XChromatograms {xcms} | R Documentation

## Containers for chromatographic and peak detection data

### Description

The `XChromatogram` object allows to store chromatographic data (e.g. an extracted ion chromatogram) along with identified chromatographic peaks within that data. The object inherits all functions from the `Chromatogram()` object in the `MSnbase` package.

Multiple `XChromatogram` objects can be stored in a `XChromatograms` object. This class extends `MChromatograms()` from the `MSnbase` package and allows thus to arrange chromatograms in a matrix-like structure, columns representing samples and rows m/z-retention time ranges.

All functions are described (grouped into topic-related sections) after the Arguments section.

### Usage

    XChromatograms(data, phenoData, featureData, chromPeaks, chromPeakData, ...)

    XChromatogram(
      rtime = numeric(),
      intensity = numeric(),
      mz = c(NA_real_, NA_real_),
      filterMz = c(NA_real_, NA_real_),
      precursorMz = c(NA_real_, NA_real_),
      productMz = c(NA_real_, NA_real_),
      fromFile = integer(),
      aggregationFun = character(),
      msLevel = 1L,
      chromPeaks,
      chromPeakData
    )

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

    ## S4 method for signature 'XChromatogram'
    chromPeaks(
      object,
      rt = numeric(),
      mz = numeric(),
      ppm = 0,
      type = c("any", "within", "apex_within"),
      msLevel
    )

    ## S4 replacement method for signature 'XChromatogram'
    chromPeaks(object) <- value

    ## S4 method for signature 'XChromatogram,ANY'
    plot(
      x,
      col = "#00000060",
      lty = 1,
      type = "l",
      xlab = "retention time",
      ylab = "intensity",
      main = NULL,
      peakType = c("polygon", "point", "rectangle", "none"),
      peakCol = "#00000060",
      peakBg = "#00000020",
      peakPch = 1,
      ...
    )

    ## S4 method for signature 'XChromatogram'
    filterMz(object, mz, ...)

    ## S4 method for signature 'XChromatogram'
    filterRt(object, rt, ...)

    ## S4 method for signature 'XChromatogram'
    hasChromPeaks(object)

    ## S4 method for signature 'XChromatogram'
    dropFilledChromPeaks(object)

    ## S4 method for signature 'XChromatogram'
    chromPeakData(object)

    ## S4 replacement method for signature 'XChromatogram'
    chromPeakData(object) <- value

    ## S4 method for signature 'XChromatogram,MergeNeighboringPeaksParam'
    refineChromPeaks(object, param = MergeNeighboringPeaksParam())

    ## S4 method for signature 'XChromatogram'
    filterChromPeaks(object, method = c("keepTop"), ...)

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

    ## S4 method for signature 'XChromatograms'
    hasChromPeaks(object)

    ## S4 method for signature 'XChromatograms'
    hasFilledChromPeaks(object)

    ## S4 method for signature 'XChromatograms'
    chromPeaks(
      object,
      rt = numeric(),
      mz = numeric(),
      ppm = 0,
      type = c("any", "within", "apex_within"),
      msLevel
    )

    ## S4 method for signature 'XChromatograms'
    chromPeakData(object)

    ## S4 method for signature 'XChromatograms'
    filterMz(object, mz, ...)

    ## S4 method for signature 'XChromatograms'
    filterRt(object, rt, ...)

    ## S4 method for signature 'XChromatograms,ANY'
    plot(
      x,
      col = "#00000060",
      lty = 1,
      type = "l",
      xlab = "retention time",
      ylab = "intensity",
      main = NULL,
      peakType = c("polygon", "point", "rectangle", "none"),
      peakCol = "#00000060",
      peakBg = "#00000020",
      peakPch = 1,
      ...
    )

