onebc: Metabolomics Fingerprint Baseline Correction

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

Description

Core function to perform baseline correction on one metabolomics fingerprint.

Usage

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  onebc(x,wsize=50,qtl=0.1,maxy=1000,plotting=TRUE,title=TRUE,
        sampid=NULL)

Arguments

x

A numeric vector to be processed.

wsize

Window size.

qtl

A numeric value of for lower quantile probability.

maxy

A numeric value specifying y axis maximal value to be plotted.

plotting

A logical value indicating whether or not plotting.

title

A logical value indicating whether or not to show plot title.

sampid

Sample ID to be written in the title if both plotting and title are TRUE (useful when onebc called from multibc).

Details

The purpose of the baseline correction is to remove undesirable effects due excess chemical noise. Possible consequences of baseline drift include the possibility that the baseline may be discriminatory and that important information may be obscured in areas affected by baseline problems. A simple consensual approach consists in fitting a monotone local minimum curve to each fingerprint. Basically, the fingerprint is divided into equally spaced m/z intervals and a local minimum intensity value is returned as the baseline estimate for this region. Finally, the whole fingerprint baseline is computed by linear interpolation based on pairs made of the centre of the interval and its corresponding local minima. Intervals (argument wsize) are in the order of 30-70 amu as a trade off between the removal of relevant chemical (small interval) or estimation bias due to use of a larger interval. Rather than using the minimum value of an interval, it is also judicious to use the value corresponding to a low quantile (argument qtl) to avoid any spurious estimates due to zeros or abnormally low signals.

Value

A list containing the following components:

x

A numeric vector of the resulting fingerprint after baseline correction.

bsl

A numeric vector of the baseline intensities.

Author(s)

David Enot [email protected] and Wanchang Lin [email protected].

See Also

multibc

Examples

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  data(abr1)
  cl  <- factor(abr1$fact$class)
  mat <- abr1$pos

  ## baseline correction
  res <- onebc(mat[1,110:2000], qtl=0.8, sampid="1")
  

wilsontom/FIEmspro documentation built on Feb. 19, 2018, 9:03 a.m.