QFeatures-class: Quantitative MS QFeatures
In QFeatures: Quantitative features for mass spectrometry data

Description Usage Arguments Value Constructors Accessors Adding assays Subsetting Author(s) See Also Examples

Conceptually, a QFeatures object holds a set of assays, each composed of a matrix (or array) containing quantitative data and row annotations (meta-data). The number and the names of the columns (samples) must always be the same across the assays, but the number and the names of the rows (features) can vary. The assays are typically defined as SummarizedExperiment objects. In addition, a QFeatures object also uses a single DataFrame to annotate the samples (columns) represented in all the matrices.

The QFeatures class extends the MultiAssayExperiment::MultiAssayExperiment and inherits all the functionality of the MultiAssayExperiment::MultiAssayExperiment class.

A typical use case for such QFeatures object is to represent quantitative proteomics (or metabolomics) data, where different assays represent quantitation data at the PSM (the main assay), peptide and protein level, and where peptide values are computed from the PSM data, and the protein-level data is calculated based on the peptide-level values. The largest assay (the one with the highest number of features, PSMs in the example above) is considered the main assay.

The recommended way to create QFeatures objects is the use the readQFeatures() function, that creates an instance from tabular data. The QFeatures constructor can be used to create objects from their bare parts. It is the user's responsability to make sure that these match the class validity requirements.

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

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

## S4 method for signature 'QFeatures'
dims(x)

## S4 method for signature 'QFeatures,character,ANY,ANY'
x[i, j, k, ..., drop = TRUE]

## S4 method for signature 'QFeatures'
rowData(x, use.names = TRUE, ...)

selectRowData(x, rowvars)

rowDataNames(x)

## S4 replacement method for signature 'QFeatures,character'
names(x) <- value

longFormat(object, colDataCols = NULL, rowDataCols = NULL, index = 1L)

QFeatures(..., assayLinks = NULL)

addAssay(x, y, name = "newAssay", assayLinks = AssayLinks(names = name))

`object`	An instance of class `QFeatures`.
`x`	An instance of class QFeatures.
`i`	`character()`, `integer()`, `logical()` or `GRanges()` object for subsetting by rows.
`j`	`character()`, `logical()`, or `numeric()` vector for subsetting by `colData` rows.
`...`	See `MultiAssayExperiment` for details.
`drop`	logical (default `TRUE`) whether to drop empty assay elements in the `ExperimentList`.
`k`	`character()`, `logical()`, or `numeric()` vector for subsetting by assays
`use.names`	A `logical(1)` indicating whether the rownames of each assay should be propagated to the corresponding `rowData`.
`rowvars`	A `character()` with the names of the `rowData` variables (columns) to retain in any assay. All other variables will be dropped. In case an element in `rowvars` isn't found in any `rowData` variable, a message is printed.
`value`	A character() with new name(s) for the assay(s) in `x`
`colDataCols`	A `character()`, `logical()`, or `numeric()` index for colData columns to be included.
`rowDataCols`	A `character()` index for colData columns to be included.
`index`	The assay indicator for `SummarizedExperiment` objects. A vector input is supported in the case that the `SummarizedExperiment` object(s) has more than one assay (default `1L`)
`assayLinks`	An optional AssayLinks.
`y`	A single assay or a named list of assays.
`name`	A `character(1)` naming the single assay (default is `"newAssay"`). Ignored if `y` is a list of assays.

See individual method description for the return value.

QFeatures(..., assayLinks) allows the manual construction of objects. It is the user's responsability to make sure these comply. The arguments in ... are those documented in MultiAssayExperiment::MultiAssayExperiment(). For details about assayLinks, see AssayLinks. An example is shown below.
The readQFeatures() function constructs a QFeatures object from text-based spreadsheet or a data.frame used to generate an assay. See the function manual page for details and an example.

The QFeatures class extends the MultiAssayExperiment::MultiAssayExperiment class and inherits all its accessors and replacement methods.
The rowDataNames accessor returns a list with the rowData variable names.
The longFormat accessor takes a QFeatures object and returns it in a long format DataFrame. Each quantitative value is reported on a separate line. colData and rowData data can also be added. This function is an extension of the longFormat function in the MultiAssayExperiment::MultiAssayExperiment.

The aggregateFeatures() function creates a new assay by aggregating features of an existing assay.
addAssay(x, y, name, assayLinks): Adds a new assay (or list of assays) y to the QFeatures instance x. name is a character(1) naming the single assay (default is "newAssay"), and is ignored if y is a list of assays. assayLinks is an optional AssayLinks.

QFeatures object can be subset using the x[i, j, k, drop = TRUE] paradigm. See the argument descriptions for details.
The subsetByFeature() function can be used to subset a QFeatures object using one or multiple feature names that will be matched across different assays, taking the aggregation relation between assays.
The selectRowData(x, rowvars) function can be used to select a limited number of rowData columns of interest named in rowvars in the x instance of class QFeatures.

Laurent Gatto

The readQFeatures() constructor and the aggregateFeatures() function. The QFeatures vignette provides an extended example.
The QFeatures-filtering manual page demonstrates how to filter features based on their rowData.
The missing-data manual page to manage missing values in QFeatures objects.
The QFeatures-processing and aggregateFeatures() manual pages and Processing vignette describe common quantitative data processing methods using in quantitative proteomics.

## ------------------------
## An empty QFeatures object
## ------------------------

QFeatures()

## -----------------------------------
## Creating a QFeatures object manually
## -----------------------------------

## two assays (matrices) with matching column names
m1 <- matrix(1:40, ncol = 4)
m2 <- matrix(1:16, ncol = 4)
sample_names <- paste0("S", 1:4)
colnames(m1) <- colnames(m2) <- sample_names
rownames(m1) <- letters[1:10]
rownames(m2) <- letters[1:4]

## two corresponding feature metadata with appropriate row names
df1 <- DataFrame(Fa = 1:10, Fb = letters[1:10],
                 row.names = rownames(m1))
df2 <- DataFrame(row.names = rownames(m2))

(se1 <- SummarizedExperiment(m1, df1))
(se2 <- SummarizedExperiment(m2, df2))

## Sample annotation (colData)
cd <- DataFrame(Var1 = rnorm(4),
                Var2 = LETTERS[1:4],
                row.names = sample_names)

el <- list(assay1 = se1, assay2 = se2)
fts1 <- QFeatures(el, colData = cd)
fts1
fts1[[1]]
fts1[["assay1"]]

## Rename assay
names(fts1) <- c("se1", "se2")

## Add an assay
fts1 <- addAssay(fts1, se1[1:2, ], name = "se3")

## Get the assays feature metadata
rowData(fts1) 

## Keep only the Fa variable
selectRowData(fts1, rowvars = "Fa")

## -----------------------------------
## See ?readQFeatures to create a
## QFeatures object from a data.frame
## or spreadsheet.
## -----------------------------------