dotools_py.pl.heatmap

dotools_py.pl.heatmap(adata, group_by, features, groups_order=None, layer=None, logcounts=True, figsize=(5, 6), ax=None, swap_axes=True, title=None, title_fontproperties=None, palette='Reds', ticks_fontproperties=None, xticks_rotation=None, yticks_rotation=None, cluster_x_axis=False, cluster_y_axis=False, legend_title='LogMean(nUMI)\\nin group', path=None, filename='Heatmap.svg', show=True, add_stats=False, test='wilcoxon', correction_method='benjamini-hochberg', df_pvals=None, stats_x_size=None, square_x_size=None, pval_cutoff=0.05, log2fc_cutoff=0.0, z_score=None, clustering_method='complete', clustering_metric='euclidean', linewidth=0.1, vmin=0.0, vcenter=None, vmax=None, square=True, **kargs)

Heatmap of the mean expression of genes across a groups.

Generate a heatmap of showing the average nUMI for a set of genes in different groups. Differential gene expression analysis between the different groups can be performed.

Parameters:

adata AnnData

Annotated data matrix.

group_by str | list

Name of a categorical column in adata.obs to groupby.

features str | list

cA valid feature in adata.var_names or column in adata.obs with continuous values.

groups_order list (default: None)

Order for the categories in adata.obs[group_by].

z_score Literal['var', 'group'] (default: None)

Apply z-score transformation.

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

Path to the folder to save the figure.

filename str (default: 'Heatmap.svg')

Name of file to use when saving the figure.

layer str (default: None)

Name of the AnnData object layer that wants to be plotted. By default, adata.X is plotted. If layer is set to a valid layer name, then the layer is plotted.

swap_axes bool (default: True)

Whether to swap the axes or not.

palette str (default: 'Reds')

String denoting matplotlib colormap.

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.

clustering_method str (default: 'complete')

Linkage method to use for calculating clusters. See scipy.cluster.hierarchy.linkage.

clustering_metric str (default: 'euclidean')

Distance metric to use for the data. See scipy.spatial.distance.pdist.

cluster_x_axis bool (default: False)

Hierarchically clustering the x-axis.

cluster_y_axis bool (default: False)

Hierarchically clustering the y-axis.

ax Axes | None (default: None)

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

figsize tuple (default: (5, 6))

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

linewidth float (default: 0.1)

Linewidth for the border of cells.

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

Dictionary which should contain ‘size’ and ‘weight’ to define the fontsize and fontweight of the font of the x/y-axis.

xticks_rotation int (default: None)

Rotation of the x-ticks.

yticks_rotation int (default: None)

Rotations of the y-ticks.

vmin float (default: 0.0)

The value representing the lower limit of the color scale.

vcenter float (default: None)

The value representing the center of the color scale.

vmax float (default: None)

The value representing the upper limit of the color scale.

legend_title str (default: 'LogMean(nUMI)\\nin group')

Title for the colorbar.

add_stats bool (default: False)

Add statistical annotation. Will add a square with an ‘*’ in the center if the expression is significantly different in a group with respect to the others.

df_pvals DataFrame (default: None)

Dataframe with the pvals. Should be gene x group or group x gene in case of swap_axes is False.

stats_x_size float (default: None)

Scaling factor to control the size of the asterisk.

square_x_size dict (default: None)

Size and thickness of the square.

test Literal['wilcoxon', 't-test'] (default: 'wilcoxon')

Name of the method to test for significance.

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

Correction method for multiple testing.

pval_cutoff float (default: 0.05)

Cutoff for the p-value.

log2fc_cutoff float (default: 0.0)

Minimum cutoff for the log2FC.

square bool (default: True)

Whether to make the cell square or not.

show bool (default: True)

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

logcounts bool (default: True)

whether the input is logcounts or not.

kargs

Additional arguments pass to sns.heatmap.

Return type:

dict | None

Returns:

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

Example

import dotools_py as do
adata = do.dt.example_10x_processed()
do.pl.heatmap(adata, 'annotation', ['CD4', 'CD79A'], add_stats=True)