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
- [Results] (#results)
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.
PyPanda requires Python 2.7. We recommand the following commands to install PyPanda (on Ubuntu and Debian derived systems, also works on OSX):
git clone https://github.com/davidvi/pypanda.git
cd pypanda
sudo python setup.py install
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
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
Import PyPanda library:
from pypanda import Panda
from pypanda import Lioness
import pandas as pdRun 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')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.
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