pypanda: A Python repository from mararie

PyPanda (Python Panda)

Python implementation of PANDA (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

Panda implementation
Installation
Usage
- iPython
- Terminal
[Results] (#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

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

With root access

git clone https://github.com/davidvi/pypanda.git
cd pypanda
sudo python setup.py install

Without root access

git clone https://github.com/davidvi/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

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:

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

Usage

Run from the terminal

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

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

To run PyPanda on the example data:

$ pypanda -e ToyData/ToyExpressionData.txt -m ToyData/ToyMotifData.txt -p ToyData/ToyPPIData.txt -f True -o test_panda.txt -q test_lioness.txt

To reconstruct a single sample Lioness Pearson correlation network:

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

Run from iPython notebook

Import PyPanda library:

from pypanda import Panda
from pypanda import Lioness
import pandas as pd

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

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

Save the results:

p.save_panda_results(file = 'Toy_Panda.pairs')

Return a network plot:

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

Calculate indegrees for further analysis:

indegree = p.return_panda_indegree()

Calculate outdegrees for further analysis:

outdegree = p.return_panda_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.

mararie/pypanda