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            "License :: OSI Approved :: MIT License",
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            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# PyGNA: a Python framework for geneset network analysis\n\nCurrent version: 2.0.8-dev\n\n[![Build Status](http://drone.stracquadaniolab.org/api/badges/stracquadaniolab/pygna/status.svg)](http://drone.stracquadaniolab.org/stracquadaniolab/pygna)\n[![Anaconda-Server Badge](https://anaconda.org/stracquadaniolab/pygna/badges/platforms.svg)](https://anaconda.org/stracquadaniolab/pygna)\n[![Anaconda-Server Badge](https://anaconda.org/stracquadaniolab/pygna/badges/version.svg)](https://anaconda.org/stracquadaniolab/pygna)\n\nPyGNA 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.\n\nFor an overview of PyGNA functionalities check the infographic below, otherwise dive into the [Getting started](#getting-started) guide.\n\n![Infographic](docs/pygna_infographic-01.png)\n\n## Installation\n\nThe easiest and fastest way to install `pygna` using `conda`:\n\n    $ conda install -c stracquadaniolab -c bioconda -c conda-forge pygna\n\nAlternatively you can install it through `pip`:\n\n    $ pip install pygna\n\nPlease note, that `pip` will not install non Python requirements.\n\n## Getting started\n\nA typical `pygna` analysis consists of 3 steps:\n\n1. 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)\n2. 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.\n3. Run the analysis you are interested into.\n4. Once you have the output tables, you can choose to visualize one or more plots.\n\nOtherwise you can check our [snakemake workflow](https://github.com/stracquadaniolab/workflow-pygna) for the full geneset analysis;\nour workflow contains sample data that you can use to familiarize with our software.\n\n\nThe examples below show some basic analysis that can be carried out with pygna\n\n### Example 1: Running pygna GNT analysis\n\nRunning `pygna` on this input as follows:\n\n    $ cd ./your-path/min-working-example/\n\n    $ pygna build-rwr-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5\n\n    $ pygna test-topology-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_topology_rwr.csv --number-of-permutations 1000 --cores 4\n\n    $ pygna paint-datasets-stats table_topology_rwr.csv barplot_rwr.pdf\n\nYou can look at the plot of the results in the `barplot_rwr.pdf` file, and the corresponding table in  `table_topology_rwr.csv`.\n\n### Example 2: Running pygna GNA analysis\n\n    $ cd ./your-path/min-working-example/\n\nskip this step if the matrix is already computed\n\n    $ pygna build-RWR-diffusion barabasi.interactome.tsv --output-file interactome_RWR.hdf5\n\nThe 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.\n\n    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_association_rwr.csv -B disgenet_cancer_groups_subset.gmt --keep --number-of-permutations 100 --cores 4\n\nIf you don't include the --results-figure flag at the comparison step, plot the matrix as follows\n\n    $ pygna paint-comparison-matrix table_association_rwr.csv heatmap_association_rwr.png --rwr --annotate\n\nThe -k flag, keeps the -B geneset and permutes only on the set A.\n\nIf setname B is not passed, the analysis is run between each couple of setnames in the geneset.\n\n    $ pygna test-association-rwr barabasi.interactome.tsv disgenet_cancer_groups_subset.gmt interactome_RWR.hdf5 table_within_comparison_rwr.csv --number-of-permutations 100 --cores 4\n\n    $ pygna paint-comparison-matrix table_within_comparison_rwr.csv heatmap_within_comparison_rwr.png --rwr --single-geneset\n\nYou can look at the plot of the results in the `heatmap_within_comparison_rwr.png` file, and the corresponding table in  `table_within_comparison_rwr.csv`.\n\n\n## Documentation\n\nThe official documentation for `pygna` can be found on [readthedocs](https://pygna.readthedocs.io/).\n\n## Authors\n\n- Viola Fanfani (v.fanfani@sms.ed.ac.uk): lead developer.\n- Giovanni Stracquadanio (giovanni.stracquadanio@ed.ac.uk)\n\n## Citation\n\nA unified framework for geneset network analysis. Viola Fanfani, Giovanni Stracquadanio. bioRxiv 699926; doi: https://doi.org/10.1101/699926\n\n## Issues\n\nPlease post an issue to report a bug or request new features.\nWe are now working on code refactoring and standardising the I/O behaviour. \n\n\n",
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