cellrank.pl.gene_trends#
- cellrank.pl.gene_trends(adata, model, genes, lineages=None, backward=False, data_key='X', time_key='latent_time', time_range=None, transpose=False, callback=None, conf_int=True, same_plot=False, hide_cells=False, perc=None, lineage_cmap=None, abs_prob_cmap=<matplotlib.colors.ListedColormap object>, cell_color=None, cell_alpha=0.6, lineage_alpha=0.2, size=15, lw=2, cbar=True, margins=0.015, sharex=None, sharey=None, gene_as_title=None, legend_loc='best', obs_legend_loc='best', ncols=2, suptitle=None, return_models=False, n_jobs=1, backend='loky', show_progress_bar=True, figsize=None, dpi=None, save=None, return_figure=False, plot_kwargs=mappingproxy({}), **kwargs)[source]#
Plot gene expression trends along lineages.
Each lineage is defined via it’s lineage weights. This function accepts any model based off
cellrank.models.BaseModel
to fit gene expression, where we take the lineage weights into account in the loss function.- Parameters:
adata (
anndata.AnnData
) – Annotated data object.model (
Union
[BaseModel
,Mapping
[str
,Mapping
[str
,BaseModel
]]]) –Model based on
cellrank.models.BaseModel
to fit.If a
dict
, gene and lineage specific models can be specified. Use'*'
to indicate all genes or lineages, for example{'gene_1': {'*': ...}, 'gene_2': {'lineage_1': ..., '*': ...}}
.genes (
Union
[str
,Sequence
[str
]]) – Genes inanndata.AnnData.var_names
or inanndata.AnnData.raw.var_names
, ifuse_raw = True
.lineages (
Union
[str
,Sequence
[str
],None
]) – Names of the lineages to plot. If None, plot all lineages.backward (
bool
) – Direction of the process.data_key (
str
) – Key inanndata.AnnData.layers
or ‘X’ foranndata.AnnData.X
where the data is stored.time_key (
str
) – Key inanndata.AnnData.obs
where the pseudotime is stored.time_range (
Union
[float
,Tuple
[Optional
[float
],Optional
[float
]],None
,List
[Union
[float
,Tuple
[Optional
[float
],Optional
[float
]],None
]]]) –Specify start and end times:
This can also be specified on per-lineage basis.
gene_symbols – Key in
anndata.AnnData.var
to use instead ofanndata.AnnData.var_names
.transpose (
bool
) – Ifsame_plot = True
, group the trends bylineages
instead ofgenes
. This forceshide_cells = True
. Ifsame_plot = False
, showlineages
in rows andgenes
in columns.callback (
Union
[Callable
,Mapping
[str
,Mapping
[str
,Callable
]],None
]) – Function which takes acellrank.models.BaseModel
and some keyword arguments forcellrank.models.BaseModel.prepare()
and returns the prepared model. Can be specified in gene- and lineage-specific manner, similarly tomodel
.conf_int (
Union
[bool
,float
]) – Whether to compute and show confidence interval. If themodel
iscellrank.models.GAMR
, it can also specify the confidence level, the default is 0.95.same_plot (
bool
) – Whether to plot all lineages for each gene in the same plot.hide_cells (
bool
) – If True, hide all cells.perc (
Union
[Tuple
[float
,float
],Sequence
[Tuple
[float
,float
]],None
]) – Percentile for colors. Valid values are in interval [0, 100]. This can improve visualization. Can be specified individually for each lineage.lineage_cmap (
Optional
[ListedColormap
]) – Categorical colormap to use when coloring in the lineages. If None andsame_plot
, use the corresponding colors inanndata.AnnData.uns
, otherwise use ‘black’.abs_prob_cmap (
ListedColormap
) – Continuous colormap to use when visualizing the absorption probabilities for each lineage. Only used whensame_plot = False
.cell_color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var_names
used for coloring the cells.cell_alpha (
float
) – Alpha channel for cells.lineage_alpha (
float
) – Alpha channel for lineage confidence intervals.size (
float
) – Size of the points.lw (
float
) – Line width of the smoothed values.cbar (
bool
) – Whether to show colorbar. Always shown when percentiles for lineages differ. Only used whensame_plot = False
.margins (
float
) – Margins around the plot.sharex (
Union
[str
,bool
,None
]) – Whether to share x-axis. Valid options are ‘row’, ‘col’ or ‘none’.sharey (
Union
[str
,bool
,None
]) – Whether to share y-axis. Valid options are ‘row’, ‘col’ or ‘none’.gene_as_title (
Optional
[bool
]) – Whether to show gene names as titles instead on y-axis.legend_loc (
Optional
[str
]) – Location of the legend displaying lineages. Only used when same_plot = True.obs_legend_loc (
Optional
[str
]) – Location of the legend whencell_color
corresponds to a categorical variable.ncols (
int
) – Number of columns of the plot when plotting multiple genes. Only used whensame_plot = True
.return_figure (
bool
) – Whether to return the figure object.return_models (
bool
) – If True, return the fitted models for each gene ingenes
and lineage inlineages
.show_progress_bar (
bool
) – Whether to show a progress bar. Disabling it may slightly improve performance.n_jobs (
Optional
[int
]) – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.backend (
Literal
['loky'
,'multiprocessing'
,'threading'
]) – Which backend to use for parallelization. Seejoblib.Parallel
for valid options.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.plot_kwargs (
Mapping
[str
,Any
]) – Keyword arguments forcellrank.models.BaseModel.plot()
.kwargs (
Any
) – Keyword arguments forcellrank.models.BaseModel.prepare()
.
- Return type:
- Returns:
: None
If
return_models = False
, just plots the figure and optionally saves it based onsave
.- Dict[str, Dict[str,
cellrank.models.BaseModel
]] Otherwise returns the fitted models as
{'gene_1': {'lineage_1': <model_11>, ...}, ...}
. Models which have failed will be instances ofcellrank.models.FailedModel
.
- Dict[str, Dict[str,