enve.recplot2.findPeaks.mower {enveomics.R}

Enveomics: Recruitment Plot (2) Mowing Peak Finder

Description

Identifies peaks in the population histogram potentially indicating sub-population mixtures, using a custom distribution-mowing method.

Usage

enve.recplot2.findPeaks.mower(
  x,
  min.points = 10,
  quant.est = c(0.002, 0.998),
  mlv.opts = list(method = "parzen"),
  fitdist.opts.sn = list(distr = "sn", method = "qme", probs = c(0.1, 0.5, 0.8), start =
    list(omega = 1, alpha = -1), lower = c(0, -Inf, -Inf)),
  fitdist.opts.norm = list(distr = "norm", method = "qme", probs = c(0.4, 0.6), start =
    list(sd = 1), lower = c(0, -Inf)),
  rm.top = 0.05,
  with.skewness = TRUE,
  optim.rounds = 200,
  optim.epsilon = 1e-04,
  merge.logdist = log(1.75),
  verbose = FALSE,
  log = TRUE
)

Arguments

x

An enve.RecPlot2 object.

min.points

Minimum number of points in the quantile-estimation-range (quant.est) to estimate a peak.

quant.est

Range of quantiles to be used in the estimation of a peak's parameters.

mlv.opts

Ignored. For backwards compatibility.

fitdist.opts.sn

Options passed to fitdist to estimate the standard deviation if with.skewness=TRUE. Note that the start parameter will be ammended with xi=estimated mode for each peak.

fitdist.opts.norm

Options passed to fitdist to estimate the standard deviation if with.skewness=FALSE. Note that the start parameter will be ammended with mean=estimated mode for each peak.

rm.top

Top-values to remove before finding peaks, as a quantile probability. This step is useful to remove highly conserved regions, but can be turned off by setting rm.top=0. The quantile is determined after removing zero-coverage windows.

with.skewness

Allow skewness correction of the peaks. Typically, the sequencing-depth distribution for a single peak is left-skewed, due partly (but not exclusively) to fragmentation and mapping sensitivity. See Lindner et al 2013, Bioinformatics 29(10):1260-7 for an alternative solution for the first problem (fragmentation) called "tail distribution".

optim.rounds

Maximum rounds of peak optimization.

optim.epsilon

Trace change at which optimization stops (unless optim.rounds is reached first). The trace change is estimated as the sum of square differences between parameters in one round and those from two rounds earlier (to avoid infinite loops from approximation).

merge.logdist

Maximum value of |log-ratio| between centrality parameters in peaks to attempt merging. The default of ~0.22 corresponds to a maximum difference of 25%.

verbose

Display (mostly debugging) information.

log

Logical value indicating if the estimations should be performed in natural logarithm units. Do not change unless you know what you're doing.

Value

Returns a list of enve.RecPlot2.Peak objects.

Author(s)

Luis M. Rodriguez-R [aut, cre]