baseline (version 1.3-8)

baseline.peakDetection: Simultaneous Peak Detection and Baseline Correction

Description

A translation from Kevin R. Coombes et al.'s MATLAB code for detecting peaks and removing baselines

Usage

baseline.peakDetection(
  spectra,
  left,
  right,
  lwin,
  rwin,
  snminimum,
  mono = 0,
  multiplier = 5,
  left.right,
  lwin.rwin
)

Value

baseline

Matrix of baselines corresponding to spectra spectra

corrected

Matrix of baseline corrected spectra

peaks

Final list of selected peaks

sn

List signal to noise ratios for peaks

y3

List of peaks prior to singal to noise selection

midspec

Mid-way baseline estimation

y

First estimate of peaks

y2

Second estimate of peaks

Arguments

spectra

Matrix with spectra in rows

left

Smallest window size for peak widths

right

Largest window size for peak widths

lwin

Smallest window size for minimums and medians in peak removed spectra

rwin

Largest window size for minimums and medians in peak removed spectra

snminimum

Minimum signal to noise ratio for accepting peaks

mono

Monotonically decreasing baseline if mono>0

multiplier

Internal window size multiplier

left.right

Sets eflt and right to value of left.right

lwin.rwin

Sets lwin and rwin to value of lwin.rwin

Author

Kristian Hovde Liland and Bjørn-Helge Mevik

Details

Peak detection is done in several steps sorting out real peaks through different criteria. Peaks are removed from spectra and minimums and medians are used to smooth the remaining parts of the spectra. If snminimum is omitted, y3, midspec, y and y2 are not returned (faster)

References

KEVIN R. COOMBES et al.: Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization.

Examples

data(milk)
bc.peakDetection <- baseline(milk$spectra[1,, drop=FALSE], method='peakDetection',
	                            left=300, right=300, lwin=50, rwin=50)

# \donttest{
  # To obtain the peak list, specify S/N threshold and call the baseline function directly:
  bc.peakDetection2 <- baseline.peakDetection(milk$spectra[1,, drop=FALSE], 
                             left=300, right=300, lwin=50, rwin=50, snminimum = 5) 
# }

if (FALSE) {
	plot(bc.peakDetection)
}