Merge branch 'dev' of https://github.com/KrishnaswamyLab/PHATE into dev

Python/phate/phate.py

@@ -544,6 +544,7 @@ def fit(self, X):
                 precomputed = "distance"
             else:
                 precomputed = "affinity"
+            log_info("Using precomputed {} matrix...".format(precomputed))
             n_pca = None
         else:
             precomputed = None

Python/phate/preprocessing.py

@@ -33,11 +33,15 @@ def library_size_normalize(data, verbose=False):
     if verbose:
         print("Normalizing library sizes for %s cells" % (data.shape[0]))
 
+    # pandas support
+    columns, index = None, None
     try:
-        if isinstance(data, pd.SparseDataFrame):
-            data = data.to_coo()
-        elif pd.api.types.is_sparse(data):
+        if isinstance(data, pd.SparseDataFrame) or \
+                pd.api.types.is_sparse(data):
+            columns, index = data.columns, data.index
             data = data.to_coo()
+        elif isinstance(data, pd.DataFrame):
+            columns, index = data.columns, data.index
     except NameError:
         pass
     except AttributeError as e:
@@ -76,4 +80,12 @@ def library_size_normalize(data, verbose=False):
     # axis = 1 independently normalizes each sample
 
     data_norm = data_norm * median_transcript_count
+    if columns is not None:
+        # pandas dataframe
+        if sparse.issparse(data_norm):
+            data_norm = pd.SparseDataFrame(data_norm, default_fill_value=0)
+        else:
+            data_norm = pd.DataFrame(data_norm)
+        data_norm.columns = columns
+        data_norm.index = index
     return data_norm