Merge branch 'master' of https://github.com/aertslab/pySCENIC

README.rst

@@ -135,6 +135,7 @@ First we import the necessary modules and declare some constants:
 .. code-block:: python
 
     import os
+    import glob
     import pandas as pd
     import numpy as np
 
@@ -222,7 +223,7 @@ for the co-expression module inference is used.
 .. code-block:: python
 
     N_SAMPLES = ex_matrix.shape[1] # Full dataset
-    adjancencies = grnboost2(expression_data=ex_matrix.T.sample(n=N_SAMPLES, replace=False),
+    adjacencies = grnboost2(expression_data=ex_matrix.T.sample(n=N_SAMPLES, replace=False),
                         tf_names=tf_names, verbose=True)
 
 Derive potential regulons from these co-expression modules

src/pyscenic/transform.py

@@ -228,7 +228,7 @@ def module2df(db: Type[RankingDatabase], module: Regulon, motif_annotations: pd.
 
 
 def modules2df(db: Type[RankingDatabase], modules: Sequence[Regulon], motif_annotations: pd.DataFrame,
-               weighted_recovery=False, module2features_func=module2features) -> pd.DataFrame:
+               weighted_recovery=False, return_recovery_curves=False, module2features_func=module2features) -> pd.DataFrame:
     # Make sure return recovery curves is always set to false because the metadata for the distributed dataframe needs
     # to be fixed for the dask framework.
     #TODO: Remove this restriction.