scpanel.utils_func

Functions

preprocess(→ anndata._core.anndata.AnnData)

standardize input data

get_X_y_from_ann(→ Union[Tuple[numpy.ndarray, ...)

check_nonnegative_integers(→ bool)

Checks values of X to ensure it is count data

check_nonnegative_float(→ bool)

Checks values of X to ensure it is logcount data

compute_cell_weight(→ numpy.ndarray)

downsample_adata(adata[, downsample_size, random_state])

Module Contents

scpanel.utils_func.preprocess(adata: object, integrated: bool = False, ct_col: str | None = None, y_col: str | None = None, pt_col: str | None = None, class_map: Dict[str, int] | None = None) → anndata._core.anndata.AnnData

standardize input data

Parameters:

  • adata (object)

  • integrated (bool=False)

  • ct_col (Optional[str]=None)

  • y_col (Optional[str]=None)

  • pt_col (Optional[str]=None)

  • class_map (Optional[Dict[str, int]]=None)

Return type:

AnnData

scpanel.utils_func.get_X_y_from_ann(adata: anndata._core.anndata.AnnData, return_adj: bool = False, n_neigh: int = 10) → Tuple[numpy.ndarray, pandas.core.arrays.categorical.Categorical] | Tuple[numpy.ndarray, pandas.core.arrays.categorical.Categorical, scipy.sparse._csr.csr_matrix]
Parameters:

  • adata

  • return_adj

  • n_neigh

scpanel.utils_func.check_nonnegative_integers(X: numpy.ndarray | scipy.sparse.spmatrix) → bool

Checks values of X to ensure it is count data

scpanel.utils_func.check_nonnegative_float(X: numpy.ndarray | scipy.sparse.spmatrix) → bool

Checks values of X to ensure it is logcount data

scpanel.utils_func.compute_cell_weight(data: anndata._core.anndata.AnnData | pandas.core.frame.DataFrame) → numpy.ndarray
scpanel.utils_func.downsample_adata(adata, downsample_size=4000, random_state=1)