crested.pl.modisco.clustermap

crested.pl.modisco.clustermap(pattern_matrix, classes, subset=None, grid=False, cmap='coolwarm', center=0, method='average', dy=0.002, save_path=None, pat_seqs=None, dendrogram_ratio=(0.05, 0.2), importance_threshold=0, plot_kws=None, width=25, height=8, figsize='deprecated')

Create a clustermap from the given pattern matrix and class labels with customizable options.

Parameters:

• pattern_matrix (ndarray) – 2D NumPy array containing pattern data.

• classes (list[str]) – List of class labels, matching the rows of the pattern matrix.

• subset (list[str] | None (default: None)) – List of class labels to subset the matrix.

• grid (bool (default: False)) – Whether to add a grid to the heatmap.

• cmap (str (default: 'coolwarm')) – Colormap for the clustermap.

• center (float (default: 0)) – Value at which to center the colormap.

• method (str (default: 'average')) – Clustering method to use.

• dy (float (default: 0.002)) – Scaling parameter for vertical distance between nucleotides (if pat_seqs is not None) in xticklabels.

• save_path (str | None (default: None)) – Path to save the figure.

• pat_seqs (list[tuple[str, ndarray]] | None (default: None)) – List of sequences to use as xticklabels.

• dendrogram_ratio (tuple[float, float] (default: (0.05, 0.2))) – Ratio of dendograms in x and y directions.

• importance_threshold (float (default: 0)) – Minimal pattern importance threshold over all classes to retain the pattern before clustering and plotting.

• width (int | float (default: 25)) – Figure width.

• height (int | float (default: 8)) – Figure height.

• plot_kws (dict | None (default: None)) – Extra keyword arguments passed to clustermap(). Default is {'cbar_pos': (1.05, 0.4, 0.01, 0.3)}.

Return type:

ClusterGrid

See also

crested.tl.modisco.create_pattern_matrix, crested.tl.modisco.generate_nucleotide_sequences

Examples

>>> pat_seqs = crested.tl.modisco.generate_nucleotide_sequences(all_patterns)
>>> crested.pl.modisco.clustermap(
...     pattern_matrix,
...     classes=list(adata.obs_names)
...     subset=["Lamp5", "Pvalb", "Sst", "Sst-Chodl", "Vip"],
...     figsize=(25, 8),
...     grid=True,
... )