remove blank figures and edit code

5_analyze_data/notebooks/Heatmap_analysis/nf1_heatmap.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# # Create correlation heatmap from normalized and feature selected NF1 data
+# # Create correlation heatmap from normalized and feature selected NF1 data for both CellProfiler and DeepProfiler
 
 # ## Import libraries
 
@@ -18,7 +18,9 @@
 import UMAPutils as utils
 
 
-# ## Read in NF1 data `csv`
+# ## CellProfiler data
+
+# ### Read in NF1 data `csv`
 
 # In[2]:
 
@@ -31,7 +33,7 @@
 data.head()
 
 
-# ## Split NF1 data `csv`
+# ### Split NF1 data `csv`
 
 # In[3]:
 
@@ -40,7 +42,7 @@
 feature_data
 
 
-# ## Transpose the NF1 dataframe
+# ### Transpose the NF1 dataframe
 
 # In[4]:
 
@@ -49,7 +51,7 @@
 data_trans
 
 
-# ## Create correlation heatmap
+# ### Create correlation heatmap
 
 # In[5]:
 
@@ -62,7 +64,7 @@
 plt.savefig(save_path, bbox_inches="tight")
 
 
-# ## Create clustermap with correlation heatmap
+# ### Create clustermap with correlation heatmap
 
 # In[6]:
 
@@ -74,3 +76,94 @@
 save_path = pathlib.Path("figures/correlation_clustermap_sc.png")
 plt.savefig(save_path, bbox_inches="tight")
 
+
+# ## DeepProfiler data
+
+# In[7]:
+
+
+norm_fs_data_nuc = pathlib.Path("../../../4_processing_features/data/nf1_sc_norm_fs_deepprofiler_nuc.csv.gz")
+norm_fs_data_cyto = pathlib.Path("../../../4_processing_features/data/nf1_sc_norm_fs_deepprofiler_cyto.csv.gz")
+
+data_nuc = pd.read_csv(norm_fs_data_nuc)
+data_cyto = pd.read_csv(norm_fs_data_cyto)
+
+print(data_nuc.shape)
+data_nuc.head()
+
+
+# In[8]:
+
+
+metadata_dataframe_nuc, feature_data_nuc = utils.split_data(data_nuc)
+
+feature_data_nuc = feature_data_nuc.drop(['Location_Center_X', 'Location_Center_Y'], axis=1)
+
+print(feature_data_nuc.shape)
+feature_data_nuc.head()
+
+
+# In[9]:
+
+
+metadata_dataframe_cyto, feature_data_cyto = utils.split_data(data_cyto)
+
+feature_data_cyto = feature_data_cyto.drop(['Location_Center_X', 'Location_Center_Y'], axis=1)
+
+print(feature_data_cyto.shape)
+feature_data_cyto.head()
+
+
+# In[10]:
+
+
+data_trans_nuc = feature_data_nuc.transpose()
+data_trans_cyto = feature_data_cyto.transpose()
+
+print(data_trans_nuc.shape)
+data_trans_nuc.head()
+
+
+# In[11]:
+
+
+data_trans_nuc_heatmap = sb.heatmap(data_trans_nuc.corr())
+
+plt.show()
+
+
+# In[12]:
+
+
+data_trans_cyto_heatmap = sb.heatmap(data_trans_cyto.corr())
+
+plt.show()
+
+
+# In[13]:
+
+
+sb.clustermap(data_trans_nuc.corr(), 
+            cmap='RdBu_r',
+            )
+
+save_path = pathlib.Path("figures/correlation_clustermap_sc_dp_nuc.png")
+plt.savefig(save_path, bbox_inches="tight")
+
+
+# In[14]:
+
+
+sb.clustermap(data_trans_cyto.corr(), 
+            cmap='RdBu_r',
+            )
+
+save_path = pathlib.Path("figures/correlation_clustermap_sc_dp_cyto.png")
+plt.savefig(save_path, bbox_inches="tight")
+
+
+# In[ ]:
+
+
+
+