powerLaw: .powerLaw

In CAGEr: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining

Description

Private funtion for normalizing CAGE tag count to a referent power-law distribution.

Usage

.powerLaw(tag.counts, fitInRange = c(10, 1000), alpha = 1.25,
  T = 10^6)

Arguments

tag.counts	Numerical values whose reverse cumulative distribution will be fitted to power-law (e.g. tag count or signal for regions, peaks, etc.)
fitInRange	Range in which the fitting is done (values outside of this range will not be considered for fitting)
alpha	Slope of the referent power-law distribution (the actual slope has negative sign and will be -1*alpha)
T	total number of tags (signal) in the referent power-law distribution.

Details

S4 Methods are provided for integer vectors, Rle objects, data.frame objects and DataFrame objects, so that the most complex objects can be deconstructed in simpler parts, normalized and reconstructed.

Value

Normalized values (vector of the same length as input values); i.e. what would be the value of input values in the referent distribution. Ouptut objects are numeric, possibly Rle-encoded or wrapped in data.frames or DataFrames according to the input.

References

Balwierz, P. J., Carninci, P., Daub, C. O., Kawai, J., Hayashizaki, Y., Van Belle, W., Beisel, C., et al. (2009). Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data. Genome Biology, 10(7), R79.

CAGEr documentation built on Jan. 17, 2021, 2 a.m.