dotools_py.pl.ridgeplot

dotools_py.pl.ridgeplot(adata, group_by, feature, layer=None, figsize=(6, 5), ax=None, title=None, title_fontproperties=None, palette=None, x_linspace=500, alpha=1, x_label='Log(nUMI)', add_y_ticks=True, catgs_order=None, reference=None, groups=None, groups_pvals=None, test='wilcoxon', corr_method='benjamini-hochberg', txt='p = ', txt_size=10, path=None, filename='Ridgeplot.pdf', show=True, bw_adjust=0.5, bw_method='scott', ridge_height=0.8, ridge_spacing=0.6)

Ridgeplot for AnnData.

Represent in a ridgeplot the expression of a feature in adata.var_names or a continuous metadata in adata.obs.

Parameters:

adata AnnData

Annotated data matrix.

group_by str

Column in adata.obs to group in the Y axis.

feature str

Valid key in adata.var_names or continuous metadata in adata.obs.

layer str (default: None)

Layer in adata.layers to use.

figsize tuple[int, int] (default: (6, 5))

Figure size, the format is (width, height).

ax Axes (default: None)

Matplotlib axes to use for plotting. If not set, a new figure will be generated.

title str (default: None)

Title for the figure.

title_fontproperties Dict[Literal['size', 'weight'], str | int] (default: None)

Dictionary which should contain ‘size’ and ‘weight’ to define the fontsize and fontweight of the title of the figure.

palette str | dict (default: None)

Can be the name of a valid matplotlib colormap or a dictionary of the groups as keys and the colors as values. If set to None will extract the colors from adata.uns[group_by_colors]

x_linspace int (default: 500)

Number of points to generate for the x-axis.

alpha float (default: 1)

Transparency level of the object, where 0 is fully transparent and 1 is fully opaque.

x_label str (default: 'Log(nUMI)')

Name of the X axis label.

add_y_ticks bool (default: True)

If set to True the groups will be shown in the y-ticks, otherwise the Y-axis is removed and the labels are displayed inside the plot.

catgs_order list (default: None)

order for the categories in adata.obs[group_by]

reference str (default: None)

Reference condition to use when testing for significance.

groups str | list (default: None)

List of the name of the groups to test against.

groups_pvals list (default: None)

If provided, these values will be plotted. If not set, the p-values will be estimated. The order of the p-values should match the order of the groups_cond categories.

test Literal['wilcoxon', 't-test', 'kruskal', 'anova', 'logreg', 't-test_overestim_var'] (default: 'wilcoxon')

Name of the method to test for significance.

corr_method Literal['benjamini-hochberg', 'bonferroni'] (default: 'benjamini-hochberg')

Correction method for multiple testing.

txt str (default: 'p = ')

Text to include before the p-value. If not set, only the p-value is shown.

txt_size int (default: 10)

Font size of the text indicating significance.

path str | PathLike[str] | Path (default: None)

Path to the folder to save the figure.

filename str (default: 'Ridgeplot.pdf')

Name of file to use when saving the figure.

show bool (default: True)

If set to False, returns a dictionary with the matplotlib axes.

bw_adjust float (default: 0.5)

Factor that multiplicatively scales the value chosen using bw_method. Increasing will make the curve smoother.

bw_method str (default: 'scott')

Method for determining the smoothing bandwidth to use; passed to scipy.stats.gaussian_kde.

ridge_height float (default: 0.8)

Scaling factor controlling the ridge height.

ridge_spacing float (default: 0.6)

Distance between consecutive ridges.

Return type:

Axes | dict | None

Returns:

Depending on show, returns the plot if set to True or a dictionary with the axes.

Example

Create a ridgeplot showing the expression of a given gene including the p-value to indicate if there is a significant statistical difference between groups.

import dotools_py as do
adata = do.dt.example_10x_processed()
do.pl.ridgeplot(adata,  'annotation', 'CD4', reference = 'pDC', groups=['B_cells'])

Plot a continuous value in adata.obs.

do.pl.ridgeplot(adata,'condition','total_counts', reference = 'healthy', groups=['disease'], figsize=(6, 4), x_label="total_counts", title="", palette={"healthy":"sandybrown", "disease":"royalblue"})