adjustRtime-peakGroups {xcms} | R Documentation

## Retention time correction based on alignment of house keeping peak groups

### Description

This method performs retention time adjustment based on the alignment of chromatographic peak groups present in all/most samples (hence corresponding to house keeping compounds). First the retention time deviation of these peak groups is described by fitting either a polynomial (`smooth = "loess"`) or a linear ( `smooth = "linear"`) model to the data points. These models are subsequently used to adjust the retention time of each spectrum in each sample.

It is also possible to exclude certain samples within an experiment from the estimation of the alignment models. The parameter `subset` allows to define the indices of samples within `object` that should be aligned. Samples not part of this `subset` are left out in the estimation of the alignment models, but their retention times are subsequently adjusted based on the alignment results of the closest sample in `subset` (close in terms of position within the `object`). Alignment could thus be performed on only real samples leaving out e.g. blanks, which are then in turn adjusted based on the closest real sample. Here it is up to the user to ensure that the samples within `object` are ordered correctly (e.g. by injection index).

How the non-subset samples are adjusted bases also on the parameter `subsetAdjust`: with `subsetAdjust = "previous"`, each non-subset sample is adjusted based on the closest previous subset sample which results in most cases with adjusted retention times of the non-subset sample being identical to the subset sample on which the adjustment bases. The second, default, option is to use `subsetAdjust = "average"` in which case each non subset sample is adjusted based on the average retention time adjustment from the previous and following subset sample. For the average a weighted mean is used with weights being the inverse of the distance of the non-subset sample to the subset samples used for alignment.

See also section Alignment of experiments including blanks in the xcms vignette for an example.

The `PeakGroupsParam` class allows to specify all settings for the retention time adjustment based on house keeping peak groups present in most samples. Instances should be created with the `PeakGroupsParam` constructor.

`adjustRtimePeakGroups` returns the features (peak groups) which would, depending on the provided `PeakGroupsParam`, be selected for alignment/retention time correction.

`minFraction`,`minFraction<-`: getter and setter for the `minFraction` slot of the object.

`extraPeaks`,`extraPeaks<-`: getter and setter for the `extraPeaks` slot of the object.

`smooth`,`smooth<-`: getter and setter for the `smooth` slot of the object.

`span`,`span<-`: getter and setter for the `span` slot of the object.

`family`,`family<-`: getter and setter for the `family` slot of the object.

`peakGroupsMatrix`,`peakGroupsMatrix<-`: getter and setter for the `peakGroupsMatrix` slot of the object.

`subset`,`subset<-`: getter and setter for the `subset` slot of the object.

`subsetAdjust`,`subsetAdjust<-`: getter and setter for the `subsetAdjust` slot of the object.

`adjustRtime,XCMSnExp,PeakGroupsParam`: performs retention time correction based on the alignment of peak groups (features) found in all/most samples. The correction function identified on these peak groups is applied to the retention time of all spectra in the object, i.e. retention times of all spectra, also MS level > 1 are adjusted.

### Usage

    PeakGroupsParam(
      minFraction = 0.9,
      extraPeaks = 1,
      smooth = "loess",
      span = 0.2,
      family = "gaussian",
      peakGroupsMatrix = matrix(nrow = 0, ncol = 0),
      subset = integer(),
      subsetAdjust = c("average", "previous")
    )

    adjustRtimePeakGroups(object, param = PeakGroupsParam(), msLevel = 1L)

    ## S4 method for signature 'PeakGroupsParam'
    show(object)

    ## S4 method for signature 'PeakGroupsParam'
    minFraction(object)

    ## S4 replacement method for signature 'PeakGroupsParam'
    minFraction(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    extraPeaks(object)

    ## S4 replacement method for signature 'PeakGroupsParam'
    extraPeaks(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    smooth(x)

    ## S4 replacement method for signature 'PeakGroupsParam'
    smooth(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    span(object)

    ## S4 replacement method for signature 'PeakGroupsParam'
    span(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    family(object)

    ## S4 replacement method for signature 'PeakGroupsParam'
    family(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    peakGroupsMatrix(object)

