Update return_panda_indegree() and return_panda_outdegree()

pypanda/lioness.py

@@ -5,6 +5,7 @@
 from .panda import Panda
 from .timer import Timer
 
+
 class Lioness(Panda):
     """Using LIONESS to infer single-sample gene regulatory networks.
 
@@ -43,7 +44,7 @@ def __init__(self, obj, start=1, end=None, save_dir='lioness_output', save_fmt='
             os.makedirs(save_dir)
 
         # Run LIONESS
-        self.lioness_network = self.__lioness_loop()
+        self.total_lioness_network = self.__lioness_loop()
 
         # create result data frame
         #self.export_lioness_results = pd.DataFrame(self.lioness_network)
@@ -78,10 +79,15 @@ def __lioness_loop(self):
                 else:
                     print("Unknown format %s! Use npy format instead." % self.save_fmt)
                     np.save(path, lioness_network)
-        return lioness_network
+            if i == 0:
+                self.total_lioness_network = np.fromstring(np.transpose(lioness_network).tostring(),dtype=lioness_network.dtype)
+            else:
+                self.total_lioness_network=np.column_stack((self.total_lioness_network ,np.fromstring(np.transpose(lioness_network).tostring(),dtype=lioness_network.dtype)))
+
+        return self.total_lioness_network
 
     def save_lioness_results(self, file='lioness.txt'):
         '''Write lioness results to file.'''
         #self.lioness_network.to_csv(file, index=False, header=False, sep="\t")
-        np.savetxt(file, self.lioness_network, delimiter="\t",header="")
-        return None
+        np.savetxt(file, self.total_lioness_network, delimiter="\t",header="")
+        return None

pypanda/panda.py

@@ -30,6 +30,7 @@ def __init__(self, expression_file, motif_file, ppi_file, save_memory = False, s
                 print('Unique TFs:', self.num_tfs)
         else:
             self.motif_data = None
+            self.num_tfs    = 0
 
         if expression_file:
             with Timer('Loading expression data ...'):
@@ -69,6 +70,7 @@ def __init__(self, expression_file, motif_file, ppi_file, save_memory = False, s
         if self.motif_data is None:
             print('Returning the correlation matrix of expression data in <Panda_obj>.correlation_matrix')
             #self.panda_network = self.correlation_matrix
+            self.panda_network = self.correlation_matrix
             self.__pearson_results_data_frame()
             return
         # Auxiliary dicts
@@ -330,12 +332,15 @@ def split_label(label):
     def return_panda_indegree(self):
         '''Return Panda indegree.'''
         #subset_indegree = self.export_panda_results.loc[:,['gene','force']]
-        subset_indegree = self.panda_results.loc[:,['gene','force']]
+        export_panda_results_pd = pd.DataFrame(self.export_panda_results,columns=['tf','gene','motif','force'])
+        subset_indegree = export_panda_results_pd.loc[:,['gene','force']]
+        subset_indegree['force']=pd.to_numeric(subset_indegree.force)
         self.panda_indegree = subset_indegree.groupby('gene').sum()
         return self.panda_indegree
     def return_panda_outdegree(self):
         '''Return Panda outdegree.'''
         export_panda_results_pd = pd.DataFrame(self.export_panda_results,columns=['tf','gene','motif','force'])
         subset_outdegree = export_panda_results_pd.loc[:,['tf','force']]
+        subset_outdegree['force']=pd.to_numeric(subset_outdegree.force)
         self.panda_outdegree = subset_outdegree.groupby('tf').sum()
-        return self.panda_outdegree
+        return self.panda_outdegree