MSnSetList-class {MSnbase} | R Documentation

## Storing multiple related MSnSets

### Description

A class for storing lists of `MSnSet` instances.

### Details

There are two ways to store different sets of measurements pertaining an experimental unit, such as replicated measures of different conditions that were recorded over more than one MS acquisition. Without focusing on any proteomics technology in particular, these multiple assays can be recorded as

- A single combined `MSnSet` (see the section Combining MSnSet instances in the MSnbase-demo section). In such cases, the different experimental (phenotypical) conditions are recorded as an `AnnotatedDataFrame` in the `phenoData` slots.

  Quantitative data for features that were missing in an assay are generally encode as missing with `NA` values. Alternatively, only features observed in all assays could be selected. See the `commonFeatureNames` functions to select only common features among two or more `MSnSet` instance.

- Each set of measurements is stored in an `MSnSet` which are combined into one `MSnSetList`. Each `MSnSet` elements can have identical or different samples and features. Unless compiled directly manually by the user, one would expect at least one of these dimensions (features/rows or samples/columns) are conserved (i.e. all feature or samples names are identical). See `split`/`unsplit` below.

### Objects from the Class

Objects can be created and manipluated with:

`MSnSetList(x, log, featureDAta)`

  The class constructor that takes a list of valid `MSnSet` instances as input `x`, an optional logging `list`, and an optional feature metadata `data.frame`.

`split(x, f)`

  An `MSnSetList` can be created from an `MSnSet` instance. `x` is a single `MSnSet` and `f` is a `factor` or a `character` of length 1. In the latter case, `f` will be matched to the feature- and phenodata variable names (in that order). If a match is found, the respective variable is extracted, converted to a factor if it is not one already, and used to split `x` along the features/rows (`f` was a feature variable name) or samples/columns (`f` was a phenotypic variable name). If `f` is passed as a factor, its length will be matched to `nrow(x)` or `ncol(x)` (in that order) to determine if `x` will be split along the features (rows) or sample (columns). Hence, the length of `f` must match exactly to either dimension.

`unsplit(value, f)`

  The `unsplit` method reverses the effect of splitting the `value` `MSnSet` along the groups `f`.

`as(x, "MSnSetList")`

  Where `x` is an instance of class MzTab. See the class documentation for details.

### Slots

`x`:

  Object of class `list` containing valid `MSnSet` instances. Can be extracted with the `msnsets()` accessor.

`log`:

  Object of class `list` containing an object creation log, containing among other elements the call that generated the object. Can be accessed with `objlog()`.

`featureData`:

  Object of class `DataFrame` that stores metadata for each object in the `x` slot. The number of rows of this `data.frame` must be equal to the number of items in the `x` slot and their respective (row)names must be identical.

`.__classVersion__`:

  The version of the instance. For development purposes only.

### Methods

`"[["`

  Extracts a single `MSnSet` at position.

`"["`

  Extracts one of more `MSnSets` as `MSnSetList`.

`length`

  Returns the number of `MSnSets`.

`names`

  Returns the names of `MSnSets`, if available. The replacement method is also available.

`show`

  Display the object by printing a short summary.

`lapply(x, FUN, ...)`

  Apply function `FUN` to each element of the input `x`. If the application of `FUN` returns and `MSnSet`, then the return value is an `MSnSetList`, otherwise a `list`

.

`sapply(x, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)`

  A `lapply` wrapper that simplifies the ouptut to a vector, matric or array is possible. See `?base::sapply` for details.

.

`fData`

  Returns the features metadata `featureData` slot.

`fData<-`

  Features metadata `featureData` replacement method.

### Author(s)

Laurent Gatto <lg390@cam.ac.uk>

### See Also

The `commonFeatureNames` function to select common features among `MSnSet` instances.

### Examples

    library("pRolocdata")
    data(tan2009r1)
    data(tan2009r2)

    ## The MSnSetList class
    ##  for an unnamed list, names are set to indices
    msnl <- MSnSetList(list(tan2009r1, tan2009r2))
    names(msnl)
    ##  a named example
    msnl <- MSnSetList(list(A = tan2009r1, B = tan2009r2))
    names(msnl)
    msnsets(msnl)
    length(msnl)
    objlog(msnl)
    msnl[[1]] ## an MSnSet
    msnl[1]   ## an MSnSetList of length 1

    ## Iterating over the elements
    lapply(msnl, dim) ## a list
    lapply(msnl, normalise) ## an MSnSetList

    fData(msnl)
    fData(msnl)$X <- sapply(msnl, nrow)
    fData(msnl)

    ## Splitting and unsplitting
    ##  splitting along the columns/samples
    data(dunkley2006)
    head(pData(dunkley2006))
    (splt <- split(dunkley2006, "replicate"))
    lapply(splt, dim) ## the number of rows and columns of the split elements
    unsplt <- unsplit(splt, dunkley2006$replicate)
    stopifnot(compareMSnSets(dunkley2006, unsplt))

    ##  splitting along the rows/features
    head(fData(dunkley2006))
    (splt <- split(dunkley2006, "markers"))
    unsplt <- unsplit(splt, factor(fData(dunkley2006)$markers))
    simplify2array(lapply(splt, dim))
    stopifnot(compareMSnSets(dunkley2006, unsplt))

[Package MSnbase version 2.12.0 Index]