QFeatures-processing: QFeatures processing
In QFeatures: Quantitative features for mass spectrometry data

Description Usage Arguments Details Value Examples

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 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 `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.

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 sweeping 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.