Peak detection parameters

Algorithms and parameters for automatic detection of peaks in chromatograms and mobilograms.

getDefPeakParams(type, algorithm, ...)

Arguments

type

The type of parameter defaults: "chrom" for chromatograms and "bruker_ims" and "agilent_ims" for mobilograms coming from Bruker and Agilent systems, respectively.

algorithm

The peak detection algorithm: "openms", "xcms3", "envipick" or "piek".

...

optional named arguments that override defaults.

Details

The algorithm and its parameters for peak detection should be in a named list with the format:

list(algorithm = <algorithm>, param1 = ..., param2 = ..., ...)

Where <algorithm> is the name of the algorithm and param1, param2 etc are the parameters. The getDefPeakParams function generates such parameter list with the algorithm and default parameters.

The following algorithms are currently supported:

• "openms": uses MRMTransitionGroupPicker tool from OpenMS.

• "xcms3": uses the xcms::peaksWithCentWave function.

• "envipick": uses the enviPick::mzpick function.

• "piek": uses the peak detection algorithm from (Dietrich et al. 2021) , which was optimized with OpenMP parallelization. See findFeaturesPiek for more details.

The parameters are discussed in the next sections.

Note

The peak detection used by algorithm="openms" is different than that of findFeaturesOpenMS.

The patRoon.threads package option sets the number of threads for the piek algorithm.

General parameters

These parameters are applicable to all algorithms

• forcePeakWidth a two-sized numeric vector with the minimum and maximum width for a peak. Peaks that are more narrow or wide will be clamped to this range. This is especially useful for algorithms that consider an extensive part of the fronting/tailing noise as part as the peak. Set to c(0, 0) to disable.

• relMinIntensity the minimum intensity threshold for a peak relative to the highest peak in the same chromatogram/mobilogram. This is e.g. useful to exclude noise in mobilograms where normally few peaks are expected.

• calcCentroid Controls how the peak centroid is calculated, which is used for retention time or mobility determination. Valid values are: "algorithm" (use the centroid as determined by the algorithm), "max" (use the apex of the peak), "weighted.mean" (use the intensity weighted mean of all data points in the peak) or "centerOfMass" (use the center of mass or first statistical moment of the peak). The latter two might of interest for assymaterical peaks. However, most algorithms, including those not interfaced by patRoon, seem to use the peak apex. Hence, calcCentroid="max" (or calcCentroid="algorithm" which is usually the same) seems a good default for comparative reasons.

Parameters for openms

The parameters directly map to the command line options for MRMTransitionGroupPicker, please see its documentation.

• minPeakWidth the minimum peak width, sets the min_peak_width option.

• backgroundSubtraction the background subtraction method, sets the -algorithm:background_subtraction option.

• SGolayFrameLength the frame length for Savitzky-Golay smoothing, sets the -algorithm:PeakPickerMRM:sgolay_frame_length option.

• SGolayPolyOrder order of the polynomial, sets the -algorithm:PeakPickerMRM:sgolay_polynomial_order option.

• useGauss set to TRUE to use Gaussian smoothing (instead of Savitzky-Golay, sets the -algorithm:PeakPickerMRM:use_gauss option.

• gauss_width the Gaussian width, estimated peak size, sets the -algorithm:PeakPickerMRM:gauss_width option.

• SN signal to noise threshold, sets the -algorithm:PeakPickerMRM:signal_to_noise option.

• SNWinLen SN window length, sets the -algorithm:PeakPickerMRM:sn_win_len option.

• SNBinCount SN bin count, sets the -algorithm:PeakPickerMRM:sn_bin_count option.

• method peak picking method, sets the -algorithm:PeakPickerMRM:method option.

• integrationType the integration technique, sets the -algorithm:PeakIntegrator:integration_type option.

• baselineType the baseline type, sets the -algorithm:PeakIntegrator:baseline_type option.

• fitEMG if TRUE then the EMG model is used for fitting, sets the -algorithm:PeakIntegrator:fit_EMG option.

Parameters for xcms3 and envipick

See the documentation for xcms::peaksWithCentWave and enviPick::mzpick for xcms3 and envipick, respectively.

Parameters for piek

• minIntensity the minimum intensity of a peak.

• SN the signal to noise ratio.

• peakWidth two-sized vector with the minimum and maximum peak width (seconds)

• RTRange two-sized vector with the minimum and maximum retention time range (seconds). Set the 2nd element to Inf for no upper limit.

• maxPeaksPerSignal upper threshold for consecutive maxima of similar size to be regarded as noise.

References

Rost HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich H, Gutenbrunner P, Kenar E, Liang X, Nahnsen S, Nilse L, Pfeuffer J, Rosenberger G, Rurik M, Schmitt U, Veit J, Walzer M, Wojnar D, Wolski WE, Schilling O, Choudhary JS, Malmstrom L, Aebersold R, Reinert K, Kohlbacher O (2016). “OpenMS: a flexible open-source software platform for mass spectrometry data analysis.” Nature Methods, 13(9), 741–748. doi:10.1038/nmeth.3959 .

pugixml (via Rcpp) is used to process OpenMS XML output.

Eddelbuettel D (2013). Seamless R and C++ Integration with Rcpp. Springer, New York. doi:10.1007/978-1-4614-6868-4 , ISBN 978-1-4614-6867-7.

Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction to Rcpp.” The American Statistician, 72(1), 28-36. doi:10.1080/00031305.2017.1375990 .

Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software, 40(8), 1–18. doi:10.18637/jss.v040.i08 .

Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2026). Rcpp: Seamless R and C++ Integration. R package version 1.1.1, https://www.rcpp.org.

Dietrich C, Wick A, Ternes TA (2021). “Open‐source feature detection for non‐target LC–MS analytics.” Rapid Communications in Mass Spectrometry, 36(2). ISSN 1097-0231, doi:10.1002/rcm.9206 , http://dx.doi.org/10.1002/rcm.9206. Benton HP, Want EJ, Ebbels TMD (2010). “Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data.” BIOINFORMATICS, 26, 2488.

Smith, C.A., Want, E.J., O'Maille, G., Abagyan,R., Siuzdak, G. (2006). “XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification.” Analytical Chemistry, 78, 779–787.

Tautenhahn R, Boettcher C, Neumann S (2008). “Highly sensitive feature detection for high resolution LC/MS.” BMC Bioinformatics, 9, 504.