QFeatures-processing {QFeatures} | R Documentation

## QFeatures processing

### Description

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.

### Usage

    ## 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"
    )

### Arguments

`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.

### Details

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()`.

### Value

An processed object of the same class as `x` or `object`.

### Examples

    MsCoreUtils::normalizeMethods()

[Package QFeatures version 1.0.0 Index]