baseline: Baseline correction - wrapper function

In acinostroza/TargetSearch: A package for the analysis of GC-MS metabolite profiling data

Baseline correction - wrapper function

Description

This function perform baseline correction by wrapping around the methods implemented on baselineCorrection and baselineCorrectionQuant.

Usage

    baseline(ncdf, bsline_method = c('classic', 'quantiles', 'none'), ...)

Arguments

ncdf	A list containing the raw chromatogram data. The list can be generated by peakCDFextraction. Expected elements are "Peaks" which is matrix of intensities where the rows are retention times and columns are mass traces, "Time" which is a vector of retention time in seconds.
bsline_method	A string to select the baseline retention method. Options are "classic" which implements Chang's method (the old or classic TargetSearch method), "quantiles" the quantiles based method, and "none" which does nothing (returns the same input).
...	Extra parameters to be passed to baselineCorrection or baselineCorrectionQuant

.

Details

This is a wrapper function around the different baseline correction algorithms. It is not intended to be executed by the average user. Please refer to the respective man pages for details.

Value

Returns a list with the same elements as the input, but the element "Peaks" containing baseline corrected values.

Author(s)

Alvaro Cuadros-Inostroza

See Also

RIcorrect, baselineCorrection, baselineCorrectionQuant

Examples

  # get a random sample CDF from TargetSearchData
  require(TargetSearchData)
  cdffile <- sample(tsd_cdffiles(), 1)
  pdata <- peakCDFextraction(cdffile)

  # restrict mass range to reduce computing time (not needed for
  # actual data)
  pdata$Peaks <- pdata$Peaks[, 1:10] ; pdata$massRange <- c(85, 94)

  # make a fake baseline as constant + noise (the CDF files have been
  # already baseline corrected by the vendor software).
  nscans <- length(pdata$Time)
  noise <- as.integer(1000 + rnorm(nscans, sd=5))
  pdata$Peaks <- pdata$Peaks + noise

  # use Classic and Quantile methods for baseline correction (def parameters)
  pdata_c <- baseline(pdata, 'classic')
  # use Quantile method
  pdata_q <- baseline(pdata, 'quantiles')

  # plot function to compare traces
  plfun <- function(p, q, k, n, titl) {
    plot(p$Time, p$Peaks[, k], col='blue', type='l', xlab='time', ylab='intensity')
    lines(q$Time, q$Peaks[, k] - n, col='red')
    legend('topleft', c('corrected', 'original'), col=c('blue', 'red'), lty=1, lwd=2)
    title(paste('method:', titl))
  }

  op <- par(mfrow=c(2,2))
    plfun(pdata_c, pdata, 1, noise, 'classic')
    plfun(pdata_q, pdata, 1, noise, 'quantile')
    plfun(pdata_c, pdata, 7, noise, 'classic')
    plfun(pdata_q, pdata, 7, noise, 'quantile')
  par(op)

acinostroza/TargetSearch documentation built on Nov. 22, 2024, 3:31 p.m.