Forked from David/pypanda. Now I switched to the more efficient QuackenbushLab/pypandas

TODO

I renamed scripts from panda to puma and rebuild the setup. I might have forgotten something somewhere
Stuff is still called pandas in scripts and in this README.
rewrite this README. Anaconda nice to mention. Update the example from pandas to puma. update citations and descriptions. check all links. figures!

Check david's python commands in this README

PyPuma (Python Puma)

Python implementation of PUMA (PANDA Using MicroRNA Associations)

PANDA: 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

Table of Contents

• Panda implementation
• Installation
• Usage
• python
• Terminal
• Results

Panda algorithm

To find agreement between the three input networks first the responsibility (R) is calculated.

Thereafter availability (A) is calculated.

Availability and responsibility are combined with the following formula.

Protein cooperativity and gene co-regulatory networks are updated.

P and C are updated to satisfy convergence.

Hamming distance is calculated every iteration.

Installation

PyPuma requires Python 2.7. We recommand the following commands to install PyPuma (on Ubuntu and Debian derived systems, also works on OSX):

Using a virtual environment

Using python virtual environment is the cleanest installation method.

Cloning git and setting up the python virtual environment:

pip install --user pipenv   #Make sure you have Pipenv
git clone https://github.com/aless80/pypanda.git
cd pypuma
virtualenv pypumaenv #Create a folder for the virtual environment inside the cloned git folder 
source pypumaenv/bin/activate

Installing pypuma:

(pypumaenv)$ pip install -r requirements.txt
(pypumaenv)$ python setup.py install #--user

Complete uninstall of pypuma:

(pypuma)$ deactivate	#Exit
rm -rf pypumaenv
write about uninstalling the setup using --record

Using pip

Using pip on the user's install directory

git clone https://github.com/aless80/pypanda.git
cd pypuma
python setup.py install --user
#to run from the command line you will need to make pypuma executable and add the bin directory to your PATH:
cd bin
chmod +x pypuma
echo "$(pwd):PATH" >> ~/.bashrc
source ~/.bashrc

To run PyPuma 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:

git clone https://github.com/aless80/pypanda.git
cd pypuma
python setup.py install

Usage

Run from the terminal

PyPandas can be run directly from the terminal with the following options:

-h help
-e, --expression (required) expression values
-m, --motif (optional) pair file of motif edges, when not provided analysis continues with Pearson correlation matrix
-p, --ppi (optional) pair file of PPI edges
-f, --remove (optional) remove missing values (default is Fales)
-o, --output (required) output file
-q, --outputlioness (optional) output lioness single sample network
-s, --s1 (required) s1
-x, --s2 (required) s2
-t, --t1 (required) t1
-z, --t2 (required) t2

To run PyPuma on the example data:

pypanda -e ToyData/ToyExpressionData.txt -m ToyData/ToyMotifData.txt -p ToyData/ToyPPIData.txt -f True -o test_puma.txt -q test_lioness.txt -s 0 -x 1 -t 1 -z 2

To reconstruct a single sample Lioness Pearson correlation network:

$ pypanda -e ToyData/ToyExpressionData.txt -o test_puma_pearson.txt -q test_lioness_pearson.txt

Run from python

Fire up your python shell or ipython notebook. Import PyPuma library:

from pypuma import Puma
from pypuma import Lioness
import pandas as pd

Run Puma algorithm, leave out motif and PPI data to use Pearson correlation network:

p = Puma('ToyData/ToyExpressionData.txt', 'ToyData/ToyMotifData.txt', 'ToyData/ToyPPIData.txt', remove_missing=False)

Save the results:

p.save_puma_results(file = 'Toy_Puma.pairs')

Return a network plot:

plot = AnalyzePuma(p)
plot.top_network_plot(top=100, file='top_100_genes.png')

Calculate indegrees for further analysis:

indegree = p.return_puma_indegree()

Calculate outdegrees for further analysis:

outdegree = p.return_puma_outdegree()

Run the Lioness algorithm for single sample networks:

l = Lioness(p)

Save Lioness results:

l.save_lioness_results(file = 'Toy_Lioness.txt')

Return a network plot for one of the Lioness single sample networks:

plot = AnalyzeLioness(l)
plot.top_network_plot(column= 0, top=100, file='top_100_genes.png')

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.