, markers, no_bins=150, smoothing_factor=1, min_delta=0.1, show_variance=False, figsize=None, return_fig=False, show=True, save=None, ax=None)

Plot marker trends along trajectory, and return trajectory branches for further analysis and visualization (heatmap, etc..)

adata : AnnDataAnnData

Annotated data matrix.

markers : Collection[str]Collection[str]

Iterable of markers/genes to be plotted.

show_variance : boolbool (default: False)

Logical indicating if the trends should be accompanied with variance.

no_bins : intint (default: 150)

Number of bins for calculating marker density.

smoothing_factor : intint (default: 1)

Parameter controlling the degree of smoothing.

min_delta : floatfloat (default: 0.1)

Minimum difference in marker expression after normalization to show separate trends for the two branches.

figsize : Tuple[float, float] | NoneOptional[Tuple[float, float]] (default: None)

width, height

return_fig : boolbool (default: False)

Return the matplotlib figure.

show : boolbool (default: True)

Show the plot, do not return axis.

save : str | bool | NoneUnion[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 : Axes | NoneOptional[Axes] (default: None)

A matplotlib axes object. Only works if plotting a single component.


Updates adata with the following fields:

trunk_wishbonepandas.DataFrame (adata.uns)

Computed values before branching

branch1_wishbonepandas.DataFrame (adata.uns)

Computed values for the first branch

branch2_wishbonepandas.DataFrame (adata.uns)

Computed values for the second branch.