scModels (version 1.0.4)

gmRNA: 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))