{
    "info": {
        "author": "Ahmed Bassiouni",
        "author_email": "ahmedbassi@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "The *omicexperiment* python package\n===================================\n\nThe omicexperiment package is an open-source, BSD-licensed, Python 3\npackage for analysis of dataframes of 'omic experiments', built upon the\nsolid foundations of the Python scientific stack.\n\nThe omicexperiment package has the goal of providing a pleasant API for\n*rapid* analysis of 'omic experiments' in interactive environments.\n\nThe R bioinformatics community has already provided similar\nimplementations for similar functionality. Examples include DESeqDataSet\n(from the package DeSeq2), MRExperiment (from the package\nmetagenomeSeq), phyloseq-class (from the package phyloseq). To my\nknowledge, there exists no similar powerful functionality available to\nusers of python.\n\nThe philosophy of this package is to build upon solid foundations of the\npython scientific stack and try not to re-invent the wheel. Packages\nsuch as numpy and pandas are powerful optimized libraries in dealing\nwith matrix and tabular data, respectively. This package's backend thus\nconsists almost entirely of pandas DataFrames and pandas APIs.\n\nExample code\n------------\n\n::\n\n    from omicexperiment.experiment.microbiome import MicrobiomeExperiment\n    from omicexperiment.filters import Sample, Taxonomy\n\n    mapping = \"example_map.tsv\"\n    biom = \"example_fungal.biom\"\n    tax = \"blast_tax_assignments.txt\"\n\n    #our Experiment object\n    exp = MicrobiomeExperiment(biom, mapping,tax)\n\n    #include only samples with more than 90000 counts\n    exp.filter(Sample.count > 90000)\n\n    ##collapse the OTUs in the _data_ DataFrame to genus level assignment\n    exp.filter(Taxonomy.groupby('genus')) #any taxonomic rank can be passed\n\nInstallation\n------------\n\nJust clone the git repository. Hopefully very shortly, the package will\nbe uploaded to PyPi and will be pip-installable.\n\nDue to bugs in handling numpy installation in setuptools, it is better\nto install numpy first using 'pip install numpy'.\n\nDependencies\n------------\n\nCurrent omicexperiment dependencies:\n\n-  numpy\n-  scipy\n-  pandas\n-  scikit-bio\n-  biom-format\n-  lxml\n-  pygal\n\nLicense\n-------\n\nThis package is released as open-source, under a BSD License. Please see\nCOPYING.txt.\n\nDocumentation\n-------------\n\nPlease see the doc folder in the package\n(https://github.com/bassio/omicexperiment/tree/master/doc), it contains\nJupyter notebooks that runs you through most of the current package\nfunctionality.\n\nAcknowledgements\n----------------\n\nThank you to all the great python bioinformatics and data science\ncommunity for releasing their code as open source.\n\nContributing and use in your research\n-------------------------------------\n\nPlease be advised that at this stage, this package is still alpha\nsoftware. Testing is still not implemented so be careful. But if you are\ninterested in contributing to this project, please contact. Contributors\nare welcome to improve the software. Nowadays, I expect the package to\ncontinue growing slowly as I add functionality and improvements as long\nas I need it in my own research. I will be initially focusing on\ndeveloping the MicrobiomeExperiment part.\n\n0.1.2-dev\n---------\n\n-  Development version(s) dedicated to fixing PyPI installation.\n\n0.1.1\n-----\n\n-  Initial version",
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        "home_page": "https://github.com/bassio/omicexperiment",
        "keywords": "bioinformatics",
        "license": "BSD",
        "maintainer": null,
        "maintainer_email": null,
        "name": "omicexperiment",
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