package

Makefile

 init:
-    pip install -r requirements.txt
+	pip install -r requirements.txt

README.md

-# Group Factor Analysis Code
-Decomposes a set of expressions into a group expression
-and calculates how it is regulated via an ANOVA that is
-relating it to the observables in the journal file. The
-final p values are then fdr corrected and the resulting
-q values are also calculated.
+# Simple Group Factor Analysis
+Decomposes a set of expressions into a group expression.
+The expression regulation can be studied via an ANOVA that
+relates it to the observables in the journal file. The
+final p values are then FDR corrected and the resulting
+q values are produced.
 
 The journal and analyte expression file must be ordered
 the same way with respect to the samples that are
 positioned on the columns.
 
+Install with:
+pip install  --index-url https://test.pypi.org/simple/ impetuous-gfa
+
+Visit the active code via:
+https://github.com/richardtjornhammar/impetuous
+
+Visit the published code: 
+https://doi.org/10.5281/zenodo.2594691
+
+Cite using 
+DOI: 10.5281/zenodo.2594691

requirements.txt

 pandas==0.23.4
 numpy==1.13.3
 statsmodels==0.9.0
-sklearn==0.19.0
+scikit-learn==0.19.0
 scipy==0.19.1
 patsy==0.5.0

setup.py

@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
 
 setuptools.setup(
     name="impetuous-gfa",
-    version="0.1.0",
+    version="0.1.2",
     author="Richard Tjörnhammar",
     author_email="richard.tjornhammar@gmail.com",
     description="Impetuous Group Factor Analysis",
@@ -15,8 +15,7 @@ setuptools.setup(
     packages=setuptools.find_packages(),
     classifiers=[
         "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: Apache License 2.0",
+        "License :: OSI Approved :: Apache Software License",
         "Operating System :: OS Independent",
-        "Domain :: Group Factor Analysis",
     ],
 )

src/impetuous.py

@@ -109,5 +109,6 @@ if __name__ == '__main__' :
 
     analyte_df = pd.read_csv(analyte_file,'\t' , index_col=0 )
     journal_df = prune_journal( pd.read_csv(journal_file,'\t', index_col=0 ) )
+
     print ( quantify_groups( analyte_df, journal_df, 'Group ~ Var + C(Cat) ', grouping_file ) )