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The SPIKE program. A collaborative development for a FT-spectroscopy processing program

Project description

What is SPIKE ?

SPIKE a collaborative development for a FT-spectroscopy processing program.

This is the version 0.99.12 - September 2019

SPIKE is a program that allows the processing, the display and the analysis of data-sets obtained from various Fourier-Transform spectroscopies. The name stands for Spectrometry Processing Innovative KErnel.

It allows the processing of 1D and 2D FT spectroscopies, implementing Real, Complex and HyperComplex n-dimensionnal Fourier Transform, as well as many other functionalities.

It is written in python (tested in python 2.7 and 3.6) and can be used as a set of tools, using for instance jupyter notebook as an interactive front-end.

To our knowledge, it is the first program freely available allowing the processing, display and analysis of 2D-FT-ICR (Fourier Transform Ion Cyclotron Resonance), as well as Orbitrap time domain data. processing.

It is still in very active development. Many features are missing, and many other while present, are not fully fixed. However, considering the amount of efforts already present in this code, we decided to make it available. We believe that even in this partial development stage, this program might prove useful for certain usages.

Citing SPIKE

If you happen to use SPIKE successfully please cite it, and refer to this site, as well as the following possible references :

  • first publication of the program itself - rejected from Anal. Chem. Reviewer 1 said "too much NMR", Reviewer 2 said "too much MS", !!
    1. Chiron L., Coutouly M-A., Starck J-P., Rolando C., Delsuc M-A. SPIKE a Processing Software dedicated to Fourier Spectroscopies https://arxiv.org/abs/1608.06777 (2016)
  • first version of the python set-up on which the current SPIKE is partially based 2. Tramesel, D., Catherinot, V. & Delsuc, M.-A. Modeling of NMR processing, toward efficient unattended processing of NMR experiments. J Magn Reson 188, 56-67 (2007).
  • first version of the 2D FT-ICR-MS processing 3. van Agthoven, M. A., Chiron, L., Coutouly, M.-A., Delsuc, M.-A. & Rolando, C. Two-Dimensional ECD FT-ICR Mass Spectrometry of Peptides and Glycopeptides. Anal Chem 84, 5589-95 (2012).
  • presentation of the automation possibilities in NMR 4. Margueritte, L., Markov, P., Chiron, L., Starck, J.-P., Vonthron Sénécheau, C., Bourjot, M., & Delsuc, M.-A. (2018). Automatic differential analysis of NMR experiments in complex samples. Magn. Reson. Chem., 80(5), 1387. http://doi.org/10.1002/mrc.4683

ref 1) is a general purpose reference, the other ones are more specific.

SPIKE features

  • FT analysis of 1D data-sets
    • apodisation, phasing, modulus, ...
  • Analysis of 2D data-sets
    • phase or amplitude modulation
    • complex or hyper-complex algebra
  • Robust processing
    • no limit in data-set size
    • parallel processing of the heaviest processing
      • on multi-core desktop using standard python ressources
      • on large clusters, using MPI library
  • High-Level features
    • noise reduction (filtering, Linear-Prediction, Cadzow, urQRd, sane, ...)
    • automatic or manual baseline correction
    • NUS data processing
    • 1D and 2D Peak-Picker
  • Plugin architecture
    • allow easy extension of the core program
    • reduces cross dependences
  • Complete spectral display using matplotlib or bokeh transparently
    • zoom, available in several units (depending on the spectroscopy : seconds, Hz, ppm, m/z, etc...)
    • store to png or pdf
  • interaction with the Jupyter Notebook environment

Handles the following Spectroscopies

  • NMR
    • 1D and 2D are fully supported
    • no nD yet
  • FT-ICR
    • 1D and 2D are fully supported
  • Orbitrap
    • 1D only (!)
  • other spectroscopies are being considered

Files can be imported from

  • NMR:
    • Bruker Topspin
    • NMRNoteBook
    • NPK - Gifa
    • SpinIt
  • FT-ICR:
    • Bruker Apex
    • Bruker Solarix
  • Orbitrap:
    • Thermofisher raw data
  • Other
    • csv and txt files
    • any data in memory in a Numpy buffer.

