recalibrate: Post-experimental recalibration of observed mass to charge...

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Mass spectrometry measurements like any other real-worls measurements are prone to systematic errors. Typically they are minimized by instrument calibration prior the analysis. Nonetheless, the calibration may drift over time or be affected by some adverse factors (temperature or space charge fluctuations).

This function estimates and removes the systematic error from the datasets. The side effect is the recalibrated experimentalMassToCharge values.

Usage

1
    recalibrate(object)

Arguments

object

An instance of class "MSnID".

Details

Currently it employs a very simple method of zero-centering the histogram of mass measurement errors. In the future it will contain more sophisticated recalibration routines.

Value

"MSnID" class instance with updated experimentalMassToCharge.

Author(s)

Vladislav A Petyuk [email protected]

See Also

MSnID mass_measurement_error correct_peak_selection

Examples

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data(c_elegans)

# first let's fix the error of picking wrong monoisotopic peak
# otherwise the mass error range will be very large
msnidObj <- correct_peak_selection(msnidObj)

# original mass error in ppm
ppm <- mass_measurement_error(msnidObj)
hist(ppm, 200, xlim=c(-20,+20))

# The dataset is well calibrated. So let's introduce 
# some mass measurement error.
msnidObj$experimentalMassToCharge <- 
    msnidObj$experimentalMassToCharge * (1+0.00001)

# mass error (in ppm) after artificial de-calibration
ppm <- mass_measurement_error(msnidObj)
hist(ppm, 200, xlim=c(-20,+20))

# recalibration
msnidObj <- recalibrate(msnidObj)
ppm <- mass_measurement_error(msnidObj)
hist(ppm, 200, xlim=c(-20,+20))

vladpetyuk/MSnID documentation built on Oct. 14, 2018, 7:54 a.m.