Spectra: Spectra Infrastructure for Mass Spectrometry Data

Description Usage Arguments Details Value Author(s) Examples

View source: R/peak-list-functions.R

combinePeaks aggregates provided peak matrices into a single peak matrix. Peaks are grouped by their m/z values with the group() function from the MsCoreUtils package. In brief, all peaks in all provided spectra are first ordered by their m/z and consecutively grouped into one group if the (pairwise) difference between them is smaller than specified with parameter tolerance and ppm (see group() for grouping details and examples).

The m/z and intensity values for the resulting peak matrix are calculated using the mzFun and intensityFun on the grouped m/z and intensity values.

The function supports also different strategies for peak combinations which can be specified with the peaks parameter:

peaks = "union" (default): report all peaks from all input spectra.
peaks = "intersect": keep only peaks in the resulting peak matrix that are present in >= minProp proportion of input spectra. This would generate a consensus or representative spectra from a set of e.g. fragment spectra measured from the same precursor ion.

As a special case it is possible to report only peaks in the resulting matrix from peak groups that contain a peak from one of the input spectra, which can be specified with parameter main. Thus, if e.g. main = 2 is specified, only (grouped) peaks that have a peak in the second input matrix are returned.

Setting timeDomain to TRUE causes grouping to be performed on the square root of the m/z values (assuming a TOF instrument was used to create the data).

combinePeaks(
  x,
  intensityFun = base::mean,
  mzFun = base::mean,
  weighted = FALSE,
  tolerance = 0,
  ppm = 0,
  timeDomain = FALSE,
  peaks = c("union", "intersect"),
  main = integer(),
  minProp = 0.5,
  ...
)

`x`	`list` of peak matrices.
`intensityFun`	`function` to be used to combine intensity values for matching peaks. By default the mean intensity value is returned.
`mzFun`	`function` to be used to combine m/z values for matching peaks. By default the mean m/z value is returned.
`weighted`	`logical(1)` defining whether m/z values for matching peaks should be calculated by an intensity-weighted average of the individuak m/z values. This overrides parameter `mzFun`.
`tolerance`	`numeric(1)` defining the (absolute) maximal accepted difference between mass peaks to group them into the same final peak.
`ppm`	`numeric(1)` defining the m/z-relative maximal accepted difference between mass peaks (expressed in parts-per-million) to group them into the same final peak.
`timeDomain`	`logical(1)` whether grouping of mass peaks is performed on the m/z values (`timeDomain = FALSE`) or on `sqrt(mz)` (`timeDomain = TRUE`).
`peaks`	`character(1)` specifying how peaks should be combined. Can be either `"peaks = "union"` (default) or `peaks = "intersect"`. See function description for details.
`main`	optional `integer(1)` to force the resulting peak list to contain only peaks that are present in the specified input spectrum. See description for details.
`minProp`	`numeric(1)` for 'peaks = "intersect": the minimal required proportion of input spectra (peak matrices) a mass peak has to be present to be included in the consensus peak matrix.
`...`	additional parameters to the `mzFun` and `intensityFun` functions.

For general merging of spectra, the tolerance and/or ppm should be manually specified based on the precision of the MS instrument. Peaks from spectra with a difference in their m/z being smaller than tolerance or smaller than ppm of their m/z are grouped into the same final peak.

Some details for the combination of consecutive spectra of an LC-MS run:

The m/z values of the same ion in consecutive scans (spectra) of a LC-MS 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. Intensities and m/z values falling within each of these m/z groups are aggregated using the intensityFun and mzFun, 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.

Peaks matrix with m/z and intensity values representing the aggregated values across the provided peak matrices.

Johannes Rainer

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 peaks matrices
p1 <- cbind(mz = mzs + rnorm(length(mzs), sd = 0.01),
    intensity = ints1)
p2 <- cbind(mz = mzs + rnorm(length(mzs), sd = 0.01),
    intensity = ints2)
p3 <- cbind(mz = mzs + rnorm(length(mzs), sd = 0.009),
    intensity = ints3)

## Combine the spectra. With `tolerance = 0` and `ppm = 0` only peaks with
## **identical** m/z are combined. The result will be a single spectrum
## containing the *union* of mass peaks from the individual input spectra.
p <- combinePeaks(list(p1, p2, p3))

## Plot the spectra before and after combining
par(mfrow = c(2, 1), mar = c(4.3, 4, 1, 1))
plot(p1[, 1], p1[, 2], xlim = range(mzs[5:25]), type = "h", col = "red")
points(p2[, 1], p2[, 2], type = "h", col = "green")
points(p3[, 1], p3[, 2], type = "h", col = "blue")

plot(p[, 1], p[, 2], xlim = range(mzs[5:25]), type = "h",
    col = "black")
## The peaks were not merged, because their m/z differs too much.

## Combine spectra with `tolerance = 0.05`. This will merge all triplets.
p <- combinePeaks(list(p1, p2, p3), tolerance = 0.05)

## Plot the spectra before and after combining
par(mfrow = c(2, 1), mar = c(4.3, 4, 1, 1))
plot(p1[, 1], p1[, 2], xlim = range(mzs[5:25]), type = "h", col = "red")
points(p2[, 1], p2[, 2], type = "h", col = "green")
points(p3[, 1], p3[, 2], type = "h", col = "blue")

plot(p[, 1], p[, 2], xlim = range(mzs[5:25]), type = "h",
    col = "black")

## With `intensityFun = max` the maximal intensity per peak is reported.
p <- combinePeaks(list(p1, p2, p3), tolerance = 0.05,
    intensityFun = max)

## Create *consensus*/representative spectrum from a set of spectra

p1 <- cbind(mz = c(12, 45, 64, 70), intensity = c(10, 20, 30, 40))
p2 <- cbind(mz = c(17, 45.1, 63.9, 70.2), intensity = c(11, 21, 31, 41))
p3 <- cbind(mz = c(12.1, 44.9, 63), intensity = c(12, 22, 32))

## No mass peaks identical thus consensus peaks are empty
combinePeaks(list(p1, p2, p3), peaks = "intersect")

## Reducing the minProp to 0.2. The consensus spectrum will contain all
## peaks
combinePeaks(list(p1, p2, p3), peaks = "intersect", minProp = 0.2)

## With a tolerance of 0.1 mass peaks can be matched across spectra
combinePeaks(list(p1, p2, p3), peaks = "intersect", tolerance = 0.1)

## Report the minimal m/z and intensity
combinePeaks(list(p1, p2, p3), peaks = "intersect", tolerance = 0.1,
    intensityFun = min, mzFun = min)