Merge pull request #2 from netZoo/devel

Devel

Author: marouenbg

CONDUCT.md

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+As contributors and maintainers of this project, we pledge to respect all people who contribute through reporting issues, posting feature requests, updating documentation, submitting pull requests or patches, and other activities.
+
+We are committed to making participation in this project a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.
+
+Examples of unacceptable behavior by participants include the use of sexual language or imagery, derogatory comments or personal attacks, trolling, public or private harassment, insults, or other unprofessional conduct.
+
+Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed from the project team.
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by opening an issue or contacting one or more of the project maintainers.
+
+This Code of Conduct is adapted from the Contributor Covenant (http://contributor-covenant.org), version 1.0.0, available at http://contributor-covenant.org/version/1/0/0/

LICENSE

@@ -1,219 +1,17 @@
 ## Description
-Forked from [https://github.com/QuackenbushLab/pypanda](https://github.com/QuackenbushLab/pypanda), 
-which was based on [https://github.com/davidvi/pypanda](https://github.com/davidvi/pypanda).  
-Compared to QuackenbushLab/pypanda this repository adds the Python implementation of PUMA ([run_puma.py](run_puma.py) and [pypanda/puma.py](pypanda/puma.py)). 
-NaN values in normalized matrices are replaced with values normalized by the overall z-score. This allows running the Toy Data provided in this repository.   
-  
-## Table of Contents
-* [Links to literature](#links-to-literature)
-* [Panda algorithm](#panda-algorithm)  
-* [Installation](#installation)  
-* [Usage](#usage)  
-  * [Run from terminal](#run-from-terminal)
-  * [Run from python](#run-from-python)
-* [Toy data](#toy-data)
-* [Results](#results)
+netZooPy is a Python package of network methods.
 
+## Quick guide
+`git clone https://github.com/netZoo/netZooPy.git` into your local disk.
 
-## Links to literature 
+## User guide
+Please refer to the [user guide](UserGuide.md) for installation instructions.
 
-* **[PUMA](https://static-content.springer.com/esm/art%3A10.1186%2Fs13045-017-0465-4/MediaObjects/13045_2017_465_MOESM3_ESM.pdf)** (PANDA Using MicroRNA Associations)  
-_Manuscript in preparation, used in [PUMA](https://static-content.springer.com/esm/art%3A10.1186%2Fs13045-017-0465-4/MediaObjects/13045_2017_465_MOESM3_ESM.pdf)._  
-C and MATLAB code: [https://github.com/mararie/PUMA](https://github.com/mararie/PUMA)
+## License
+The software is free and is licensed under the GNU General License v3.0, see the file [LICENSE](LICENSE) for details.
 
-* **[PANDA](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064832)** Passing Attributes between Networks for Data Assimilation  
-_Glass K, Huttenhower C, Quackenbush J, Yuan GC. Passing Messages Between Biological Networks to Refine Predicted Interactions, PLoS One, 2013 May 31;8(5):e64832_  
-Original PANDA C++ code: [http://sourceforge.net/projects/panda-net/](http://sourceforge.net/projects/panda-net/).  
+## Feedback/Issues
+Please report any issues to the [issues page](https://github.com/netZoo/netZooPy/issues).
 
