spateo.tools.spatially_variable_gene_ot
=======================================

.. py:module:: spateo.tools.spatially_variable_gene_ot

.. autoapi-nested-parse::

   Wasserstein distance would be calculated by ot python package, see following:

   Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer,
   POT Python Optimal Transport library,
   Journal of Machine Learning Research, 22(78):1−8, 2021.
   Website: https://pythonot.github.io/



Functions
---------

.. autoapisummary::

   spateo.tools.spatially_variable_gene_ot._cal_dis
   spateo.tools.spatially_variable_gene_ot._cal_wass_dis
   spateo.tools.spatially_variable_gene_ot.bin_adata
   spateo.tools.spatially_variable_gene_ot._cal_geodesic_distance
   spateo.tools.spatially_variable_gene_ot._cal_wass_dis_on_genes
   spateo.tools.spatially_variable_gene_ot.shuffle_adata
   spateo.tools.spatially_variable_gene_ot.cal_wass_dis_bs


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

.. py:function:: _cal_dis(adata, x1)

   Compute distance between samples in x1.

   :param adata:

   :rtype: adata


.. py:function:: _cal_wass_dis(M, a, b=[], numItermax=1000000)

   Computing Wasserstein distance.

   :param M: (ns,nt) array-like, float – Loss matrix (c-order array in numpy with type float64)
   :param a: (ns,) array-like, float – Source histogram (uniform weight if empty list)
   :param b: (nt,) array-like, float – Target histogram (uniform weight if empty list)

   :returns: (float, array-like) – Optimal transportation loss for the given parameters
   :rtype: W


.. py:function:: bin_adata(adata, bin_size=1)

.. py:function:: _cal_geodesic_distance(adata, n_neighbors=30, min_dis_cutoff=2.0, max_dis_cutoff=4.0)

.. py:function:: _cal_wass_dis_on_genes(M, inp)

.. py:function:: shuffle_adata(adata: anndata.AnnData, seed: int = 0)

   Shuffle X in anndata object randomly.

   :param adata: AnnData object
   :param seed: seed for randomly shuffling

   :returns: AnnData object
   :rtype: adata


.. py:function:: cal_wass_dis_bs(adata: anndata.AnnData, bin_size: int = 1, numItermax: int = 1000000, gene_set: Union[List, numpy.ndarray] = None, compare_to: typing_extensions.Literal[uniform, allUMI] = 'allUMI', processes: int = 1, bootstrap: int = 100, min_dis_cutoff: float = 2.0, max_dis_cutoff: float = 6.0) -> pandas.DataFrame

   Computing Wasserstein distance for a AnnData to identify spatially variable genes.

   :param adata: AnnData object
   :param bin_size: bin size for mergeing cells.
   :param numItermax: The maximum number of iterations before stopping the optimization algorithm if it has not converged
   :param gene_set: Gene set for computing, default is for all genes.
   :param compare_to: compare distance to uniform distribution or allUMI distribution.
   :param processes: process number for parallelly running
   :param bootstrap: bootstrap number for permutation to calculate p-value
   :param min_dis_cutoff: Cells/Bins whose min distance to 30 neighbors are larger than this cutoff would be filtered.
   :param max_dis_cutoff: Cells/Bins whose max distance to 30 neighbors are larger than this cutoff would be filtered.

   :returns: a dataframe
             adata0: binned AnnData object
   :rtype: w_df