QFeatures processing

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"
)

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.

Value

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

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 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.

For further details and examples about normalisation, see MsCoreUtils::normalize_matrix().

Examples

MsCoreUtils::normalizeMethods()
#>  [1] "sum"              "max"              "center.mean"      "center.median"   
#>  [5] "div.mean"         "div.median"       "diff.median"      "quantiles"       
#>  [9] "quantiles.robust" "vsn"