number of interactions parameter updated

mikelkou · May 31, 2023 · 1ac2f10 · 1ac2f10
1 parent ea0f479
commit 1ac2f10
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Showing 3 changed files with 10 additions and 9 deletions.
diff --git a/setup.cfg b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = favapy
-version = 0.3.9.3
+version = 0.3.9.4
 author = Mikaela Koutrouli
 author_email = mikaela.koutrouli@cpr.ku.dk
 description = Infer Functional Associations using Variational Autoencoders on -Omics data.

diff --git a/setup.py b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="favapy",
-    version="0.3.9.3",
+    version="0.3.9.4",
     author="Mikaela Koutrouli",
     author_email="mikaela.koutrouli@cpr.ku.dk",
     description="Infer Functional Associations using Variational Autoencoders on -Omics data.",

diff --git a/src/favapy/fava.py b/src/favapy/fava.py
@@ -154,7 +154,7 @@ def pairs_after_cutoff(correlation, interaction_count=100000, PCC_cutoff=None):
         logging.info(" A cut-off of " + str(PCC_cutoff) + " is applied.")
         correlation_df_new = correlation.loc[(correlation['Score'] >= PCC_cutoff)]
     else:
-        correlation_df_new = correlation.head(interaction_count+1)
+        correlation_df_new = correlation.iloc[:interaction_count,:]
         logging.warn(" The number of interactions in the output file is " + str(interaction_count) + " in which both directions are included: proteinA - proteinB and proteinB - proteinA.")
     return correlation_df_new
 
@@ -208,9 +208,9 @@ def cook(data,
     vae = VAE(opt, x_train, x_test, batch_size, original_dim, hidden_layer, latent_dim, epochs)
     x_test_encoded = np.array(vae.encoder.predict(x_test, batch_size=batch_size))
     correlation = create_protein_pairs(x_test_encoded, row_names)
-    final_pairs =  pairs_after_cutoff(correlation, interaction_count=interaction_count, PCC_cutoff=PCC_cutoff)
-    final_pairs = final_pairs[final_pairs.iloc[:,0] != final_pairs.iloc[:,1]]
+    final_pairs = correlation[correlation.iloc[:,0] != correlation.iloc[:,1]]
     final_pairs = final_pairs.sort_values(by=['Score'], ascending=False)
+    final_pairs =  pairs_after_cutoff(correlation=final_pairs, interaction_count=interaction_count, PCC_cutoff=PCC_cutoff)
     return final_pairs
 
 
@@ -242,13 +242,14 @@ def main():
     x_test_encoded = np.array(vae.encoder.predict(x_test, batch_size=args.batch_size))
 
     logging.info(" Calculating Pearson correlation scores.")
-
     correlation = create_protein_pairs(x_test_encoded, row_names)    
-    final_pairs =  pairs_after_cutoff(correlation, interaction_count=args.interaction_count, PCC_cutoff=args.PCC_cutoff)
-    logging.warn(" If it is not the desired cut-off, please check again the value assigned to the related parameter (-n or interaction_count | -c or PCC_cutoff).")
 
-    final_pairs = final_pairs[final_pairs.iloc[:,0] != final_pairs.iloc[:,1]]
+    final_pairs = correlation[correlation.iloc[:,0] != correlation.iloc[:,1]]
     final_pairs = final_pairs.sort_values(by=['Score'], ascending=False)
+    final_pairs =  pairs_after_cutoff(correlation=final_pairs, interaction_count=args.interaction_count, PCC_cutoff=args.PCC_cutoff)
+    final_pairs.Score = final_pairs.Score.astype(float).round(5)
+    logging.warn(" If it is not the desired cut-off, please check again the value assigned to the related parameter (-n or interaction_count | -c or PCC_cutoff).")
+
     logging.info(" Saving the file with the interactions in the chosen directory ...")
 
     # Save the file