Plotting

mina.pl.plot_view_samples(anndata_dict, min_samples, table=False, figsize=(5, 5), dpi=100, ax=None, return_fig=False, **kwargs)

Quality control plot to assess the quality of the obtained pseudobulk samples.

Parameters:

Name	Type	Description	Default
anndata_dict	dict[str, AnnData]	Dictionary mapping view names to AnnData objects.	required
min_samples	int	Minimum number of samples required for a view to be included.	required
table	bool	Whether to return the underlying summary table instead of plotting. Default is False.	False
figsize	tuple[int, int]	Size of the figure in inches. Default is (5, 5).	(5, 5)
dpi	int	Resolution of the figure in dots per inch. Default is 100.	100
ax	Axes or None	Matplotlib Axes object to plot on. If None, a new figure and axes are created.	None
return_fig	bool	Whether to return the Figure object. Default is False.	False
**kwargs	dict	Additional keyword arguments passed to seaborn.scatterplot.	{}

Returns:

Name	Type	Description
fig	Figure or None	The created Figure object if return_fig is True, otherwise None.

mina.pl.plot_view_genes(anndata_dict, min_genes, table=False, figsize=(5, 5), dpi=100, ax=None, return_fig=False, **kwargs)

Quality control plot to assess the quality of the obtained pseudobulk samples.

Parameters:

Name	Type	Description	Default
anndata_dict	dict[str, AnnData]	Dictionary mapping view names to AnnData objects.	required
min_genes	int	Minimum number of genes required for a view to be included.	required
table	bool	Whether to return the underlying summary table instead of plotting. Default is False.	False
figsize	tuple[int, int]	Size of the figure in inches. Default is (5, 5).	(5, 5)
dpi	int	Resolution of the figure in dots per inch. Default is 100.	100
ax	Axes or None	Matplotlib Axes object to plot on. If None, a new figure and axes are created.	None
return_fig	bool	Whether to return the Figure object. Default is False.	False
**kwargs	dict	Additional keyword arguments passed to seaborn.scatterplot.	{}

Returns:

Name	Type	Description
fig	Figure or None	The created Figure object if return_fig is True, otherwise None.

mina.pl.plot_sample_coverage(anndata_dict, threshold, proportion, table=False, figsize=(5, 5), dpi=100, return_fig=False, **kwargs)

Visualize coverage for each AnnData in a dictionary and highlight samples below a given proportion threshold.

One figure is produced per dictionary key.

Parameters:

Name	Type	Description	Default
anndata_dict	dict[str, AnnData]	Dictionary mapping view names to AnnData objects.	required
threshold	float or dict[str, float]	Gene expression threshold. If a dict, must contain all keys of anndata_dict.	required
proportion	float or dict[str, float]	Minimum proportion of genes above threshold. If a dict, must contain all keys.	required
table	bool	If True, return summary tables instead of plotting. Default is False.	False
figsize	tuple[int, int]	Figure size per subplot. Default is (5, 5).	(5, 5)
dpi	int	Figure resolution in dots per inch. Default is 100.	100
return_fig	bool	If True, return the generated Figure objects. Default is False.	False
**kwargs	dict	Additional keyword arguments passed to matplotlib.axes.Axes.scatter.	{}

Returns:

Type	Description
dict[str, DataFrame] or dict[str, Figure] or None	Summary tables if table is True, figures if return_fig is True, otherwise None.

mina.pl.plot_pval_tiles(p_df: pd.DataFrame, star_threshold: float = 0.05, ax=None, title: str | None = None)

Create a tile plot colored by -log10(p) values, with tiles annotated by a star when p <= star_threshold.

Parameters:

Name	Type	Description	Default
p_df	DataFrame	DataFrame of p-values with rows and columns defining the tile grid.	required
star_threshold	float	P-value threshold for star annotation. Default is 0.05.	0.05
ax	Axes or None	Axes to draw on. If None, a new figure and axes are created.	None
title	str or None	Optional title for the plot.	None

mina.pl.plot_mcell_funcomics(result_dict: dict[str, dict[str, pd.DataFrame]], result_key: str = 'pw_acts', pval_key: str = 'pw_padj', p_threshold: float = 0.05, top_n: int = 10, cmap: str = 'coolwarm', figsize: tuple = (14, 5), ytick_rotation: int = 0, use_var: bool = False)

Plot grouped heatmaps per view using a selected result matrix.

Features are filtered by adjusted p-value and ranked either by mean absolute value or variance.

Parameters:

Name	Type	Description	Default
result_dict	dict[str, dict[str, DataFrame]]	Output of run_ulm_per_view with one entry per view.	required
result_key	str	Key within each view result containing values to plot.	'pw_acts'
pval_key	str	Key within each view result containing adjusted p-values.	'pw_padj'
p_threshold	float	Adjusted p-value significance threshold.	0.05
top_n	int	Number of top significant features per view to display.	10
cmap	str	Colormap for the heatmaps.	'coolwarm'
figsize	tuple[int, int]	Overall figure size.	(14, 5)
ytick_rotation	int	Rotation angle for y-axis tick labels.	0
use_var	bool	If True, rank features by variance instead of mean absolute value.	False

mina.pl.plot_mcell_network(df: pd.DataFrame, weight_col: str = 'coef', abs_cutoff: float = 0.0, keep_negative: bool = True, edge_width_range: tuple = (0.8, 6), node_size: int = 1100, arrowsize: int = 18, reciprocal_curvature: float = 0.25, default_curvature: float = 0.04, positive_color: str = 'tab:purple', negative_color: str = 'tab:red', show_edge_labels: bool = False, label_fmt: str = '{:.2f}', title: str | None = None, save_path: str | None = None, edge_margin_factor: float = 0.55, arrows_on_top: bool = True)

Given the inference of a multicellular information network, plot the resulting directed graph with edges colored and scaled by weight. The results are shown solely from one subset (positive or negative loadings).

Parameters:

Name	Type	Description	Default
df	DataFrame	DataFrame defining directed edges. Must contain at least source, target, and edge weight columns.	required
weight_col	str	Column name containing edge weights. Default is "coef".	'coef'
abs_cutoff	float	Minimum absolute weight required to keep an edge.	0.0
keep_negative	bool	Whether to retain negatively weighted edges.	True
edge_width_range	tuple[float, float]	Minimum and maximum edge widths used for scaling.	(0.8, 6)
node_size	int	Size of network nodes.	1100
arrowsize	int	Size of arrow heads.	18
reciprocal_curvature	float	Curvature used for reciprocal edges.	0.25
default_curvature	float	Curvature used for non-reciprocal edges.	0.04
positive_color	str	Color for positively weighted edges.	'tab:purple'
negative_color	str	Color for negatively weighted edges.	'tab:red'
show_edge_labels	bool	Whether to display edge weight labels.	False
label_fmt	str	Format string used for edge labels.	'{:.2f}'
title	str or None	Optional plot title.	None
save_path	str or None	If provided, save the figure to this path.	None
edge_margin_factor	float	Factor controlling spacing between nodes and edges.	0.55
arrows_on_top	bool	Whether arrows are drawn above nodes.	True

Returns:

Type	Description
Figure or None	The generated figure, or None if not returned explicitly.