upgrade to v1.2.1

Author: mrland99

build/lib/paste/PASTE.py

@@ -1,33 +1,53 @@
+from typing import List, Tuple, Optional
 import numpy as np
-import anndata
+from anndata import AnnData
 import ot
 from sklearn.decomposition import NMF
-from .helper import kl_divergence, intersect, kl_divergence_backend, to_dense_array, extract_data_matrix
-import time
+from .helper import intersect, kl_divergence_backend, to_dense_array, extract_data_matrix
 
-def pairwise_align(sliceA, sliceB, alpha = 0.1, dissimilarity='kl', use_rep = None, G_init = None, a_distribution = None, b_distribution = None, norm = False, numItermax = 200, backend=ot.backend.NumpyBackend(), use_gpu = False, return_obj = False, verbose = False, gpu_verbose = True, **kwargs):
+def pairwise_align(
+    sliceA: AnnData, 
+    sliceB: AnnData, 
+    alpha: float = 0.1, 
+    dissimilarity: str ='kl', 
+    use_rep: Optional[str] = None, 
+    G_init = None, 
+    a_distribution = None, 
+    b_distribution = None, 
+    norm: bool = False, 
+    numItermax: int = 200, 
+    backend = ot.backend.NumpyBackend(), 
+    use_gpu: bool = False, 
+    return_obj: bool = False, 
+    verbose: bool = False, 
+    gpu_verbose: bool = True, 
+    **kwargs) -> Tuple[np.ndarray, Optional[int]]:
     """
     Calculates and returns optimal alignment of two slices. 
     
-    param: sliceA - AnnData object of spatial slice
-    param: sliceB - AnnData object of spatial slice
-    param: alpha - Alignment tuning parameter. Note: 0 ≤ alpha ≤ 1
-    param: dissimilarity - Expression dissimilarity measure: 'kl' or 'euclidean'
-    param: use_rep - If none, uses slice.X to calculate dissimilarity between spots, otherwise uses the representation given by slice.obsm[use_rep]
-    param: G_init - initial mapping to be used in FGW-OT, otherwise default is uniform mapping
-    param: a_distribution - distribution of sliceA spots (1-d numpy array), otherwise default is uniform
-    param: b_distribution - distribution of sliceB spots (1-d numpy array), otherwise default is uniform
-    param: numItermax - max number of iterations during FGW-OT
-    param: norm - scales spatial distances such that neighboring spots are at distance 1 if True, otherwise spatial distances remain unchanged
-     param: backend - type of backend to run calculations. For list of backends available on system: ot.backend.get_backend_list()
-    param: use_gpu - Whether to run on gpu or cpu. Currently we only have gpu support for Pytorch.
-    param: return_obj - returns objective function output of FGW-OT if True, nothing if False
-    param: verbose - FGW-OT is verbose if True, nothing if False
-    param: gpu_verbose - Print whether gpu is being used to user, nothing if False
+    Args:
+        sliceA: Slice A to align.
+        sliceB: Slice B to align.
+        alpha:  Alignment tuning parameter. Note: 0 <= alpha <= 1.
+        dissimilarity: Expression dissimilarity measure: ``'kl'`` or ``'euclidean'``.
+        use_rep: If ``None``, uses ``slice.X`` to calculate dissimilarity between spots, otherwise uses the representation given by ``slice.obsm[use_rep]``.
+        G_init (array-like, optional): Initial mapping to be used in FGW-OT, otherwise default is uniform mapping.
+        a_distribution (array-like, optional): Distribution of sliceA spots, otherwise default is uniform.
+        b_distribution (array-like, optional): Distribution of sliceB spots, otherwise default is uniform.
+        numItermax: Max number of iterations during FGW-OT.
+        norm: If ``True``, scales spatial distances such that neighboring spots are at distance 1. Otherwise, spatial distances remain unchanged.
+        backend: Type of backend to run calculations. For list of backends available on system: ``ot.backend.get_backend_list()``.
+        use_gpu: If ``True``, use gpu. Otherwise, use cpu. Currently we only have gpu support for Pytorch.
+        return_obj: If ``True``, additionally returns objective function output of FGW-OT.
+        verbose: If ``True``, FGW-OT is verbose.
+        gpu_verbose: If ``True``, print whether gpu is being used to user.
    
