sccoda.util.data_visualization.boxplots¶

sccoda.util.data_visualization.boxplots(data, feature_name, y_scale='relative', plot_facets=False, add_dots=False, cell_types=None, args_boxplot={}, args_swarmplot={}, figsize=None, dpi=100, cmap='Blues', plot_legend=True, level_order=None)¶

Grouped boxplot visualization. The cell counts for each cell type are shown as a group of boxplots, with intra–group separation by a covariate from data.obs.

The cell type groups can either be ordered along the x-axis of a single plot (plot_facets=False) or as plot facets (plot_facets=True).

Parameters

data : AnnDataAnnData: A scCODA-compatible data object
feature_name : strstr: The name of the feature in data.obs to plot
y_scale : strstr (default: 'relative'): Transformation to of cell counts. Options: “relative” - Relative abundance, “log” - log(count), “count” - absolute abundance (cell counts)
plot_facets : boolbool (default: False): If False, plot cell types on the x-axis. If True, plot as facets
add_dots : boolbool (default: False): If True, overlay a scatterplot with one dot for each data point
cell_types : list, NoneOptional[list] (default: None): Subset of cell types that should be plotted
args_boxplot : dict, NoneOptional[dict] (default: {}): Arguments passed to sns.boxplot
args_swarmplot : dict, NoneOptional[dict] (default: {}): Arguments passed to sns.swarmplot
figsize : Tuple[int, int], NoneOptional[Tuple[int, int]] (default: None): figure size
dpi : int, NoneOptional[int] (default: 100): dpi setting
cmap : str, NoneOptional[str] (default: 'Blues'): The seaborn color map for the barplot
plot_legend : bool, NoneOptional[bool] (default: True): If True, adds a legend
level_order : List[str], NoneOptional[List[str]] (default: None): Custom ordering of bars on the x-axis

Return type

Tuple[Axes, FacetGrid], NoneOptional[Tuple[Axes, FacetGrid]]

Returns

Depending on plot_facets, returns a AxesSubplot (plot_facets = False) or FacetGrid (plot_facets = True) object
ax – if plot_facets = False
g – if plot_facets = True