Usage

As a processing library

SPIKE is primary meant for being used as a library, code can as simple as :

from spike.File import Solarix 

dd = Solarix.Import_1D('FTICR-Files/ESI_pos_Ubiquitin_000006.d')  # Import create a basic SPIKE object

dd.hamming().zf(2).rfft().modulus()    # we have a simple piped processing scheme
  # here doing apodisation - zerofilling (doubling the size) - FT and modulus.
  # calibration is imported from Bruker - advanced calibration is available

dd.unit = "m/z"
dd.display(zoom=(500,2000))     # display the spectrum for m/z ranging from 500 to 2000

dd.pp(threshold=1E7)         # peak-pick the spectrum in this range
dd.centroid()                # compute centroids

dd.display(zoom=(856.5, 858.5))    # and zoom on the isotopic peak
dd.display_peaks(zoom=(856.5, 858.5), peak_label=True)

interactive mode

SPIKE allows to process datasets interactively from an jupyter (IPython) prompt, and is perfectly working in jupyter notebook or even jupyter lab

  • Look at the examples files ( eg_*.py and *.ipynb ) for examples and some documentation. ( * not fully up to data * )
  • display is performed using the Matplotlib library.
  • large 2D-FT-ICR are handled in batch using the processing.py batch program, controlled by parameter file called *.mscf
  • The batch mode supports multiprocessing, both with MPI and natively on multi-core machines (still in-progress)
  • large 2D-FT-ICR are stored in a hierarchical format, easyly displayed with an interactive program.
  • data-sets are handled in the HDF5 standard file-format, which allows virtually unlimited file size ( tested up to 500 Gb ).

running stand-alone programs

processing.py and visu2D.py are two stand alone programs, written on the top of SPIKE.

  • processing.py allowing the efficient processing of FT-ICR 2D datasets, with no limit on the size of the final file Produces multi-resolution files

syntax :

python -m spike.processing param_file.mscf

How do I get SPIKE ?

SPIKE is written in pure Python, and relies on several external libraries. It is compatible and fully tested with both python 2.7 and python 3.6

However it relies on mathematical libraries which should be installed independently.

  • matplotlib
  • numpy
  • scipy
  • tables
  • pandas

some plugins or extension require additional libraries ( MPI, bokeh, mayavi, ...)

To get it, you can simply

  • rely on a scientific distribution such as Anaconda or Enthough
  • install the above python distributions yourself (tricky)

Then you can install it using pip:

pip install spike-py

Or, if you want to play with the code,

python setup.py install

or, if you do not want to instal it permanently

python setup.py develop

using pip

pip install spike_py

dependencies

It requires the following non-standard Python libraries :

Optionnaly

  • MPI / mpi4py used for parallel processing of large FTICR 2D files
  • cupy for computating in the GPU using Cuda, which allows to speed up considerably certain processing steps.

It has been successfully tested in the Enthought and Anaconda distributions.

History

SPIKE is originated from the Gifa program, developed by M-A Delsuc and others in FORTRAN 77 since the late eighties. Gifa has known several mutations, and finally ended as a partial rewrite called NPK. The NPK program is based on some of the original FORTRAN code, wrapped in Java and Python, which allows to control all the program possibilities from the Python level. NPK is purely a computing kernel, with no graphical possibilities, and has been used as a kernel embedded in the commercial program NMRNoteBook, commercialized by NMRTEC.

However, NPK was showing many weaknesses, mostly due to the 32bits organization, and a poor file format. So, when a strong scientific environment became available in Python, a rewrite in pure Python was undertaken. To this initial project, called NPK-V2, many new functionalities were added, and mostly the capability to work in other spectroscopies than NMR.

At some point in 2014, we chose to fork NPK-V2 to SPIKE, and make it public.

Developing for SPIKE

SPIKE is an open-source program, this means that external contributions are welcomed. If you believe your improvement is useful for other people, please submit a pull request. Note that pull request should be associated to the devel branch. This branch is devoted to new features not fully tested yet and still susceptible of changes, while the default branch is meant for stable code.

plugins

If you consider adding some new feature, it is probably a good idea to implement it as a plugin. The code contains already quite a few plugins, some are quite sophisticated - see Peaks.py for instance which implements a 1D and 2D peak picker, as well as a centroid evaluation and a full listing capability.

