decoupler.pl.source_targets

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decoupler.pl.source_targets#

decoupler.pl.source_targets(data, net, x, y, name, top=5, thr_x=0.0, thr_y=0.0, max_x=None, max_y=None, color_pos='#D62728', color_neg='#1F77B4', kw_scatter=None, **kwargs)#

Plots target features of a given source as a scatter plot.

Parameters:

  • data (DataFrame) – DataFrame containing feature-level statistics. Feature names must be in df.index.

  • net (DataFrame) – Dataframe in long format. Must include source and target columns, and optionally a weight column.

  • x (str) – Name of the column containing values to place on the x-axis.

  • y (str) – Name of the column containing values to place on the y-axis.

  • name (str) – Name of the source to plot.

  • top (int | None (default: 5)) – Number of top features to show labels based on the product of x and y to label. Can be None.

  • thr_x (float (default: 0.0)) – Value were to place a baseline for the x-axis.

  • thr_y (float (default: 0.0)) – Value were to place a baseline for the y-axis.

  • max_x (float | None (default: None)) – Maximum value to plot on x-axis.

  • max_y (float | None (default: None)) – Maximum value to plot on y-axis.

  • color_pos (str (default: '#D62728')) – Color to plot positively associated features.

  • color_neg (str (default: '#1F77B4')) – Color to plot negatively associated features.

  • kw_scatter (dict | None (default: None)) – Keyword arguments passed to matplotlib.pyplot.scatter.

  • ax – An existing matplotlib.axes._axes.Axes instance to plot on. If None, a new figure and axes will be created.

  • figsize – Size of the figure in inches as (width, height).

  • dpi – Dots per inch for the figure resolution.

  • return_fig – If True, plotting methods should return the figure object instead of showing it.

  • save – If set, path to save the plot automatically to a file.

Return type:

None | Figure

Returns:

If return_fig=True, returns matplotlib.figure.Figure instance.

Example

import decoupler as dc
import scanpy as sc

adata, net = dc.ds.toy()
sc.tl.rank_genes_groups(adata, groupby="group")
deg = sc.get.rank_genes_groups_df(adata, group="A").set_index("names")
dc.pl.source_targets(data=deg, x="weight", y="scores", net=net, name="T1")
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