slicepi: Process raw data to compute density estimate.

In hansenlab/yamss: Tools for high-throughput metabolomics

Description

The slicepi method uses the bivariate approximate kernel density estimate computed by bakedpi and uses a cutoff to bound and quantify peaks.

Usage

slicepi(object, cutoff = NULL, verbose = TRUE)

Arguments

object	An object of class CMSproc.
cutoff	A number indicating the threshold to apply to the density estimate. NULL indicates that a data-driven threshold should be chosen.
verbose	Should the function be verbose?

Details

slicepi uses the most intense features in set regions of the M/Z space to identify a data-driven density cutoff to detect peaks. Once peak bounds have been computed, the extracted ion chromatograms for the peaks are computed, and the EICs are integrated to obtain peak quantifications.

Value

An object of class CMSslice containing peak bounds and quantifications as well as sample and preprocessing metadata.

Examples

data(cmsRawExample)
cmsProc <- bakedpi(cmsRawExample, dbandwidth = c(0.01, 10), dgridstep = c(0.01, 1),
                   outfileDens = NULL, dortalign = FALSE, verbose = TRUE)
dqs <- densityQuantiles(cmsProc)
cmsSlice <- slicepi(cmsProc, cutoff = dqs[996], verbose = TRUE)
cmsSlice

hansenlab/yamss documentation built on Feb. 8, 2022, 4:28 p.m.