixplot()).
plot.rank_features_groups_tracksplot	Plot ranking of genes using tracksplot plot (see tracksplot()).
plot.rank_features_supervised	Plot features with greatest absolute importances as a barplot.

Survival Analysis

plot.ols	Plots an Ordinary Least Squares (OLS) Model result, scatter plot, and line plot.
plot.kaplan_meier	Plots a pretty figure of the Fitted KaplanMeierFitter model.
plot.cox_ph_forestplot	Generates a forest plot to visualize the coefficients and confidence intervals of a Cox Proportional Hazards model.

Causal Inference

plot.causal_effect

Plot the causal effect estimate.