Source code for

# -*- coding: utf-8 -*-
"""Module for plotting lineage-related stuff."""
from typing import Union, Optional, Sequence

import pandas as pd

import cellrank.logging as logg
from cellrank.ul._docs import d
from import AnnData
from import DirPrefix
from import GPCCA
from import A, P
from import DummyKernel

[docs]@d.dedent def lineages( adata: AnnData, lineages: Optional[Union[str, Sequence[str]]] = None, backward: bool = False, cluster_key: Optional[str] = None, mode: str = "embedding", time_key: str = "latent_time", **kwargs, ) -> None: """ Plot lineages that were uncovered using :func:``. For each lineage, we show all cells in an embedding (default is UMAP) and color them by their probability of belonging to this lineage. For cells that are already committed, this probability will be one for their respective lineage and zero otherwise. For naive cells, these probabilities will be more balanced, reflecting the fact that naive cells have the potential to develop towards multiple endpoints. Parameters ---------- %(adata)s lineages Plot only these lineages. If `None`, plot all lineages. %(backward)s cluster_key If given, plot cluster annotations left of the lineage probabilities. %(time_mode)s time_key Key in ``adata.obs`` where the pseudotime is stored. %(basis)s **kwargs Keyword arguments for :meth:``. Returns ------- %(just_plots)s """ pk = DummyKernel(adata, backward=backward) mc = GPCCA(pk, read_from_adata=True, write_to_adata=False) if mc._get(P.ABS_PROBS) is None: raise RuntimeError( f"Compute absorption probabilities first as `, backward={backward})`." ) # plot using the MC object mc.plot_absorption_probabilities( lineages=lineages, cluster_key=cluster_key, mode=mode, time_key=time_key, **kwargs, )
[docs]@d.dedent def lineage_drivers( adata: AnnData, lineage: str, backward: bool = False, n_genes: int = 8, use_raw: bool = False, **kwargs, ) -> None: """ Plot lineage drivers that were uncovered using :func:``. Parameters ---------- %(adata)s %(backward)s %(plot_lineage_drivers.parameters)s Returns ------- %(just_plots)s """ pk = DummyKernel(adata, backward=backward) mc = GPCCA(pk, read_from_adata=True, write_to_adata=False) if use_raw and adata.raw is None: logg.warning("No raw attribute set. Using `adata.var` instead") use_raw = False direction = DirPrefix.BACKWARD if backward else DirPrefix.FORWARD needle = f"{direction} {lineage} corr" haystack = adata.raw.var if use_raw else adata.var if needle not in haystack: raise RuntimeError( f"Unable to find lineage drivers in " f"`{'adata.raw.var' if use_raw else 'adata.var'}[{needle!r}]`. " f"Compute lineage drivers first as `{lineage!r}, " f"use_raw={use_raw}, backward={backward}).`" ) drivers = pd.DataFrame(haystack[[needle, f"{direction} {lineage} qval"]]) drivers.columns = [f"{lineage} corr", f"{lineage} qval"] mc._set(A.LIN_DRIVERS, drivers) mc.plot_lineage_drivers(lineage, n_genes=n_genes, use_raw=use_raw, **kwargs)