generate_counts: Simulate counts which are distributed using a zero-inflated...

In dynverse/dyntoy: Generating simple toy data of cellular differentiation

Description

Simulate counts which are distributed using a zero-inflated negative biniomal distribution

Usage

generate_counts(trajectory, num_features = 101,
  sample_mean_count = function() runif(1, 100, 1000),
  sample_dispersion_count = function(mean) map_dbl(mean, ~runif(1, ./10,
  ./4)), dropout_probability_factor = 100, dropout_rate = 0.2,
  differentially_expressed_rate = 1)

Arguments

trajectory	The dynwrap trajectory
num_features	Number of features
sample_mean_count	Function used to sample the mean expression
sample_dispersion_count	Function to sample the dispersion (size) of the negative biniomal given the expression. Higher dispersion values generate less noise
dropout_probability_factor	Factor used to calculate the probabilities of dropouts, relative to expression. Higher values (> 10000) have a lot of dropouts, lower values (< 10) have almost none
dropout_rate	Base rate of drop-outs
differentially_expressed_rate	Percentage of differentially expressed genes

dynverse/dyntoy documentation built on May 25, 2019, 4:26 p.m.