scpanel.utils_func
==================

.. py:module:: scpanel.utils_func


Functions
---------

.. autoapisummary::

   scpanel.utils_func.preprocess
   scpanel.utils_func.get_X_y_from_ann
   scpanel.utils_func.check_nonnegative_integers
   scpanel.utils_func.check_nonnegative_float
   scpanel.utils_func.compute_cell_weight
   scpanel.utils_func.downsample_adata


Module Contents
---------------

.. py:function:: preprocess(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) -> anndata._core.anndata.AnnData

   standardize input data

   :param adata:
   :type adata: object
   :param integrated:
   :type integrated: bool=False
   :param ct_col:
   :type ct_col: Optional[str]=None
   :param y_col:
   :type y_col: Optional[str]=None
   :param pt_col:
   :type pt_col: Optional[str]=None
   :param class_map:
   :type class_map: Optional[Dict[str, int]]=None

   :rtype: AnnData


.. py:function:: get_X_y_from_ann(adata: anndata._core.anndata.AnnData, return_adj: bool = False, n_neigh: int = 10) -> Union[Tuple[numpy.ndarray, pandas.core.arrays.categorical.Categorical], Tuple[numpy.ndarray, pandas.core.arrays.categorical.Categorical, scipy.sparse._csr.csr_matrix]]

   :param adata:
   :param return_adj:
   :param n_neigh:


.. py:function:: check_nonnegative_integers(X: Union[numpy.ndarray, scipy.sparse.spmatrix]) -> bool

   Checks values of X to ensure it is count data


.. py:function:: check_nonnegative_float(X: Union[numpy.ndarray, scipy.sparse.spmatrix]) -> bool

   Checks values of X to ensure it is logcount data


.. py:function:: compute_cell_weight(data: Union[anndata._core.anndata.AnnData, pandas.core.frame.DataFrame]) -> numpy.ndarray

.. py:function:: downsample_adata(adata, downsample_size=4000, random_state=1)