View source: R/normalization.R
renormalizeBorders | R Documentation |
Renormalizes a list of signal matrices or an EnrichmentSE object.
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,
...
)
ml |
A named matrix list or EnrichmentSE object as produced by
|
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"'. |
* '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
.
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.
renormalizeBorders()
: deprecated > renormalizeSignalMatrices
# we first get an EnrichmentSE object:
data(exampleESE)
# we normalize them
m <- renormalizeSignalMatrices(m)
# see the `vignette("multiRegionPlot")` for more info on normalization.
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