rxSeq (version 0.99.3)

simRX: Produce simulated counts

Description

This function is producing simulated counts for the joint model with Negative-Binomial distribution for TReC and Beta-Binomial for ASE counts. The simulated dataset should be reformatted to readCounts format to be used for optimization.

Usage

simRX(b0f, b0m, b1f, b1m, beta_sex, beta_dom, beta_k=1, phi=1, theta=1, n=6,  mean.base.cnt=50, range.base.cnt=60, perc.ase=.35, n.simu=1E4,  is.X=FALSE, tauB=NULL, seed=NULL)

Arguments

b0f

a female additive strain effect

b0m

a male additive strain effect

b1f

a female parent of origin effect

b1m

a male parent of origin effect

beta_sex

a sex effect

beta_dom

a dominance effect

beta_k

an effect associated with the library size kappas

phi

a Negative-Binomial overdispersion, default value is 1

theta

a Beta-Binomial overdispersion, default value is 1

n

a vector defining number of mice in each cross, default value is 6

mean.base.cnt

a target expected number of counts for the base group (with no effects), default value is 50

range.base.cnt

a range in which the expected number of counts for the base group will vary, default value is 60

perc.ase

a percent reads that are allele-specific, default value is 35%

n.simu

a number of simulations, default value is 1E4

is.X

a flag if the value to be simulated is X for chromosome (otherwise autosome), default value is FALSE

tauB

a value describing allelic imbalance - Xce effect for the cross, default value is NULL, in which case 50% will be simulated

seed

a random seed to be set, no set by default.

Value

output - 3 matrices with one row - one gene, one column - one mouse:

See Also

process,readCounts.

Examples

# simulating autosomal data:
dat.A = simRX(b0f=.5, b0m=.6, b1f=.3, b1m=.4, beta_sex=.1, beta_dom=.1, n.simu=1E1)
names(dat.A)
# simulating autosomal data:
dat.X = simRX(b0f=.5, b0m=.6, b1f=.3, b1m=.4, beta_sex=.1, beta_dom=.1, n.simu=1E1, 
              is.X=TRUE, tauB=.3)
names(dat.X)