combineSpectra {MSnbase} | R Documentation

## Combine profile-mode spectra signals

### Description

Combine signals from profile-mode spectra of consecutive scans into the values for the main spectrum. This can improve centroiding of profile-mode data by increasing the signal-to-noise ratio.

### Usage

    combineSpectra(x, mzFun = base::mean, intensityFun = base::mean,
      main = floor(length(x)/2L) + 1L, mzd, timeDomain = TRUE)

### Arguments

`x` | `list` of `Spectrum` objects.
`mzFun` | `function` to aggregate the m/z values per m/z group. Should be a function or the name of a function. The function is expected to return a `numeric(1)`. For `mzFun = "weighted.mean"` (note that the name of the function is passed!) the resulting m/z is determined as an intensity-weighted mean of spectras' m/z values.
`intensityFun` | `function` to aggregate the intensity values per m/z group. Should be a function or the name of a function. The function is expected to return a `numeric(1)`.
`main` | `integer(1)` defining the main spectrum, i.e. the spectrum which m/z and intensity values get replaced and is returned.
`mzd` | `numeric(1)` defining the maximal m/z difference below which values are grouped. If not provided, this value is estimated from the distribution of differences of m/z values from the provided spectra (see details).
`timeDomain` | `logical(1)` whether definition of the m/z values to be combined into one m/z is performed on m/z values (`timeDomain = FALSE`) or on `sqrt(mz)` (`timeDomain = TRUE`). Profile data from TOF MS instruments should be aggregated based on the time domain (see details). Note that a pre-defined `mzd` should also be estimated on the square root of m/z values if `timeDomain = TRUE`.

### Details

The m/z values of the same ion in consecutive scans (spectra) of a LCMS run will not be identical. Assuming that this random variation is much smaller than the resolution of the MS instrument (i.e. the difference between m/z values within each single spectrum), m/z value groups are defined across the spectra and those containing m/z values of the `main` spectrum are retained. The maximum allowed difference between m/z values for the same ion is estimated as in `estimateMzScattering()`. Alternatively it is possible to define this maximal m/z difference with the `mzd` parameter. All m/z values with a difference smaller than this value are combined to a m/z group. Intensities and m/z values falling within each of these m/z groups are aggregated using the `intensity_fun` and `mz_fun`, respectively. It is highly likely that all QTOF profile data is collected with a timing circuit that collects data points with regular intervals of time that are then later converted into m/z values based on the relationship `t = k * sqrt(m/z)`. The m/z scale is thus non-linear and the m/z scattering (which is in fact caused by small variations in the time circuit) will thus be different in the lower and upper m/z scale. m/z-intensity pairs from consecutive scans to be combined are therefore defined by default on the square root of the m/z values. With `timeDomain = FALSE`, the actual m/z values will be used.

### Value

`Spectrum` with m/z and intensity values representing the aggregated values across the provided spectra. The returned spectrum contains the same number of m/z and intensity pairs than the spectrum with index `main` in `x`, also all other related information is taken from this spectrum.

### Author(s)

Johannes Rainer, Sigurdur Smarason

### See Also

`estimateMzScattering()` for a function to estimate m/z scattering in consecutive scans.

`estimateMzResolution()` for a function estimating the m/z resolution of a spectrum.

`combineSpectraMovingWindow()` for the function to combine consecutive spectra of an `MSnExp` object using a moving window approach.

### Examples

    library(MSnbase)
    ## Create 3 example profile-mode spectra with a resolution of 0.1 and small
    ## random variations on these m/z values on consecutive scans.
    set.seed(123)
    mzs <- seq(1, 20, 0.1)
    ints1 <- abs(rnorm(length(mzs), 10))
    ints1[11:20] <- c(15, 30, 90, 200, 500, 300, 100, 70, 40, 20) # add peak
    ints2 <- abs(rnorm(length(mzs), 10))
    ints2[11:20] <- c(15, 30, 60, 120, 300, 200, 90, 60, 30, 23)
    ints3 <- abs(rnorm(length(mzs), 10))
    ints3[11:20] <- c(13, 20, 50, 100, 200, 100, 80, 40, 30, 20)

    ## Create the spectra.
    sp1 <- new("Spectrum1", mz = mzs + rnorm(length(mzs), sd = 0.01),
        intensity = ints1)
    sp2 <- new("Spectrum1", mz = mzs + rnorm(length(mzs), sd = 0.01),
        intensity = ints2)
    sp3 <- new("Spectrum1", mz = mzs + rnorm(length(mzs), sd = 0.009),
        intensity = ints3)

    ## Combine the spectra
    sp_agg <- combineSpectra(list(sp1, sp2, sp3))

    ## Plot the spectra before and after combining
    par(mfrow = c(2, 1), mar = c(4.3, 4, 1, 1))
    plot(mz(sp1), intensity(sp1), xlim = range(mzs[5:25]), type = "h", col = "red")
    points(mz(sp2), intensity(sp2), type = "h", col = "green")
    points(mz(sp3), intensity(sp3), type = "h", col = "blue")
    plot(mz(sp_agg), intensity(sp_agg), xlim = range(mzs[5:25]), type = "h",
        col = "black")

[Package MSnbase version 2.6.4 Index]