Simulation of generalized dynamical system model.

SimulateDynamics() simulates any specified dynamical system of interconverting RNA species. Its required input is similar to that of EstimateDynamics(), i.e., an adjacency matrix describing the set of species and how they are related to one another and a list of formula relating actually assayed species to the modeled species. Currently, SimulateDynamics() implements a naive heteroskedastic replicate variability simulation and is not designed to simulate multiple experimental conditions.

Usage

SimulateDynamics(
  nfeatures,
  graph,
  metadf,
  log_means,
  log_sds,
  ntreatments = 1,
  treatment_effects = NULL,
  formula_list = NULL,
  unassigned_name = "__no_feature",
  seqdepth = nfeatures * 2500,
  dispersion = 100,
  lfn_sd = 0.2,
  effect_avg_default = 0,
  effect_sd_default = 0.5,
  fraction_affected_default = 0.5,
  ...
)

Arguments

nfeatures: Number of "features" to simulate data for. A "feature" in this case may contain a number of "sub-features". For example, you may want to simulate pre-RNA and mature RNA for a set of "genes", in which case the number of features is the number of genes.
graph: An adjacency matrix describing the reaction diagram graph relating the various RNA species to one another.
metadf: Data frame with two required columns (sample and tl). sample represents names given to each simulated sample. tl represents the label time for that sample. Additional columns can specify other features of the sample, like what subcellular compartment the sample is taken from. NOTE: Not sure I am actually using these optional columns in any useful capacity anymore.
log_means: Vector of log-Normal logmeans from which the distribution of feature-specific parameters will be drawn from. Length of vector should be the same as max(entries in graph), i.e., the number of parameters in your specified model.
log_sds: Vector of log-Normal logsds from which the distribution of feature-specific parameters will be drawn from.
ntreatments: Number of distinct experimental treatments to simulate. By default, only a single "treatment" (you might refer to this as wild-type, or control) is simulated. Increase this if you would like to explore performing comparative dynamical systems modeling.
treatment_effects: Data frame describing effects of treatment on each parameter. Should have five columns: "parameter_index", "treatment_index", "mean", "sd", and "fraction_affected". Each row corresponds to the effect the ith (i = treatment_index) treatment has on the jth (j = parameter_index) kinetic parameter. Effect sizes, on a log-scale, are drawn from a Normal distribution with mean and standard deviation set by the mean and sd columns, respectively. The number of non-zero effects is set by "fraction_affected", and is equal to ceiling(nfeatures * fraction_affected). treatment_index of 1 will be ignored and can either be included or not.
formula_list: A list of named lists. The names of each sub-list should be the same as the sample names as they are found in metadf. Each sub-list should be a list of formula relating feature names that will show up as columns of the simulated cB to species modeled in your graph. This only needs to be specified if you want to simulate the scenario where some of the measured species are a sum of modeled species.
unassigned_name: String to give to reads not assigned to a given feature.
seqdepth: Total number of reads in each sample.
dispersion: Negative binomial size parameter to use for simulating read counts
lfn_sd: Logit(fn) replicate variability.
effect_avg_default: If ntreatments > 1, and treatment_effects is not provided, this will be the value of mean for all treatments and parameters imputed in treatment_effects.
effect_sd_default: If ntreatments > 1, and treatment_effects is not provided, this will be the value of sd for all treatments and parameters imputed in treatment_effects.
fraction_affected_default: If ntreatments > 1, and treatment_effects is not provided, this will be the value of fraction_affected for all treatments and parameters imputed in treatment_effects.
...: Parameters passed to SimulateOneRep().