    ## S4 replacement method for signature 'PeakGroupsParam'
    peakGroupsMatrix(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    subset(x)

    ## S4 replacement method for signature 'PeakGroupsParam'
    subset(object) <- value

    ## S4 method for signature 'PeakGroupsParam'
    subsetAdjust(object)

    ## S4 replacement method for signature 'PeakGroupsParam'
    subsetAdjust(object) <- value

    ## S4 method for signature 'XCMSnExp,PeakGroupsParam'
    adjustRtime(object, param, msLevel = 1L)

### Arguments

`minFraction` | `numeric(1)` between 0 and 1 defining the minimum required fraction of samples in which peaks for the peak group were identified. Peak groups passing this criteria will aligned across samples and retention times of individual spectra will be adjusted based on this alignment. For `minFraction = 1` the peak group has to contain peaks in all samples of the experiment. Note that if `subset` is provided, the specified fraction is relative to the defined subset of samples and not to the total number of samples within the experiment (i.e. a peak has to be present in the specified proportion of subset samples).
`extraPeaks` | `numeric(1)` defining the maximal number of additional peaks for all samples to be assigned to a peak group (i.e. feature) for retention time correction. For a data set with 6 samples, `extraPeaks = 1` uses all peak groups with a total peak count `<= 6 + 1`. The total peak count is the total number of peaks being assigned to a peak group and considers also multiple peaks within a sample being assigned to the group.
`smooth` | character defining the function to be used, to interpolate corrected retention times for all peak groups. Either `"loess"` or `"linear"`.
`span` | `numeric(1)` defining the degree of smoothing (if `smooth = "loess"`). This parameter is passed to the internal call to `loess`.
`family` | character defining the method to be used for loess smoothing. Allowed values are `"gaussian"` and `"symmetric"`.See `loess` for more information.
`peakGroupsMatrix` | optional `matrix` of (raw) retention times for the peak groups on which the alignment should be performed. Each column represents a sample, each row a feature/peak group. Such a matrix is for example returned by the `adjustRtimePeakGroups` method.
`subset` | `integer` with the indices of samples within the experiment on which the alignment models should be estimated. Samples not part of the subset are adjusted based on the closest subset sample. See description above for more details.
`subsetAdjust` | `character` specifying the method with which non-subset samples should be adjusted. Supported options are `"previous"` and `"average"` (default). See description above for more information.
`object` | For `adjustRtime`: an `XCMSnExp` object containing the results from a previous chromatographic peak detection (see `findChromPeaks`) and alignment analysis (see `groupChromPeaks`). For all other methods: a `PeakGroupsParam` object.
`param` | A `PeakGroupsParam` object containing all settings for the retention time correction method..
`msLevel` | `integer(1)` specifying the MS level. Currently only MS level 1 is supported.
`value` | The value for the slot.
`x` | a `PeakGroupsParam` object.

### Value

The `PeakGroupsParam` function returns a `PeakGroupsParam` class instance with all of the settings specified for retention time adjustment based on house keeping features/peak groups.

For `adjustRtimePeakGroups`: a `matrix`, rows being features, columns samples, of retention times. The features are ordered by the median retention time across columns.

For `adjustRtime`: a `XCMSnExp` object with the results of the retention time adjustment step. These can be accessed with the `adjustedRtime` method. Retention time correction does also adjust the retention time of the identified chromatographic peaks (accessed via `chromPeaks`. Note that retention time correction drops all previous alignment results from the result object.

### Slots

`.__classVersion__,minFraction,extraPeaks,smooth,span,family,peakGroupsMatrix,subset,subsetAdjust`

  See corresponding parameter above. `.__classVersion__` stores the version from the class. Slots values should exclusively be accessed via the corresponding getter and setter methods listed above.

### Note

These methods and classes are part of the updated and modernized `xcms` user interface which will eventually replace the `group` methods. All of the settings to the alignment algorithm can be passed with a `PeakGroupsParam` object.

