scHopfield.plotting.plot_gene_correlation_scatter

scHopfield.plotting.plot_gene_correlation_scatter(adata: AnnData, clus1: str, clus2: str, energy: str = 'total', cluster_key: str = 'cell_type', ax: Axes | None = None, annotate: int | None = None, clus1_low: float = -0.5, clus1_high: float = 0.5, clus2_low: float = -0.5, clus2_high: float = 0.5) → Axes[source]

Plot scatter of gene correlations between two clusters.

Creates a scatter plot comparing the gene correlations with energy landscapes between two clusters, highlighting genes with divergent behavior (strongly positive in one cluster, strongly negative in the other).

Parameters:

  • adata (AnnData) – Annotated data object with computed correlations

  • clus1 (str) – First cluster name (x-axis)

  • clus2 (str) – Second cluster name (y-axis)

  • energy (str, optional (default: 'total')) – Energy type: ‘total’, ‘interaction’, ‘degradation’, or ‘bias’

  • cluster_key (str, optional (default: 'cell_type')) – Key in adata.obs for cluster labels

  • ax (plt.Axes, optional) – Axes to plot on. If None, creates new figure

  • annotate (int, optional) – If provided, annotates top N divergent genes

  • clus1_low (float, optional (default: -0.5)) – Lower threshold for clus1 to identify divergent genes

  • clus1_high (float, optional (default: 0.5)) – Upper threshold for clus1 to identify divergent genes

  • clus2_low (float, optional (default: -0.5)) – Lower threshold for clus2 to identify divergent genes

  • clus2_high (float, optional (default: 0.5)) – Upper threshold for clus2 to identify divergent genes

Returns:

Axes with plot

Return type:

plt.Axes