support sparse matrix

.gitignore

@@ -0,0 +1,21 @@
+paste_output/
+.DS_Store
+AD.ipynb
+BC.ipynb
+cortex.ipynb
+DH.ipynb
+sample_data/adpolb*
+sample_data/dh*
+sample_data/DLPFC
+sample_data/151507*
+sample_data/Developmental*
+sample_data/expr*
+sample_data/RA-and-SP*
+.ipynb_checkpoints/
+!Tutorial-checkpoint.ipynp
+ST_sim.ipynb
+src/__pycache__/visualization.cpython-37.pyc
+__pycache__/paste.cpython-37.pyc
+src/helper_ron.py
+src/PASTE_ron.py
+cortex_ron.ipynb

Tutorial.ipynb

@@ -27,7 +27,7 @@
    "source": [
     "import math\n",
     "import time\n",
-    "import pandas as pds\n",
+    "import pandas as pd\n",
     "import numpy as np\n",
     "import scanpy as sc\n",
     "import seaborn as sns\n",

src/paste/PASTE.py

@@ -3,8 +3,9 @@
 import ot
 from sklearn.decomposition import NMF
 from scipy.spatial import distance_matrix
+import scipy
 from numpy import linalg as LA
-from .helper import kl_divergence, intersect
+from .helper import kl_divergence, intersect, to_dense_array
 
 def pairwise_align(sliceA, sliceB, alpha = 0.1, G_init = None, a_distribution = None, b_distribution = None, norm = False, numItermax = 200, return_obj = False, verbose = False, **kwargs):
     """
@@ -33,8 +34,8 @@ def pairwise_align(sliceA, sliceB, alpha = 0.1, G_init = None, a_distribution =
 
     D_A = distance_matrix(sliceA.obsm['spatial'], sliceA.obsm['spatial'])
     D_B = distance_matrix(sliceB.obsm['spatial'], sliceB.obsm['spatial'])
-    s_A = sliceA.X + 0.01
-    s_B = sliceB.X + 0.01
+    s_A = to_dense_array(sliceA.X) + 0.01
+    s_B = to_dense_array(sliceB.X) + 0.01
     M = kl_divergence(s_A, s_B)
 
     if a_distribution is None:
@@ -48,8 +49,8 @@ def pairwise_align(sliceA, sliceB, alpha = 0.1, G_init = None, a_distribution =
         b = b_distribution
 
     if norm:
-        D1 /= D1[D1>0].min().min()
-        D2 /= D2[D2>0].min().min()
+        D_A /= D_A[D_A>0].min().min()
+        D_B /= D_B[D_B>0].min().min()
 
     if G_init is None:
         pi, logw = ot.gromov.fused_gromov_wasserstein(M, D_A, D_B, a, b, loss_fun='square_loss', alpha= alpha, log=True, numItermax=numItermax,verbose=verbose)
@@ -99,7 +100,7 @@ def center_align(A, slices, lmbda, alpha = 0.1, n_components = 15, threshold = 0
         W = model.fit_transform(A.X)
     else:
         pis = pis_init
-        W = model.fit_transform(A.shape[0]*sum([lmbda[i]*np.dot(pis[i], slices[i].X) for i in range(len(slices))]))
+        W = model.fit_transform(A.shape[0]*sum([lmbda[i]*np.dot(pis[i], to_dense_array(slices[i].X)) for i in range(len(slices))]))
     H = model.components_
     center_coordinates = A.obsm['spatial']
 
@@ -146,7 +147,7 @@ def center_ot(W, H, slices, center_coordinates, common_genes, alpha, norm = Fals
 def center_NMF(W, H, slices, pis, lmbda, n_components, random_seed, verbose = False):
     print('Solving Center Mapping NMF Problem:')
     n = W.shape[0]
-    B = n*sum([lmbda[i]*np.dot(pis[i], slices[i].X) for i in range(len(slices))])
+    B = n*sum([lmbda[i]*np.dot(pis[i], to_dense_array(slices[i].X)) for i in range(len(slices))])
     model = NMF(n_components=n_components, solver = 'mu', beta_loss = 'kullback-leibler', init='random', random_state = random_seed, verbose = verbose)
     W_new = model.fit_transform(B)
     H_new = model.components_

src/paste/helper.py

@@ -74,4 +74,6 @@ def match_spots_using_spatial_heuristic(X,Y,use_ot=True):
         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
+    return pi
+
+to_dense_array = lambda X: np.array(X.todense()) if isinstance(X,scipy.sparse.csr.spmatrix) else X