Metadata-Version: 1.2
Name: pygna
Version: 1.1.0.dev0
Summary: Geneset Network Analysis
Home-page: https://github.com/stracquadaniolab/pygna
Author: Viola Fanfani, Giovanni Stracquadanio
Author-email: v.fanfani@sms.ed.ac.uk
License: MIT
Description: # PyGNA: a Python framework for geneset network analysis
        
        Current version: 1.1.0-dev
        
        [![Build Status](http://drone.stracquadaniolab.org/api/badges/stracquadaniolab/pygna/status.svg)](http://drone.stracquadaniolab.org/stracquadaniolab/pygna)
        ![platform](https://anaconda.org/stracquadaniolab/pygna/badges/platforms.svg)
        ![anaconda](https://anaconda.org/stracquadaniolab/pygna/badges/version.svg)
        
        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.
        
        ![Infographic](docs/pygna_infographic-01.png)
        
        ## 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 visualize one or more plots.
        
        Otherwise you can check our [snakemake workflow](https://github.com/stracquadaniolab/workflow-pygna) for the full geneset analysis;
        our workflow contains sample data that you can use to familiarize with our software.
        
        
        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 (v.fanfani@sms.ed.ac.uk): lead developer.
        - Giovanni Stracquadanio (giovanni.stracquadanio@ed.ac.uk)
        
        ## Citation
        
        A unified framework for geneset network analysis. Viola Fanfani and Giovanni Stracquadanio. bioRxiv 699926 [To appear]
        
        ## Issues
        
        Please post an issue to report a bug or request new features.
        
Keywords: Bioinformatics Network Statistics
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3
