QFeaturesprocessing  R Documentation 
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)
logtransforms (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 signature 'SummarizedExperiment'
logTransform(object, base = 2, pc = 0)
## S4 method for signature 'QFeatures'
logTransform(object, i, name = "logAssay", base = 2, pc = 0)
## S4 method for signature 'SummarizedExperiment'
scaleTransform(object, center = TRUE, scale = TRUE)
## S4 method for signature 'QFeatures'
scaleTransform(object, i, name = "scaledAssay", center = TRUE, scale = TRUE)
## S4 method for signature 'SummarizedExperiment'
normalize(object, method, ...)
## S4 method for signature 'QFeatures'
normalize(object, i, name = "normAssay", method, ...)
## S4 method for signature 'SummarizedExperiment'
sweep(x, MARGIN, STATS, FUN = "", check.margin = TRUE, ...)
## S4 method for signature 'QFeatures'
sweep(
x,
MARGIN,
STATS,
FUN = "",
check.margin = TRUE,
...,
i,
name = "sweptAssay"
)
object 
An object of class 
base 

pc 

i 
A numeric vector or a character vector giving the index or the name, respectively, of the assay(s) to be processed. 
name 
A 
center 

scale 

method 

... 
Additional parameters passed to inner functions. 
x 
An object of class 
MARGIN 
As in 
STATS 
As in 
FUN 
As in 
check.margin 
As in 
The method
parameter in normalize
can be one of "sum"
,
"max"
, "center.mean"
, "center.median"
, "div.mean"
,
"div.median"
, "diff.meda"
, "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 glogtransforms the
intensities. See respective manuals for more details and function
arguments.
For further details and examples about normalisation, see
MsCoreUtils::normalize_matrix()
.
An processed object of the same class as x
or object
.
MsCoreUtils::normalizeMethods()
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