Simulate a complete time course of metabolic labeling - nucleotide conversion RNA-seq data.

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.

Usage

SimulateTimeCourseNonConstant(
  condition,
  gene.info,
  s,
  d,
  dispersion,
  num.reads = 1e+07,
  t = 2,
  replicates = 3,
  beta.approx = FALSE,
  conversion.reads = FALSE,
  verbose = TRUE,
  seed = NULL,
  ...
)

Arguments

condition

A user-defined condition name (which is placed into the Coldata of the final grandR object)

gene.info

either a data frame containing gene annotation or a vector of gene names

s

a vector of synthesis rates (see details)

d

a vector of degradation rates (see details)

dispersion

a vector of dispersion parameters (estimate from data using DESeq2, e.g. by the estimate.dispersion utility function)

num.reads

a vector representing the number of reads for each sample

t

a single number denoting the time

replicates

a single number denoting the number of replicates

beta.approx

should the beta approximation of the NTR posterior be computed?

conversion.reads

also output the number of reads with conversion

verbose

Print status updates

seed

seed value for the random number generator (set to make it deterministic!)

...

provided to SimulateReadsForSample

Value

a grandR object containing the simulated data in its data slots and the true parameters in the gene annotation table

Details

Both rates can be either (i) a single number (constant rate), (ii) a data frame with names "offset", "factor" and "exponent" (for linear functions, see ComputeNonConstantParam; only one row allowed) or (iii) a unary function time->rate. Functions

See also

SimulateTimeCourse