from collections.abc import Iterable
from anndata import AnnData
from scanpy.get import obs_df as scanpy_obs_df
from scanpy.get import rank_genes_groups_df
from scanpy.get import var_df as scanpy_var_df
[docs]
def obs_df( # pragma: no cover
adata: AnnData,
keys: Iterable[str] = (),
obsm_keys: Iterable[tuple[str, int]] = (),
*,
layer: str = None,
features: str = None,
):
"""Return values for observations in adata.
Args:
adata: AnnData object to get values from.
keys: Keys from either `.var_names`, `.var[gene_symbols]`, or `.obs.columns`.
obsm_keys: Tuple of `(key from obsm, column index of obsm[key])`.
layer: Layer of `adata`.
features: Column of `adata.var` to search for `keys` in.
Returns:
A DataFrame with `adata.obs_names` as index, and values specified by `keys` and `obsm_keys`.
Examples:
>>> import ehrapy as ep
>>> adata = ep.dt.mimic_2(encoded=True)
>>> ages = ep.get.obs_df(adata, keys=["age"])
"""
return scanpy_obs_df(adata=adata, keys=keys, obsm_keys=obsm_keys, layer=layer, gene_symbols=features)
[docs]
def var_df( # pragma: no cover
adata: AnnData,
keys: Iterable[str] = (),
varm_keys: Iterable[tuple[str, int]] = (),
*,
layer: str = None,
):
"""Return values for observations in adata.
Args:
adata: AnnData object to get values from.
keys: Keys from either `.obs_names`, or `.var.columns`.
varm_keys: Tuple of `(key from varm, column index of varm[key])`.
layer: Layer of `adata`.
Returns:
A DataFrame with `adata.var_names` as index, and values specified by `keys` and `varm_keys`.
Examples:
>>> import ehrapy as ep
>>> adata = ep.dt.mimic_2(encoded=True)
>>> four_patients = ep.get.var_df(adata, keys=["0", "1", "2", "3"])
"""
return scanpy_var_df(adata=adata, keys=keys, varm_keys=varm_keys, layer=layer)
[docs]
def rank_features_groups_df(
adata: AnnData,
group: str | Iterable[str],
*,
key: str = "rank_features_groups",
pval_cutoff: float | None = None,
log2fc_min: float | None = None,
log2fc_max: float | None = None,
features: str | None = None,
):
""":func:`ehrapy.tools.rank_features_groups` results in the form of a :class:`~pandas.DataFrame`.
Args:
adata: AnnData object to get values from.
group: Which group (as in :func:`ehrapy.tools.rank_features_groups`'s `groupby` argument)
to return results from. Can be a list. All groups are returned if groups is `None`.
key: Key differential groups were stored under.
pval_cutoff: Return only adjusted p-values below the cutoff.
log2fc_min: Minimum logfc to return.
log2fc_max: Maximum logfc to return.
features: Column name in `.var` DataFrame that stores gene symbols.
Specifying this will add that column to the returned DataFrame.
Returns:
A Pandas DataFrame of all rank genes groups results.
Examples:
>>> import ehrapy as ep
>>> adata = ep.dt.mimic_2(encoded=True)
>>> ep.tl.rank_features_groups(adata, "service_unit")
>>> df = ep.get.rank_features_groups_df(adata, group="FICU")
"""
return rank_genes_groups_df(
adata=adata,
group=group,
key=key,
pval_cutoff=pval_cutoff,
log2fc_min=log2fc_min,
log2fc_max=log2fc_max,
gene_symbols=features,
)
