Description Usage Arguments Value Examples
Simulate a (possibly unbalanced) count matrix from NBMM.
Under NBMM, an observed number of reads aligned to feature/gene g,
Y_{gsr}, follows a negative binomial (NB) distribution with mean
μ_{gs} and variance μ_{gs}+φ_{g} μ_{gs}^2, where
φ_g is the dispersion parameter, shared across strains. The
generalized linear model uses a \log-link:
\log(μ_{gs}) = α_g+ b_{gs}, \;\;b_{gs}\sim N(0, σ^2_g).
1 | getReadMatrix.NB(vec.num.rep, alphas, sigma2s, 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. |
phis |
Dispersion parameter in NB models, φ_g's, a 1 x num.features vector. |
A G x N matrix with NB 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 | ## 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)
phis <- c(0.5, 1, 0.05, 0.01, 0.1)
set.seed(1234)
## Generate reads:
nbData <- getReadMatrix.NB(rep.num, a0s, sig2s, phis)
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