dynamo.pl.jacobian_kinetics

dynamo.pl.jacobian_kinetics(adata, basis='umap', regulators=None, effectors=None, mode='pseudotime', tkey='potential', color_map='bwr', gene_order_method='raw', show_colorbar=False, cluster_row_col=[False, True], figsize=(11.5, 6), standard_scale=1, n_convolve=30, save_show_or_return='show', save_kwargs={}, **kwargs)[source]

Plot the Jacobian dynamics over time (pseudotime or inferred real time) in a heatmap.

Note that by default potential estimated with the diffusion graph built from reconstructed vector field will be used as the measure of pseudotime.

Parameters
  • adata (AnnData) – an Annodata object.

  • basis (str) – The reduced dimension basis.

  • regulators (list or None (default: None)) – The list of genes that will be used as regulators for plotting the Jacobian heatmap, only limited to genes that have already performed Jacobian analysis.

  • effectors (List or None (default: None)) – The list of genes that will be used as targets for plotting the Jacobian heatmap, only limited to genes that have already performed Jacobian analysis.

  • mode (str (default: vector_field)) – Which data mode will be used, either vector_field or pseudotime. if mode is vector_field, the trajectory predicted by vector field function will be used, otherwise pseudotime trajectory (defined by time argument) will be used. By default potential estimated with the diffusion graph built reconstructed vector field will be used as pseudotime.

  • tkey (str (default: potential)) – The .obs column that will be used for timing each cell, only used when mode is not vector_field.

  • color_map (str (default: BrBG)) – Color map that will be used to color the gene expression. If half_max_ordering is True, the color map need to be divergent, good examples, include BrBG, RdBu_r or coolwarm, etc.

  • gene_order_method (str (default: half_max_ordering) [half_max_ordering, maximum]) – Supports two different methods for ordering genes when plotting the heatmap: either half_max_ordering, or maximum. For half_max_ordering, it will order genes into up, down and transit groups by the half max ordering algorithm (HA Pliner, et. al, Molecular cell 71 (5), 858-871. e8). While for maximum, it will order by the position of the highest gene expression.

  • show_colorbar (bool (default: False)) – Whether to show the color bar.

  • cluster_row_col ([bool, bool] (default: [False, False])) – Whether to cluster the row or columns.

  • figsize (str (default: (11.5, 6)) – Size of figure

  • standard_scale (int (default: 1)) – Either 0 (rows, cells) or 1 (columns, genes). Whether or not to standardize that dimension, meaning for each row or column, subtract the minimum and divide each by its maximum.

  • n_convolve (int (default: 30)) – Number of cells for convolution.

  • save_show_or_return ({‘show’, ‘save_fig’, ‘return’} (default: show)) – Whether to save_fig, show or return the figure.

  • save_kwargs (dict (default: {})) – A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘kinetic_curves’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys according to your needs.

  • kwargs – All other keyword arguments are passed to heatmap(). Currently xticklabels=False, yticklabels=’auto’ is passed to heatmap() by default.

Returns

Return type

Nothing but plots a heatmap that shows the element of Jacobian matrix dynamics over time (potential decreasing)

Examples

>>> import dynamo as dyn
>>> adata = dyn.sample_data.hgForebrainGlutamatergic()
>>> adata = dyn.pp.recipe_monocle(adata)
>>> dyn.tl.dynamics(adata)
>>> dyn.vf.VectorField(adata, basis='pca')
>>> valid_gene_list = adata[:, adata.var.use_for_transition].var.index[:2]
>>> dyn.vf.jacobian(adata, regulators=valid_gene_list[0], effectors=valid_gene_list[1])
>>> dyn.pl.jacobian_kinetics(adata)