pygna 1.0.2.dev0

Geneset Network Analysis

# PyGNA: a Python framework for geneset network analysis

Current version: 1.0.2-dev

[](http://drone.stracquadaniolab.org/stracquadaniolab/pygna)  

PyGNA is a unified framework for network analysis of high-throughput experiment results. It can be used both as a standalone command line application or it can be included as a package in your own python code.

For an overview of PyGNA functionalities check the infographic below, otherwise dive into the [Getting started](#getting-started) guide. For the complete API check the official [Documentation](#documentation)


## Installation

The easiest and fastest way to install pygna using conda:

$ conda install -c stracquadaniolab -c bioconda -c conda-forge pygna

Alternatively you can install it through pip:

$ pip install pygna

Please note, that pip will not install non Python requirements.

## Getting started

A typical pygna analysis consists of 3 steps:

1. Generate the RWR and SP matrices for the network you are using ( once they are generated, you won’t need to repeat the same step again)

2. Make sure that the input genesets are in the right format. If a network uses entrez ID, and your file is in HUGO symbols, use the pygna utility for the name conversion.

3. Run the analysis you are interested into.

4. Once you have the output tables, you can choose to visualise one or more plots.

Otherwise you can check the snakemake pipeline for the full analysis of a geneset.

We provide the data for a minimum working example in the zip folder named min_working_example. The examples below show some basic analysis that can be carried out with pygna

### Example 1: Running pygna GNT analysis

Running pygna on this input as follows:

$ cd ./your-path/min-working-example/

$ pygna build-RWR-diffusion barabasi.interactome.tsv –output-file interactome_RWR.hdf5

$ pygna test-topology-rwr –number-of-permutations 50 barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 ./ example1

$ pygna paint-datasets-stats interactome_table_RW.csv ./ example1

You can look at the plot of the results in the example1_results.pdf file, and the corresponding table in example1_table_RW.csv.

### Example 2: Running pygna GNA analysis

$ cd ./your-path/min-working-example/

skip this step if the matrix is already computed

$ pygna build-RWR-diffusion barabasi.interactome.tsv –output-file interactome_RWR.hdf5

The association analysis is run N x M times (N number of genesets, M number of pathways), we use only 50 permutations in this example to avoid long computations; however, the recommended value is 1000.

$ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 ./ example2 -B GO_cc_subset.gmt -k –number-of-permutations 50 –show-results

If you don’t include the –show-results flag at the comparison step, plot the matrix as follows

$ pygna paint-comparison-RW example2_table_association_rwr.csv ./ comparison_stats

The -k flag, keeps the -B geneset and permutes only on the set A.

If setname B is not passed, the analysis is run between each couple of setnames in the geneset.

$ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 ./ example2_full –number-of-permutations 50 –show-results

You can look at the plot of the results in the example2_full_RWR_comparison_heatmap.pdf file, and the corresponding table in example_full_table_association_rwr.csv.

## Documentation

The official documentation for pygna can be found on [readthedocs](https://pygna.readthedocs.io/).

## Authors

• Viola Fanfani ([e-mail]): lead developer.

• Giovanni Stracquadanio ([e-mail])

## Citation

A unified framework for geneset network analysis. Viola Fanfani and Giovanni Stracquadanio. bioRxiv [To appear]

## Issues

Please post an issue to report a bug or request new features.