findTargetFeatures: Find and integrate target features in each ROI

In peakPantheR: Peak Picking and Annotation of High Resolution Experiments

Description

For each ROI, fit a curve and integrate the largest feature in the box. Each entry in ROIsDataPoints must match the corresponding row in ROI. The curve shape to employ for fitting can be changed with curveModel while fitting parameters can be changed with params (list with one param per ROI window). rtMin and rtMax are established at 0.5

Usage

findTargetFeatures(ROIsDataPoints, ROI, curveModel = "skewedGaussian",
  params = "guess", sampling = 250, maxApexResidualRatio = 0.2,
  verbose = FALSE, ...)

Arguments

ROIsDataPoints	(list) A list (one entry per ROI window) of data.frame with signal as row and retention time ("rt"), mass ("mz") and intensity ("int) as columns. Must match each row of ROI.
ROI	(data.frame) A data.frame of compounds to target as rows. Columns: rtMin (float in seconds), rtMax (float in seconds), mzMin (float), mzMax (float)
curveModel	(str) Name of the curve model to fit (currently skewedGaussian)
params	(list or str) Either 'guess' for automated parametrisation or list (one per ROI windows) of "guess" or list of curve fit parameters
sampling	(int) Number of points to employ when subsampling the fittedCurve (rt, rtMin, rtMax, integral calculation)
maxApexResidualRatio	(float) Ratio of maximum allowed fit residual at the peak apex, compared to the fit max intensity. (e.g. 0.2 for a maximum residual of 20% of apex intensity)
verbose	(bool) If TRUE message the time taken and number of features found
...	Passes arguments to fitCurve to alter peak fitting (params)

Value

A list: list()$peakTable (data.frame) with targeted features as rows and peak measures as columns (see Details), list()$curveFit (list) a list of peakPantheR_curveFit or NA for each ROI.

Details:

The returned data.frame is structured as follow:

found	was the peak found
rt	retention time of peak apex (sec)
rtMin	leading edge of peak retention time (sec) determined at 0.5% of apex intensity
rtMax	trailing edge of peak retention time (sec) determined at 0.5% of apex intensity
mz	weighted (by intensity) mean of peak m/z across scans
mzMin	m/z peak minimum (between rtMin, rtMax)
mzMax	m/z peak maximum (between rtMin, rtMax)
peakArea	integrated peak area
maxIntMeasured	maximum peak intensity in raw data
maxIntPredicted	maximum peak intensity based on curve fit (at apex)

Examples

## Not run: 
## Load data
library(faahKO)
library(MSnbase)
netcdfFilePath <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
raw_data       <- MSnbase::readMSData(netcdfFilePath, centroided=TRUE, mode='onDisk')

## targetFeatTable
targetFeatTable     <- data.frame(matrix(vector(), 2, 8, dimnames=list(c(), c("cpdID",
                         "cpdName", "rtMin", "rt", "rtMax", "mzMin", "mz", "mzMax"))),
                         stringsAsFactors=F)
targetFeatTable[1,] <- c("ID-1", "Cpd 1", 3310., 3344.888, 3390., 522.194778, 522.2, 522.205222)
targetFeatTable[2,] <- c("ID-2", "Cpd 2", 3280., 3385.577, 3440., 496.195038, 496.2, 496.204962)
targetFeatTable[,3:8] <- sapply(targetFeatTable[,3:8], as.numeric)

ROIsPt         <- extractSignalRawData(raw_data, rt=targetFeatTable[,c('rtMin','rtMax')],
                                       mz=targetFeatTable[,c('mzMin','mzMax')], verbose=TRUE)
# Reading data from 2 windows

foundPeaks <- findTargetFeatures(ROIsPt, targetFeatTable, verbose=T)
# Warning: rtMin/rtMax outside of ROI; datapoints cannot be used for mzMin/mzMax calculation,
# approximate mz and returning ROI$mzMin and ROI$mzMax for ROI #1
# Found 2/2 features in 0.07 secs

foundPeaks
# $peakTable
#   found    rtMin       rt    rtMax    mzMin    mz    mzMax peakArea maxIntMeasured maxIntPredicted
# 1  TRUE 3309.759 3346.828 3385.410 522.1948 522.2 522.2052 26133727         889280        901015.8
# 2  TRUE 3345.377 3386.529 3428.279 496.2000 496.2 496.2000 35472141        1128960       1113576.7
#
# $curveFit
# $curveFit[[1]]
# $amplitude
# [1] 162404.8
# 
# $center
# [1] 3341.888
# 
# $sigma
# [1] 0.07878613
# 
# $gamma
# [1] 0.00183361
# 
# $fitStatus
# [1] 2
# 
# $curveModel
# [1] "skewedGaussian"
# 
# attr(,"class")
# [1] "peakPantheR_curveFit"
# 
# $curveFit[[2]]
# $amplitude
# [1] 199249.1
# 
# $center
# [1] 3382.577
# 
# $sigma
# [1] 0.07490442
# 
# $gamma
# [1] 0.00114719
# 
# $fitStatus
# [1] 2
# 
# $curveModel
# [1] "skewedGaussian"
# 
# attr(,"class")
# [1] "peakPantheR_curveFit"

## End(Not run)

peakPantheR documentation built on May 1, 2019, 10:53 p.m.