pilotpy.tl.cell_importance
- pilotpy.tl.cell_importance(adata, width=20, height=35, xlim=5, p_val=1, plot_cell=True, point_size=100, color_back='white', fontsize=20, alpha=1, cmap_proportions='viridis', cmap_heatmap='Blues', save_as_pdf=True, figsize=(12, 12), col_cluster=True, row_cluster=False)
Order cells based on estimated time and visualize cell type importance.
Parameters
- adataAnnData
Annotated data matrix containing necessary data.
- widthint, optional
Width of the plot, by default 40.
- heightint, optional
Height of the plot, by default 35.
- xlimint, optional
Limit for x-axis in the plot, by default 5.
- p_valfloat, optional
P-value for filtering the fitting models, by default 0.05.
- plot_cellbool, optional
Whether to plot the cell type importance, by default True.
- point_sizeint, optional
Size of points in the plot, by default 100.
- color_backstr, optional
Background color of the plot, by default ‘white’.
- fontsizeint, optional
Font size for labels and annotations, by default 20.
- alphafloat, optional
Transparency level for plotting, by default 1.
- cmap_proportionsstr, optional
Colormap for plotting proportions over time, by default ‘viridis’.
- cmap_heatmapstr, optional
Colormap for plotting heatmap, by default ‘Blues_r’.
- save_as_pdfbool, optional
Whether to save the plot as PDF, by default False.
- figsizetuple, optional
Figure size of the heatmap (width, height) in inches, by default (12, 12).
- col_clusterbool, optional
Whether to cluster columns in the heatmap, by default True.
- row_clusterbool, optional
Whether to cluster rows in the heatmap, by default False.
Returns
- None
Visualizes and saves the cell type importance plot.