Metadata-Version: 1.1
Name: dimepy
Version: 0.1.1
Summary: Python package for the high-thoroughput nontargeted metabolite fingerprinting of nominal mass direct injection mass spectrometry.
Home-page: http://www.github.com/KeironO/dimepy
Author: Keiron O'Shea
Author-email: keo7@aber.ac.uk
License: GPLv2
Description: DIMEpy: Direct Infusion MEtablomics (DIME) Processing in Python
        ===============================================================
        
        Python package for the high-thoroughput nontargeted metabolite
        fingerprinting of nominal mass direct injection mass spectrometry from
        ``mzML`` files.
        
        Implementation of the methods detailed in:
        
        ::
        
            High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry
        
            Beckmann, et al. (2008) - doi:10.1038/nprot.2007.500
        
        Installation
        ------------
        
        DIMEpy requires Python 2.7.+ and is unfortunately not compatible with
        Python 3.
        
        You can install it through ``pypi`` using ``pip``:
        
        ::
        
            pip install dimepy
        
        alternatively install it manually using ``git``:
        
        ::
        
            git clone https://www.github.com/KeironO/DIMEpy
            cd DIMEpy
            python setup.py install
        
        Or use ``git`` and ``pip`` in unison.
        
        ::
        
            pip install git+https://www.github.com/KeironO/DIMEpy
        
        Bug reporting
        -------------
        
        Please report all bugs you find in the issues tracker. We would welcome
        all sorts of contribution, so please be as candid as you want.
        
        Contributors
        ------------
        
        -  Keiron O'Shea (keo7@aber.ac.uk)
        
        Usage
        -----
        
        The following script takes a path containing mzML files, processes them
        following the Beckmann, et al protocol and exports the result to an
        Excel file.
        
        .. code:: python
        
        
            # Importing modules required to run this script.
            import dimepy
            import os
        
            # Path containing mzML files.
            mzMLpaths = "/dir/to/mzMLs/"
        
            # Where we'll store the spectrum.
            spectrum_list = dimepy.SpectrumList()
        
            for index, file in enumerate(os.listdir(mzMLpaths)):
              # Load in the spectrum directly using default parameters.
              spectrum = dimepy.Spectrum(os.path.join(mzMLpaths, file))
              # Correct for baseline.
              spectrum.baseline_correction(qtl=0.6)
              spectrum_list.append(spectrum)
        
            # Write the raw spectrum to a comma seperated file.
            spectrum_list.to_csv("raw.csv")
            # Convert the object to a SpectrumListProcessor for processing.
        
            # Apply outlier detection to remove spurious samples.
            spectrum_list.outlier_detection()
            # Bin masses over 0.125 m/z.
            spectrum_list.binning(bin_size=0.125)
            # Value imputate where < 50% of the values are lost across all samples.
            spectrum_list.value_imputation(method="basic", threshold=0.5)
            # Normalise over the total ion count.
            spectrum_list.normalise(method="TIC")
            # Apply generalised log transformation
            spectrum_list.transform(method="glog")
        
            # Write the processed spectrum to a comma seperated file.
            spectrum_list.to_csv("processed.csv")
        
        License
        -------
        
        DIMEpy is licensed under the GNU General Public License v2.0.
        
Platform: Windows
Platform: UNIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