    ## S4 method for signature 'XChromatograms'
    processHistory(object, fileIndex, type)

    ## S4 method for signature 'XChromatograms'
    hasFeatures(object, ...)

    ## S4 method for signature 'XChromatograms'
    dropFeatureDefinitions(object, ...)

    ## S4 method for signature 'XChromatograms,PeakDensityParam'
    groupChromPeaks(object, param)

    ## S4 method for signature 'XChromatograms'
    featureDefinitions(
      object,
      mz = numeric(),
      rt = numeric(),
      ppm = 0,
      type = c("any", "within", "apex_within")
    )

    ## S4 method for signature 'XChromatograms,ANY,ANY,ANY'
    x[i, j, drop = TRUE]

    ## S4 method for signature 'XChromatograms'
    featureValues(
      object,
      method = c("medret", "maxint", "sum"),
      value = "into",
      intensity = "into",
      missing = NA,
      ...
    )

    ## S4 method for signature 'XChromatograms'
    plotChromPeakDensity(
      object,
      param,
      col = "#00000060",
      xlab = "retention time",
      main = NULL,
      peakType = c("polygon", "point", "rectangle", "none"),
      peakCol = "#00000060",
      peakBg = "#00000020",
      peakPch = 1,
      simulate = TRUE,
      ...
    )

    ## S4 method for signature 'XChromatograms'
    dropFilledChromPeaks(object)

    ## S4 method for signature 'XChromatograms,MergeNeighboringPeaksParam'
    refineChromPeaks(object, param = MergeNeighboringPeaksParam())