-* **[LIONESS](https://arxiv.org/abs/1505.06440)** (Linear Interpolation to Obtain Network Estimates for Single Samples)   
-_Marieke Lydia Kuijjer, Matthew Tung,GuoCheng Yuan,John Quackenbush, Kimberly Glass. Estimating sample-specific regulatory networks_  
-
-LIONESS can be used to estimate single-sample networks using aggregate networks made with any network reconstruction algorithm (http://arxiv.org/pdf/1505.06440.pdf).
-
-## Panda algorithm
-<img src="img/panda.png" height="300">  
-
-To find agreement between the three input networks first the responsibility (R) is calculated.  
-
-<img src="img/responsibility.png" height="30">  
-
-Thereafter availability (A) is calculated.  
-
-<img src="img/availability.png" height="30">  
-
-Availability and responsibility are combined with the following formula.  
-
-<img src="img/combine.png" height="30">  
-
-Protein cooperativity and gene co-regulatory networks are updated.  
-
-<img src="img/cooperativity.png" height="30">  
-<img src="/img/co-regulatory.png" height="30">  
-
-P and C are updated to satisfy convergence.  
-
-<img src="img/p.png" height="30">  
-<img src="/img/c.png" height="30">  
-
-Hamming distance is calculated every iteration.  
-
-<img src="img/hamming.png" height="40">  
-
-
-## Installation
-PyPanda runs on Python 2.7. You can either run the pypanda script directly (see [Usage](#usage)) or install it on your system. We recommend the following commands to install pypandas on UNIX systems:
-#### Using  a virtual environment
-Using [python virtual environments](http://docs.python-guide.org/en/latest/dev/virtualenvs/) is the cleanest installation method. 
-
-Cloning git and setting up a [python virtual environment](http://docs.python-guide.org/en/latest/dev/virtualenvs/):
-```no-highlight
-pip install --user pipenv   #Make sure you have pipenv
-git clone https://github.com/aless80/pypanda.git
-cd pypanda
-```
-Creating a virtual environment and installing pypanda:
-```no-highlight
-virtualenv pypandaenv #virtual environment created in a folder inside the git folder 
-source pypandaenv/bin/activate
-(pypandaenv)$ pip install -r requirements.txt
-(pypandaenv)$ python setup.py install --record files.txt
-```
-Uninstall pypanda from virtual environment:
-```no-highlight
-cat files.txt | xargs rm -rf
-```
-Complete removal of virtual environment and pypanda:
-```no-highlight
-(pypanda)$ deactivate	#Quit virtual environment
-rm -rf pypandaenv
-```
-
-#### Using pip 
-Never use ~~sudo pip~~. Instead you can use pip on the user's install directory:
-```no-highlight
-git clone https://github.com/aless80/pypanda.git
-cd pypanda
-python setup.py install --user
-#to run from the command line you will need to make pypanda executable and add the bin directory to your PATH:
-cd bin
-chmod +x pypanda
-echo "$(pwd):PATH" >> ~/.bashrc
-source ~/.bashrc
-```
-
-```no-highlight
-pip uninstall pypanda
-```
-To run pypanda from Windows (tested on Windows 10) install Git (https://git-scm.com/downloads) and Anaconda Python2.7 (https://www.continuum.io/downloads) and from the Anaconda prompt run:
-```no-highlight
-git clone https://github.com/aless80/pypanda.git
-cd pypanda
-python setup.py install
-```
-
-## Usage
-#### Run from terminal
-pypanda (or pypuma) can be run directly from the terminal with the following options:
-```
--h help
--e, --expression: expression values
--m, --motif: pair file of motif edges, or Pearson correlation matrix when not provided 
--p, --ppi: pair file of PPI edges
--o, --output: output file
--i, --mir: mir data miR file (only for pypuma)
--r, --rm_missing
--q, --lioness: output for Lioness single sample networks 
-```
-To run pypanda on toy data:
-```
-python run_panda.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData.txt -p ./ToyData/ToyPPIData.txt -o output_panda.txt
-```
-To reconstruct a single sample Lioness Pearson correlation network (this can take some time):
-```python
-python run_panda.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData.txt -p ./ToyData/ToyPPIData.txt -o output_panda.txt -q output_lioness.txt
-```
-
