Description Usage Arguments Value Examples
Simulate a (possibly unbalanced) read matrix from CPMM.
For a compound Poisson (CP) random variable Y_{gsr} with mean
μ_{gs}, its variance can be expressed as
φ_gμ_{gs}^{p_g}, for some 1<p_g<2. Under the CPMM, with
a \log-link, the regression on the mean has the form:
\log(μ_{gs}) = α_g+ b_{gs}, \;\;b_{gs}\sim N(0, σ^2_g).
1 | getReadMatrix.CP(vec.num.rep, alphas, sigma2s, ps, phis)
|
vec.num.rep |
A vector of replicate numbers for each strain. |
alphas |
Intercept vector α_g's, 1 x num.features. |
sigma2s |
Random effect variance vector σ^2_g's, 1 x num.features. |
ps |
Tweedie parameter in CP models, p_g's, a 1 x num.features vector. |
phis |
Dispersion parameter in CP models, φ_g's, a 1 x num.features vector. |
A G x N matrix with CP reads. N is the total number of samples; G is the number of features. Column names are sample names of the form "Ss_r", where S stands for sample, s is the strain number, r is the replicate number within the strain. Row names are the feature names of the form "Gene g", where g is the feature index.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## Generate a sequencing dataset with 5 features and 6 strains.
## Assign parameter values.
rep.num <- c(3, 5, 2, 3, 4, 2)
a0s <- c(-1, 1, 2, 5, 10)
sig2s <- c(10, 0.2, 0.1, 0.03, 0.01)
ps <- rep(1.5, 5)
phis <- c(1.5, 1, 0.5, 0.1, 0.1)
set.seed(1234)
## Generate reads:
cpData <- getReadMatrix.CP(rep.num, a0s, sig2s, ps, phis)
## Generate strain names:
str <- sapply(1:length(rep.num), function(x){
str.x <- paste0("S", x)
return(rep(str.x, rep.num[x]))
})
str <- do.call(c, str)
|
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