-    
-    return: pi - alignment of spots
-    return: log['fgw_dist'] - objective function output of FGW-OT
+    Returns:
+        - Alignment of spots.
+
+        If ``return_obj = True``, additionally returns:
+        
+        - Objective function output of FGW-OT.
     """
 
     # Determine if gpu or cpu is being used
@@ -131,31 +151,47 @@ def pairwise_align(sliceA, sliceB, alpha = 0.1, dissimilarity='kl', use_rep = No
     return pi
 
 
-def center_align(A, slices, lmbda = None, alpha = 0.1, n_components = 15, threshold = 0.001, max_iter = 10, dissimilarity='kl', use_rep = None, norm = False, random_seed = None, pis_init = None, distributions=None, backend = ot.backend.NumpyBackend(), use_gpu = False, verbose = False, gpu_verbose = True):
+def center_align(
+    A: AnnData, 
+    slices: List[AnnData], 
+    lmbda = None, 
+    alpha: float = 0.1, 
+    n_components: int = 15, 
+    threshold: float = 0.001, 
+    max_iter: int = 10, 
+    dissimilarity: str ='kl', 
+    norm: bool = False, 
+    random_seed: Optional[int] = None, 
+    pis_init: Optional[List[np.ndarray]] = None, 
+    distributions = None, 
+    backend = ot.backend.NumpyBackend(), 
+    use_gpu: bool = False, 
+    verbose: bool = False, 
+    gpu_verbose: bool = True) -> Tuple[AnnData, List[np.ndarray]]:
     """
     Computes center alignment of slices.
     
-    param: A - Initialization of starting AnnData Spatial Object; Make sure to include gene expression AND spatial info
-    param: slices - List of slices (AnnData objects) used to calculate center alignment
-    param: lmbda - List of probability weights assigned to each slice; default is uniform weights
-    param: n_components - Number of components in NMF decomposition
-    param: threshold - Threshold for convergence of W and H
-    param: max_iter - maximum number of iterations for solving for center slice
-    param: dissimilarity - Expression dissimilarity measure: 'kl' or 'euclidean'
-    param: use_rep - If none, uses slice.X to calculate dissimilarity between spots, otherwise uses the representation given by slice.obsm[use_rep]
-    param: norm - scales spatial distances such that neighboring spots are at distance 1 if True, otherwise spatial distances remain unchanged
-    param: random_seed - set random seed for reproducibility
-    param: pis_init - initial list of mappings between 'A' and 'slices' to solver, otherwise will calculate default mappings
-    param: distributions - distributions of spots for each slice (list of 1-d numpy array), otherwise default is uniform
-    param: backend - type of backend to run calculations. For list of backends available on system: ot.backend.get_backend_list()
-    param: use_gpu - Whether to run on gpu or cpu. Currently we only have gpu support for Pytorch.
-    param: verbose - FGW-OT is verbose if True, nothing if False
-    param: gpu_verbose - Print whether gpu is being used to user, nothing if False
+    Args:
+        A: Slice to use as the initialization for center alignment; Make sure to include gene expression and spatial information.
+        slices: List of slices to use in the center alignment.
+        lmbda (array-like, optional): List of probability weights assigned to each slice; If ``None``, use uniform weights.
+        alpha:  Alignment tuning parameter. Note: 0 <= alpha <= 1.
+        n_components: Number of components in NMF decomposition.
+        threshold: Threshold for convergence of W and H during NMF decomposition.
+        max_iter: Maximum number of iterations for our center alignment algorithm.
+        dissimilarity: Expression dissimilarity measure: ``'kl'`` or ``'euclidean'``.
+        norm:  If ``True``, scales spatial distances such that neighboring spots are at distance 1. Otherwise, spatial distances remain unchanged.
+        random_seed: Set random seed for reproducibility.
+        pis_init: Initial list of mappings between 'A' and 'slices' to solver. Otherwise, default will automatically calculate mappings.
+        distributions (List[array-like], optional): Distributions of spots for each slice. Otherwise, default is uniform.
+        backend: Type of backend to run calculations. For list of backends available on system: ``ot.backend.get_backend_list()``.
+        use_gpu: If ``True``, use gpu. Otherwise, use cpu. Currently we only have gpu support for Pytorch.
+        verbose: If ``True``, FGW-OT is verbose.
+        gpu_verbose: If ``True``, print whether gpu is being used to user.
 
