findTargetFeatures {peakPantheR} | R Documentation

## Find and integrate target features in each ROI

### 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 outward (the window is the ROI width); if after 8 iterations `rtMin` or `rtMax` is not found, NA is returned and the peak fit rejected. `peakArea` is calculated from `rtMin` to `rtMax`. `mz` is the weighted (by intensity) average mz of datapoints falling into the `rtMin` to `rtMax` range, `mzMin` and `mzMax` are the minimum and maxmimum mass in these range. If `rtMin` or `rtMax` falls outside of ROI (extracted scans), `mzMin` or `mzMax` are returned as the input ROI limits and `mz` is an approximation on the datapoints available (if no scan of the ROI fall between rtMin/rtMax, mz would be NA, the peak is rejected). If any of the two following ratio are superior to `maxApexResidualRatio`, the fit is rejected: 1) ratio of fit residuals at the apex (predicted apex fit intensity vs measured apex intensity: fit overshoots the apex), 2) ratio of predicted apex fit intensity vs maximum measured peak intensity (fit misses the real apex in the peak).

### 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` and `emgGaussian`)
`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`)

### Details

## Examples cannot be computed as the function is not exported: ## 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=FALSE) 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] <- vapply(targetFeatTable[,3:8], as.numeric, FUN.VALUE=numeric(2))

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=TRUE) # 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 # 1 TRUE 3309.759 3346.828 3385.410 522.1948 522.2 522.2052 26133727 # 2 TRUE 3345.377 3386.529 3428.279 496.2000 496.2 496.2000 35472141 # maxIntMeasured maxIntPredicted # 1 889280 901015.8 # 2 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'

### 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)

[Package peakPantheR version 1.4.0 Index]