Metadata-Version: 1.1
Name: dimepy
Version: 0.1.2
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.
        
        
        ```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
Platform: Mac OS
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