-    
-    return: center_slice - inferred center slice (AnnData object) with full and low dimensional representations (W, H) of
-                            the gene expression matrix
-    return: pi - List of pairwise alignment mappings of the center slice (rows) to each input slice (columns)
+    Returns:
+        - Inferred center slice with full and low dimensional representations (W, H) of the gene expression matrix.
+        - List of pairwise alignment mappings of the center slice (rows) to each input slice (columns).
     """
 
     # Determine if gpu or cpu is being used
@@ -213,10 +249,10 @@ def center_align(A, slices, lmbda = None, alpha = 0.1, n_components = 15, thresh
     center_coordinates = A.obsm['spatial']
 
     if not isinstance(center_coordinates, np.ndarray):
-        print("Warning: A.obsm['spatial'] is not of type numpy array .")
+        print("Warning: A.obsm['spatial'] is not of type numpy array.")
 
     # Initialize center_slice
-    center_slice = anndata.AnnData(np.dot(W,H))
+    center_slice = AnnData(np.dot(W,H))
     center_slice.var.index = common_genes
     center_slice.obs.index = A.obs.index
     center_slice.obsm['spatial'] = center_coordinates
@@ -246,7 +282,7 @@ def center_align(A, slices, lmbda = None, alpha = 0.1, n_components = 15, thresh
 #--------------------------- HELPER METHODS -----------------------------------
 
 def center_ot(W, H, slices, center_coordinates, common_genes, alpha, backend, use_gpu, dissimilarity = 'kl', norm = False, G_inits = None, distributions=None, verbose = False):
-    center_slice = anndata.AnnData(np.dot(W,H))
+    center_slice = AnnData(np.dot(W,H))
     center_slice.var.index = common_genes
     center_slice.obsm['spatial'] = center_coordinates
 
@@ -313,4 +349,4 @@ def df(G):
         return res, log
 
     else:
-        return ot.gromov.cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
+        return ot.gromov.cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

build/lib/paste/__init__.py

@@ -1,3 +1,3 @@
 from .PASTE import pairwise_align, center_align
-from .helper import kl_divergence, kl_divergence_backend, intersect, match_spots_using_spatial_heuristic, filter_for_common_genes
+from .helper import match_spots_using_spatial_heuristic
 from .visualization import plot_slice, stack_slices_pairwise, stack_slices_center

build/lib/paste/helper.py

@@ -2,27 +2,44 @@
 import scipy
 import ot
 
-def filter_for_common_genes(slices):
+def match_spots_using_spatial_heuristic(
+    X,
+    Y,
+    use_ot: bool = True) -> np.ndarray:
     """
-    param: slices - list of slices (AnnData objects)
-    """
-    assert len(slices) > 0, "Cannot have empty list."
+    Calculates and returns a mapping of spots using a spatial heuristic.
+
+    Args:
+        X (array-like, optional): Coordinates for spots X.
+        Y (array-like, optional): Coordinates for spots Y.
+        use_ot: If ``True``, use optimal transport ``ot.emd()`` to calculate mapping. Otherwise, use Scipy's ``min_weight_full_bipartite_matching()`` algorithm.
     