    ## S4 method for signature 'XChromatograms'
    filterChromPeaks(object, method = c("keepTop"), ...)

### Arguments

`data` | For `XChromatograms`: `list` of `Chromatogram` or `XChromatogram` objects.
`phenoData` | For `XChromatograms`: either a `data.frame`, `AnnotatedDataFrame` or `NAnnotatedDataFrame` describing the phenotypical information of the samples.
`featureData` | For `XChromatograms`: either a `data.frame` or `AnnotatedDataFrame` with additional information for each row of chromatograms.
`chromPeaks` | For `XChromatogram`: `matrix` with required columns `"rt"`, `"rtmin"`, `"rtmax"`, `"into"`, `"maxo"` and `"sn"`. For `XChromatograms`: `list`, same length than `data`, with the chromatographic peaks for each chromatogram. Each element has to be a `matrix`, the ordering has to match the order of the chromatograms in `data`.
`chromPeakData` | For `XChromatogram`: `DataFrame` with optional additional annotations for each chromatographic peak. The number of rows has to match the number of chromatographic peaks.
`...` | For `filterChromPeaks`: additional parameters defining how to filter chromatographic peaks. See function description below for details.
`rtime` | For `XChromatogram`: `numeric` with the retention times (length has to be equal to the length of `intensity`).
`intensity` | For `XChromatogram`: `numeric` with the intensity values (length has to be equal to the length of `rtime`).
    For `featureValues`: `character(1)` specifying the name
    of the column in `chromPeaks(object)` containing the intensity value
    of the peak that should be used for the `method = "maxint"` conflict
    resolution if.
`mz` | For `XChromatogram`: `numeric(2)` representing the m/z value range (min, max) on which the chromatogram was created. This is supposed to contain the real range of m/z values in contrast to the `filterMz` below. For `chromPeaks` and `featureDefinitions`: `numeric(2)` defining the m/z range for which chromatographic peaks or features should be returned. For `filterMz`: `numeric(2)` defining the m/z range for which chromatographic peaks should be retained.#'
`filterMz` | For `XChromatogram`: `numeric(2)` representing the m/z value range (min, max) that was used to filter the original object on m/z dimension. If not applicable use `filterMz = c(0, 0)`.
`precursorMz` | For `XChromatogram`: `numeric(2)` for SRM/MRM transitions. Represents the mz of the precursor ion. See details for more information.
`productMz` | For `XChromatogram`: `numeric(2)` for SRM/MRM transitions. Represents the mz of the product. See details for more information.
`fromFile` | For `XChromatogram`: `integer(1)` the index of the file within the `OnDiskMSnExp` or `MSnExp` object from which the chromatogram was extracted.
`aggregationFun` | For `XChromatogram`: `character(1)` specifying the function that was used to aggregate intensity values for the same retention time across the m/z range.
`msLevel` | For `XChromatogram`: `integer` with the MS level from which the chromatogram was extracted. For `chromPeaks` and `chromPeakData`: extract chromatographic peaks of a certain MS level.
`object` | An `XChromatogram` or `XChromatograms` object.
`rt` | For `chromPeaks` and `featureDefinitions`: `numeric(2)` defining the retention time range for which chromatographic peaks or features should be returned. For `filterRt`: `numeric(2)` defining the retention time range to reduce `object` to.
`ppm` | For `chromPeaks` and `featureDefinitions`: `numeric(1)` defining a ppm to expand the provided m/z range.
`type` | For `chromPeaks` and `featureDefinitions`: `character(1)` defining which peaks or features to return if `rt` or `mz` is provided: `"any"` (default) return all peaks that are even partially overlapping with `rt`, `"within"` return peaks that are completely within `rt` and `"apex_within"` return peaks which apex is within `rt`.
    For `plot`: what type of plot should be used for the
    chromatogram (such as `"l"` for lines, `"p"` for points etc), see help
    of [plot()] in the `graphics` package for more details.
    For `processHistory`: restrict returned processing steps to specific
    types. Use [processHistoryTypes()] to list all supported values.
`value` | For `chromPeaks<-`: a numeric `matrix` with required columns `"rt"`, `"rtmin"`, `"rtmax"`, `"into"` and `"maxo"`.
    For `featureValues`: `character(1)` specifying the name of the column in
    `chromPeaks(object)` that should be returned or `"index"` (default) to
    return the index of the peak associated with the feature in each sample.
    To return the integrated peak area instead of the index use
    `value = "into"`.
`x` | For `plot`: an `XChromatogram` or `XChromatograms` object.
`col` | For `plot`: the color to be used to draw the chromatogram.
`lty` | For `plot` and `plotChromPeakDensity`: the line type.
`xlab` | For `plot` and `plotChromPeakDensity`: the x axis label.
`ylab` | For `plot`: the y axis label.
`main` | For `plot` and `plotChromPeakDensity`: an optional title for the plot.
`peakType` | For `plot` and `plotChromPeakDensity`: `character(1)` defining how (and if) identified chromatographic peak within the chromatogram should be plotted. Options are `"polygon"` (default): draw the peak borders with the `peakCol` color and fill the peak area with the `peakBg` color, `"point"`: indicate the peak's apex with a point, `"rectangle"`: draw a rectangle around the identified peak and `"none"`: don't draw peaks.
`peakCol` | For `plot` and `plotChromPeakDensity`: the foreground color for the peaks. For `peakType = "polygon"` and `peakType = "rectangle"` this is the color for the border. Use `NA` to not use a foreground color. This should either be a single color or a vector of colors with the same length than `chromPeaks(x)` has rows.
`peakBg` | For `plot` and `plotChromPeakDensity`: the background color for the peaks. For `peakType = "polygon"` and `peakType = "rectangle"` the peak are or rectangle will be filled with this color. Use `NA` to skip. This should be either a single color or a vector of colors with the same length than `chromPeaks(x)` has rows.
`peakPch` | For `plot` and `plotChromPeakDensity`: the point character to be used for `peakType = "point"`. See `plot()` in the `graphics` package for more details.
`param` | For `groupChromPeaks` and `plotChromPeakDensity`: a `PeakDensityParam()` object with the settings for the peak density correspondence analysis algorithm.
`method` | For `featureValues`: `character(1)` specifying the method to resolve multi-peak mappings within the sample sample, i.e. to select the representative peak for a feature for which more than one peak was assigned in one sample. Options are `"medret"` (default): select the peak closest to the median retention time of the feature, `"maxint"`: select the peak with the largest signal and `"sum"`: sum the values of all peaks (only if `value` is `"into"` or `"maxo"`). For `filterChromPeaks`: `character(1)` defining the method that should be used to filter chromatographic peaks. See help on `filterChromPeaks` below for details.
`fileIndex` | For `processHistory`: optional `integer` specifying the index of the files/samples for which the ProcessHistory objects should be returned.
`i` | For `[`: `integer` with the row indices to subset the `XChromatograms` object.
`j` | For `[`: `integer` with the column indices to subset the `XChromatograms` object.
`drop` | For `[`: `logical(1)` whether the dimensionality should be dropped (if possible). Defaults to `drop = TRUE`, thus, if length of `i` and `j` is 1 a `XChromatogram` is returned. Note that `drop` is ignored if length of `i` or `j` is larger than 1, thus a `XChromatograms` is returned.
`missing` | For `featureValues`: how missing values should be reported. Allowed values are `NA` (default), a `numeric(1)` to replace `NA`s with that value or `missing = "rowmin_half"` to replace `NA`s with half of the row's minimal (non-missing) value.
`simulate` | For `plotChromPeakDensity`: `logical(1)` whether a correspondence analysis should be simulated based on the available data and the provided `PeakDensityParam()` `param` argument. See section Correspondence analysis for details.

### Value

See help of the individual functions.

### Creation of objects

Objects can be created with the contructor function `XChromatogram` and `XChromatograms`, respectively. Also, they can be coerced from Chromatogram or `MChromatograms()` objects using `as(object, "XChromatogram")` or `as(object, "XChromatograms")`.

### Filtering and subsetting

Besides classical subsetting with `[` specific filter operations on `MChromatograms()` and `XChromatograms` objects are available. See `filterColumnsIntensityAbove()` for more details.

- `[` allows to subset a `XChromatograms` object by row (`i`) and column (`j`), with `i` and `j` being of type `integer`. The `featureDefinitions` will also be subsetted accordingly and the `peakidx` column updated.

- `filterMz` filters the chromatographic peaks within an `XChromatogram` or `XChromatograms`, if a column `"mz"` is present in the `chromPeaks` matrix. This would be the case if the `XChromatogram` was extracted from an `XCMSnExp()` object with the `chromatogram()` function. All chromatographic peaks with their m/z within the m/z range defined by `mz` will be retained. Also feature definitions (if present) will be subset accordingly. The function returns a filtered `XChromatogram` or `XChromatograms` object.

- `filterRt` filters chromatogram(s) by the provided retention time range. All eventually present chromatographic peaks with their apex within the retention time range specified with `rt` will be retained. Also feature definitions, if present, will be filtered accordingly. The function returns a filtered `XChromatogram` or `XChromatograms` object.

### Accessing data

See also help of Chromatogram in the `MSnbase` package for general information and data access. The methods listed here are specific for `XChromatogram` and `XChromatograms` objects.

- `chromPeaks`, `chromPeaks<-`: extract or set the matrix with the chromatographic peak definitions. Parameter `rt` allows to specify a retention time range for which peaks should be returned along with parameter `type` that defines how overlapping is defined (parameter description for details). For `XChromatogram` objects the function returns a `matrix` with columns `"rt"` (retention time of the peak apex), `"rtmin"` (the lower peak boundary), `"rtmax"` (the upper peak boundary), `"into"` (the ingegrated peak signal/area of the peak), `"maxo"` (the maximum instensity of the peak and `"sn"` (the signal to noise ratio). Note that, depending on the peak detection algorithm, the matrix may contain additional columns. For `XChromatograms` objects the `matrix` contains also columns `"row"` and `"column"` specifying in which chromatogram of `object` the peak was identified. Chromatographic peaks are ordered by row.

- `chromPeakData`, `chromPeakData<-`: extract or set the `DataFrame()` with optional chromatographic peak annotations.

- `hasChromPeaks`: infer whether a `XChromatogram` (or `XChromatograms`) has chromatographic peaks. For `XChromatogram`: returns a `logical(1)`, for `XChromatograms`: returns a `matrix`, same dimensions than `object` with either `TRUE` or `FALSE` if chromatographic peaks are available in the chromatogram at the respective position.

- `hasFilledChromPeaks`: whether a `XChromatogram` (or a `XChromatogram` in a `XChromatograms`) has filled-in chromatographic peaks. For `XChromatogram`: returns a `logical(1)`, for `XChromatograms`: returns a `matrix`, same dimensions than `object` with either `TRUE` or `FALSE` if chromatographic peaks are available in the chromatogram at the respective position.

- `dropFilledChromPeaks`: removes filled-in chromatographic peaks. See `dropFilledChromPeaks()` help for `XCMSnExp()` objects for more information.

- `hasFeatures`: for `XChromatograms` objects only: if correspondence analysis has been performed and m/z-rt feature definitions are present. Returns a `logical(1)`.

- `dropFeatureDefinitions`: for `XChrmomatograms` objects only: delete any correspondence analysis results (and related process history).

- `featureDefinitions`: for `XChromatograms` objects only. Extract the results from the correspondence analysis (performed with `groupChromPeaks`). Returns a `DataFrame` with the properties of the defined m/z-rt features: their m/z and retention time range. Columns `peakidx` and `row` contain the index of the chromatographic peaks in the `chromPeaks` matrix associated with the feature and the row in the `XChromatograms` object in which the feature was defined. Similar to the `chromPeaks` method it is possible to filter the returned feature matrix with the `mz`, `rt` and `ppm` parameters.

- `featureValues`: for `XChromatograms` objects only. Extract the abundance estimates for the individuals features. Note that by default (with parameter `value = "index"` a `matrix` of indices of the peaks in the `chromPeaks` matrix associated to the feature is returned. To extract the integrated peak area use `value = "into"`. The function returns a `matrix` with one row per feature (in `featureDefinitions`) and each column being a sample (i.e. column of `object`). For features without a peak associated in a certain sample `NA` is returned. This can be changed with the `missing` argument of the function.

- `filterChromPeaks`: filters chromatographic peaks in `object` depending on parameter `method` and method-specific parameters passed as additional arguments with `...`. Available methods are:

  - `method = "keepTop"`: keep top `n` (default `n = 1L`) peaks in each chromatogram ordered by column `order` (defaults to `order = "maxo"`). Parameter `decreasing` (default `decreasing = TRUE`) can be used to order peaks in descending (`decreasing = TRUE`) or ascending ( `decreasing = FALSE`) order to keep the top `n` peaks with largest or smallest values, respectively.

- `processHistory`: returns a `list` of ProcessHistory objects representing the individual performed processing steps. Optional parameters `type` and `fileIndex` allow to further specify which processing steps to return.

### Plotting and visualizing

- `plot` draws the chromatogram and highlights in addition any chromatographic peaks present in the `XChromatogram` or `XChromatograms` (unless `peakType = "none"` was specified). To draw peaks in different colors a vector of color definitions with length equal to `nrow(chromPeaks(x))` has to be submitted with `peakCol` and/or `peakBg` defining one color for each peak (in the order as peaks are in `chromPeaks(x))`. For base peak chromatograms or total ion chromatograms it might be better to set `peakType = "none"` to avoid generating busy plots.

- `plotChromPeakDensity`: visualize peak density-based correspondence analysis results. See section Correspondence analysis for more details.

### Chromatographic peak detection

See findChromPeaks-Chromatogram-CentWaveParam for information.

After chromatographic peak detection it is also possible to refine identified chromatographic peaks with the `refineChromPeaks` method (e.g. to reduce peak detection artifacts). Currently, only peak refinement using the merge neighboring peaks method is available (see `MergeNeighboringPeaksParam()` for a detailed description of the approach.

### Correspondence analysis

Identified chromatographic peaks in an `XChromatograms` object can be grouped into features with the `groupChromPeaks` function. Currently, such a correspondence analysis can be performed with the peak density method (see groupChromPeaks for more details) specifying the algorithm settings with a `PeakDensityParam()` object. A correspondence analysis is performed separately for each row in the `XChromatograms` object grouping chromatographic peaks across samples (columns).

The analysis results are stored in the returned `XChromatograms` object and can be accessed with the `featureDefinitions` method which returns a `DataFrame` with one row for each feature. Column `"row"` specifies in which row of the `XChromatograms` object the feature was identified.

The `plotChromPeakDensity` method can be used to visualize peak density correspondence results, or to simulate a peak density correspondence analysis on chromatographic data. The resulting plot consists of two panels, the upper panel showing the chromatographic data as well as the identified chromatographic peaks, the lower panel the distribution of peaks (the peak density) along the retention time axis. This plot shows each peak as a point with it's peak's retention time on the x-axis, and the sample in which it was found on the y-axis. The distribution of peaks along the retention time axis is visualized with a density estimate. Grouped chromatographic peaks are indicated with grey shaded rectangles. Parameter `simulate` allows to define whether the correspondence analysis should be simulated ( `simulate=TRUE`, based on the available data and the provided `PeakDensityParam()` parameter class) or not (`simulate=FALSE`). For the latter it is assumed that a correspondence analysis has been performed with the peak density method on the `object`. See examples below.

Abundance estimates for each feature can be extracted with the `featureValues` function using parameter `value = "into"` to extract the integrated peak area for each feature. The result is a `matrix`, columns being samples and rows features.

### Note

Highlighting the peak area(s) in an `XChromatogram` or `XChromatograms` object (`plot` with `peakType = "polygon"`) draws a polygon representing the displayed chromatogram from the peak's minimal retention time to the maximal retention time. If the `XChromatograms` was extracted from an `XCMSnExp()` object with the `chromatogram()` function this might not represent the actual identified peak area if the m/z range that was used to extract the chromatogram was larger than the peak's m/z.

### Author(s)

Johannes Rainer

### See Also

findChromPeaks-centWave for peak detection on `MChromatograms()` objects.

### Examples

    ## ---- Creation of XChromatograms ----
    ##
    ## Create a XChromatograms from Chromatogram objects
    dta <- list(Chromatogram(rtime = 1:7, c(3, 4, 6, 12, 8, 3, 2)),
        Chromatogram(1:10, c(4, 6, 3, 4, 7, 13, 43, 34, 23, 9)))

    ## Create an XChromatograms without peak data
    xchrs <- XChromatograms(dta)

    ## Create an XChromatograms with peaks data
    pks <- list(matrix(c(4, 2, 5, 30, 12, NA), nrow = 1,
        dimnames = list(NULL, c("rt", "rtmin", "rtmax", "into", "maxo", "sn"))),
        NULL)
    xchrs <- XChromatograms(dta, chromPeaks = pks)

    ## Create an XChromatograms from XChromatogram objects
    dta <- lapply(dta, as, "XChromatogram")
    chromPeaks(dta[[1]]) <- pks[[1]]

    xchrs <- XChromatograms(dta, nrow = 1)

    hasChromPeaks(xchrs)

    ## Loading a test data set with identified chromatographic peaks
    data(faahko_sub)
    ## Update the path to the files for the local system
    dirname(faahko_sub) <- system.file("cdf/KO", package = "faahKO")

    ## Subset the dataset to the first and third file.
    xod_sub <- filterFile(faahko_sub, file = c(1, 3))

    od <- as(xod_sub, "OnDiskMSnExp")

    ## Extract chromatograms for a m/z - retention time slice
    chrs <- chromatogram(od, mz = 344, rt = c(2500, 3500))
    chrs

    ## --------------------------------------------------- ##
    ##       Chromatographic peak detection                ##
    ## --------------------------------------------------- ##
    ## Perform peak detection using CentWave
    xchrs <- findChromPeaks(chrs, param = CentWaveParam())
    xchrs

    ## Do we have chromatographic peaks?
    hasChromPeaks(xchrs)

    ## Process history
    processHistory(xchrs)

    ## The chromatographic peaks, columns "row" and "column" provide information
    ## in which sample the peak was identified.
    chromPeaks(xchrs)

    ## Spectifically extract chromatographic peaks for one sample/chromatogram
    chromPeaks(xchrs[1, 2])

    ## Plot the results
    plot(xchrs)

    ## Plot the results using a different color for each sample
    sample_colors <- c("#ff000040", "#00ff0040", "#0000ff40")
    cols <- sample_colors[chromPeaks(xchrs)[, "column"]]
    plot(xchrs, col = sample_colors, peakBg = cols)

    ## Indicate the peaks with a rectangle
    plot(xchrs, col = sample_colors, peakCol = cols, peakType = "rectangle",
        peakBg = NA)

    ## --------------------------------------------------- ##
    ##       Correspondence analysis                       ##
    ## --------------------------------------------------- ##
    ## Group chromatographic peaks across samples
    prm <- PeakDensityParam(sampleGroup = rep(1, 2))
    res <- groupChromPeaks(xchrs, param = prm)

    hasFeatures(res)
    featureDefinitions(res)

    ## Plot the correspondence results. Use simulate = FALSE to show the
    ## actual results. Grouped chromatographic peaks are indicated with
    ## grey shaded rectangles.
    plotChromPeakDensity(res, simulate = FALSE)

    ## Simulate a correspondence analysis based on different settings. Larger
    ## bw will increase the smoothing of the density estimate hence grouping
    ## chromatographic peaks that are more apart on the retention time axis.
    prm <- PeakDensityParam(sampleGroup = rep(1, 3), bw = 60)
    plotChromPeakDensity(res, param = prm)

    ## Delete the identified feature definitions
    res <- dropFeatureDefinitions(res)
    hasFeatures(res)

    ## Create a XChromatogram object
    pks <- matrix(nrow = 1, ncol = 6)
    colnames(pks) <- c("rt", "rtmin", "rtmax", "into", "maxo", "sn")
    pks[, "rtmin"] <- 2
    pks[, "rtmax"] <- 9
    pks[, "rt"] <- 4
    pks[, "maxo"] <- 19
    pks[, "into"] <- 93

    xchr <- XChromatogram(rtime = 1:10,
        intensity = c(4, 8, 14, 19, 18, 12, 9, 8, 5, 2),
        chromPeaks = pks)
    xchr

    ## Add arbitrary peak annotations
    df <- DataFrame(peak_id = c("a"))
    xchr <- XChromatogram(rtime = 1:10,
        intensity = c(4, 8, 14, 19, 18, 12, 9, 8, 5, 2),
        chromPeaks = pks, chromPeakData = df)
    xchr
    chromPeakData(xchr)

    ## Extract the chromatographic peaks
    chromPeaks(xchr)

    ## Plotting of a single XChromatogram object
    ## o Don't highlight chromatographic peaks
    plot(xchr, peakType = "none")

    ## o Indicate peaks with a polygon
    plot(xchr)

    ## Add a second peak to the data.
    pks <- rbind(chromPeaks(xchr), c(7, 7, 10, NA, 15, NA))
    chromPeaks(xchr) <- pks

    ## Plot the peaks in different colors
    plot(xchr, peakCol = c("#ff000080", "#0000ff80"),
        peakBg = c("#ff000020", "#0000ff20"))

    ## Indicate the peaks as rectangles
    plot(xchr, peakCol = c("#ff000060", "#0000ff60"), peakBg = NA,
        peakType = "rectangle")

    ## Filter the XChromatogram by retention time
    xchr_sub <- filterRt(xchr, rt = c(4, 6))
    xchr_sub
    plot(xchr_sub)

[Package xcms version 3.16.1 Index]