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49 changes: 26 additions & 23 deletions UserGuide.md
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Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
## 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)).
This repo is based on the following repos:
- [https://github.com/aless80/pypanda](https://github.com/aless80/pypanda),
- [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](netZooPy/netZooPy/pypuma/run_puma.py) and [netZooPy/netZooPy/pypuma/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
Expand All @@ -25,7 +27,8 @@ C and MATLAB code: [https://github.com/mararie/PUMA](https://github.com/mararie/
_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/).

* **[LIONESS](https://arxiv.org/abs/1505.06440)** (Linear Interpolation to Obtain Network Estimates for Single Samples)
* **[LIONESS](https://arxiv.org/abs/1505.06440)** (Linear Interpolation to Obtain Network Estimates for Single Samples)
* **[LIONESSR](https://doi.org/10.1016/j.isci.2019.03.02)** The R version
_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).
Expand Down Expand Up @@ -61,22 +64,22 @@ Hamming distance is calculated every iteration.


## 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:
PyPanda runs on Python 3. 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
git clone https://github.com/netZoo/netZooPy.git
cd netZooPy
```
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
(pypandaenv)$ pip3 install -r requirements.txt
(pypandaenv)$ python3 setup.py install --record files.txt
```
Uninstall pypanda from virtual environment:
```no-highlight
Expand All @@ -91,9 +94,9 @@ 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
git clone https://github.com/netZooPy/netZooPy.git
cd pypanda
python setup.py install --user
python3 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
Expand All @@ -104,11 +107,11 @@ 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:
To run pypanda from Windows (tested on Windows 10) install Git (https://git-scm.com/downloads) and Anaconda Python3 (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
git clone https://github.com/netZooPy/netZooPy.git
cd netZooPy
python3 setup.py install
```

## Usage
Expand All @@ -130,18 +133,18 @@ python run_panda.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData
```
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
python3 run_panda.py -e ../../tests/ToyData/ToyExpressionData.txt -m ../../tests/ToyData/ToyMotifData.txt -p ../../tests/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
python3 run_puma.py -e ../../tests/ToyData/ToyExpressionData.txt -m ../../tests/ToyData/ToyMotifData.txt -p ../../tests/ToyData/ToyPPIData.txt -o output_puma.txt -i ../../tests//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
python3 run_puma.py -e ../../tests/ToyData/ToyExpressionData.txt -m ../../tests/ToyData/ToyMotifData.txt -p ../../tests/ToyData/ToyPPIData.txt -i ../../tests/ToyData/ToyMiRList.txt -o output_puma.txt -q output_lioness.txt
```
For pypuma see also [PyPuma](https://github.com/aless80/pypuma#installation).
For pypuma see also [PyPuma](https://github.com/netZooPy/netZooPy/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.
Expand All @@ -154,7 +157,7 @@ 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)
panda_obj = Panda('../../tests/ToyData/ToyExpressionData.txt', '../../tests/ToyData/ToyMotifData.txt', '../../tests/ToyData/ToyPPIData.txt', remove_missing=False)
```
Save the results:
```python
Expand All @@ -172,7 +175,7 @@ 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)
panda_obj = Panda('../../tests/ToyData/ToyExpressionData.txt', '../../tests/ToyData/ToyMotifData.txt', '../../tests/ToyData/ToyPPIData.txt', remove_missing=False, keep_expression_matrix=True)
lioness_obj = Lioness(panda_obj)
```
Save Lioness results:
Expand All @@ -187,7 +190,7 @@ 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')
puma_obj = Puma('../../tests/ToyData/ToyExpressionData.txt', '../../tests/ToyData/ToyMotifData.txt', '../../tests/ToyData/ToyPPIData.txt','../../tests/ToyData/ToyMiRList.txt')
```

## Toy data
Expand Down Expand Up @@ -216,4 +219,4 @@ Sample1 Sample2 Sample3 Sample4
-0.117475863987 0.494923925853 0.0518448588965 -0.0584810456421

TF, Gene and Motif order is identical to the panda output file.
```
```