The matrix with the (raw) retention times of the peak groups used in the alignment is added to the `peakGroupsMatrix` slot of the `PeakGroupsParam` object that is stored into the corresponding process history step (see `processHistory` for how to access the process history).

`adjustRtimePeakGroups` is supposed to be called before the sample alignment, but after a correspondence (peak grouping).

This method requires that a correspondence analysis has been performed on the data, i.e. that grouped chromatographic peaks/features are present (see `groupChromPeaks` for details).

Calling `adjustRtime` on an `XCMSnExp` object will cause all peak grouping (correspondence) results and any previous retention time adjustments to be dropped. In some instances, the `adjustRtime,XCMSnExp,PeakGroupsParam` re-adjusts adjusted retention times to ensure them being in the same order than the raw (original) retention times.

### Author(s)

Colin Smith, Johannes Rainer

### References

Colin A. Smith, Elizabeth J. Want, Grace O'Maille, Ruben Abagyan and Gary Siuzdak. "XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification" Anal. Chem. 2006, 78:779-787.

### See Also

The `do_adjustRtime_peakGroups` core API function and `retcor.peakgroups` for the old user interface. `plotAdjustedRtime` for visualization of alignment results.

`XCMSnExp` for the object containing the results of the alignment.

Other retention time correction methods: `adjustRtime-obiwarp`, `adjustRtime()`

### Examples

    ##############################
    ## Chromatographic peak detection and grouping.
    ##
    ## Below we perform first a peak detection (using the matchedFilter
    ## method) on some of the test files from the faahKO package followed by
    ## a peak grouping.
    library(faahKO)
    library(xcms)
    fls <- dir(system.file("cdf/KO", package = "faahKO"), recursive = TRUE,
               full.names = TRUE)

    ## Reading one of the KO samples
    raw_data <- readMSData(fls[1:2], mode = "onDisk")

    ## Disabling parallel processing for this example
    register(SerialParam())

    ## Perform the peak detection using the matchedFilter method.
    mfp <- MatchedFilterParam(snthresh = 20, binSize = 1)
    res <- findChromPeaks(raw_data, param = mfp)

    head(chromPeaks(res))
    ## The number of peaks identified per sample:
    table(chromPeaks(res)[, "sample"])

    ## Performing the peak grouping using the "peak density" method.
    p <- PeakDensityParam(sampleGroups = c(1, 1))
    res <- groupChromPeaks(res, param = p)

    ## Perform the retention time adjustment using peak groups found in both
    ## files.
    fgp <- PeakGroupsParam(minFraction = 1)

    ## Before running the alignment we can evaluate which features (peak groups)
    ## would be used based on the specified parameters.
    pkGrps <- adjustRtimePeakGroups(res, param = fgp)

    ## We can also plot these to evaluate if the peak groups span a large portion
    ## of the retention time range.
    plot(x = pkGrps[, 1], y = rep(1, nrow(pkGrps)), xlim = range(rtime(res)),
        ylim = c(1, 2), xlab = "rt", ylab = "", yaxt = "n")
    points(x = pkGrps[, 2], y = rep(2, nrow(pkGrps)))
    segments(x0 = pkGrps[, 1], x1 = pkGrps[, 2],
        y0 = rep(1, nrow(pkGrps)), y1 = rep(2, nrow(pkGrps)))
    grid()
    axis(side = 2, at = c(1, 2), labels = colnames(pkGrps))

    ## Next we perform the alignment.
    res <- adjustRtime(res, param = fgp)

    ## Any grouping information was dropped
    hasFeatures(res)

    ## Plot the raw against the adjusted retention times.
    plot(rtime(raw_data), rtime(res), pch = 16, cex = 0.25, col = fromFile(res))

    ## Adjusterd retention times can be accessed using
    ## rtime(object, adjusted = TRUE) and adjustedRtime
    all.equal(rtime(res), adjustedRtime(res))

    ## To get the raw, unadjusted retention times:
    all.equal(rtime(res, adjusted = FALSE), rtime(raw_data))

    ## To extract the retention times grouped by sample/file:
    rts <- rtime(res, bySample = TRUE)

[Package xcms version 3.10.2 Index]