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Description:
PyMassSpec 2.4.2
Python Toolkit for Mass Spectrometry
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PyMassSpec is a Python package for processing gas chromatography-mass spectrometry data.
PyMassSpec provides a framework and a set of components for rapid development and testing of methods for processing of chromatography–mass spectrometry data.
PyMassSpec can be used interactively through the Python shell, in a Jupyter Notebook, or the functions can be collected into scripts when it is preferable to perform data processing in the batch mode.
Forked from the original PyMS Repository: https://github.com/ma-bio21/pyms.
Originally by Andrew Isaac, Sean O’Callaghan and Vladimir Likić. The original publication can be found here: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-115
The original project seems to have been abandoned as there has been no activity since 2017.
Table of Contents
The PyMassSpec project
Features
Installation
Usage
Example processing GC-MS data
Contributing
License
Issues
The PyMassSpec project
The directory structure of PyMassSpec is as follows:
/
├── pyms: The PyMassSpec code
│
├── pyms-data: Example GC-MS data files
│
├── pyms-demo: Examples of how to use PyMassSpec
│
├── tests: pytest tests
│
└── doc-source: Sphinx source for documentation
Features
Installation
PyMassSpec can be installed with the following command:
$ pip --user install PyMassSpec
This will also install the following dependencies:
numpy >= 1.16.2
scipy >= 1.2.1
pymzml >= 2.2.1
matplotlib >= 3.0.2
openpyxl >= 2.6.2
netCDF4 >= 1.5.0
biopython >= 1.74
deprecation >= 2.0.6
PyMassSpec can also make use of ‘mpi4py’ if it is installed. See https://mpi4py.readthedocs.io/en/stable/ for further information.
Usage
A tutorial illustrating various PyMassSpec features in detail is provided
in subsequent chapters of this User Guide. The commands executed
interactively are grouped together by example, and can be found
here.
The data used in the PyMassSpec documentation and examples is available
here.
In the “Demos and Examples” section there
is a page corresponding to each example, coded with the chapter number
(ie. “pyms-demo/20a/” corresponds to the Example 20a, from Chapter 2).
Each example has a script named ‘proc.py’ which contains the commands given in the example.
These scripts can be run with the following command:
$ python3 proc.py
Example processing GC-MS data
Download the file gc01_0812_066.jdx and save it in the folder data.
This file contains GC-MS data in the the JCAMP-DX format.
First the raw data is loaded:
>>> from pyms.GCMS.IO.JCAMP import JCAMP_reader
>>> jcamp_file = "data/gc01_0812_066.jdx"
>>> data = JCAMP_reader(jcamp_file)
-> Reading JCAMP file 'Data/gc01_0812_066.jdx'
>>> data
<pyms.GCMS.Class.GCMS_data at 0x7f3ec77da0b8>
The intensity matrix object is then built by binning the data:
>>> from pyms.IntensityMatrix import build_intensity_matrix_i
>>> im = build_intensity_matrix_i(data)
In this example, we show how to obtain the dimensions of the
newly created intensity matrix, then loop over all ion chromatograms,
and for each ion chromatogram apply Savitzky-Golay noise filter
and tophat baseline correction:
>>> n_scan, n_mz = im.size
>>> from pyms.Noise.SavitzkyGolay import savitzky_golay
>>> from pyms.TopHat import tophat
>>> for ii in range(n_mz):
... print("working on IC", ii)
... ic = im.get_ic_at_index(ii)
... ic1 = savitzky_golay(ic)
... ic_smooth = savitzky_golay(ic1)
... ic_base = tophat(ic_smooth, struct="1.5m")
... im.set_ic_at_index(ii, ic_base)
The resulting noise and baseline corrected ion chromatogram is saved back into the intensity matrix.
Further examples can be found in the documentation
Contributing
Contributions are very welcome. Tests can be run with pytest.
Please ensure the coverage is at least
before you submit a pull request.
For further information see the section Contributing to PyMassSpec
License
PyMassSpec is Free and Open Source software released under the GNU General Public License version 2.
Issues
If you encounter any problems, please file an issue along with a
detailed description.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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