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Python package for the high-thoroughput nontargeted metabolite fingerprinting of nominal mass direct injection mass spectrometry.

Project description

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

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.

# 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.

Project details


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Files for DIMEpy, version 0.1.1
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