pseudodynamics.plotting_fns.density_plot

Functions

celltype_proportion(p_celltype_melt, ...[, ...])	cell type proportion
obs_composition(adata, x_var, y_var[, kind, ...])
plot_along_pseudotime(color_col, anndata[, ...])
plot_tmap(tmap[, log, ax, return_fig])
resampling_animation_meshgrid(s, train_DS, ...)	s : cellstate coordinates, [n_grid**2, 2] train_DS : reader.MeshGrid_logDS classs
resampling_animation_umap(train_DS, save_path)	train_DS : reader.HigDim_AnnDS classs save_path : str, a path ends with .gif
scatter_density(color_col, anndata[, ...])
stack_catplot(x, y, cat, stack, data[, palette])
umap_by_time(attribute, anndata[, ...])	A very basic functions plotting cellular attribute in the umap and stratified by time

pseudodynamics.plotting_fns.density_plot.celltype_proportion(p_celltype_melt, timepoints, cm_celltype, ct_key, density_key='value', x_lim=None)

cell type proportion

pseudodynamics.plotting_fns.density_plot.resampling_animation_meshgrid(s, train_DS, save_path)

s : cellstate coordinates, [n_grid**2, 2] train_DS : reader.MeshGrid_logDS classs

pseudodynamics.plotting_fns.density_plot.resampling_animation_umap(train_DS, save_path)

train_DS : reader.HigDim_AnnDS classs save_path : str, a path ends with .gif

pseudodynamics.plotting_fns.density_plot.umap_by_time(attribute, anndata, timepoints=[3, 7, 12, 27, 49, 76, 112, 161, 269], time_mask=True, subplot_kws=None, cell_of_t=True, umap_kws=None)

A very basic functions plotting cellular attribute in the umap and stratified by time

Parameters:

• attribute (str or callable, a function of time or a obs_key of the anndata)

• anndata (anndata)

• time_mask (bool or str, default to True. If bool, only visualize cells of each timepoint (True) or show all cells in each panel (False). If str, use the given obs key for timepoint selection.)

• timepoints (iterable, list of real-time , like the number of columns)

Returns:

matplotlb figure and axes

Example

>>> u_b = DataSet.u_b
>>> adata = DataSet.adata
>>> for i, t in enumerate(DataSet.popD['t']):
        adata.obs[f'Day{t}_u'] = u_b[i]
>>> # use a lambda function as attributes to plot
>>> PINN.pl.umap_by_time(lambda x: f'Day{x}_u', adata, DataSet.popD['t']);