Merge pull request #3 from biomedbigdata/dev

Dev updates

.github/workflows/python-publish.yml

@@ -18,7 +18,7 @@ permissions:
 jobs:
   deploy:
 
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
 
     steps:
     - uses: actions/checkout@v3

README.md

@@ -34,7 +34,7 @@ The patients id or samples ids must be the same in the "expression_data" and  "m
 
 In the condition column of the meta DataFrame, the control samples should be encoded as 0 and case samples as 1.
 
-GRN network should be provided a prior, You can either use an experimental validated GRN or learn it from control samples, we recommend using software like [arboreto](https://github.com/aertslab/arboreto), since you can use its output directly to DysRegNet.
+The gene regulatory network should be provided by the user. You can either use an experimental validated GRN or learn it from control samples. We recommend using software like [arboreto](https://github.com/aertslab/arboreto) since you can use its output directly to DysRegNet.
 
 
 
@@ -53,23 +53,23 @@ Additionally, you can provide the following parameters:
 
 - ConCov: List of continuous covariates. They should match the name of their columns in the meta Dataframe.
 
-- zscoring: Boolean, default: True. zscoring of expression data (if needed).
+- zscoring: Boolean, default: False. zscoring of expression data (if needed).
 
-- bonferroni_alpha:P-value threshold for multiple testing correction
+- bonferroni_alpha: P-value threshold for multiple testing correction
 
 - normaltest: Boolean. If True, Run a normality test for residuals "scipy.stats.normaltest". If residuals are not normal, the edge will not be considered in the analysis. 
 
 - normaltest_alpha: p-value threshold for normaltest (if True).
 
 - R2_threshold: R-squared (R2) threshold from 0 to 1 (optional).  If the fit is weaker, the edge will not be considered in the analysis. 
 
-- direction_condition: Boolean. If True: only include dysregulation that are relevant for the interactions (down regulation of an activation or up regulation of a supressions). Please check the paper for more details.
+- direction_condition: Boolean. If True: only include dysregulation that are relevant for the interactions (down-regulation of an activation or up-regulation of a supressions). Please check the paper for more details.
 
 
 ## Get Started
 
 
-Please note, that the functions are annotated with dockstrings for more details.
+Please note that the functions are annotated with dockstrings for more details.
 
 Import the package and pandas:
 
@@ -127,11 +127,11 @@ The expected run time for the installation and running the demo dataset on a "no
 
 ## The output
 
-The package output a DataFrame that represents patient-specific dysregulated edges. The columns represent edges and the rows patient ids. 
+The package outputs a data frame that represents patient-specific dysregulated edges. The columns represent edges, and the rows are patient IDs. 
 
 In the result table, a value of 0 means that the edge is not significantly dysregulated (different from control samples). Otherwise, the z-score is reported, with a positive in case of activation and a negative sign in case of repression (different than the sign of the residual). 
 
-The method "get_results_binary()", outputs binarized dysregulations instead of z-scores. 
+The method "get_results_binary()" outputs binarized dysregulations instead of z-scores. 
 
 
 ## Example

setup.py

@@ -11,7 +11,7 @@
 
 
 setup(name='dysregnet',
-      version='0.0.4',
+      version='0.0.5',
       description='DysRegNet',
       long_description=README,
       long_description_content_type="text/markdown",
@@ -24,6 +24,8 @@
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
         "Topic :: Scientific/Engineering :: Bio-Informatics",
       ],

src/dysregnet/functions.py

@@ -105,7 +105,7 @@ def dyregnet_model(data):
 
                         # condition of direction
                         cond = True
-                        direction = np.sign(results.params[1])
+                        direction = np.sign(results.params.iloc[1])
 
 
                         # two sided p_value as default