Merge pull request #6 from biomedbigdata/dev

Release 0.1.0

Author: JohannesKersting

README.md

@@ -1,103 +1,75 @@
 [![PyPI version](https://badge.fury.io/py/dysregnet.svg)](https://badge.fury.io/py/dysregnet)
 
 # DysRegNet package
-
-
 DysRegNet, is a  method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. DysRegNet uses linear models to account for confounders and residual-derived z-scores to assess significance.
-
-
 ## Installation
 To install the package from PyPI please run:
-
-`pip install dysregnet`
-
+```bash
+pip install dysregnet
+```
 
 or you can install it from git:
-
-`git clone https://github.com/biomedbigdata/DysRegNet_package.git  && cd DysRegNet_package`
-
-`python setup.py install`
-
-
+```bash
+git clone https://github.com/biomedbigdata/DysRegNet_package.git  && cd DysRegNet_package
+python setup.py install
+```
 
 ## Data input
+The inputs of the  package are the following Pandas DataFrame objects:
 
-The inputs of the  package are the following Pandas DataFrame object:
-
-
-- expression_data  - Gene expression matrix with the format: patients as rows (first column - patients/samples ids), and genes as columns.
+- expression_data  - Gene expression matrix in the format: patients as rows (first column - patients/samples ids), and genes as columns.
 - GRN - Gene Regulatory Network (GRN) with two columns in the following order ['TF', 'target'].
 - meta -  Metadata with the first column containing patients/samples ids and other columns for the condition and the covariates.
 
-
 The patients id or samples ids must be the same in the "expression_data" and  "meta". Additionally, gene names or ids must match the ones in the "GRN" DataFrame. 
 
 In the condition column of the meta DataFrame, the control samples should be encoded as 0 and case samples as 1.
 
 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.
 
-
-
-
-
 ## Parameters 
-
-
 Additionally, you can provide the following parameters:
 
-
-            
 - conCol: Column name for the condition in the meta DataFrame.
 
 - CatCov: List of categorical variable names. They should match the name of their columns in the meta Dataframe.
 
 - ConCov: List of continuous covariates. They should match the name of their columns in the meta Dataframe.
 
-- zscoring: Boolean, default: False. zscoring of expression data (if needed).
+- zscoring: If True, DysRegNet will scale the expression of each gene and all continuous confounders based on their mean and standard deviation in the control samples.
 
 - 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: If True, DysRegNet runs 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).
+- 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:  If True, DysRegNet will only consider case samples with positive residuals (target gene overexpressed) for models with a negative TF coefficient as potentially dysregulated. Similarly, for positive TF coefficients, only case samples with negative residuals are considered. Please check the paper for more details.
 
+The parameters are also annotated with dockstrings for more details.
 
 ## Get Started
-
-
-Please note that the functions are annotated with dockstrings for more details.
-
 Import the package and pandas:
-
-
 ```python
 import dysregnet
 import pandas as pd
 ```
 
-
-
 Define the confounding variables or the design matrix 
-
 ```python
-# The condition column
+# define condition column (0 indicated control, 1 indicates case)
 conCol='condition'
 
-# categorical variable columns in meta dataframe.
-# these columns will be transformed to variables for regression 
+# define categorical confounder columns in meta dataframe 
 CatCov=['race','gender']  
 
-# continuous variable columns in meta dataframe.
+# define continuous confounder columns in meta dataframe.
 ConCov=['birth_days_to']
 ```
 
-
 Run DysRegNet
-
 ```python
 data=dysregnet.run(expression_data=expr,
                    meta=meta, 
@@ -107,46 +79,36 @@ data=dysregnet.run(expression_data=expr,
                    ConCov=ConCov,
                    direction_condition=True,
                    normaltest=True,
-                   R2_threshold=.2 )
+                   R2_threshold=.2)
 
-# results table
+# get the patient-specific dysregulate networks
 data.get_results()
 
-# or a binary result
-
+# or with binary edges
 data.get_results_binary()
 
 # get R2 values, coefficients, and coefficient p-values for all models/edges
 data.get_model_stats()
-
 ```
 
-The expected run time for the installation and running the demo dataset on a "normal" desktop computer is around 3~5 minutes.
-
-
-
 ## The output
-
 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). 
+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. 
 
 The method "get_results_binary()" outputs binarized dysregulations instead of z-scores. 
 
+"get_model_stats()" outputs R2 values, coefficients, and coefficient p-values for all models/edges.
 
 ## Example
 
 A simple example for running DysRegNet:
 ([Notebook](https://github.com/biomedbigdata/DysRegNet_package/blob/main/test.ipynb)/[Google Colab](https://colab.research.google.com/github/biomedbigdata/DysRegNet_package/blob/main/test.ipynb)).
 
-
 You will need to download the demo dataset and extract the files into test dataset/
 
 Link for the demo dataset: https://figshare.com/ndownloader/files/35142652
 
-
-
 ## Cite
-
 "DysRegNet: Patient-specific and confounder-aware dysregulated network inference"
-Olga Lazareva*, Zakaria Louadi*, Johannes Kersting, Jan Baumbach, David B. Blumenthal, Markus List. bioRxiv 2022.04.29.490015; doi: https://doi.org/10.1101/2022.04.29.490015. * equal first-authors
+Johannes Kersting*, Olga Lazareva*, Zakaria Louadi*, David B. Blumenthal, Jan Baumbach, Markus List. bioRxiv 2022.04.29.490015; doi: https://doi.org/10.1101/2022.04.29.490015. * equal first-authors