| gmRNA {scModels} | R Documentation |
Gillespie algorithm for mRNA generating processes
Description
Gillespie algorithms allow synthetic data simulation via three different underlying mRNA generating processes: the basic process consists of a simple death-birth model of mRNA transcription and degradation; the switching process considers additionally gene activation and deactivation, with mRNA transcription only happening in active gene states; the bursting process, transcribes mRNA in bursts with geometrically distributed burst sizes. The basic_burst model combines both the basic and the burst model. The IGbasic burst model describes the basic model with non-constant transcription rates, but transcription rates follow an inverse Gaussian distribution governed by one parameter, the mean parameter of the inverse Gaussian distribution. Additionally a burst transcription occures (with NB distributed burst sizes), the whole burst (rate and burst sizes) are determined by the rate parameter.
Usage
gmRNA_basic(n, r.on, r.degr)
gmRNA_switch(n, r.act, r.deact, r.on, r.degr)
gmRNA_burst(n, r.burst, s.burst, r.degr)
gmRNA_basic_burst(n, r.on, r.burst, s.burst, r.degr)
gmRNA_IGbasic_burst(n, r.mu, r.burst, r.degr)
Arguments
n |
Number of observations |
r.on |
Transcription rate during gene activation (Switching model) |
r.degr |
mRNA degradation rate (all models) |
r.act |
DNA activation rate (Switching Model) |
r.deact |
DNA deactivation rate (Switching Model) |
r.burst |
Bursty transcription rate (Bursting model, Basic Burst model and IG Basic Burst model) |
s.burst |
Mean burst size (Bursting Model and Basic Burst model) |
r.mu |
Mean parameter for the inverse Gaussian distribution (IG Basic Burst model) |
Examples
x <- gmRNA_basic(100, 0.75, 0.001)
plot(density(x))
x <- gmRNA_switch(100, 0.23, 0.15, 0.75, 0.001)
plot(density(x))
x <- gmRNA_burst(10, 0.15, 0.75, 0.001)
plot(density(x))
x <- gmRNA_basic_burst(10, 0.75, 0.15, 0.5, 0.001)
plot(density(x))
x <- gmRNA_IGbasic_burst(10, 2, 0.5, 0.1)
plot(density(x))