This manual page describes common quantitative proteomics data
processing methods using QFeatures objects. In the following
functions, if object
is of class QFeatures
, and optional assay
index or name i
can be specified to define the assay (by name of
index) on which to operate.
The following functions are currently available:
logTransform(object, base = 2, i, pc = 0)
log-transforms (with
an optional pseudocount offset) the assay(s).
normalize(object, method, i)
normalises the assay(s) according
to method
(see Details).
scaleTransform(object, center = TRUE, scale = TRUE, i)
applies
base::scale()
to SummarizedExperiment
and QFeatures
objects.
sweep(x, MARGIN, STATS, FUN = "-", check.margin = TRUE, ...)
sweeps out array summaries from SummarizedExperiment
and
QFeatures
objects. See base::sweep()
for details.
See the Processing vignette for examples.
# S4 method for class 'SummarizedExperiment'
logTransform(object, base = 2, pc = 0)
# S4 method for class 'QFeatures'
logTransform(object, i, name = "logAssay", base = 2, pc = 0)
# S4 method for class 'SummarizedExperiment'
scaleTransform(object, center = TRUE, scale = TRUE)
# S4 method for class 'QFeatures'
scaleTransform(object, i, name = "scaledAssay", center = TRUE, scale = TRUE)
# S4 method for class 'SummarizedExperiment'
normalize(object, method, ...)
# S4 method for class 'QFeatures'
normalize(object, i, name = "normAssay", method, ...)
# S4 method for class 'SummarizedExperiment'
sweep(x, MARGIN, STATS, FUN = "-", check.margin = TRUE, ...)
# S4 method for class 'QFeatures'
sweep(
x,
MARGIN,
STATS,
FUN = "-",
check.margin = TRUE,
...,
i,
name = "sweptAssay"
)
An object of class QFeatures
or SummarizedExperiment
.
numeric(1)
providing the base with respect to which
logarithms are computed. Defaults is 2.
numeric(1)
with a pseudocount to add to the
quantitative data. Useful when (true) 0 are present in the
data. Default is 0 (no effect).
A numeric vector or a character vector giving the index or the name, respectively, of the assay(s) to be processed.
A character(1)
naming the new assay name. Defaults
are logAssay
for logTransform
, scaledAssay
for
scaleTranform
and normAssay
for normalize
.
logical(1)
(default is TRUE
) value or
numeric-alike vector of length equal to the number of columns
of object
. See base::scale()
for details.
logical(1)
(default is TRUE
) or a numeric-alike
vector of length equal to the number of columns of
object
. See base::scale()
for details.
character(1)
defining the normalisation method to
apply. See Details.
Additional parameters passed to inner functions.
An object of class QFeatures
or SummarizedExperiment
in sweep
.
As in base::sweep()
, a vector of indices giving the
extent(s) of x
which correspond to STATS
.
As in base::sweep()
, the summary statistic which is
to be swept out.
As in base::sweep()
, the function to be used to carry
out the sweep.
As in base::sweep()
, a logical
. If TRUE
(the default), warn if the length or dimensions of STATS
do
not match the specified dimensions of x
. Set to FALSE
for
a small speed gain when you know that dimensions match.
An processed object of the same class as x
or object
.
The method
parameter in normalize
can be one of "sum"
,
"max"
, "center.mean"
, "center.median"
, "div.mean"
,
"div.median"
, "diff.median"
, "quantiles
", "quantiles.robust
"
or "vsn"
. The MsCoreUtils::normalizeMethods()
function returns
a vector of available normalisation methods.
For "sum"
and "max"
, each feature's intensity is divided by
the maximum or the sum of the feature respectively. These two
methods are applied along the features (rows).
"center.mean"
and "center.median"
center the respective
sample (column) intensities by subtracting the respective column
means or medians. "div.mean"
and "div.median"
divide by the
column means or medians. These are equivalent to sweep
ing the
column means (medians) along MARGIN = 2
with FUN = "-"
(for
"center.*"
) or FUN = "/"
(for "div.*"
).
"diff.median"
centers all samples (columns) so that they all
match the grand median by subtracting the respective columns
medians differences to the grand median.
Using "quantiles"
or "quantiles.robust"
applies (robust) quantile
normalisation, as implemented in preprocessCore::normalize.quantiles()
and preprocessCore::normalize.quantiles.robust()
. "vsn"
uses the
vsn::vsn2()
function. Note that the latter also glog-transforms the
intensities. See respective manuals for more details and function
arguments.
For further details and examples about normalisation, see
MsCoreUtils::normalize_matrix()
.
MsCoreUtils::normalizeMethods()
#> [1] "sum" "max" "center.mean" "center.median"
#> [5] "div.mean" "div.median" "diff.median" "quantiles"
#> [9] "quantiles.robust" "vsn"