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.
QFeatures(..., assayLinks = NULL)
# S4 method for QFeatures
show(object)
# S3 method for QFeatures
plot(x, interactive = FALSE, ...)
# S4 method for QFeatures,ANY,ANY,ANY
[(x, i, j, ..., drop = TRUE)
# S4 method for QFeatures,character,ANY,ANY
[(x, i, j, k, ..., drop = TRUE)
# S4 method for QFeatures
c(x, ...)
# S4 method for QFeatures
dims(x, use.names = TRUE)
# S4 method for QFeatures
nrows(x, use.names = TRUE)
# S4 method for QFeatures
ncols(x, use.names = TRUE)
# S4 method for QFeatures
rowData(x, use.names = TRUE, ...)
# S4 method for QFeatures,DataFrameList
rowData(x) <- value
# S4 method for QFeatures,ANY
rowData(x) <- value
rbindRowData(object, i)
selectRowData(x, rowvars)
rowDataNames(x)
# S4 method for QFeatures,character
names(x) <- value
longFormat(object, colvars = NULL, rowvars = NULL, index = 1L)
addAssay(x, y, name, assayLinks)
removeAssay(x, i)
replaceAssay(x, y, i)
# S4 method for QFeatures,ANY,ANY
[[(x, i, j, ...) <- value
# S4 method for QFeatures
updateObject(object, ..., verbose = FALSE)
dropEmptyAssays(object, dims = 1:2)See MultiAssayExperiment for details. For plot,
further arguments passed to igraph::plot.igraph.
An optional AssayLinks.
An instance of class QFeatures.
An instance of class QFeatures.
A logical(1). If TRUE, an interactive graph
is generated using plotly. Else, a static plot using igraph
is generated. We recommend interactive exploration when the
QFeatures object contains more than 50 assays.
An indexing vector. See the corresponding section in the documentation for more details.
character(), logical(), or numeric() vector for
subsetting by colData rows.
logical (default TRUE) whether to drop empty assay
elements in the ExperimentList.
character(), logical(), or numeric() vector for
subsetting by assays
A logical(1) indicating whether the rownames of
each assay should be propagated to the corresponding rowData.
The values to use as a replacement. See the corresponding section in the documentation for more details.
A character() with the names of the rowData
variables (columns) to retain in any assay.
A character() that selects column(s) in the
colData.
The assay indicator within each SummarizedExperiment
object. A vector input is supported in the case that the
SummarizedExperiment object(s) has more than one assay
(default 1L)
An object that inherits from SummarizedExperiment or a
named list of assays. When y is a list, each element must
inherit from a SummarizedExperiment and the names of the
list are used as the names of the assays to add. Hence, the
list names must be unique and cannot overlap with the names of
the assays already present in x.
A character(1) naming the single assay. Ignored if
y is a list of assays.
logical (default FALSE) whether to print extra messages
numeric() that defines the dimensions to consider to
drop empty assays. 1 for rows (i.e. assays without any
features) and 2 for columns (i.e. assays without any
samples). Default is 1:2. Any value other that 1 and/or 2
will trigger an error.
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 rowData method returns a DataFrameList containing the
rowData for each assay of the QFeatures object. On the other
hand, rowData can be modified using rowData(x) <- value,
where value is a list of tables that can be coerced to DFrame
tables. The names of value point to the assays for
which the rowData must be replaced. The column names of each
table are used to replace the data in the existing rowData. If
the column name does not exist, a new column is added to the
rowData.
The rbindRowData functions returns a DFrame table that
contains the row binded rowData tables from the selected
assays. In this context, i is a character(), integer() or
logical() object for subsetting assays. Only rowData variables
that are common to all assays are kept.
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 one or more
new assay(s) y to the QFeatures instance x. name
is a character(1) naming the assay if only one assay is
provided, and is ignored if y is a list of assays. assayLinks
is an optional AssayLinks. The colData(y) is
automatically added to colData(x) by matching sample
names, that is colnames(y). If the samples are not present in
x, the rows of colData(x) are extended to account for the
new samples. Be aware that conflicting information between the
colData(y) and the colData(x) will result in an
error.
removeAssay(x, i): Removes one or more assay(s) from the
QFeatures instance x. In this context, i is a character(),
integer() or logical() that indicates which assay(s) to
remove.
replaceAssay(x, y, i): Replaces one or more
assay(s) from the QFeatures instance x. In this context, i
is a character(), integer() or logical() that indicates
which assay(s) to replace. The AssayLinks from or to
any replaced assays are automatically removed, unless the
replacement has the same dimension names (columns and row, order
agnostic). Be aware that conflicting information between
colData(y) and colData(x) will result in an error.
x[[i]] <- value: a generic method for adding (when i is not
in names(x)), removing (when value is null) or replacing (when
i is in names(x)). Note that the arguments j and ... from
the S4 replacement method signature are not allowed.
QFeatures object can be subset using the x[i, j, k, drop = TRUE]
paradigm. In this context, i is a character(), integer(),
logical() or GRanges() object for subsetting by rows. See
the argument descriptions for details on the remaining arguments.
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. All other
variables than rowvars will be dropped. In case an element in
rowvars isn't found in any rowData variable, a message is
printed.
The dropEmptyAssays(object, dims) function removes empty
assays from a QFeatures. Empty assays are defined as having 0
rows and/or 0 columns, as defined by the dims argument.
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()
#> A empty instance of class 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))
#> class: SummarizedExperiment
#> dim: 10 4
#> metadata(0):
#> assays(1): ''
#> rownames(10): a b ... i j
#> rowData names(2): Fa Fb
#> colnames(4): S1 S2 S3 S4
#> colData names(0):
(se2 <- SummarizedExperiment(m2, df2))
#> class: SummarizedExperiment
#> dim: 4 4
#> metadata(0):
#> assays(1): ''
#> rownames(4): a b c d
#> rowData names(0):
#> colnames(4): S1 S2 S3 S4
#> colData names(0):
## 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
#> An instance of class QFeatures containing 2 assays:
#> [1] assay1: SummarizedExperiment with 10 rows and 4 columns
#> [2] assay2: SummarizedExperiment with 4 rows and 4 columns
fts1[[1]]
#> class: SummarizedExperiment
#> dim: 10 4
#> metadata(0):
#> assays(1): ''
#> rownames(10): a b ... i j
#> rowData names(2): Fa Fb
#> colnames(4): S1 S2 S3 S4
#> colData names(0):
fts1[["assay1"]]
#> class: SummarizedExperiment
#> dim: 10 4
#> metadata(0):
#> assays(1): ''
#> rownames(10): a b ... i j
#> rowData names(2): Fa Fb
#> colnames(4): S1 S2 S3 S4
#> colData names(0):
## 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)
#> DataFrameList of length 3
#> names(3): se1 se2 se3
## Keep only the Fa variable
selectRowData(fts1, rowvars = "Fa")
#> An instance of class QFeatures containing 3 assays:
#> [1] se1: SummarizedExperiment with 10 rows and 4 columns
#> [2] se2: SummarizedExperiment with 4 rows and 4 columns
#> [3] se3: SummarizedExperiment with 2 rows and 4 columns
## -----------------------------------
## See ?readQFeatures to create a
## QFeatures object from a data.frame
## or spreadsheet.
## -----------------------------------