crested.pl.patterns.clustermap

crested.pl.patterns.clustermap(pattern_matrix, classes, subset=None, figsize=(25, 8), grid=False, cmap='coolwarm', center=0, method='average', dy=0.002, fig_path=None, pat_seqs=None, dendrogram_ratio=(0.05, 0.2), importance_threshold=0)

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

• figsize (tuple[int, int] (default: (25, 8))) – Size of the figure.

• 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.

• fig_path (Optional[str] (default: None)) – Path to save the figure.

• pat_seqs (Optional[list[tuple[str, ndarray]]] (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.

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.patterns.clustermap(
...     pattern_matrix,
...     classes=list(adata.obs_names)
...     subset=["Lamp5", "Pvalb", "Sst", "Sst-Chodl", "Vip"],
...     figsize=(25, 8),
...     grid=True,
... )