omicverse.pl.plot_boxplots¶

omicverse.pl.plot_boxplots(data, feature_name, modality_key='coda', y_scale='relative', plot_facets=False, add_dots=False, cell_types=None, args_boxplot=None, args_swarmplot=None, palette='Blues', show_legend=True, level_order=None, figsize=None, dpi=100, return_fig=None, ax=None, show=None, save=None)[source]¶

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.

Parameters:

data – AnnData object or MuData object
feature_name (str) – The name of the feature in data.obs to plot
modality_key (str (default: 'coda')) – If data is a MuData object, specify which modality to use.
y_scale (default: 'relative') – Transformation to of cell counts. Options: “relative” - Relative abundance, “log” - log(count), “log10” - log10(count), “count” - absolute abundance (cell counts).
plot_facets (bool (default: False)) – If False, plot cell types on the x-axis. If True, plot as facets.
add_dots (bool (default: False)) – If True, overlay a scatterplot with one dot for each data point.
cell_types (default: None) – Subset of cell types that should be plotted.
args_boxplot (default: None) – Arguments passed to sns.boxplot.
args_swarmplot (default: None) – Arguments passed to sns.swarmplot.
figsize (default: None) – Figure size.
dpi (default: 100) – Dpi setting.
palette (default: 'Blues') – The seaborn color map for the barplot.
show_legend (default: True) – If True, adds a legend.
level_order (default: None) – Custom ordering of bars on the x-axis.

Returns:

Depending on plot_facets, returns a Axes (plot_facets = False) or: class:~sns.axisgrid.FacetGrid (plot_facets = True) object

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> sccoda.plot_boxplots(mdata, feature_name="condition", add_dots=True)

Preview: