averageMSnSet {MSnbase} | R Documentation

## Generate an average `MSnSet`

### Description

Given a list of `MSnSet` instances, typically representing replicated experiments, the function returns an average `MSnSet`.

### Usage

    averageMSnSet(x, avg = function(x) mean(x, na.rm = TRUE), disp = npcv)

### Arguments

`x` | A `list` of valid `MSnSet` instances to be averaged.
`avg` | The averaging function. Default is the mean after removing missing values, as computed by `function(x) mean(x, na.rm = TRUE)`.
`disp` | The disperion function. Default is an non-parametric coefficient of variation that replaces the standard deviation by the median absolute deviation as computed by `mad(x)/abs(mean(x))`. See `npcv` for details. Note that the `mad` of a single value is 0 (as opposed to `NA` for the standard deviation, see example below).

### Details

This function is aimed at facilitating the visualisation of replicated experiments and should not be used as a replacement for a statistical analysis.

The samples of the instances to be averaged must be identical but can be in a different order (they will be reordered by default). The features names of the result will correspond to the union of the feature names of the input `MSnSet` instances. Each average value will be computed by the `avg` function and the dispersion of the replicated measurements will be estimated by the `disp` function. These dispersions will be stored as a `data.frame` in the feature metadata that can be accessed with `fData(.)$disp`. Similarly, the number of missing values that were present when average (and dispersion) were computed are available in `fData(.)$disp`.

Currently, the feature metadata of the returned object corresponds the the feature metadata of the first object in the list (augmented with the missing value and dispersion values); the metadata of the features that were missing in this first input are missing (i.e. populated with `NA`s). This may change in the future.

### Value

A new average `MSnSet`.

### Author(s)

Laurent Gatto

### See Also

`compfnames` to compare MSnSet feature names.

### Examples

    library("pRolocdata")
    ## 3 replicates from Tan et al. 2009
    data(tan2009r1)
    data(tan2009r2)
    data(tan2009r3)
    x <- MSnSetList(list(tan2009r1, tan2009r2, tan2009r3))
    avg <- averageMSnSet(x)
    dim(avg)
    head(exprs(avg))
    head(fData(avg)$nNA)
    head(fData(avg)$disp)
    ## using the standard deviation as measure of dispersion
    avg2 <-averageMSnSet(x, disp = sd)
    head(fData(avg2)$disp)
    ## keep only complete observations, i.e proteins
    ## that had 0 missing values for all samples
    sel <- apply(fData(avg)$nNA, 1 , function(x) all(x == 0))
    avg <- avg[sel, ]
    disp <- rowMax(fData(avg)$disp)
    library("pRoloc")
    setStockcol(paste0(getStockcol(), "AA"))
    plot2D(avg, cex = 7.7 * disp)
    title(main = paste("Dispersion: non-parametric CV",
                       paste(round(range(disp), 3), collapse = " - ")))

[Package MSnbase version 2.6.4 Index]