apLCMS: Adaptive processing of LC-MS data

proc.cdf

R Documentation

Filter noise and detect peaks from LC/MS data in CDF format

Description

This function applies the run filter to remove noise. Data points are grouped into EICs in this step.

Usage

proc.cdf(filename, min.pres=0.5, min.run=12, tol=1e-5, baseline.correct=0, baseline.correct.noise.percentile=0, do.plot=TRUE, intensity.weighted=FALSE)

Arguments

`filename`	The cdf file name. If the file is not in the working directory, the path needs to be given.
`min.pres`	Run filter parameter. The minimum proportion of presence in the time period for a series of signals grouped by m/z to be considered a peak.
`min.run`	Run filter parameter. The minimum length of elution time for a series of signals grouped by m/z to be considered a peak.
`tol`	m/z tolerance level for the grouping of data points. This value is expressed as the fraction of the m/z value. This value, multiplied by the m/z value, becomes the cutoff level. The recommended value is the machine's nominal accuracy level. Divide the ppm value by 1e6. For FTMS, 1e-5 is recommended.
`baseline.correct`	After grouping the observations, the highest intensity in each group is found. If the highest is lower than this value, the entire group will be deleted. The default value is NA, in which case the program uses a percentile of the height of the noise groups. If given a value, the value will be used as the threshold, and baseline.correct.noise.percentile will be ignored.
`baseline.correct.noise.percentile`	The perenctile of signal strength of those EIC that don't pass the run filter, to be used as the baseline threshold of signal strength.
`do.plot`	Whether to generate diagnostic plots.
`intensity.weighted`	Whether to weight the local density by signal intensities.

Details

The subroutine takes CDF, mxml etc LC/MS profile. The m/z are grouped based on the tolerance level using multi-stage smoothing and peak finding. Non-parametric density estimation is used in both m/z dimension and elution time dimension to fine-tune the signal grouping. A run filter is applied, which requires a "true peak" to have a minimum length in the retention time dimension (parameter: min.run), as well as being detected at or higher than a proportion of the time points within the time period (parameter: min.pres).