Plotting

pl.barplot(data, name[, top, vertical, ...])	Plot barplots showing top scores.
pl.dotplot(df, x, y, c, s[, top, scale, ...])	Plot results of enrichment analysis as dots.
pl.filter_by_expr(adata[, group, lib_size, ...])	Plot to help determining the thresholds of the decoupler.pp.filter_by_expr function.
pl.filter_by_prop(adata[, min_prop, ...])	Plot to help determining the thresholds of the decoupler.pp.filter_by_prop function.
pl.filter_samples(adata[, groupby, log, ...])	Plot to assess the quality of the obtained pseudobulk samples from decoupler.pp.pseudobulk.
pl.leading_edge(df, net, stat, name[, cmap, ...])	Plot the running score of GSEA.
pl.network(net[, data, score, sources, ...])	Plot results of enrichment analysis as network.
pl.obsbar(adata, y[, hue, kw_barplot])	Plot adata.obs metadata as a grouped barplot.
pl.obsm(adata[, key, names, nvar, ...])	Plot metadata associations with features in adata.obsm.
pl.order_targets(adata, net, source, order)	Plot a source score, together with its targets readouts, along a continuous ordered process such as pseudotime.
pl.order(df[, mode, kw_order])	Plot features along a continuous, ordered process such as pseudotime.
pl.source_targets(data, net, x, y, name[, ...])	Plots target features of a given source as a scatter plot.
pl.volcano(data, x, y[, net, name, top, ...])	Plot logFC and p-values from a long formated data-frame.