This function takes a vector of true synthesis rates and RNA half-lives, and then simulates data for multiple time points and replicates. Both synthesis rate and RNA half-lives are assumed to be constant, but the system might not be in steady-state.
SimulateTimeCourse(
condition,
gene.info,
s,
d,
f0 = s/d,
s.variation = 1,
d.variation = 1,
dispersion,
num.reads = 1e+07,
timepoints = c(0, 0, 0, 1, 1, 1, 2, 2, 2, 4, 4, 4),
beta.approx = FALSE,
conversion.reads = FALSE,
verbose = TRUE,
seed = NULL,
...
)a grandR object containing the simulated data in its data slots and the true parameters in the gene annotation table
A user-defined condition name (which is placed into the Coldata of the final grandR object)
either a data frame containing gene annotation or a vector of gene names
a vector of synthesis rates
a vector of degradation rates (to get a specific half-life HL, use d=log(2)/HL)
the abundance at time t=0
biological variability of s among all samples (see details)
biological variability of d among all samples (see details)
a vector of dispersion parameters (estimate from data using DESeq2, e.g. by the estimate.dispersion utility function)
a vector representing the number of reads for each sample
a vector representing the labeling duration (in h) for each sample
should the beta approximation of the NTR posterior be computed?
also output the number of reads with conversion
Print status updates
seed value for the random number generator (set to make it deterministic!)
provided to SimulateReadsForSample
If s.variation or d.variation are > 1, then for each gene a random gaussian is added to s (or d) such that 90 of the gaussian is log2(s.variation).