Metadata-Version: 2.1
Name: cimr
Version: 0.1.7
Summary: continuous integration of association summary statistics for network analysis
Home-page: https://github.com/greenelab/cimr
Author: YoSon Park
Author-email: yoson.park@gmail.com
License: BSD 3-Clause
Description: 
        
        ****
        cimr
        ****
        
        
        ***************************************
        cimr is not yet released for public use
        ***************************************
        
        =====================================================
        continuous integration and analysis of complex traits
        =====================================================
        
        ==========
        YoSon Park
        ==========
        
        **Useful links**:
        `Source repository <https://github.com/greenelab/cimr>`_ |
        `Issues & Ideas <https://github.com/greenelab/cimr/issues>`_ |
        `Documentation <https://cimr.readthedocs.io>`_ |
        `cimr-d <https://github.com/greenelab/cimr-d>`_
        
        
        *cimr* (continuously integrated meta-resource) is a convenience tool
        for continuous analyses of variant-based association results from
        GWAS (genome-wide association studies), eQTL (expression-quantitative
        trait loci mapping) or other association studies. cimr aims to
        streamline the pre-analysis processing steps, provide standardized
        input files and automate scripting for standard downstream analyses.
        
        
        
        ============
        Installation
        ============
        
        -----------------
        Installing python
        -----------------
        
        cimr requires python :math: `\ge` 3.6. Installation of data analysis bundles
        such as `miniconda <https://conda.io/miniconda.html>`_ or
        `anaconda <https://www.anaconda.com/download/>`_ are recommended and will
        install all python packages cimr depends on. However, all required python
        packages can be downloaded and installed with setup.py or requirements.txt
        provided here.
        
        
        ------------------
        Installing git lfs
        ------------------
        
        cimr-d and some functionalities in cimr may use
        `git large file storage (LFS) <https://git-lfs.github.com/>`_ .
        See how to install
        `git <https://www.atlassian.com/git/tutorials/install-git>`_ .
        
        
        To install git-lfs on Ubuntu, run::
        
            curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
            sudo apt-get install -y git git-lfs
            git-lfs install
        
        
        Alternatively, you can install git-lfs through conda::
        
            conda install -c conda-forge git-lfs && git lfs install
        
        
        ---------------
        Installing cimr
        ---------------
        
        You can use pip to install the latest stable release of cimr::
        
            pip3 install cimr
        
        
        If you want to try out the nightly build of cimr at your own risk,
        clone the repository from git::
        
            git clone https://github.com/greenelab/cimr.git
            cd cimr
            pip3 install -r requirements.txt
            python3 setup.py build
            python3 setup.py install
        
        
        
        =================
        Analysis examples
        =================
        
        ------------------------------------------------------------------------
        Quality assurance and processing of association summary statistics files
        ------------------------------------------------------------------------
        
        You can use cimr to standardize public datasets using a yaml file, e.g.::
        
            # example.yaml
        
            data_file:
                description: >-
                    Global Lipid Genetics Consortium GWAS results for high-density
                    cholesterol levels
                location:
                    url: https://zenodo.org/record/3338180/files/HDL_Cholesterol.txt.gz
                    md5: 2b28816a0a363db1a09ad9a6ba1a6620
                columns:
                    variant_id: panel_variant_id
                    variant_chrom: chromosome
                    variant_pos: position
                    rsnum: variant_id
        
            data_info:
                citation: 10.1038/ng.2797
                data_source: http://lipidgenetics.org/
                data_type: gwas
                context: hdl cholesterol
                build: b38
                sample_size: 187167
                n_cases: na
                can_be_public: true
        
            method:
                name: linear regression
                tool: PLINK;SNPTEST;EMMAX;Merlin;GENABEL;MMAP
                website: >-
                    http://zzz.bwh.harvard.edu/plink/download.shtml;
                    https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html;
                    https://genome.sph.umich.edu/wiki/EMMAX;
                    https://csg.sph.umich.edu/abecasis/Merlin/tour/assoc.html;
                    http://www.genabel.org/sites/default/files/html_for_import/GenABEL_tutorial_html/GenABEL-tutorial.html;
                    https://mmap.github.io/
        
            contributor:
                name: Contributor Name
                github: contributorgithub
                email: contributoremail@emaildomain.emailextension
        
        
        
        Details can be found in the
        `cimr-d contributions.md <https://github.com/greenelab/cimr-d/blob/master/doc/contributing.md>`_.
        
        
        Once the yaml file is prepared, you can run cimr locally::
        
            cimr processor -process -yaml-file example.yaml
        
Keywords: gene network ci
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
