do_findChromPeaks_massifquant: Core API function for massifquant peak detection
In xcms: LC-MS and GC-MS Data Analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/do_findChromPeaks-functions.R

Massifquant is a Kalman filter (KF)-based chromatographic peak detection for XC-MS data in centroid mode. The identified peaks can be further refined with the centWave method (see do_findChromPeaks_centWave for details on centWave) by specifying withWave = TRUE.

do_findChromPeaks_massifquant(
  mz,
  int,
  scantime,
  valsPerSpect,
  ppm = 10,
  peakwidth = c(20, 50),
  snthresh = 10,
  prefilter = c(3, 100),
  mzCenterFun = "wMean",
  integrate = 1,
  mzdiff = -0.001,
  fitgauss = FALSE,
  noise = 0,
  verboseColumns = FALSE,
  criticalValue = 1.125,
  consecMissedLimit = 2,
  unions = 1,
  checkBack = 0,
  withWave = FALSE
)

`mz`	Numeric vector with the individual m/z values from all scans/ spectra of one file/sample.
`int`	Numeric vector with the individual intensity values from all scans/spectra of one file/sample.
`scantime`	Numeric vector of length equal to the number of spectra/scans of the data representing the retention time of each scan.
`valsPerSpect`	Numeric vector with the number of values for each spectrum.
`ppm`	`numeric(1)` defining the maximal tolerated m/z deviation in consecutive scans in parts per million (ppm) for the initial ROI definition.
`peakwidth`	`numeric(2)` with the expected approximate peak width in chromatographic space. Given as a range (min, max) in seconds.
`snthresh`	`numeric(1)` defining the signal to noise ratio cutoff.
`prefilter`	`numeric(2)`: `c(k, I)` specifying the prefilter step for the first analysis step (ROI detection). Mass traces are only retained if they contain at least `k` peaks with intensity `>= I`.
`mzCenterFun`	Name of the function to calculate the m/z center of the chromatographic peak. Allowed are: `"wMean"`: intensity weighted mean of the peak's m/z values, `"mean"`: mean of the peak's m/z values, `"apex"`: use the m/z value at the peak apex, `"wMeanApex3"`: intensity weighted mean of the m/z value at the peak apex and the m/z values left and right of it and `"meanApex3"`: mean of the m/z value of the peak apex and the m/z values left and right of it.
`integrate`	Integration method. For `integrate = 1` peak limits are found through descent on the mexican hat filtered data, for `integrate = 2` the descent is done on the real data. The latter method is more accurate but prone to noise, while the former is more robust, but less exact.
`mzdiff`	`numeric(1)` representing the minimum difference in m/z dimension required for peaks with overlapping retention times; can be negative to allow overlap. During peak post-processing, peaks defined to be overlapping are reduced to the one peak with the largest signal.
`fitgauss`	`logical(1)` whether or not a Gaussian should be fitted to each peak. This affects mostly the retention time position of the peak.
`noise`	`numeric(1)` allowing to set a minimum intensity required for centroids to be considered in the first analysis step (centroids with intensity `< noise` are omitted from ROI detection).
`verboseColumns`	`logical(1)` whether additional peak meta data columns should be returned.
`criticalValue`	`numeric(1)`. Suggested values: (`0.1-3.0`). This setting helps determine the the Kalman Filter prediciton margin of error. A real centroid belonging to a bonafide peak must fall within the KF prediction margin of error. Much like in the construction of a confidence interval, `criticalVal` loosely translates to be a multiplier of the standard error of the prediction reported by the Kalman Filter. If the peak in the XC-MS sample have a small mass deviance in ppm error, a smaller critical value might be better and vice versa.
`consecMissedLimit`	`integer(1)` Suggested values: (`1,2,3`). While a peak is in the proces of being detected by a Kalman Filter, the Kalman Filter may not find a predicted centroid in every scan. After 1 or more consecutive failed predictions, this setting informs Massifquant when to stop a Kalman Filter from following a candidate peak.
`unions`	`integer(1)` set to `1` if apply t-test union on segmentation; set to `0` if no t-test to be applied on chromatographically continous peaks sharing same m/z range. Explanation: With very few data points, sometimes a Kalman Filter stops tracking a peak prematurely. Another Kalman Filter is instantiated and begins following the rest of the signal. Because tracking is done backwards to forwards, this algorithmic defect leaves a real peak divided into two segments or more. With this option turned on, the program identifies segmented peaks and combines them (merges them) into one with a two sample t-test. The potential danger of this option is that some truly distinct peaks may be merged.
`checkBack`	`integer(1)` set to `1` if turned on; set to `0` if turned off. The convergence of a Kalman Filter to a peak's precise m/z mapping is very fast, but sometimes it incorporates erroneous centroids as part of a peak (especially early on). The `scanBack` option is an attempt to remove the occasional outlier that lies beyond the converged bounds of the Kalman Filter. The option does not directly affect identification of a peak because it is a postprocessing measure; it has not shown to be a extremely useful thus far and the default is set to being turned off.
`withWave`	`logical(1)` if `TRUE`, the peaks identified first with Massifquant are subsequently filtered with the second step of the centWave algorithm, which includes wavelet estimation.

This algorithm's performance has been tested rigorously on high resolution LC/OrbiTrap, TOF-MS data in centroid mode. Simultaneous kalman filters identify peaks and calculate their area under the curve. The default parameters are set to operate on a complex LC-MS Orbitrap sample. Users will find it useful to do some simple exploratory data analysis to find out where to set a minimum intensity, and identify how many scans an average peak spans. The consecMissedLimit parameter has yielded good performance on Orbitrap data when set to (2) and on TOF data it was found best to be at (1). This may change as the algorithm has yet to be tested on many samples. The criticalValue parameter is perhaps most dificult to dial in appropriately and visual inspection of peak identification is the best suggested tool for quick optimization. The ppm and checkBack parameters have shown less influence than the other parameters and exist to give users flexibility and better accuracy.

A matrix, each row representing an identified chromatographic peak, with columns:

mz: Intensity weighted mean of m/z values of the peaks across scans.
mzmin: Minumum m/z of the peak.
mzmax: Maximum m/z of the peak.
rtmin: Minimum retention time of the peak.
rtmax: Maximum retention time of the peak.
rt: Retention time of the peak's midpoint.
into: Integrated (original) intensity of the peak.
maxo: Maximum intensity of the peak.

If withWave is set to TRUE, the result is the same as returned by the do_findChromPeaks_centWave method.

Christopher Conley

Conley CJ, Smith R, Torgrip RJ, Taylor RM, Tautenhahn R and Prince JT "Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection" Bioinformatics 2014, 30(18):2636-43.

massifquant for the standard user interface method.

Other core peak detection functions: do_findChromPeaks_centWaveWithPredIsoROIs(), do_findChromPeaks_centWave(), do_findChromPeaks_matchedFilter(), do_findPeaks_MSW()

## Load the test file
data(faahko_sub)
## Update the path to the files for the local system
dirname(faahko_sub) <- system.file("cdf/KO", package = "faahKO")

## Subset to one file and restrict to a certain retention time range
data <- filterRt(filterFile(faahko_sub, 1), c(2500, 3000))

## Get m/z and intensity values
mzs <- mz(data)
ints <- intensity(data)

## Define the values per spectrum:
valsPerSpect <- lengths(mzs)

## Perform the peak detection using massifquant - setting prefilter to
## a high value to speed up the call for the example
res <- do_findChromPeaks_massifquant(mz = unlist(mzs), int = unlist(ints),
    scantime = rtime(data), valsPerSpect = valsPerSpect,
    prefilter = c(3, 10000))
head(res)