Fix KL 2D

meyer-lab · Sep 20, 2023 · a8bdeca · a8bdeca
1 parent fd8bfe6
commit a8bdeca
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Showing 2 changed files with 9 additions and 6 deletions.
diff --git a/bicytok/figures/common.py b/bicytok/figures/common.py
@@ -514,7 +514,7 @@ def KL_divergence_2D(dataset, signal_receptor, target_cells, ax):
         target_receptor_counts[:, 1] *= conversion_factor
         off_target_receptor_counts[:, 1] *= conversion_factor
 
-        KL_div = calculate_kl_divergence_2D(target_receptor_counts[:, 1], off_target_receptor_counts[:, 1])
+        KL_div = calculate_kl_divergence_2D(target_receptor_counts[:, 0:2], off_target_receptor_counts[:, 0:2])
         if np.mean(target_receptor_counts[:, 1]) > np.mean(off_target_receptor_counts[:, 1]): 
             results.append((KL_div, receptor_name, signal_receptor))
         else:
@@ -738,11 +738,12 @@ def EMD_2D_pair(dataset, target_cells, signal_receptor, special_receptor):
     return optimal_transport
 
 def calculate_kl_divergence_2D(targCellMark, offTargCellMark):
-    kdeTarg = KernelDensity(kernel='gaussian').fit(targCellMark.reshape(-1, 1))
-    kdeOffTarg = KernelDensity(kernel='gaussian').fit(offTargCellMark.reshape(-1, 1))
+    kdeTarg = KernelDensity(kernel='gaussian').fit(targCellMark.reshape(-1, 2))
+    kdeOffTarg = KernelDensity(kernel='gaussian').fit(offTargCellMark.reshape(-1, 2))
     minVal = np.minimum(targCellMark.min(), offTargCellMark.min()) - 10
     maxVal = np.maximum(targCellMark.max(), offTargCellMark.max()) + 10
-    outcomes = np.arange(minVal, maxVal + 1).reshape(-1, 1)
+    X, Y = np.mgrid[minVal:maxVal:((maxVal-minVal) / 100), minVal:maxVal:((maxVal-minVal) / 100)]
+    outcomes = np.concatenate((X.reshape(-1, 1), Y.reshape(-1, 1)), axis=1)
     distTarg = np.exp(kdeTarg.score_samples(outcomes))
     distOffTarg = np.exp(kdeOffTarg.score_samples(outcomes))
     KL_div = stats.entropy(distOffTarg.flatten() + 1e-200, distTarg.flatten() + 1e-200, base=2)

diff --git a/bicytok/figures/figure4.py b/bicytok/figures/figure4.py
@@ -33,9 +33,11 @@ def makeFigure():
     ax, f = getSetup((40, 40), (1,1))
     target_cells = 'Treg' 
     ######################################################
+    '''
     resultsEMD = []
     for receptor in receptors: 
         val = EMD_2D(new_df, receptor, target_cells, ax = None) 
+        print(val)
         resultsEMD.append(val)
     flattened_results = [result_tuple for inner_list in resultsEMD for result_tuple in inner_list]
     # Create a DataFrame from the flattened_results
@@ -52,7 +54,7 @@ def makeFigure():
     '''
 
     resultsKL = []
-    for receptor in receptors:
+    for receptor in receptors[0:5]:
         val = KL_divergence_2D(new_df, receptor, target_cells, ax = None) 
         resultsKL.append(val)
     flattened_resultsKL = [result_tuple for inner_list in resultsKL for result_tuple in inner_list]
@@ -67,5 +69,5 @@ def makeFigure():
     ax[0].set_xlabel('Receptor')
     ax[0].set_ylabel('Receptor')
     ax[0].set_title('KL Heatmap')
-    '''
+
     return f