peakPantheR_singleFileSearch {peakPantheR} | R Documentation

## Search, integrate and report targeted features in a raw spectra

### Description

Report for a raw spectra the TIC, acquisition time, integrated targeted features, fitted curves and datapoints for each region of interest. Optimised to reduce the number of file access. Features not detected can be integrated using fallback integration regions (FIR).

### Usage

    peakPantheR_singleFileSearch(singleSpectraDataPath, targetFeatTable,
        peakStatistic = FALSE, plotEICsPath = NA, getAcquTime = FALSE,
        FIR = NULL, centroided = TRUE, verbose = TRUE, ...)

### Arguments

`singleSpectraDataPath` | (str) path to netCDF or mzML raw data file (centroided, only with the channel of interest).
`targetFeatTable` | a `data.frame` of compounds to target as rows. Columns: `cpdID` (str), `cpdName` (str), `rtMin` (float in seconds), `rt` (float in seconds, or NA), `rtMax` (float in seconds), `mzMin` (float), `mz` (float or NA), `mzMax` (float).
`peakStatistic` | (bool) If TRUE calculates additional peak statistics: 'ppm_error', 'rt_dev_sec', 'tailing factor' and 'asymmetry factor'
`plotEICsPath` | (str or NA) If not NA, will save a .png of all ROI EICs at the path provided (`'filepath/filename.png'` expected). If NA no plot saved
`getAcquTime` | (bool) If TRUE will extract sample acquisition date-time from the mzML metadata (the additional file access will impact run time)
`FIR` | (data.frame or NULL) If not NULL, integrate Fallback Integration Regions (FIR) when a feature is not found. Compounds as row are identical to `targetFeatTable`, columns are `rtMin` (float in seconds), `rtMax` (float in seconds), `mzMin` (float), `mzMax` (float).
`centroided` | (bool) use TRUE if the data is centroided, used by `readMSData` when reading the raw data file
`verbose` | (bool) If TRUE message calculation progress, time taken and number of features found
`...` | Passes arguments to `findTargetFeatures` to alter peak-picking parameters (e.g. `curveModel`, `sampling`, `params` as a list of parameters for each ROI or 'guess',...)

### Value

a list: `list()$TIC` (int) TIC value, `list()$peakTable` (data.frame) targeted features results (see Details), `list()$curveFit` (list) list of `peakPantheR_curveFit` or NA for each ROI, `list()$acquTime` (POSIXct or NA) date-time of sample acquisition from mzML metadata, `list()$ROIsDataPoint` (list) a list of `data.frame` of raw data points for each ROI (retention time 'rt', mass 'mz' and intensity 'int' (as column) of each raw data points (as row)).

#### Details:

The returned peakTable `data.frame` is structured as follow:

cpdID | database compound ID
cpdName | compound name
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
is_filled | Logical indicate if the feature was integrated using FIR (Fallback Integration Region)
ppm_error | difference in ppm between the expected and measured m/z
rt_dev_sec | difference in seconds between the expected and measured rt
tailingFactor | the tailing factor is a measure of peak tailing.It is defined as the distance from the front slope of the peak to the back slope divided by twice the distance from the center line of the peak to the front slope, with all measurements made at 5% of the maximum peak height. The tailing factor of a peak will typically be similar to the asymmetry factor for the same peak, but the two values cannot be directly converted
asymmetryFactor | the asymmetry factor is a measure of peak tailing. It is defined as the distance from the center line of the peak to the back slope divided by the distance from the center line of the peak to the front slope, with all measurements made at 10% of the maximum peak height. The asymmetry factor of a peak will typically be similar to the tailing factor for the same peak, but the two values cannot be directly converted

### See Also

Other peakPantheR: `peakPantheRAnnotation`, `peakPantheR_parallelAnnotation`

Other parallelAnnotation: `peakPantheRAnnotation`, `peakPantheR_parallelAnnotation`

### Examples

    if(requireNamespace('faahKO')){
    ## Load data
    library(faahKO)
    netcdfFilePath <- system.file('cdf/KO/ko15.CDF', package = 'faahKO')

    ## 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[,c(3:8)] <- vapply(targetFeatTable[,c(3:8)], as.numeric,
                                        FUN.VALUE=numeric(2))

    res <- peakPantheR_singleFileSearch(netcdfFilePath,targetFeatTable,
                                        peakStatistic=TRUE)
    # Polarity can not be extracted from netCDF files, please set manually the
    #    polarity with the 'polarity' method.
    # Reading data from 2 windows
    # Data read in: 0.16 secs
    # 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 0.05 secs
    # Peak statistics done in: 0 secs
    # Feature search done in: 0.75 secs

    res
    # $TIC
    # [1] 2410533091
    #
    # $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 cpdID cpdName is_filled  ppm_error
    # 1         889280        901015.8  ID-1   Cpd 1     FALSE 0.02337616
    # 2        1128960       1113576.7  ID-2   Cpd 2     FALSE 0.02460103
    #   rt_dev_sec  tailingFactor  asymmetryFactor
    # 1  1.9397590       1.015357         1.026824
    # 2  0.9518072       1.005378         1.009318
    #
    # $acquTime
    # [1] NA
    #
    #
    # $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'
    #
    #
    # $ROIsDataPoint
    # $ROIsDataPoint[[1]]
    #          rt    mz    int
    # 1  3315.154 522.2   2187
    # 2  3316.719 522.2   3534
    # 3  3318.284 522.2   6338
    # 4  3319.849 522.2  11718
    # 5  3321.414 522.2  21744
    # 6  3322.979 522.2  37872
    # 7  3324.544 522.2  62424
    # 8  3326.109 522.2  98408
    # 9  3327.673 522.2 152896
    # 10 3329.238 522.2 225984
    # ...
    #
    # $ROIsDataPoint[[2]]
    #          rt    mz     int
    # 1  3280.725 496.2    1349
    # 2  3290.115 496.2    2069
    # 3  3291.680 496.2    3103
    # 4  3293.245 496.2    5570
    # 5  3294.809 496.2   10730
    # 6  3296.374 496.2   20904
    # 7  3297.939 496.2   38712
    # 8  3299.504 496.2   64368
    # 9  3301.069 496.2   97096
    # 10 3302.634 496.2  136320
    # ...
    }

[Package peakPantheR version 1.0.0 Index]