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

Project description

# DIMEpy: Direct Infusion MEtablomics (DIME) Processing in Python

**HERE BE DRAGONS:** This project is largely undocumented and untested, I do aim on sorting it all out eventually.

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

Or alternatively install it manually using ```git```:

git clone
cd DIMEpy
python install

## 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 (

## 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/"

# Path to save the output.
output_directory = "/output/directory/"

# mzML parameters.
parameters = {
"MS1 Precision" : 1e-6,
"MSn Precision" : 1e-6,
"Measured Precision" : 1e-6,
"Scan Range" : "apex",
"Peak Type" : "peaks"

# Object to store processed spectrum.

for polarity in ["negative", "positive"]:
spectrum_list = dimepy.SpectrumList()

for index, file in enumerate(os.listdir(mzMLpaths)):
# Read a mzML file from a given directory, and process it using given parameters.
spectrum = dimepy.Spectrum(file_path=os.path.join(mzMLpaths, file),
polarity=polarity, parameters=parameters)
# Applying TIC normalisation
# Applying generalised log transformation.
# Adding the processed spectrum to the spectrum list.

# Create a spectrum list processor.
processor = dimepy.SpectrumListProcessor(spectrum_list)

# Apply MAD outlier detection.

# Bin the spectrum to 0.25 m/z widths.

# Value imputation and value thresholding.
processor.value_imputation(method="basic", threshold=0.5)

# Applying mass-wise pareto scaling to the spectrum list.

# Export the processed spectrum list back to a spectrum list object.
processed_spectrum_list = processor.to_spectrumlist()

# Convert the spectrum list to a Pandas DataFrame.
df = processed_spectrum_list.flatten_to_dataframe()

# Export processed spectrum to to Excel.
df.to_excel(os.path.join(output_directory, polarity+".xlsx"))

## License

DIMEpy is licensed under the GNU General Public License v2.0.

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