API batches module

Metabo Feature Finder

emzed.batches.runMetaboFeatureFinder(pattern=None, destination=None, configid='std', **params)[source]

runs MetaboFeatureFinding from OpenMS in batch mode.

  • pattern is a used for file globbing, eg “/data/experiment1/*.mzML”, allowed are files of type .mzML, .mzXML and .mzData.
  • destination is a target folder where the results are stored as .csv files
  • configid is a preconfiugred setting id, but following key pairs as ppm=20 override this.

Examples:

  • runMetaboFeatureFinder():

    asks for source files and target directory asks for config if multiple algorithm_configs are defined

  • runMetaboFeatureFinder(configid=”std”, ffm_local_mz_range=0.01)

    uses config with id “std”, overwrites ffm_local_mz_range parameter with value 0.01

  • runMetaboFeatureFinder(ppm=13):

    asks for source files and target directory runs matched filter with modified ppm=13 parameter.

  • runMetaboFeatureFinder(pattern):

    looks for map files matching pattern resulting csv files are stored next to input map file

  • runMetaboFeatureFinder(pattern, mzDiff=0.003):

    looks for map files matching pattern resulting csv files are stored next to input map file runs matched filter with modified mzDiff parameter

  • runMetaboFeatureFinder(pattern, destination):

    looks for map files matching pattern resulting csv files are stored at destination directory

  • runMetaboFeatureFinder(pattern, destination, ppm=17, peakwidth=(5,100) ):

    looks for map files matching pattern resulting csv files are stored at destination directory runs matched filter with modified ppm and peakwidth parameters.

For the available parameter settings see runMetaboFeatureFinder().

Centwave Feature Detector

emzed.batches.runCentwave(pattern=None, destination=None, configid='std', **params)[source]

runs centwave algorithm from xcms in batch mode

  • pattern is used for file globbing, eg “/data/experiment1/*.mzML”, allowed are files of type .mzML, .mzXML and .mzData.
  • destination is a target folder where the results are stored as .csv files
  • configid is a preconfiugred setting id, but following key pairs as ppm=20 override this.

Examples:

  • runCentwave()

    asks for source files and target directory asks for config if multiple algorithm_configs are defined

  • runCentwave(configid=”std”, ppm=17)

    uses confi with id “std”, overwrites ppm parameter with ppm=17.

  • runCentwave(ppm=13):

    asks for source files and target directory runs centwave with modified ppm=13 parameter.

  • runCentwave(pattern):

    looks for map files matching pattern resulting csv files are stored next to input map file

  • runCentwave(pattern, mzDiff=0.003):

    looks for map files matching pattern resulting csv files are stored next to input map file runs centwave with modified mzDiff parameter

  • runCentwave(pattern, destination):

    looks for map files matching pattern resulting csv files are stored at destination directory

  • runCentwave(pattern, destination, ppm=17, peakwidth=(5,100) ):

    looks for map files matching pattern resulting csv files are stored at destination directory runs centwave with modified ppm and peakwidth parameters.

Docs from XCMS library

Matched Filter Feature Detector

emzed.batches.runMatchedFilter(pattern=None, destination=None, configid='std', **params)[source]

runs matched filters algorithm from XCMS in batch mode

  • pattern is a used for file globbing, eg “/data/experiment1/*.mzML”, allowed are files of type .mzML, .mzXML and .mzData.
  • destination is a target folder where the results are stored as .csv files
  • configid is a preconfiugred setting id, but following key pairs as ppm=20 override this.

Examples:

  • runMatchedFilter():

    asks for source files and target directory asks for config if multiple algorithm_configs are defined

  • runMatchedFilter(configid=”std”, ppm=17)

    uses config with id “std”, overwrites ppm parameter with ppm=17.

  • runMatchedFilter(ppm=13):

    asks for source files and target directory runs matched filter with modified ppm=13 parameter.

  • runMatchedFilter(pattern):

    looks for map files matching pattern resulting csv files are stored next to input map file

  • runMatchedFilter(pattern, mzDiff=0.003):

    looks for map files matching pattern resulting csv files are stored next to input map file runs matched filter with modified mzDiff parameter

  • runMatchedFilter(pattern, destination):

    looks for map files matching pattern resulting csv files are stored at destination directory

  • runMatchedFilter(pattern, destination, ppm=17, peakwidth=(5,100) ):

    looks for map files matching pattern resulting csv files are stored at destination directory runs matched filter with modified ppm and peakwidth parameters.

Docs from XCMS library

Centroiding Data

emzed.batches.runPeakPickerHiRes(pattern=None, destination=None, configid=None, **params)[source]

import runPeakPickerHiRes. runs peakPickerHiRes from openMs in batch mode. - input files are map files (mzXML, mxML, mzData), - ouput files are mzML files, with extended file name. - pattern is - destination is the path of the output file - configid is

You can add modifications to the standard parameters, e. g. signal_to_noise, as named arguments.

If you have multiple configs for the peakpicker, you can give an configid as defined in algorithm_configs.py, or you are asked to choose a config.

If you have a single config this one is used automatically.

Examples:

  • runPeakPickerHiRes()

    asks for source files and target directory asks for config if multiple algorithm_configs are defined

  • runPeakPickerHiRes(configid=”std”, signal_to_noise = 2.0)

    uses config with id “std”, overwrites signal_to_noise parameter with signal_to_noise=2.0.

  • runPeakPickerHiRes(signal_to_noise = 2.0)

    asks for source files and target directory runs peak picking with modified parameter.

  • runPeakPickerHiRes(pattern)

    looks for map files matching pattern resulting mzML files are stored next to input map file

  • runPeakPickerHiRes(pattern, signal_to_noise = 2.0)

    looks for map files matching pattern resulting mzML files are stored next to input map file runs peak picking with modified parameter

  • runPeakPickerHiRes(pattern, destination)

    looks for map files matching pattern resulting mzML files are stored at destination directory

  • runPeakPickerHiRes(pattern, destination, signal_to_noise = 2.0)

    looks for map files matching pattern resulting csv files are stored at destination directory runs peak picking with modified parameter