renormalizeSignalMatrices

Renormalizes a list of signal matrices or an EnrichmentSE object.

Usage

renormalizeBorders(ml, trim = NULL, assay = "input", nWindows = NULL)

renormalizeSignalMatrices(
  ml,
  method = c("border", "top", "manual"),
  trim = NULL,
  fromAssay = "input",
  toAssay = NULL,
  nWindows = NULL,
  scaleFactors = NULL,
  ...
)

Arguments

ml

A named matrix list or EnrichmentSE object as produced by signal2Matrix.

trim

Quantiles trimmed at each extreme before calculating normalization factors.

method

Either "border" or "top" (see details below).

fromAssay

Assay to use (ignored unless `ml` is an EnrichmentSE object), defaults to the first assay.

toAssay

Assay in which to store the normalized data (ignored unless `ml` is an EnrichmentSE object). By default an assay name will be set based on the normalization method used.

scaleFactors

A numeric vector of same length as `ml`, indicating the scaling factors by which to multiply each matrix. Alternatively, a numeric matrix with a number of rows equal to the length of `ml`, and two columns indicating the alpha and beta arguments of a s3norm normalization. Ignored unless `method="manual"`.

Value

Either a renormalized list of signal matrices or, if `ml` was an `EnrichmentSE` object, the same object with an additional normalized assay automatically put at the front.

Details

* `method="border"` works on the assumption that the left/right borders of the matrices represent background signal which should be equal across samples. As a result, it will work only if 1) the left/right borders of the matrices are sufficiently far from the signal (e.g. peaks) to be chiefly noise, and 2) the signal-to-noise ratio is comparable across tracks/samples. * `method="top"` instead works on the assumption that the highest signal should be the same across tracks/samples. By default, extreme values are trimmed before establishing either kind of normalization factor. The proportion trimmed can be set using the `trim` argument, and is by default 10 * `method="manual"` enables the use of independently computed normalization factors, for instance obtained through getNormFactors.

Functions

• renormalizeBorders(): deprecated > renormalizeSignalMatrices

Examples

# we first get an EnrichmentSE object:
data(exampleESE)
# we normalize them
m <- renormalizeSignalMatrices(m)
#> Error: object 'm' not found
# see the `vignette("multiRegionPlot")` for more info on normalization.