pyVIA.plotting_via.via_streamplot

pyVIA.plotting_via.via_streamplot(via_object, embedding=None, density_grid=0.5, arrow_size=0.7, arrow_color='k', color_dict=None, arrow_style='-|>', max_length=4, linewidth=1, min_mass=1, cutoff_perc=5, scatter_size=500, scatter_alpha=0.5, marker_edgewidth=0.1, density_stream=2, smooth_transition=1, smooth_grid=0.5, color_scheme='annotation', add_outline_clusters=False, cluster_outline_edgewidth=0.001, gp_color='white', bg_color='black', dpi=300, title='Streamplot', b_bias=20, n_neighbors_velocity_grid=None, labels=None, use_sequentially_augmented=False, cmap='rainbow', show_text_labels=True)[source]

Construct vector streamplot on the embedding to show a fine-grained view of inferred directions in the trajectory

Parameters:

  • via_object –

  • embedding (ndarray) – np.ndarray of shape (n_samples, 2) umap or other 2-d embedding on which to project the directionality of cells

  • density_grid (float) –

  • arrow_size (float) –

  • arrow_color (str) –

  • arrow_style –

  • max_length (int) –

  • linewidth (float) – width of lines in streamplot, default = 1

  • min_mass –

  • cutoff_perc (int) –

  • scatter_size (int) – size of scatter points default =500

  • scatter_alpha (float) – transpsarency of scatter points

  • marker_edgewidth (float) – width of outline arround each scatter point, default = 0.1

  • density_stream (int) –

  • smooth_transition (int) –

  • smooth_grid (float) –

  • color_scheme (str) – str, default = ‘annotation’ corresponds to self.true_labels. Other options are ‘time’ (uses single-cell pseudotime) and ‘cluster’ (via cluster graph) and ‘other’. Alternatively provide labels as a list

  • add_outline_clusters (bool) –

  • cluster_outline_edgewidth –

  • gp_color –

  • bg_color –

  • dpi –

  • title –

  • b_bias – default = 20. higher value makes the forward bias of pseudotime stronger

  • n_neighbors_velocity_grid –

  • labels (list) – list (will be used for the color scheme) or if a color_dict is provided these labels should match

  • use_sequentially_augmented –

  • cmap (str) –

Returns:

fig, ax