You can check also fastclean.py for a very simple plugin, or wavelet.py for a plugin relying on an external library which has to be installed.

Some Good Practice

  • Spike contains many tools, most of the basic function for data interaction are found in the NPKData.py master file; utilities are also scattered in the util module. Use then, life will be easier for the users.
  • Please write tests, even for the plugins ! We use standard python unittest, so nothing fancy. All the tests are run automatically every night (code is Tests.py), so it will detect rapidly all potential problem.
  • push your pull requests to the devel branch - default is for the stable releases.

Organisation of the Code

The main program is NPKData.py, which defines NPKData object on which everything is built.

Spectroscopies are defined in the FTICR.py and Orbitrap.py code, which sub class NPKData. It is prototyped as an NMR data-set. This set-up is temporary.

Many programs contain routines tests (in an object unittest) that also serve as an example of use. The code goes through extensive tests daily, using the unittest Python library. However, many tests rely on a set of tests data-sets which is more than 1Go large, and not distributed here.

Main programs :

a small description of the files:

  • NPKData.py the main library, allows all processing for NMR experiments (1D, 2D and 3D) to be used as a library, in a stand-alone program or in IPython interactive session

  • FTICR.py an extension of NPKData for processing FT-ICR datasets (1D and 2D)

  • Orbitrap.py an extension of NPKData for processing Orbitrap datasets (1D)

  • processing.py a stand alone program, written on the top of FTICR.py, allowing the efficient processing of FT-ICR 2D datasets, with no limit on the size of the final file Produces multi-resolution files syntax :

    python -m spike.processing param_file.mscf
    
  • visu2D.py an interactive tool for visualizing 2D FT-ICR multi-resolution files

    python -m spike.visu2D param_file.mscf
    

Directories

  • Algo
    contains algorithms to process data-sets (MaxEnt, Laplace, etc...) not everything active !

  • Display
    a small utility to choose either for regular Matplotlib display of fake no-effect display (for tests)

  • File
    Importers for various file format for spectrometry, as well as the HDF5 SPIKE native format.

  • plugins
    Tools automatically plugged in NPK kernel : display utilities, urQRd algorithm and various other tools.

  • Miscellaneous
    "en vrac"

  • util
    set of low-level tools used all over in the code

  • v1
    a library implementing a partial compatibility with the NPKV_V1 program

  • SPIKE_usage_eg
    example of Python programs using the various libraries available

  • example of configuration files

    • process_eg.mscf
    • test.mscf
  • and various utilities

    • NPKConfigParser.py
      reads .mscf files
    • NPKError.py
      generates error msg
    • QC.py
      Quality Check
    • Tests.py
      runs all tests
    • dev_setup.py
      rolls a new version
    • version.py
      defines version number
    • _init_.py
      defines library
    • rcpylint
    • To_Do_list.txt
    • QC.txt
    • Release.txt

Authors and Licence

Current Active authors for SPIKE are:

  • Marc-André Delsuc madelsuc -at- unistra.fr
  • Laura Duciel laura.duciel -at- casc4de.eu

Previous authors:

  • Christian Rolando christian.rolando -at- univ-lille1.fr
  • Lionel Chiron Lionel.Chiron -at- casc4de.eu
  • Petar Markov petar.markov -at- igbmc.fr
  • Marie-Aude Coutouly . Marie-Aude.COUTOULY - at- datastorm.fr

Covered code is provided under this license on an "as is" basis, without warranty of any kind, either expressed or implied, including, without limitation, warranties that the covered code is free of defects. The entire risk as to the quality and performance of the covered code is with you. Should any covered code prove defective in any respect, you (not the initial developer or any other contributor) assume the cost of any necessary servicing, repair or correction.

Downloading code and datasets from this page signifies acceptance of the hereunder License Agreement. The code distributed here is covered under the CeCILL license : http://www.cecill.info/index.en.html

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