dotools_py.bm.eval_integration

dotools_py.bm.eval_integration(adata_post, adata_pre, batch_key, annotation_key, use_rep, figsize=(6, 5), ax=None, palette=None, cmap='tab10', path=None, filename=None, title=None, title_fontsize=15, title_legend=None, legend_fontsize=12, show=True, scale=True, compute_metrics='all')

Evaluate the integration.

This function calculate several metrics to evaluate how well the integration worked. The batch correction metrics values are scaled by default between 0 and 1, in which larger scores represent better batch removal. It will generate a barplot to summarize all the metrics.

Parameters:

adata_post AnnData

Annotated data matrix after integration.

adata_pre AnnData

Annotated data matrix before integration.

batch_key str

Column in adata.obs with batch information.

annotation_key str

Column in adata.obs with clustering information.

use_rep str | list

Key(s) in adata.obsp with the embedding generated from the integration.

figsize tuple (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.

palette dict (default: None)

Dictionary with the embedding names (keys) and the color as values.

cmap str (default: 'tab10')

Matplotlib colormap to use for the different embeddings.

path str (default: None)

Path to the folder to save the figure.

filename str (default: None)

Name of file to use when saving the figure.

title str (default: None)

Title for the figure.

title_fontsize int (default: 15)

Size of the title font.

title_legend str (default: None)

Title of the legend.

legend_fontsize int (default: 12)

Size of the legend title font.

show bool (default: True)

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

scale bool (default: True)

If set to True scale score between 0 and 1.

compute_metrics Union[Literal['GraphConnectivity', 'kBET', 'pcr_comparison', 'silhouette_batch', 'silhouette_global', 'all'], list] (default: 'all')

List of the metrics to compute. Set to “all” to compute all metrics.

Return type:

DataFrame | tuple[DataFrame | dict[str, Axes]]

Returns:

Returns a pd.Dataframe with the metrics for each embedding in use_rep. If show is set to False it returns a tuple with the first element being the DataFrame with the metrics and the second a dictionary with the matplotlib axes for the figure.

Examples

Evaluate the metrics across different integration methods.

import dotools_py as do
adata = do.dt.example_10x_processed()
adata_unintegrated = adata.copy()
del adata_unintegrated.obsm
database = do.bm.eval_integration(adata_post=adata, adata_pre=adata_unintegrated, batch_key="batch", annotation_key="annotation", use_rep=["X_CCA", "X_pca"], compute_metrics = ["GraphConnectivity", "silhouette_batch", "silhouette_global"] )