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 SummarizedExperiment
logTransform(object, base = 2, pc = 0)
# S4 method for QFeatures
logTransform(object, i, name = "logAssay", base = 2, pc = 0)
# S4 method for SummarizedExperiment
scaleTransform(object, center = TRUE, scale = TRUE)
# S4 method for QFeatures
scaleTransform(object, i, name = "scaledAssay", center = TRUE, scale = TRUE)
# S4 method for SummarizedExperiment
normalize(object, method, ...)
# S4 method for QFeatures
normalize(object, i, name = "normAssay", method, ...)
# S4 method for SummarizedExperiment
sweep(x, MARGIN, STATS, FUN = "-", check.margin = TRUE, ...)
# S4 method for QFeatures
sweep(
x,
MARGIN,
STATS,
FUN = "-",
check.margin = TRUE,
...,
i,
name = "sweptAssay"
)
```

- object
An object of class

`QFeatures`

or`SummarizedExperiment`

.- base
`numeric(1)`

providing the base with respect to which logarithms are computed. Defaults is 2.- pc
`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).- i
A numeric vector or a character vector giving the index or the name, respectively, of the assay(s) to be processed.

- name
A

`character(1)`

naming the new assay name. Defaults are`logAssay`

for`logTransform`

,`scaledAssay`

for`scaleTranform`

and`normAssay`

for`normalize`

.- center
`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.- scale
`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.- method
`character(1)`

defining the normalisation method to apply. See Details.- ...
Additional parameters passed to inner functions.

- x
An object of class

`QFeatures`

or`SummarizedExperiment`

in`sweep`

.- MARGIN
As in

`base::sweep()`

, a vector of indices giving the extent(s) of`x`

which correspond to`STATS`

.- STATS
As in

`base::sweep()`

, the summary statistic which is to be swept out.- FUN
As in

`base::sweep()`

, the function to be used to carry out the sweep.- check.margin
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.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 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"
```