fitBimodal: Fit a Bimodal Gaussian Distribution

In jlaffy/scalop: Single Cell Analysis Operations

Fit a Bimodal Gaussian Distribution

Description

Fit a bimodal gaussian distribution to a set of observations.

Usage

fitBimodal(
  x,
  prob = 0.95,
  coverage = 0.8,
  size = 10,
  assign = FALSE,
  boolean = FALSE,
  verbose = TRUE,
  maxit = 5000,
  maxrestarts = 100,
  bySampling = FALSE,
  sample.size = 100,
  tries = 2000,
  force.tries = FALSE,
  ...
)

Arguments

x	a named numeric vector of cells/observations or a matrix of genes X cells (variables X observations). If the latter, the column means are first computed.
prob	a numeric value >= 0 and <= 1; the minimum posterior probability required for an observation to be assigned to a mode. Default: 0.95
coverage	the fraction of observations that must have a posterior probability higher than <prob> to one of two modes in order for the distribution to qualify as bimodal. Default: 0.8
size	the minimum number of observations that must be assigned to a mode in order for the distribution to qualify as bimodal. Default: 10
assign	if set to TRUE, returns a list of length two containing the vector names that were assigned to each mode. Default: FALSE
boolean	if set to TRUE, returns a boolean value indicating whether the distribution is bimodal. Default: FALSE
verbose	print progress messages. Default: TRUE
maxit	the maximum number of iterations. Default: 5000
maxrestarts	the maximum number of restarts allowed. See normalmixEM for details. Default: 100
bySampling	logical; if TRUE, the function uses a bootstrapping method to subsample values and identify the two modes iteratively. This method is more sensitive to differing mode sizes, so will be useful if you believe one group to be much smaller than the other. Default: TRUE
sample.size	the number of observations to be sampled. Default: 100
tries	the number of bootstrap replicates.
force.tries	Should bootstrap replicates continue to run if bimodality has already been found? Logical. Default: FALSE

Value

The posterior probabilities of each observation to one of two modes. If boolean = TRUE, return a boolean value indicating whether bimodality was found. If assign = TRUE, return a list of length two with the observations (IDs) in each mode.

See Also

normalmixEM

jlaffy/scalop documentation built on March 24, 2024, 9 a.m.