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Debug classify_clone_cell_type() #627

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merged 1 commit into from
Dec 10, 2023
Merged

Debug classify_clone_cell_type() #627

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3 changes: 2 additions & 1 deletion dynamo/prediction/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
rank_perturbation_cells,
rank_perturbation_genes,
)
from .state_graph import state_graph, tree_model
from .state_graph import classify_clone_cell_type, state_graph, tree_model
from .trajectory import GeneTrajectory, Trajectory
from .trajectory_analysis import (
calc_mean_exit_time,
Expand Down Expand Up @@ -45,5 +45,6 @@
"get_pulse_r0",
"calc_mean_exit_time",
"calc_mean_first_passage_time",
"classify_clone_cell_type",
"mean_first_passage_time",
]
28 changes: 21 additions & 7 deletions dynamo/prediction/state_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,15 +24,29 @@
# from sklearn.preprocessing import OrdinalEncoder


def classify_clone_cell_type(adata, clone, clone_column, cell_type_column, cell_type_to_excluded):
"""find the dominant cell type of all the cells that are from the same clone"""
cell_ids = np.where(adata.obs[clone_column] == clone)[0]

to_check = adata[cell_ids].obs[cell_type_column].value_counts().index.isin(list(cell_type_to_excluded))
def classify_clone_cell_type(
adata: AnnData,
clone: str,
clone_column: str,
cell_type_column: str,
cell_type_to_excluded: Union[List, str],
) -> str:
"""Find the dominant cell type of all the cells that are from the given clone.

cell_type = np.where(to_check)[0]
Args:
adata: AnnData object.
clone: The clone name that specifies the clone group used to find the dominant cell type.
clone_column: The column name in `adata.obs` that corresponds to the clone information.
cell_type_column: The column name in `adata.obs` that corresponds to cell type information.
cell_type_to_excluded: The cell type name that will be excluded from the dominant cell type calculation.

return cell_type
Returns:
The dominant cell type of all the cells that are from the given clone.
"""
cell_ids = np.where(adata.obs[clone_column] == clone)[0]
cell_types_counts = adata[cell_ids].obs[cell_type_column].value_counts()
to_check = cell_types_counts.index.isin(list(cell_type_to_excluded))
return cell_types_counts[np.where(~to_check)[0]].index[0]


def prune_transition(
Expand Down