-    common_genes = slices[0].var.index
-    for s in slices:
-        common_genes = intersect(common_genes, s.var.index)
-    for i in range(len(slices)):
-        slices[i] = slices[i][:, common_genes]
-    print('Filtered all slices for common genes. There are ' + str(len(common_genes)) + ' common genes.')
+    Returns:
+        Mapping of spots using a spatial heuristic.
+    """
+    n1,n2=len(X),len(Y)
+    X,Y = norm_and_center_coordinates(X),norm_and_center_coordinates(Y)
+    dist = scipy.spatial.distance_matrix(X,Y)
+    if use_ot:
+        pi = ot.emd(np.ones(n1)/n1, np.ones(n2)/n2, dist)
+    else:
+        row_ind, col_ind = scipy.sparse.csgraph.min_weight_full_bipartite_matching(scipy.sparse.csr_matrix(dist))
+        pi = np.zeros((n1,n2))
+        pi[row_ind, col_ind] = 1/max(n1,n2)
+        if n1<n2: pi[:, [(j not in col_ind) for j in range(n2)]] = 1/(n1*n2)
+        elif n2<n1: pi[[(i not in row_ind) for i in range(n1)], :] = 1/(n1*n2)
+    return pi
 
 def kl_divergence(X, Y):
     """
     Returns pairwise KL divergence (over all pairs of samples) of two matrices X and Y.
     
-    param: X - np array with dim (n_samples by n_features)
-    param: Y - np array with dim (m_samples by n_features)
+    Args:
+        X: np array with dim (n_samples by n_features)
+        Y: np array with dim (m_samples by n_features)
     
-    return: D - np array with dim (n_samples by m_samples). Pairwise KL divergence matrix.
+    Returns:
+        D: np array with dim (n_samples by m_samples). Pairwise KL divergence matrix.
     """
     assert X.shape[1] == Y.shape[1], "X and Y do not have the same number of features."
 
@@ -40,10 +57,12 @@ def kl_divergence_backend(X, Y):
     
     Takes advantage of POT backend to speed up computation.
     
-    param: X - np array with dim (n_samples by n_features)
-    param: Y - np array with dim (m_samples by n_features)
+    Args:
+        X: np array with dim (n_samples by n_features)
+        Y: np array with dim (m_samples by n_features)
     
-    return: D - np array with dim (n_samples by m_samples). Pairwise KL divergence matrix.
+    Returns:
+        D: np array with dim (n_samples by m_samples). Pairwise KL divergence matrix.
     """
     assert X.shape[1] == Y.shape[1], "X and Y do not have the same number of features."
 
@@ -61,10 +80,14 @@ def kl_divergence_backend(X, Y):
 
 def intersect(lst1, lst2): 
     """
-    param: lst1 - list
-    param: lst2 - list
+    Gets and returns intersection of two lists.
+
+    Args:
+        lst1: List
+        lst2: List
     
-    return: list of common elements
+    Returns:
+        lst3: List of common elements.
     """
 
     temp = set(lst2)
@@ -73,33 +96,17 @@ def intersect(lst1, lst2):
 
 def norm_and_center_coordinates(X): 
     """
-    param: X - numpy array
+    Normalizes and centers coordinates at the origin.
+
+    Args:
+        X: Numpy array
     
-    return: 
+    Returns:
+        X_new: Updated coordiantes.
     """
     return (X-X.mean(axis=0))/min(scipy.spatial.distance.pdist(X))
 
 
-def match_spots_using_spatial_heuristic(X,Y,use_ot=True):
-    """
-    param: X - numpy array
-    param: Y - numpy array
-    
-    return: pi- mapping of spots using spatial heuristic
-    """
-    n1,n2=len(X),len(Y)
-    X,Y = norm_and_center_coordinates(X),norm_and_center_coordinates(Y)
-    dist = scipy.spatial.distance_matrix(X,Y)
-    if use_ot:
-        pi = ot.emd(np.ones(n1)/n1, np.ones(n2)/n2, dist)
-    else:
-        row_ind, col_ind = scipy.sparse.csgraph.min_weight_full_bipartite_matching(scipy.sparse.csr_matrix(dist))
-        pi = np.zeros((n1,n2))
-        pi[row_ind, col_ind] = 1/max(n1,n2)
-        if n1<n2: pi[:, [(j not in col_ind) for j in range(n2)]] = 1/(n1*n2)
-        elif n2<n1: pi[[(i not in row_ind) for i in range(n1)], :] = 1/(n1*n2)
-    return pi
-
 ## Covert a sparse matrix into a dense np array
 to_dense_array = lambda X: X.toarray() if isinstance(X,scipy.sparse.csr.spmatrix) else np.array(X)