scanpy.pl.paga

scanpy.pl.paga(adata, threshold=None, color=None, layout=None, layout_kwds=mappingproxy({}), init_pos=None, root=0, labels=None, single_component=False, solid_edges='connectivities', dashed_edges=None, transitions=None, fontsize=None, fontweight='bold', fontoutline=None, text_kwds=mappingproxy({}), node_size_scale=1.0, node_size_power=0.5, edge_width_scale=1.0, min_edge_width=None, max_edge_width=None, arrowsize=30, title=None, left_margin=0.01, random_state=0, pos=None, normalize_to_color=False, cmap=None, cax=None, colorbar=None, cb_kwds=mappingproxy({}), frameon=None, add_pos=True, export_to_gexf=False, use_raw=True, colors=None, groups=None, plot=True, show=None, save=None, ax=None)

Plot the PAGA graph through thresholding low-connectivity edges.

Compute a coarse-grained layout of the data. Reuse this by passing init_pos='paga' to umap() or draw_graph() and obtain embeddings with more meaningful global topology [Wolf19].

This uses ForceAtlas2 or igraph’s layout algorithms for most layouts [Csardi06].

Parameters:

adata : AnnData

Annotated data matrix.

threshold : Optional[float] (default: None)

Do not draw edges for weights below this threshold. Set to 0 if you want all edges. Discarding low-connectivity edges helps in getting a much clearer picture of the graph.

color : Union[str, Mapping[Union[str, int], Mapping[Any, float]], None] (default: None)

Gene name or obs annotation defining the node colors. Also plots the degree of the abstracted graph when passing {'degree_dashed', 'degree_solid'}.

Can be also used to visualize pie chart at each node in the following form: {<group name or index>: {<color>: <fraction>, ...}, ...}. If the fractions do not sum to 1, a new category called 'rest' colored grey will be created.

labels : Union[str, Sequence[str], Mapping[str, str], None] (default: None)

The node labels. If None, this defaults to the group labels stored in the categorical for which paga() has been computed.

pos : Union[ndarray, str, Path, None] (default: None)

Two-column array-like storing the x and y coordinates for drawing. Otherwise, path to a .gdf file that has been exported from Gephi or a similar graph visualization software.

layout : Optional[Literal['fa', 'fr', 'rt', 'rt_circular', 'drl', 'eq_tree', Ellipsis]] (default: None)

Plotting layout that computes positions. 'fa' stands for “ForceAtlas2”, 'fr' stands for “Fruchterman-Reingold”, 'rt' stands for “Reingold-Tilford”, 'eq_tree' stands for “eqally spaced tree”. All but 'fa' and 'eq_tree' are igraph layouts. All other igraph layouts are also permitted. See also parameter pos and draw_graph().

layout_kwds : Mapping[str, Any] (default: mappingproxy({}))

Keywords for the layout.

init_pos : Optional[ndarray] (default: None)

Two-column array storing the x and y coordinates for initializing the layout.

random_state : Optional[int] (default: 0)

For layouts with random initialization like 'fr', change this to use different intial states for the optimization. If None, the initial state is not reproducible.

root : Union[int, str, Sequence[int], None] (default: 0)

If choosing a tree layout, this is the index of the root node or a list of root node indices. If this is a non-empty vector then the supplied node IDs are used as the roots of the trees (or a single tree if the graph is connected). If this is None or an empty list, the root vertices are automatically calculated based on topological sorting.

transitions : Optional[str] (default: None)

Key for .uns['paga'] that specifies the matrix that stores the arrows, for instance 'transitions_confidence'.

solid_edges : str (default: 'connectivities')

Key for .uns['paga'] that specifies the matrix that stores the edges to be drawn solid black.

dashed_edges : Optional[str] (default: None)

Key for .uns['paga'] that specifies the matrix that stores the edges to be drawn dashed grey. If None, no dashed edges are drawn.

single_component : bool (default: False)

Restrict to largest connected component.

fontsize : Optional[int] (default: None)

Font size for node labels.

fontoutline : Optional[int] (default: None)

Width of the white outline around fonts.

text_kwds : Mapping[str, Any] (default: mappingproxy({}))

Keywords for text().

node_size_scale : float (default: 1.0)

Increase or decrease the size of the nodes.

node_size_power : float (default: 0.5)

The power with which groups sizes influence the radius of the nodes.

edge_width_scale : float (default: 1.0)

Edge with scale in units of rcParams['lines.linewidth'].

min_edge_width : Optional[float] (default: None)

Min width of solid edges.

max_edge_width : Optional[float] (default: None)

Max width of solid and dashed edges.

arrowsize : int (default: 30)

For directed graphs, choose the size of the arrow head head’s length and width. See :py:class: matplotlib.patches.FancyArrowPatch for attribute mutation_scale for more info.

export_to_gexf : bool (default: False)

Export to gexf format to be read by graph visualization programs such as Gephi.

normalize_to_color : bool (default: False)

Whether to normalize categorical plots to color or the underlying grouping.

cmap : Union[str, Colormap, None] (default: None)

The color map.

cax : Optional[Axes] (default: None)

A matplotlib axes object for a potential colorbar.

cb_kwds : Mapping[str, Any] (default: mappingproxy({}))

Keyword arguments for Colorbar, for instance, ticks.

add_pos : bool (default: True)

Add the positions to adata.uns['paga'].

title : Optional[str] (default: None)

Provide a title.

frameon : Optional[bool] (default: None)

Draw a frame around the PAGA graph.

plot : bool (default: True)

If False, do not create the figure, simply compute the layout.

save : Union[str, bool, None] (default: None)

If True or a str, save the figure. A string is appended to the default filename. Infer the filetype if ending on {'.pdf', '.png', '.svg'}.

ax : Optional[Axes] (default: None)

A matplotlib axes object.

Return type:

Union[Axes, List[Axes], None]

Returns:

: If show==False, one or more Axes objects. Adds 'pos' to adata.uns['paga'] if add_pos is True.

Examples

import scanpy as sc
adata = sc.datasets.pbmc3k_processed()
sc.tl.paga(adata, groups='louvain')
sc.pl.paga(adata)

You can increase node and edge sizes by specifying additional arguments.

sc.pl.paga(adata, node_size_scale=10, edge_width_scale=2)

Notes

When initializing the positions, note that – for some reason – igraph mirrors coordinates along the x axis… that is, you should increase the maxiter parameter by 1 if the layout is flipped.