-To run pypuma on toy data:
-```python
-python run_puma.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData.txt -p ./ToyData/ToyPPIData.txt -o output_puma.txt -i ./ToyData/ToyMiRList.txt
-```
-To reconstruct a single sample Lioness Pearson correlation network using pypuma (this can take some time):
-```python
-python run_puma.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData.txt -p ./ToyData/ToyPPIData.txt -i ToyData/ToyMiRList.txt -o output_puma.txt -q output_lioness.txt
-```
-For pypuma see also [PyPuma](https://github.com/aless80/pypuma#installation). 
-
-#### Run from python
-Fire up your python shell or ipython notebook. Use the python installation in the virtual environment if you installed pypanda there. 
-
-Import the classes in the pypanda library:
-```python
-from pypanda.panda import Panda
-from pypuma.puma import Puma
-from pypanda.lioness import Lioness
-```
-Run the Panda algorithm, leave out motif and PPI data to use Pearson correlation network:
-```python
-panda_obj = Panda('ToyData/ToyExpressionData.txt', 'ToyData/ToyMotifData.txt', 'ToyData/ToyPPIData.txt', remove_missing=False)
-```
-Save the results:
-```python
-panda_obj.save_panda_results('Toy_Panda.pairs.txt')
-```
-Return a network plot:
-
-```python
-panda_obj.top_network_plot(top=70, file='top_genes.png')
-```
-Calculate in- and outdegrees for further analysis:
-```python
-indegree = panda_obj.return_panda_indegree()
-outdegree = panda_obj.return_panda_outdegree()
-```
-To run the Lioness algorithm for single sample networks, first run panda (or puma) using the keep_expression_matrix flag, then use Lioness as follows:
-```python
-panda_obj = Panda('ToyData/ToyExpressionData.txt', 'ToyData/ToyMotifData.txt', 'ToyData/ToyPPIData.txt', remove_missing=False, keep_expression_matrix=True)
-lioness_obj = Lioness(panda_obj)
-```
-Save Lioness results:
-```python
-lioness_obj.save_lioness_results('Toy_Lioness.txt')
-```
-Return a network plot for one of the Lioness single sample networks:
-```python
-plot = AnalyzeLioness(lioness_obj)
-plot.top_network_plot(column= 0, top=100, file='top_100_genes.png')
-```
-
-Run the Puma algorithm, leave out motif and PPI data to use Pearson correlation network:
-```python
-puma_obj = Puma('ToyData/ToyExpressionData.txt', 'ToyData/ToyMotifData.txt', 'ToyData/ToyPPIData.txt','ToyData/ToyMiRList.txt')
-```
-
-## Toy data
-The example gene expression data that we have available here contains gene expression profiles for different samples in the columns. Of note, this is just a small subset of a larger gene expression dataset. We provided these "toy" data so that the user can test the method. 
-
-However, if you plan to model gene regulatory networks on your own dataset, you should use your own expression data as input.
-
-## Results
-```
-Example Panda output:
-TF  Gene  Motif Force
----------------------
-CEBPA	AACSL	0.0	-0.951416589143
-CREB1	AACSL	0.0	-0.904241609324
-DDIT3	AACSL	0.0	-0.956471642313
-E2F1	AACSL	1.0	3.6853160511
-EGR1	AACSL	0.0	-0.695698519643
-
-Example lioness output:
-Sample1 Sample2 Sample3 Sample4
--------------------------------
--0.667452814003	-1.70433776179	-0.158129613892	-0.655795512803
--0.843366539284	-0.733709815256	-0.84849895139	-0.915217389738
-3.23445386464	2.68888472802	3.35809757371	3.05297381396
-2.39500370135	1.84608635425	2.80179804094	2.67540878165
--0.117475863987	0.494923925853	0.0518448588965	-0.0584810456421
-
-TF, Gene and Motif order is identical to the panda output file.
-```
+## Code of conduct
+Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.

README.md

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-<<<<<<< HEAD
-# netZooPy
-=======
 ## Description
 Forked from [https://github.com/QuackenbushLab/pypanda](https://github.com/QuackenbushLab/pypanda), 
 which was based on [https://github.com/davidvi/pypanda](https://github.com/davidvi/pypanda).  
@@ -219,5 +216,4 @@ Sample1 Sample2 Sample3 Sample4
 -0.117475863987	0.494923925853	0.0518448588965	-0.0584810456421
 
 TF, Gene and Motif order is identical to the panda output file.
-```
->>>>>>> pypanda/devel-update
+```